WorldWideScience

Sample records for risk prediction equations

  1. Developing prediction equations and a mobile phone application to identify infants at risk of obesity.

    Science.gov (United States)

    Santorelli, Gillian; Petherick, Emily S; Wright, John; Wilson, Brad; Samiei, Haider; Cameron, Noël; Johnson, William

    2013-01-01

    Advancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant's risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App). Anthropometry and childhood obesity risk data were obtained for 1868 UK-born White or South Asian infants in the Born in Bradford cohort. Logistic regression was used to develop prediction equations (at 6 ± 1.5, 9 ± 1.5 and 12 ± 1.5 months) for risk of childhood obesity (BMI at 2 years >91(st) centile and weight gain from 0-2 years >1 centile band) incorporating sex, birth weight, and weight gain as predictors. The discrimination accuracy of the equations was assessed by the area under the curve (AUC); internal validity by comparing area under the curve to those obtained in bootstrapped samples; and external validity by applying the equations to an external sample. An App was built to incorporate six final equations (two at each age, one of which included maternal BMI). The equations had good discrimination (AUCs 86-91%), with the addition of maternal BMI marginally improving prediction. The AUCs in the bootstrapped and external validation samples were similar to those obtained in the development sample. The App is user-friendly, requires a minimum amount of information, and provides a risk assessment of low, medium, or high accompanied by advice and website links to government recommendations. Prediction equations for risk of childhood obesity have been developed and incorporated into a novel App, thereby providing proof of concept that childhood obesity prediction research can be integrated with advancements in technology.

  2. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis

    NARCIS (Netherlands)

    Tangri, N.; Grams, M.E.; Levey, A.S.; Coresh, J.; Appel, L.J.; Astor, B.C.; Chodick, G.; Collins, A.J.; Djurdjev, O.; Elley, C.R.; Evans, M.; Garg, A.X.; Hallan, S.I.; Inker, L.A.; Ito, S.; Jee, S.H.; Kovesdy, C.P.; Kronenberg, F.; Heerspink, H.J.; Marks, A.; Nadkarni, G.N.; Navaneethan, S.D.; Nelson, R.G.; Titze, S.; Sarnak, M.J.; Stengel, B.; Woodward, M.; Iseki, K.; Wetzels, J.F.M.; et al.,

    2016-01-01

    IMPORTANCE: Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations, including such factors as age, sex, estimated glomerular filtration rate, and calcium and phosphate concentrations, were previously

  3. Development and validation of risk prediction equations to estimate survival in patients with colorectal cancer: cohort study

    OpenAIRE

    Hippisley-Cox, Julia; Coupland, Carol

    2017-01-01

    Objective: To develop and externally validate risk prediction equations to estimate absolute and conditional survival in patients with colorectal cancer. \\ud \\ud Design: Cohort study.\\ud \\ud Setting: General practices in England providing data for the QResearch database linked to the national cancer registry.\\ud \\ud Participants: 44 145 patients aged 15-99 with colorectal cancer from 947 practices to derive the equations. The equations were validated in 15 214 patients with colorectal cancer ...

  4. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure : A Meta-analysis

    NARCIS (Netherlands)

    Tangri, Navdeep; Grams, Morgan E.; Levey, Andrew S.; Coresh, Josef; Appel, Lawrence J.; Astor, Brad C.; Chodick, Gabriel; Collins, Allan J.; Djurdjev, Ognjenka; Elley, Raina; Evans, Marie; Garg, Amit X.; Hallan, Stein I.; Nicer, Lesley A.; Ito, Sadayoshi; Jee, Sun Ha; Kovesdy, Csaba P.; Kronenberg, Florian; Heerspink, Hiddo J. Lambers; Marks, Angharad; Nadkarni, Girish N.; Navaneethan, Sankar D.; Nelson, Robert G.; Titze, Stephanie; Sarnak, Mark J.; Stengel, Benedicte; Woodward, Mark; Iseki, Kunitoshi

    2016-01-01

    IMPORTANCE Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations were previously developed and validated in 2 Canadian cohorts. Validation in other regions and in CKD populations not under the care of a

  5. ANTHROPOMETRIC PREDICTIVE EQUATIONS FOR ...

    African Journals Online (AJOL)

    Keywords: Anthropometry, Predictive Equations, Percentage Body Fat, Nigerian Women, Bioelectric Impedance ... such as Asians and Indians (Pranav et al., 2009), ... size (n) of at least 3o is adjudged as sufficient for the ..... of people, gender and age (Vogel eta/., 1984). .... Fish Sold at Ile-Ife Main Market, South West Nigeria.

  6. Development and validation of risk prediction equations to estimate future risk of blindness and lower limb amputation in patients with diabetes: cohort study.

    Science.gov (United States)

    Hippisley-Cox, Julia; Coupland, Carol

    2015-11-11

    Is it possible to develop and externally validate risk prediction equations to estimate the 10 year risk of blindness and lower limb amputation in patients with diabetes aged 25-84 years? This was a prospective cohort study using routinely collected data from general practices in England contributing to the QResearch and Clinical Practice Research Datalink (CPRD) databases during the study period 1998-2014. The equations were developed using 763 QResearch practices (n=454,575 patients with diabetes) and validated in 254 different QResearch practices (n=142,419) and 357 CPRD practices (n=206,050). Cox proportional hazards models were used to derive separate risk equations for blindness and amputation in men and women that could be evaluated at 10 years. Measures of calibration and discrimination were calculated in the two validation cohorts. Risk prediction equations to quantify absolute risk of blindness and amputation in men and women with diabetes have been developed and externally validated. In the QResearch derivation cohort, 4822 new cases of lower limb amputation and 8063 new cases of blindness occurred during follow-up. The risk equations were well calibrated in both validation cohorts. Discrimination was good in men in the external CPRD cohort for amputation (D statistic 1.69, Harrell's C statistic 0.77) and blindness (D statistic 1.40, Harrell's C statistic 0.73), with similar results in women and in the QResearch validation cohort. The algorithms are based on variables that patients are likely to know or that are routinely recorded in general practice computer systems. They can be used to identify patients at high risk for prevention or further assessment. Limitations include lack of formally adjudicated outcomes, information bias, and missing data. Patients with type 1 or type 2 diabetes are at increased risk of blindness and amputation but generally do not have accurate assessments of the magnitude of their individual risks. The new algorithms calculate

  7. Prediction of atherosclerotic cardiovascular disease mortality in a nationally representative cohort using a set of risk factors from pooled cohort risk equations.

    Directory of Open Access Journals (Sweden)

    Zefeng Zhang

    Full Text Available The American College of Cardiology/American Heart Association developed Pooled Cohort equations to estimate atherosclerotic cardiovascular disease (ASCVD risk. It is unclear how well the equations predict ASCVD mortality in a nationally representative cohort. We used the National Health and Nutrition Examination Survey (NHANES 1988-1994 and Linked Mortality through 2006 (n = 6,644. Among participants aged 40-79 years without ASCVD at baseline, we used Cox proportional hazard models to estimate the 10-year probability of ASCVD death by sex and race-ethnicity (non-Hispanic white (NHW, non-Hispanic black (NHB and Mexican American (MA. We estimated the discrimination and calibration for each sex-race-ethnicity model. We documented 288 ASCVD deaths during 62,335 person years. The Pooled Cohort equations demonstrated moderate to good discrimination for ASCVD mortality, with modified C-statistics of 0.716 (95% CI 0.663-0.770, 0.794 (0.734-0.854, and 0.733 (0.654-0.811 for NHW, NHB and MA men, respectively. The corresponding C-statistics for women were 0.781 (0.718-0.844, 0.702 (0.633-0.771, and 0.789 (CI 0.721-0.857. Modified Hosmer-Lemeshow χ2 suggested adequate calibration for NHW, NHB and MA men, and MA women (p-values: 0.128, 0.295, 0.104 and 0.163 respectively. The calibration was inadequate for NHW and NHB women (p<0.05. In this nationally representative cohort, the Pooled Cohort equations performed adequately to predict 10-year ASCVD mortality for NHW and NHB men, and MA population, but not for NHW and NHB women.

  8. Unbalanced Regressions and the Predictive Equation

    DEFF Research Database (Denmark)

    Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

    Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...... in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest...

  9. Height - Diameter predictive equations for Rubber (Hevea ...

    African Journals Online (AJOL)

    BUKOLA

    They proffer logistic data for modeling and futuristic prediction for sustainable forest management. Diameter is one of the most ... in various quantitative estimation following the intricacy of time, availability of modern equipments .... growth functions. This trend is shown in Figure 1 where the prediction equations are plotted.

  10. Low RMRratio as a Surrogate Marker for Energy Deficiency, the Choice of Predictive Equation Vital for Correctly Identifying Male and Female Ballet Dancers at Risk.

    Science.gov (United States)

    Staal, Sarah; Sjödin, Anders; Fahrenholtz, Ida; Bonnesen, Karen; Melin, Anna Katarina

    2018-06-22

    Ballet dancers are reported to have an increased risk for energy deficiency with or without disordered eating behavior. A low ratio between measured ( m ) and predicted ( p ) resting metabolic rate (RMR ratio  energy deficiency. We aimed to evaluate the prevalence of suppressed RMR using different methods to calculate p RMR and to explore associations with additional markers of energy deficiency. Female (n = 20) and male (n = 20) professional ballet dancers, 19-35 years of age, were enrolled. m RMR was assessed by respiratory calorimetry (ventilated open hood). p RMR was determined using the Cunningham and Harris-Benedict equations, and different tissue compartments derived from whole-body dual-energy X-ray absorptiometry assessment. The protocol further included assessment of body composition and bone mineral density, blood pressure, disordered eating (Eating Disorder Inventory-3), and for females, the Low Energy Availability in Females Questionnaire. The prevalence of suppressed RMR was generally high but also clearly dependent on the method used to calculate p RMR, ranging from 25% to 80% in males and 35% to 100% in females. Five percent had low bone mineral density, whereas 10% had disordered eating and 25% had hypotension. Forty percent of females had elevated Low Energy Availability in Females Questionnaire score and 50% were underweight. Suppressed RMR was associated with elevated Low Energy Availability in Females Questionnaire score in females and with higher training volume in males. In conclusion, professional ballet dancers are at risk for energy deficiency. The number of identified dancers at risk varies greatly depending on the method used to predict RMR when using RMR ratio as a marker for energy deficiency.

  11. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

    2016-01-01

    Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...

  12. The new pooled cohort equations risk calculator

    DEFF Research Database (Denmark)

    Preiss, David; Kristensen, Søren L

    2015-01-01

    disease and any measure of social deprivation. An early criticism of the Pooled Cohort Equations Risk Calculator has been its alleged overestimation of ASCVD risk which, if confirmed in the general population, is likely to result in statin therapy being prescribed to many individuals at lower risk than...

  13. Validation of the mortality prediction equation for damage control ...

    African Journals Online (AJOL)

    , preoperative lowest pH and lowest core body temperature to derive an equation for the purpose of predicting mortality in damage control surgery. It was shown to reliably predict death despite damage control surgery. The equation derivation ...

  14. A risk augmented mincer earnings equation? Taking stock

    NARCIS (Netherlands)

    Hartog, J.

    2009-01-01

    We survey the literature on the Risk Augmented Mincer equation that seeks to estimate the compensation for uncertainty in the future wage to be earned after completing an education. There is wide empirical support for the predicted positive effect of wage variance and the negative effect of wage

  15. A risk augmented Mincer earnings equation? Taking stock

    NARCIS (Netherlands)

    Hartog, J.

    2011-01-01

    We survey the literature on the Risk Augmented Mincer equation that seeks to estimate the compensation for uncertainty in the future wage to be earned after completing an education. There is wide empirical support for the predicted positive effect of wage variance and the negative effect of wage

  16. Predictive Temperature Equations for Three Sites at the Grand Canyon

    Science.gov (United States)

    McLaughlin, Katrina Marie Neitzel

    Climate data collected at a number of automated weather stations were used to create a series of predictive equations spanning from December 2009 to May 2010 in order to better predict the temperatures along hiking trails within the Grand Canyon. The central focus of this project is how atmospheric variables interact and can be combined to predict the weather in the Grand Canyon at the Indian Gardens, Phantom Ranch, and Bright Angel sites. Through the use of statistical analysis software and data regression, predictive equations were determined. The predictive equations are simple or multivariable best fits that reflect the curvilinear nature of the data. With data analysis software curves resulting from the predictive equations were plotted along with the observed data. Each equation's reduced chi2 was determined to aid the visual examination of the predictive equations' ability to reproduce the observed data. From this information an equation or pair of equations was determined to be the best of the predictive equations. Although a best predictive equation for each month and season was determined for each site, future work may refine equations to result in a more accurate predictive equation.

  17. Low RMRratio as a surrogate marker for energy deficiency, the choice of predictive equation vital for correctly identifying male and female ballet dancers at risk

    DEFF Research Database (Denmark)

    Staal, Sarah; Sjödin, Anders Mikael; Fahrenholtz, Ida Lysdahl

    2018-01-01

    Ballet dancers are reported to have an increased risk for energy deficiency with or without disordered eating (DE) behavior. A low ratio between measured (m) and predicted (p) resting metabolic rate (RMRratio... the prevalence of suppressed RMR using different methods to calculatepRMR and to explore associations with additional markers of energy deficiency. Female (n=20) and male (n=20) professional ballet dancers, 19-35 years of age were enrolled. mRMR was assessed by respiratory calorimetry (ventilated open hood). p......% hypotension. Forty percent of females had elevated LEAF-Q score, and 50% were underweight. Suppressed RMR was associated with elevated LEAF-Q score in females and with higher training volume in males. In conclusion, professional ballet dancers are at risk for energy deficiency. The number of identified...

  18. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk)

    DEFF Research Database (Denmark)

    Hajifathalian, Kaveh; Ueda, Peter; Lu, Yuan

    2015-01-01

    BACKGROUND: Treatment of cardiovascular risk factors based on disease risk depends on valid risk prediction equations. We aimed to develop, and apply in example countries, a risk prediction equation for cardiovascular disease (consisting here of coronary heart disease and stroke) that can be reca...

  19. Revised predictive equations for salt intrusion modelling in estuaries

    NARCIS (Netherlands)

    Gisen, J.I.A.; Savenije, H.H.G.; Nijzink, R.C.

    2015-01-01

    For one-dimensional salt intrusion models to be predictive, we need predictive equations to link model parameters to observable hydraulic and geometric variables. The one-dimensional model of Savenije (1993b) made use of predictive equations for the Van der Burgh coefficient $K$ and the dispersion

  20. Evaluation of abutment scour prediction equations with field data

    Science.gov (United States)

    Benedict, S.T.; Deshpande, N.; Aziz, N.M.

    2007-01-01

    The U.S. Geological Survey, in cooperation with FHWA, compared predicted abutment scour depths, computed with selected predictive equations, with field observations collected at 144 bridges in South Carolina and at eight bridges from the National Bridge Scour Database. Predictive equations published in the 4th edition of Evaluating Scour at Bridges (Hydraulic Engineering Circular 18) were used in this comparison, including the original Froehlich, the modified Froehlich, the Sturm, the Maryland, and the HIRE equations. The comparisons showed that most equations tended to provide conservative estimates of scour that at times were excessive (as large as 158 ft). Equations also produced underpredictions of scour, but with less frequency. Although the equations provide an important resource for evaluating abutment scour at bridges, the results of this investigation show the importance of using engineering judgment in conjunction with these equations.

  1. Unbalanced Regressions and the Predictive Equation

    DEFF Research Database (Denmark)

    Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

    Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...

  2. Ground Motion Prediction Equations Empowered by Stress Drop Measurement

    Science.gov (United States)

    Miyake, H.; Oth, A.

    2015-12-01

    Significant variation of stress drop is a crucial issue for ground motion prediction equations and probabilistic seismic hazard assessment, since only a few ground motion prediction equations take into account stress drop. In addition to average and sigma studies of stress drop and ground motion prediction equations (e.g., Cotton et al., 2013; Baltay and Hanks, 2014), we explore 1-to-1 relationship for each earthquake between stress drop and between-event residual of a ground motion prediction equation. We used the stress drop dataset of Oth (2013) for Japanese crustal earthquakes ranging 0.1 to 100 MPa and K-NET/KiK-net ground motion dataset against for several ground motion prediction equations with volcanic front treatment. Between-event residuals for ground accelerations and velocities are generally coincident with stress drop, as investigated by seismic intensity measures of Oth et al. (2015). Moreover, we found faster attenuation of ground acceleration and velocities for large stress drop events for the similar fault distance range and focal depth. It may suggest an alternative parameterization of stress drop to control attenuation distance rate for ground motion prediction equations. We also investigate 1-to-1 relationship and sigma for regional/national-scale stress drop variation and current national-scale ground motion equations.

  3. Breast cancer risks and risk prediction models.

    Science.gov (United States)

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  4. Predicting logging residues: an interim equation for Appalachian oak sawtimber

    Science.gov (United States)

    A. Jeff Martin

    1975-01-01

    An equation, using dbh, dbh², bole length, and sawlog height to predict the cubic-foot volume of logging residue per tree, was developed from data collected on 36 mixed oaks in southwestern Virginia. The equation produced reliable results for small sawtimber trees, but additional research is needed for other species, sites, and utilization practices.

  5. Predicting Career Advancement with Structural Equation Modelling

    Science.gov (United States)

    Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia

    2012-01-01

    Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…

  6. Evaluation of peak power prediction equations in male basketball players.

    Science.gov (United States)

    Duncan, Michael J; Lyons, Mark; Nevill, Alan M

    2008-07-01

    This study compared peak power estimated using 4 commonly used regression equations with actual peak power derived from force platform data in a group of adolescent basketball players. Twenty-five elite junior male basketball players (age, 16.5 +/- 0.5 years; mass, 74.2 +/- 11.8 kg; height, 181.8 +/- 8.1 cm) volunteered to participate in the study. Actual peak power was determined using a countermovement vertical jump on a force platform. Estimated peak power was determined using countermovement jump height and body mass. All 4 prediction equations were significantly related to actual peak power (all p jump prediction equations, 12% for the Canavan and Vescovi equation, and 6% for the Sayers countermovement jump equation. In all cases peak power was underestimated.

  7. Cardiovascular risk prediction

    DEFF Research Database (Denmark)

    Graversen, Peter; Abildstrøm, Steen Z.; Jespersen, Lasse

    2016-01-01

    Aim European society of cardiology (ESC) guidelines recommend that cardiovascular disease (CVD) risk stratification in asymptomatic individuals is based on the Systematic Coronary Risk Evaluation (SCORE) algorithm, which estimates individual 10-year risk of death from CVD. We assessed the potential...

  8. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Prediction Equations for Spirometry for Children from Northern India.

    Science.gov (United States)

    Chhabra, Sunil K; Kumar, Rajeev; Mittal, Vikas

    2016-09-08

    To develop prediction equations for spirometry for children from northern India using current international guidelines for standardization. Re-analysis of cross-sectional data from a single school. 670 normal children (age 6-17 y; 365 boys) of northern Indian parentage. After screening for normal health, we carried out spirometry with recommended quality assurance according to current guidelines. We developed linear and nonlinear prediction equations using multiple regression analysis. We selected the final models on the basis of the highest coefficient of multiple determination (R2) and statistical validity. Spirometry parameters: FVC, FEV1, PEFR, FEF50, FEF75 and FEF25-75. The equations for the main parameters were as follows: Boys, Ln FVC = -1.687+0.016*height +0.022*age; Ln FEV1 = -1.748+0.015*height+0.031*age. Girls, Ln FVC = -9.989 +(2.018*Ln(height)) + (0.324*Ln(age)); Ln FEV1 = -10.055 +(1.990*Ln(height))+(0.358*Ln(age)). Nonlinear regression yielded substantially greater R2 values compared to linear models except for FEF50 for girls. Height and age were found to be the significant explanatory variables for all parameters on multiple regression with weight making no significant contribution. We developed prediction equations for spirometry for children from northern India. Nonlinear equations were superior to linear equations.

  10. Quantifying prognosis with risk predictions.

    Science.gov (United States)

    Pace, Nathan L; Eberhart, Leopold H J; Kranke, Peter R

    2012-01-01

    Prognosis is a forecast, based on present observations in a patient, of their probable outcome from disease, surgery and so on. Research methods for the development of risk probabilities may not be familiar to some anaesthesiologists. We briefly describe methods for identifying risk factors and risk scores. A probability prediction rule assigns a risk probability to a patient for the occurrence of a specific event. Probability reflects the continuum between absolute certainty (Pi = 1) and certified impossibility (Pi = 0). Biomarkers and clinical covariates that modify risk are known as risk factors. The Pi as modified by risk factors can be estimated by identifying the risk factors and their weighting; these are usually obtained by stepwise logistic regression. The accuracy of probabilistic predictors can be separated into the concepts of 'overall performance', 'discrimination' and 'calibration'. Overall performance is the mathematical distance between predictions and outcomes. Discrimination is the ability of the predictor to rank order observations with different outcomes. Calibration is the correctness of prediction probabilities on an absolute scale. Statistical methods include the Brier score, coefficient of determination (Nagelkerke R2), C-statistic and regression calibration. External validation is the comparison of the actual outcomes to the predicted outcomes in a new and independent patient sample. External validation uses the statistical methods of overall performance, discrimination and calibration and is uniformly recommended before acceptance of the prediction model. Evidence from randomised controlled clinical trials should be obtained to show the effectiveness of risk scores for altering patient management and patient outcomes.

  11. Cardiovascular risk prediction tools for populations in Asia.

    Science.gov (United States)

    Barzi, F; Patel, A; Gu, D; Sritara, P; Lam, T H; Rodgers, A; Woodward, M

    2007-02-01

    Cardiovascular risk equations are traditionally derived from the Framingham Study. The accuracy of this approach in Asian populations, where resources for risk factor measurement may be limited, is unclear. To compare "low-information" equations (derived using only age, systolic blood pressure, total cholesterol and smoking status) derived from the Framingham Study with those derived from the Asian cohorts, on the accuracy of cardiovascular risk prediction. Separate equations to predict the 8-year risk of a cardiovascular event were derived from Asian and Framingham cohorts. The performance of these equations, and a subsequently "recalibrated" Framingham equation, were evaluated among participants from independent Chinese cohorts. Six cohort studies from Japan, Korea and Singapore (Asian cohorts); six cohort studies from China; the Framingham Study from the US. 172,077 participants from the Asian cohorts; 25,682 participants from Chinese cohorts and 6053 participants from the Framingham Study. In the Chinese cohorts, 542 cardiovascular events occurred during 8 years of follow-up. Both the Asian cohorts and the Framingham equations discriminated cardiovascular risk well in the Chinese cohorts; the area under the receiver-operator characteristic curve was at least 0.75 for men and women. However, the Framingham risk equation systematically overestimated risk in the Chinese cohorts by an average of 276% among men and 102% among women. The corresponding average overestimation using the Asian cohorts equation was 11% and 10%, respectively. Recalibrating the Framingham risk equation using cardiovascular disease incidence from the non-Chinese Asian cohorts led to an overestimation of risk by an average of 4% in women and underestimation of risk by an average of 2% in men. A low-information Framingham cardiovascular risk prediction tool, which, when recalibrated with contemporary data, is likely to estimate future cardiovascular risk with similar accuracy in Asian

  12. Cell membrane temperature rate sensitivity predicted from the Nernst equation.

    Science.gov (United States)

    Barnes, F S

    1984-01-01

    A hyperpolarized current is predicted from the Nernst equation for conditions of positive temperature derivatives with respect to time. This ion current, coupled with changes in membrane channel conductivities, is expected to contribute to a transient potential shift across the cell membrane for silent cells and to a change in firing rate for pacemaker cells.

  13. Soil loss prediction using universal soil loss equation (USLE ...

    African Journals Online (AJOL)

    Soil loss prediction using universal soil loss equation (USLE) simulation model in a mountainous area in Ag lasun district, Turkey. ... The need for sufficient knowledge and data for decision makers is obvious hence the present study was carried out. The study area, the Alasun district, is in the middle west of Turkey and is ...

  14. Modeling a Predictive Energy Equation Specific for Maintenance Hemodialysis.

    Science.gov (United States)

    Byham-Gray, Laura D; Parrott, J Scott; Peters, Emily N; Fogerite, Susan Gould; Hand, Rosa K; Ahrens, Sean; Marcus, Andrea Fleisch; Fiutem, Justin J

    2017-03-01

    Hypermetabolism is theorized in patients diagnosed with chronic kidney disease who are receiving maintenance hemodialysis (MHD). We aimed to distinguish key disease-specific determinants of resting energy expenditure to create a predictive energy equation that more precisely establishes energy needs with the intent of preventing protein-energy wasting. For this 3-year multisite cross-sectional study (N = 116), eligible participants were diagnosed with chronic kidney disease and were receiving MHD for at least 3 months. Predictors for the model included weight, sex, age, C-reactive protein (CRP), glycosylated hemoglobin, and serum creatinine. The outcome variable was measured resting energy expenditure (mREE). Regression modeling was used to generate predictive formulas and Bland-Altman analyses to evaluate accuracy. The majority were male (60.3%), black (81.0%), and non-Hispanic (76.7%), and 23% were ≥65 years old. After screening for multicollinearity, the best predictive model of mREE ( R 2 = 0.67) included weight, age, sex, and CRP. Two alternative models with acceptable predictability ( R 2 = 0.66) were derived with glycosylated hemoglobin or serum creatinine. Based on Bland-Altman analyses, the maintenance hemodialysis equation that included CRP had the best precision, with the highest proportion of participants' predicted energy expenditure classified as accurate (61.2%) and with the lowest number of individuals with underestimation or overestimation. This study confirms disease-specific factors as key determinants of mREE in patients on MHD and provides a preliminary predictive energy equation. Further prospective research is necessary to test the reliability and validity of this equation across diverse populations of patients who are receiving MHD.

  15. Korean risk assessment model for breast cancer risk prediction.

    Science.gov (United States)

    Park, Boyoung; Ma, Seung Hyun; Shin, Aesun; Chang, Myung-Chul; Choi, Ji-Yeob; Kim, Sungwan; Han, Wonshik; Noh, Dong-Young; Ahn, Sei-Hyun; Kang, Daehee; Yoo, Keun-Young; Park, Sue K

    2013-01-01

    We evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk. Using 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort. The major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (p = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls (pKorean women, especially urban women.

  16. Stochastic Optimal Prediction with Application to Averaged Euler Equations

    Energy Technology Data Exchange (ETDEWEB)

    Bell, John [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Chorin, Alexandre J. [Univ. of California, Berkeley, CA (United States); Crutchfield, William [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2017-04-24

    Optimal prediction (OP) methods compensate for a lack of resolution in the numerical solution of complex problems through the use of an invariant measure as a prior measure in the Bayesian sense. In first-order OP, unresolved information is approximated by its conditional expectation with respect to the invariant measure. In higher-order OP, unresolved information is approximated by a stochastic estimator, leading to a system of random or stochastic differential equations. We explain the ideas through a simple example, and then apply them to the solution of Averaged Euler equations in two space dimensions.

  17. Prediction equations for spirometry in adults from northern India.

    Science.gov (United States)

    Chhabra, S K; Kumar, R; Gupta, U; Rahman, M; Dash, D J

    2014-01-01

    Most of the Indian studies on prediction equations for spirometry in adults are several decades old and may have lost their utility as these were carried out with equipment and standardisation protocols that have since changed. Their validity is further questionable as the lung health of the population is likely to have changed over time. To develop prediction equations for spirometry in adults of north Indian origin using the 2005 American Thoracic Society/European Respiratory Society (ATS/ERS) recommendations on standardisation. Normal healthy non-smoker subjects, both males and females, aged 18 years and above underwent spirometry using a non-heated Fleisch Pneumotach spirometer calibrated daily. The dataset was randomly divided into training (70%) and test (30%) sets and the former was used to develop the equations. These were validated on the test data set. Prediction equations were developed separately for males and females for forced vital capacity (FVC), forced expiratory volume in first second (FEV1), FEV1/FVC ratio, and instantaneous expiratory flow rates using multiple linear regression procedure with different transformations of dependent and/or independent variables to achieve the best-fitting models for the data. The equations were compared with the previous ones developed in the same population in the 1960s. In all, 685 (489 males, 196 females) subjects performed spirometry that was technically acceptable and repeatable. All the spirometry parameters were significantly higher among males except the FEV1/FVC ratio that was significantly higher in females. Overall, age had a negative relationship with the spirometry parameters while height was positively correlated with each, except for the FEV1/FVC ratio that was related only to age. Weight was included in the models for FVC, forced expiratory flow (FEF75) and FEV1/FVC ratio in males, but its contribution was very small. Standard errors of estimate were provided to enable calculation of the lower

  18. Developmental dyslexia: predicting individual risk.

    Science.gov (United States)

    Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

    2015-09-01

    Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as 'dyslexic' or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. © 2015 The Authors. Journal of Child Psychology and Psychiatry published by

  19. Validation of the Kidney Failure Risk Equation in Manitoba

    Directory of Open Access Journals (Sweden)

    Reid H. Whitlock

    2017-04-01

    Full Text Available Background: Patients with chronic kidney disease (CKD are at risk to progress to kidney failure. We previously developed the Kidney Failure Risk Equation (KFRE to predict progression to kidney failure in patients referred to nephrologists. Objective: The objective of this study was to determine the ability of the KFRE to discriminate which patients will progress to kidney failure in an unreferred population. Design: A retrospective cohort study was conducted using administrative databases. Setting: This study took place in Manitoba, Canada. Measurements: Age, sex, estimated glomerular filtration rate (eGFR, and urine albumin-to-creatinine ratio (ACR were measured. Methods: We included patients from the Diagnostic Services of Manitoba database with an eGFR <60 mL/min/1.73 m 2 and ACR measured between October 2006 and March 2007. Five-year kidney failure risk was predicted using the 4-variable KFRE and compared with treated kidney failure events from the Manitoba Renal Program database. Sensitivity and specificity for KFRE risk thresholds (3% and 10% over 5 years were compared with eGFR thresholds (30 and 45 mL/min/1.73 m 2 . Results: Of 1512 included patients, 151 developed kidney failure over the 5-year follow-up period. The 4-variable KFRE showed a superior prognostic discrimination compared with eGFR alone (area under the receiver operating characteristic curve [AUROC] values, 0.90 [95% confidence interval {CI}: 0.88-0.92] for KFRE vs 0.78 [95% CI: 0.74-0.83] for eGFR. At a 3% threshold over 5 years, the KFRE had a sensitivity of 97% and a specificity of 62%. At 10% risk, sensitivity was 86%, and specificity was 80%. Limitations: Only 11.7% of stage 3-5 CKD patients had simultaneous ACR measurement. The KFRE does not account for other indications for referral such as suspected glomerulonephritis, polycystic kidney disease, and recurrent stone disease. Conclusions: The KFRE has been validated in a population with a demographic and referral

  20. Stochastic Ocean Predictions with Dynamically-Orthogonal Primitive Equations

    Science.gov (United States)

    Subramani, D. N.; Haley, P., Jr.; Lermusiaux, P. F. J.

    2017-12-01

    The coastal ocean is a prime example of multiscale nonlinear fluid dynamics. Ocean fields in such regions are complex and intermittent with unstationary heterogeneous statistics. Due to the limited measurements, there are multiple sources of uncertainties, including the initial conditions, boundary conditions, forcing, parameters, and even the model parameterizations and equations themselves. For efficient and rigorous quantification and prediction of these uncertainities, the stochastic Dynamically Orthogonal (DO) PDEs for a primitive equation ocean modeling system with a nonlinear free-surface are derived and numerical schemes for their space-time integration are obtained. Detailed numerical studies with idealized-to-realistic regional ocean dynamics are completed. These include consistency checks for the numerical schemes and comparisons with ensemble realizations. As an illustrative example, we simulate the 4-d multiscale uncertainty in the Middle Atlantic/New York Bight region during the months of Jan to Mar 2017. To provide intitial conditions for the uncertainty subspace, uncertainties in the region were objectively analyzed using historical data. The DO primitive equations were subsequently integrated in space and time. The probability distribution function (pdf) of the ocean fields is compared to in-situ, remote sensing, and opportunity data collected during the coincident POSYDON experiment. Results show that our probabilistic predictions had skill and are 3- to 4- orders of magnitude faster than classic ensemble schemes.

  1. Expanded prediction equations of human sweat loss and water needs.

    Science.gov (United States)

    Gonzalez, R R; Cheuvront, S N; Montain, S J; Goodman, D A; Blanchard, L A; Berglund, L G; Sawka, M N

    2009-08-01

    The Institute of Medicine expressed a need for improved sweating rate (msw) prediction models that calculate hourly and daily water needs based on metabolic rate, clothing, and environment. More than 25 years ago, the original Shapiro prediction equation (OSE) was formulated as msw (g.m(-2).h(-1))=27.9.Ereq.(Emax)(-0.455), where Ereq is required evaporative heat loss and Emax is maximum evaporative power of the environment; OSE was developed for a limited set of environments, exposures times, and clothing systems. Recent evidence shows that OSE often overpredicts fluid needs. Our study developed a corrected OSE and a new msw prediction equation by using independent data sets from a wide range of environmental conditions, metabolic rates (rest to losses were carefully measured in 101 volunteers (80 males and 21 females; >500 observations) by using a variety of metabolic rates over a range of environmental conditions (ambient temperature, 15-46 degrees C; water vapor pressure, 0.27-4.45 kPa; wind speed, 0.4-2.5 m/s), clothing, and equipment combinations and durations (2-8 h). Data are expressed as grams per square meter per hour and were analyzed using fuzzy piecewise regression. OSE overpredicted sweating rates (Pdata (21 males and 9 females; >200 observations). OSEC and PW were more accurate predictors of sweating rate (58 and 65% more accurate, Perror (standard error estimate<100 g.m(-2).h(-1)) for conditions both within and outside the original OSE domain of validity. The new equations provide for more accurate sweat predictions over a broader range of conditions with applications to public health, military, occupational, and sports medicine settings.

  2. Validation of resting metabolic rate prediction equations for teenagers

    Directory of Open Access Journals (Sweden)

    Paulo Henrique Santos da Fonseca

    2007-09-01

    Full Text Available The resting metabolic rate (RMR can be defi ned as the minimum rate of energy spent and represents the main component of the energetic outlay. The purpose of this study is to validate equations to predict the resting metabolic rate in teenagers (103 individuals, being 51 girls and 52 boys, with age between 10 and 17 years from Florianópolis – SC – Brazil. It was measured: the body weight, body height, skinfolds and obtained the lean and body fat mass through bioimpedance. The nonproteic RMR was measured by Weir’s equation (1949, utilizing AeroSport TEEM-100 gas analyzer. The studied equations were: Harry and Benedict (1919, Schofi eld (1985, WHO/FAO/UNU (1985, Henry and Rees (1991, Molnár et al. (1998, Tverskaya et al. (1998 and Müller et al. (2004. In order to study the cross-validation of the RMR prediction equations and its standard measure (Weir 1949, the following statistics procedure were calculated: Pearson’s correlation (r ≥ 0.70, the “t” test with the signifi cance level of p0.05 in relation to the standard measure, with exception of the equations suggested for Tverskaya et al. (1998, and the two models of Müller et al (2004. Even though there was not a signifi cant difference, only the models considered for Henry and Rees (1991, and Molnár et al. (1995 had gotten constant error variation under 5%. All the equations analyzed in the study in girls had not reached criterion of correlation values of 0.70 with the indirect calorimetry. Analyzing the prediction equations of RMR in boys, all of them had moderate correlation coeffi cients with the indirect calorimetry, however below 0.70. Only the equation developed for Tverskaya et al. (1998 presented differences (p ABSTRACT0,05 em relação à medida padrão (Weir 1949, com exceção das equações sugeridas por Tverskaya et al. (1998 e os dois modelos de Müller et al (2004. Mesmo não havendo diferença signifi cativa, somente os modelos propostos por Henry e Rees (1991

  3. The physics behind Van der Burgh's empirical equation, providing a new predictive equation for salinity intrusion in estuaries

    Science.gov (United States)

    Zhang, Zhilin; Savenije, Hubert H. G.

    2017-07-01

    The practical value of the surprisingly simple Van der Burgh equation in predicting saline water intrusion in alluvial estuaries is well documented, but the physical foundation of the equation is still weak. In this paper we provide a connection between the empirical equation and the theoretical literature, leading to a theoretical range of Van der Burgh's coefficient of 1/2 residual circulation. This type of mixing is relevant in the wider part of alluvial estuaries where preferential ebb and flood channels appear. Subsequently, this dispersion equation is combined with the salt balance equation to obtain a new predictive analytical equation for the longitudinal salinity distribution. Finally, the new equation was tested and applied to a large database of observations in alluvial estuaries, whereby the calibrated K values appeared to correspond well to the theoretical range.

  4. Predictive model for early math skills based on structural equations.

    Science.gov (United States)

    Aragón, Estíbaliz; Navarro, José I; Aguilar, Manuel; Cerda, Gamal; García-Sedeño, Manuel

    2016-12-01

    Early math skills are determined by higher cognitive processes that are particularly important for acquiring and developing skills during a child's early education. Such processes could be a critical target for identifying students at risk for math learning difficulties. Few studies have considered the use of a structural equation method to rationalize these relations. Participating in this study were 207 preschool students ages 59 to 72 months, 108 boys and 99 girls. Performance with respect to early math skills, early literacy, general intelligence, working memory, and short-term memory was assessed. A structural equation model explaining 64.3% of the variance in early math skills was applied. Early literacy exhibited the highest statistical significance (β = 0.443, p < 0.05), followed by intelligence (β = 0.286, p < 0.05), working memory (β = 0.220, p < 0.05), and short-term memory (β = 0.213, p < 0.05). Correlations between the independent variables were also significant (p < 0.05). According to the results, cognitive variables should be included in remedial intervention programs. © 2016 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

  5. 7 CFR 610.12 - Equations for predicting soil loss due to water erosion.

    Science.gov (United States)

    2010-01-01

    .... (a) The equation for predicting soil loss due to erosion for both the USLE and the RUSLE is A = R × K... 22161.) (b) The factors in the USLE equation are: (1) A is the estimation of average annual soil loss in... 7 Agriculture 6 2010-01-01 2010-01-01 false Equations for predicting soil loss due to water...

  6. Tail Risk Premia and Return Predictability

    DEFF Research Database (Denmark)

    Bollerslev, Tim; Todorov, Viktor; Xu, Lai

    The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may be attribu......The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may......-varying economic uncertainty and changes in risk aversion, or market fears, respectively....

  7. Comparison of equations for predicting energy expenditure from accelerometer counts in children

    DEFF Research Database (Denmark)

    Nilsson, A; Brage, S; Riddoch, C

    2008-01-01

    calorimeter-based (CAL) equation (mixture of activities). Predicted physical activity energy expenditure (PAEE) was the main outcome variable. In comparison with DLW-predicted PAEE, both laboratory-derived equations significantly (PPAEE by 17% and 83%, respectively, when based on a 24-h...... prediction, while the TM equation significantly (PPAEE by 46%, when based on awake time only. In contrast, the CAL equation agreed better with the DLW equation under the awake time assumption. Predicted PAEE differ substantially between equations, depending on time-frame assumptions......, and interpretations of average levels of PAEE in children from available equations should be made with caution. Further development of equations applicable to free-living scenarios is needed....

  8. An Extrapolation of a Radical Equation More Accurately Predicts Shelf Life of Frozen Biological Matrices.

    Science.gov (United States)

    De Vore, Karl W; Fatahi, Nadia M; Sass, John E

    2016-08-01

    Arrhenius modeling of analyte recovery at increased temperatures to predict long-term colder storage stability of biological raw materials, reagents, calibrators, and controls is standard practice in the diagnostics industry. Predicting subzero temperature stability using the same practice is frequently criticized but nevertheless heavily relied upon. We compared the ability to predict analyte recovery during frozen storage using 3 separate strategies: traditional accelerated studies with Arrhenius modeling, and extrapolation of recovery at 20% of shelf life using either ordinary least squares or a radical equation y = B1x(0.5) + B0. Computer simulations were performed to establish equivalence of statistical power to discern the expected changes during frozen storage or accelerated stress. This was followed by actual predictive and follow-up confirmatory testing of 12 chemistry and immunoassay analytes. Linear extrapolations tended to be the most conservative in the predicted percent recovery, reducing customer and patient risk. However, the majority of analytes followed a rate of change that slowed over time, which was fit best to a radical equation of the form y = B1x(0.5) + B0. Other evidence strongly suggested that the slowing of the rate was not due to higher-order kinetics, but to changes in the matrix during storage. Predicting shelf life of frozen products through extrapolation of early initial real-time storage analyte recovery should be considered the most accurate method. Although in this study the time required for a prediction was longer than a typical accelerated testing protocol, there are less potential sources of error, reduced costs, and a lower expenditure of resources. © 2016 American Association for Clinical Chemistry.

  9. Prediction of Pure Component Adsorption Equilibria Using an Adsorption Isotherm Equation Based on Vacancy Solution Theory

    DEFF Research Database (Denmark)

    Marcussen, Lis; Aasberg-Petersen, K.; Krøll, Annette Elisabeth

    2000-01-01

    An adsorption isotherm equation for nonideal pure component adsorption based on vacancy solution theory and the Non-Random-Two-Liquid (NRTL) equation is found to be useful for predicting pure component adsorption equilibria at a variety of conditions. The isotherm equation is evaluated successfully...... adsorption systems, spreading pressure and isosteric heat of adsorption are also calculated....

  10. Correlation and prediction equations for eight-week bodyweight in ...

    African Journals Online (AJOL)

    Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives ... Cubic; Compound; Power; Sigmoidal; Growth; and Exponential equation. ... logarithmic, inverse, compound, growth and exponential) have significant ...

  11. Lipoprotein metabolism indicators improve cardiovascular risk prediction.

    Directory of Open Access Journals (Sweden)

    Daniël B van Schalkwijk

    Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

  12. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  19. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  20. Developmental Dyslexia: Predicting Individual Risk

    Science.gov (United States)

    Thompson, Paul A.; Hulme, Charles; Nash, Hannah M.; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J.

    2015-01-01

    Background: Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods: The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6…

  1. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  2. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  3. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  4. Model Selection and Risk Estimation with Applications to Nonlinear Ordinary Differential Equation Systems

    DEFF Research Database (Denmark)

    Mikkelsen, Frederik Vissing

    eective computational tools for estimating unknown structures in dynamical systems, such as gene regulatory networks, which may be used to predict downstream eects of interventions in the system. A recommended algorithm based on the computational tools is presented and thoroughly tested in various......Broadly speaking, this thesis is devoted to model selection applied to ordinary dierential equations and risk estimation under model selection. A model selection framework was developed for modelling time course data by ordinary dierential equations. The framework is accompanied by the R software...... package, episode. This package incorporates a collection of sparsity inducing penalties into two types of loss functions: a squared loss function relying on numerically solving the equations and an approximate loss function based on inverse collocation methods. The goal of this framework is to provide...

  5. Validity of one-repetition maximum predictive equations in men with spinal cord injury.

    Science.gov (United States)

    Ribeiro Neto, F; Guanais, P; Dornelas, E; Coutinho, A C B; Costa, R R G

    2017-10-01

    Cross-sectional study. The study aimed (a) to test the cross-validation of current one-repetition maximum (1RM) predictive equations in men with spinal cord injury (SCI); (b) to compare the current 1RM predictive equations to a newly developed equation based on the 4- to 12-repetition maximum test (4-12RM). SARAH Rehabilitation Hospital Network, Brasilia, Brazil. Forty-five men aged 28.0 years with SCI between C6 and L2 causing complete motor impairment were enrolled in the study. Volunteers were tested, in a random order, in 1RM test or 4-12RM with 2-3 interval days. Multiple regression analysis was used to generate an equation for predicting 1RM. There were no significant differences between 1RM test and the current predictive equations. ICC values were significant and were classified as excellent for all current predictive equations. The predictive equation of Lombardi presented the best Bland-Altman results (0.5 kg and 12.8 kg for mean difference and interval range around the differences, respectively). The two created equation models for 1RM demonstrated the same and a high adjusted R 2 (0.971, Ppredictive equations are accurate to assess individuals with SCI at the bench press exercise. However, the predictive equation of Lombardi presented the best associated cross-validity results. A specific 1RM prediction equation was also elaborated for individuals with SCI. The created equation should be tested in order to verify whether it presents better accuracy than the current ones.

  6. Cardiovascular risk prediction in the Netherlands

    NARCIS (Netherlands)

    Dis, van S.J.

    2011-01-01

    Background: In clinical practice, Systematic COronary Risk Evaluation (SCORE) risk prediction functions and charts are used to identify persons at high risk for cardiovascular diseases (CVD), who are considered eligible for drug treatment of elevated blood pressure and serum cholesterol. These

  7. Validity of predictive equations for basal metabolic rate in Japanese adults.

    Science.gov (United States)

    Miyake, Rieko; Tanaka, Shigeho; Ohkawara, Kazunori; Ishikawa-Takata, Kazuko; Hikihara, Yuki; Taguri, Emiko; Kayashita, Jun; Tabata, Izumi

    2011-01-01

    Many predictive equations for basal metabolic rate (BMR) based on anthropometric measurements, age, and sex have been developed, mainly for healthy Caucasians. However, it has been reported that many of these equations, used widely, overestimate BMR not only for Asians, but also for Caucasians. The present study examined the accuracy of several predictive equations for BMR in Japanese subjects. In 365 healthy Japanese male and female subjects, aged 18 to 79 y, BMR was measured in the post-absorptive state using a mask and Douglas bag. Six predictive equations were examined. Total error was used as an index of the accuracy of each equation's prediction. Predicted BMR values by Dietary Reference Intakes for Japanese (Japan-DRI), Adjusted Dietary Reference Intakes for Japanese (Adjusted-DRI), and Ganpule equations were not significantly different from the measured BMR in either sex. On the other hand, Harris-Benedict, Schofield, and Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University equations were significantly higher than the measured BMR in both sexes. The prediction error by Japan-DRI, Adjusted-DRI, and Harris-Benedict equations was significantly correlated with body weight in both sexes. Total error using the Ganpule equation was low in both males and females (125 and 99 kcal/d, respectively). In addition, total error using the Adjusted-DRI equation was low in females (95 kcal/d). Thus, the Ganpule equation was the most accurate in predicting BMR in our healthy Japanese subjects, because the difference between the predicted and measured BMR was relatively small, and body weight had no effect on the prediction error.

  8. Prediction of eyespot infection risks

    Directory of Open Access Journals (Sweden)

    M. Váòová

    2012-12-01

    Full Text Available The objective of the study was to design a prediction model for eyespot (Tapesia yallundae infection based on climatic factors (temperature, precipitation, air humidity. Data from experiment years 1994-2002 were used to study correlations between the eyespot infection index and individual weather characteristics. The model of prediction was constructed using multiple regression when a separate parameter is assigned to each factor, i.e. the frequency of days with optimum temperatures, humidity, and precipitation. The correlation between relative air humidity and precipitation and the infection index is significant.

  9. A suspended sediment yield predictive equation for river basins in ...

    African Journals Online (AJOL)

    The fit was found to be better than those relating mean annual specific suspended sediment yield to basin area or runoff only. Since many stream gauging stations in the country have no records on fluvial sediment, the empirical equation can be used to obtain preliminary estimates of expected sediment load of streams for ...

  10. Predictive equations for spirometric reference values in a healthy ...

    African Journals Online (AJOL)

    men and 98 women were selected to the reference value group. ... adult population in Dar es Salaam, Tanzania, and compare these equations to already ... which has proven to be suitable in field work as it operates on batteries and requires no ..... thought to be partly due to difference in body build and that Blacks have ...

  11. Predictive equation of state method for heavy materials based on the Dirac equation and density functional theory

    Science.gov (United States)

    Wills, John M.; Mattsson, Ann E.

    2012-02-01

    Density functional theory (DFT) provides a formally predictive base for equation of state properties. Available approximations to the exchange/correlation functional provide accurate predictions for many materials in the periodic table. For heavy materials however, DFT calculations, using available functionals, fail to provide quantitative predictions, and often fail to be even qualitative. This deficiency is due both to the lack of the appropriate confinement physics in the exchange/correlation functional and to approximations used to evaluate the underlying equations. In order to assess and develop accurate functionals, it is essential to eliminate all other sources of error. In this talk we describe an efficient first-principles electronic structure method based on the Dirac equation and compare the results obtained with this method with other methods generally used. Implications for high-pressure equation of state of relativistic materials are demonstrated in application to Ce and the light actinides. Sandia National Laboratories is a multi-program laboratory managed andoperated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

  12. Predictive equations underestimate resting energy expenditure in female adolescents with phenylketonuria

    Science.gov (United States)

    Quirk, Meghan E.; Schmotzer, Brian J.; Schmotzer, Brian J.; Singh, Rani H.

    2010-01-01

    Resting energy expenditure (REE) is often used to estimate total energy needs. The Schofield equation based on weight and height has been reported to underestimate REE in female children with phenylketonuria (PKU). The objective of this observational, cross-sectional study was to evaluate the agreement of measured REE with predicted REE for female adolescents with PKU. A total of 36 females (aged 11.5-18.7 years) with PKU attending Emory University’s Metabolic Camp (June 2002 – June 2008) underwent indirect calorimetry. Measured REE was compared to six predictive equations using paired Student’s t-tests, regression-based analysis, and assessment of clinical accuracy. The differences between measured and predicted REE were modeled against clinical parameters to determine to if a relationship existed. All six selected equations significantly under predicted measured REE (P< 0.005). The Schofield equation based on weight had the greatest level of agreement, with the lowest mean prediction bias (144 kcal) and highest concordance correlation coefficient (0.626). However, the Schofield equation based on weight lacked clinical accuracy, predicting measured REE within ±10% in only 14 of 36 participants. Clinical parameters were not associated with bias for any of the equations. Predictive equations underestimated measured REE in this group of female adolescents with PKU. Currently, there is no accurate and precise alternative for indirect calorimetry in this population. PMID:20497783

  13. Prediction equation of resting energy expenditure in an adult Spanish population of obese adult population.

    Science.gov (United States)

    de Luis, D A; Aller, R; Izaola, O; Romero, E

    2006-01-01

    The aim of our study was to evaluate the accuracy of the equations to estimate REE in obese patents and develop a new equation in our obese population. A population of 200 obesity outpatients was analyzed in a prospective way. The following variables were specifically recorded: age, weight, body mass index (BMI), waist circumference, and waist-to-hip ratio. Basal glucose, insulin, and TSH (thyroid-stimulating hormone) were measured. An indirect calorimetry and a tetrapolar electrical bioimpedance were performed. REE measured by indirect calorimetry was compared with REE obtained by prediction equations to obese or nonobese patients. The mean age was 44.8 +/- 16.81 years and the mean BMI 34.4 +/- 5.3. Indirect calorimetry showed that, as compared to women, men had higher resting energy expenditure (REE) (1,998.1 +/- 432 vs. 1,663.9 +/- 349 kcal/day; p consumption (284.6 +/- 67.7 vs. 238.6 +/- 54.3 ml/min; p predicted by prediction equations showed the next data; Berstein's equation (r = 0.65; p prediction equation was REE = 58.6 + (6.1 x weight (kg)) + (1,023.7 x height (m)) - (9.5 x age). The female model was REE = 1,272.5 + (9.8 x weight (kg)) - (61.6 x height (m)) - (8.2 x age). Our prediction equations showed a nonsignificant difference with REE measured (-3.7 kcal/day) with a significant correlation coefficient (r = 0.67; p prediction equations overestimated and underestimated REE measured. WHO equation developed in normal weight individuals provided the closest values. The two new equations (male and female equations) developed in our study had a good accuracy. Copyright 2006 S. Karger AG, Basel.

  14. A prediction equation for enteric methane emission from dairy cows for use in NorFor

    DEFF Research Database (Denmark)

    Nielsen, N I; Volden, H; Åkerlind, M

    2013-01-01

    A data-set with 47 treatment means (N = 211) was compiled from research institutions in Denmark, Norway, and Sweden in order to develop a prediction equation for enteric methane (CH4) emissions from dairy cows. The aim was to implement the equation in the Nordic feed evaluation system NorFor. The...

  15. Trends of Abutment-Scour Prediction Equations Applied to 144 Field Sites in South Carolina

    Science.gov (United States)

    Benedict, Stephen T.; Deshpande, Nikhil; Aziz, Nadim M.; Conrads, Paul

    2006-01-01

    The U.S. Geological Survey conducted a study in cooperation with the Federal Highway Administration in which predicted abutment-scour depths computed with selected predictive equations were compared with field measurements of abutment-scour depth made at 144 bridges in South Carolina. The assessment used five equations published in the Fourth Edition of 'Evaluating Scour at Bridges,' (Hydraulic Engineering Circular 18), including the original Froehlich, the modified Froehlich, the Sturm, the Maryland, and the HIRE equations. An additional unpublished equation also was assessed. Comparisons between predicted and observed scour depths are intended to illustrate general trends and order-of-magnitude differences for the prediction equations. Field measurements were taken during non-flood conditions when the hydraulic conditions that caused the scour generally are unknown. The predicted scour depths are based on hydraulic conditions associated with the 100-year flow at all sites and the flood of record for 35 sites. Comparisons showed that predicted scour depths frequently overpredict observed scour and at times were excessive. The comparison also showed that underprediction occurred, but with less frequency. The performance of these equations indicates that they are poor predictors of abutment-scour depth in South Carolina, and it is probable that poor performance will occur when the equations are applied in other geographic regions. Extensive data and graphs used to compare predicted and observed scour depths in this study were compiled into spreadsheets and are included in digital format with this report. In addition to the equation-comparison data, Water-Surface Profile Model tube-velocity data, soil-boring data, and selected abutment-scour data are included in digital format with this report. The digital database was developed as a resource for future researchers and is especially valuable for evaluating the reasonableness of future equations that may be developed.

  16. Predictive Model Equations for Palm Kernel (Elaeis guneensis J ...

    African Journals Online (AJOL)

    Estimated error of ± 0.18 and ± 0.2 are envisaged while applying the models for predicting palm kernel and sesame oil colours respectively. Keywords: Palm kernel, Sesame, Palm kernel, Oil Colour, Process Parameters, Model. Journal of Applied Science, Engineering and Technology Vol. 6 (1) 2006 pp. 34-38 ...

  17. Saturated properties prediction in critical region by a quartic equation of state

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2011-08-01

    Full Text Available A diverse substance library containing extensive PVT data for 77 pure components was used to critically evaluate the performance of a quartic equation of state and other four famous cubic equations of state in critical region. The quartic EOS studied in this work was found to significantly superior to the others in both vapor pressure prediction and saturated volume prediction in vicinity of critical point.

  18. Regression equations to predict 6-minute walk distance in Chinese adults aged 55–85 years

    OpenAIRE

    Shirley P.C. Ngai, PhD; Alice Y.M. Jones, PhD; Sue C. Jenkins, PhD

    2014-01-01

    The 6-minute walk distance (6MWD) is used as a measure of functional exercise capacity in clinical populations and research. Reference equations to predict 6MWD in different populations have been established, however, available equations for Chinese population are scarce. This study aimed to develop regression equations to predict the 6MWD for a Hong Kong Chinese population. Fifty-three healthy individuals (25 men, 28 women; mean age = 69.3 ± 6.5 years) participated in this cross-sectional st...

  19. Predicting the 10-Year Risks of Atherosclerotic Cardiovascular Disease in Chinese Population: The China-PAR Project (Prediction for ASCVD Risk in China).

    Science.gov (United States)

    Yang, Xueli; Li, Jianxin; Hu, Dongsheng; Chen, Jichun; Li, Ying; Huang, Jianfeng; Liu, Xiaoqing; Liu, Fangchao; Cao, Jie; Shen, Chong; Yu, Ling; Lu, Fanghong; Wu, Xianping; Zhao, Liancheng; Wu, Xigui; Gu, Dongfeng

    2016-11-08

    The accurate assessment of individual risk can be of great value to guiding and facilitating the prevention of atherosclerotic cardiovascular disease (ASCVD). However, prediction models in common use were formulated primarily in white populations. The China-PAR project (Prediction for ASCVD Risk in China) is aimed at developing and validating 10-year risk prediction equations for ASCVD from 4 contemporary Chinese cohorts. Two prospective studies followed up together with a unified protocol were used as the derivation cohort to develop 10-year ASCVD risk equations in 21 320 Chinese participants. The external validation was evaluated in 2 independent Chinese cohorts with 14 123 and 70 838 participants. Furthermore, model performance was compared with the Pooled Cohort Equations reported in the American College of Cardiology/American Heart Association guideline. Over 12 years of follow-up in the derivation cohort with 21 320 Chinese participants, 1048 subjects developed a first ASCVD event. Sex-specific equations had C statistics of 0.794 (95% confidence interval, 0.775-0.814) for men and 0.811 (95% confidence interval, 0.787-0.835) for women. The predicted rates were similar to the observed rates, as indicated by a calibration χ 2 of 13.1 for men (P=0.16) and 12.8 for women (P=0.17). Good internal and external validations of our equations were achieved in subsequent analyses. Compared with the Chinese equations, the Pooled Cohort Equations had lower C statistics and much higher calibration χ 2 values in men. Our project developed effective tools with good performance for 10-year ASCVD risk prediction among a Chinese population that will help to improve the primary prevention and management of cardiovascular disease. © 2016 American Heart Association, Inc.

  20. Prediction Equations Overestimate the Energy Requirements More for Obesity-Susceptible Individuals.

    Science.gov (United States)

    McLay-Cooke, Rebecca T; Gray, Andrew R; Jones, Lynnette M; Taylor, Rachael W; Skidmore, Paula M L; Brown, Rachel C

    2017-09-13

    Predictive equations to estimate resting metabolic rate (RMR) are often used in dietary counseling and by online apps to set energy intake goals for weight loss. It is critical to know whether such equations are appropriate for those susceptible to obesity. We measured RMR by indirect calorimetry after an overnight fast in 26 obesity susceptible (OSI) and 30 obesity resistant (ORI) individuals, identified using a simple 6-item screening tool. Predicted RMR was calculated using the FAO/WHO/UNU (Food and Agricultural Organisation/World Health Organisation/United Nations University), Oxford and Miflin-St Jeor equations. Absolute measured RMR did not differ significantly between OSI versus ORI (6339 vs. 5893 kJ·d -1 , p = 0.313). All three prediction equations over-estimated RMR for both OSI and ORI when measured RMR was ≤5000 kJ·d -1 . For measured RMR ≤7000 kJ·d -1 there was statistically significant evidence that the equations overestimate RMR to a greater extent for those classified as obesity susceptible with biases ranging between around 10% to nearly 30% depending on the equation. The use of prediction equations may overestimate RMR and energy requirements particularly in those who self-identify as being susceptible to obesity, which has implications for effective weight management.

  1. Derivation of governing equation for predicting thermal conductivity of composites with spherical inclusions and its applications

    International Nuclear Information System (INIS)

    Lee, Jae-Kon; Kim, Jin-Gon

    2011-01-01

    A governing differential equation for predicting the effective thermal conductivity of composites with spherical inclusions is shown to be simply derived by using the result of the generalized self-consistent model. By applying the equation to composites including spherical inclusions such as graded spherical inclusions, microballoons, mutiply-coated spheres, and spherical inclusions with an interphase, their effective thermal conductivities are easily predicted. The results are compared with those in the literatures to be consistent. It can be stated from the investigations that the effective thermal conductivity of composites with spherical inclusions can be estimated as long as their conductivities are expressed as a function of their radius. -- Highlights: → We derive equation for predicting the effective thermal conductivity of composites. → The equation is derived using the results of the generalized self-consistent model. → The inclusions are graded sphere, microballoons, and mutiply-coated spheres.

  2. Prediction equations of forced oscillation technique: the insidious role of collinearity.

    Science.gov (United States)

    Narchi, Hassib; AlBlooshi, Afaf

    2018-03-27

    Many studies have reported reference data for forced oscillation technique (FOT) in healthy children. The prediction equation of FOT parameters were derived from a multivariable regression model examining the effect of age, gender, weight and height on each parameter. As many of these variables are likely to be correlated, collinearity might have affected the accuracy of the model, potentially resulting in misleading, erroneous or difficult to interpret conclusions.The aim of this work was: To review all FOT publications in children since 2005 to analyze whether collinearity was considered in the construction of the published prediction equations. Then to compare these prediction equations with our own study. And to analyse, in our study, how collinearity between the explanatory variables might affect the predicted equations if it was not considered in the model. The results showed that none of the ten reviewed studies had stated whether collinearity was checked for. Half of the reports had also included in their equations variables which are physiologically correlated, such as age, weight and height. The predicted resistance varied by up to 28% amongst these studies. And in our study, multicollinearity was identified between the explanatory variables initially considered for the regression model (age, weight and height). Ignoring it would have resulted in inaccuracies in the coefficients of the equation, their signs (positive or negative), their 95% confidence intervals, their significance level and the model goodness of fit. In Conclusion with inaccurately constructed and improperly reported models, understanding the results and reproducing the models for future research might be compromised.

  3. Predicted equations for ventilatory function among Kuching (Sarawak, Malaysia) population.

    Science.gov (United States)

    Djojodibroto, R D; Pratibha, G; Kamaluddin, B; Manjit, S S; Sumitabha, G; Kumar, A Deva; Hashami, B

    2009-12-01

    Spirometry data of 869 individuals (males and females) between the ages of 10 to 60 years were analyzed. The analysis yielded the following conclusions: 1. The pattern of Forced Vital Capacity (FVC) and Forced Expiratory Volume in One Second (FEV1) for the selected subgroups seems to be gender dependant: in males, the highest values were seen in the Chinese, followed by the Malay, and then the Dayak; in females, the highest values were seen in the Chinese, followed by the Dayak, and then the Malay. 2. Smoking that did not produce respiratory symptom was not associated with a decline in lung function, in fact we noted higher values in smokers as compared to nonsmokers. 3. Prediction formulae (54 in total) are worked out for FVC & FEV1 for the respective gender and each of the selected subgroups.

  4. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  5. Predicting Eight Grade Students' Equation Solving Performances via Concepts of Variable and Equality

    Science.gov (United States)

    Ertekin, Erhan

    2017-01-01

    This study focused on how two algebraic concepts- equality and variable- predicted 8th grade students' equation solving performance. In this study, predictive design as a correlational research design was used. Randomly selected 407 eight-grade students who were from the central districts of a city in the central region of Turkey participated in…

  6. Subclinical organ damage and cardiovascular risk prediction

    DEFF Research Database (Denmark)

    Sehestedt, Thomas; Olsen, Michael H

    2010-01-01

    Traditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided...... by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii...

  7. Development of 1RM Prediction Equations for Bench Press in Moderately Trained Men.

    Science.gov (United States)

    Macht, Jordan W; Abel, Mark G; Mullineaux, David R; Yates, James W

    2016-10-01

    Macht, JW, Abel, MG, Mullineaux, DR, and Yates, JW. Development of 1RM prediction equations for bench press in moderately trained men. J Strength Cond Res 30(10): 2901-2906, 2016-There are a variety of established 1 repetition maximum (1RM) prediction equations, however, very few prediction equations use anthropometric characteristics exclusively or in part, to estimate 1RM strength. Therefore, the purpose of this study was to develop an original 1RM prediction equation for bench press using anthropometric and performance characteristics in moderately trained male subjects. Sixty male subjects (21.2 ± 2.4 years) completed a 1RM bench press and were randomly assigned a load to complete as many repetitions as possible. In addition, body composition, upper-body anthropometric characteristics, and handgrip strength were assessed. Regression analysis was used to develop a performance-based 1RM prediction equation: 1RM = 1.20 repetition weight + 2.19 repetitions to fatigue - 0.56 biacromial width (cm) + 9.6 (R = 0.99, standard error of estimate [SEE] = 3.5 kg). Regression analysis to develop a nonperformance-based 1RM prediction equation yielded: 1RM (kg) = 0.997 cross-sectional area (CSA) (cm) + 0.401 chest circumference (cm) - 0.385%fat - 0.185 arm length (cm) + 36.7 (R = 0.81, SEE = 13.0 kg). The performance prediction equations developed in this study had high validity coefficients, minimal mean bias, and small limits of agreement. The anthropometric equations had moderately high validity coefficient but larger limits of agreement. The practical applications of this study indicate that the inclusion of anthropometric characteristics and performance variables produce a valid prediction equation for 1RM strength. In addition, the CSA of the arm uses a simple nonperformance method of estimating the lifter's 1RM. This information may be used to predict the starting load for a lifter performing a 1RM prediction protocol or a 1RM testing protocol.

  8. Development and validation of a predictive equation for lean body mass in children and adolescents.

    Science.gov (United States)

    Foster, Bethany J; Platt, Robert W; Zemel, Babette S

    2012-05-01

    Lean body mass (LBM) is not easy to measure directly in the field or clinical setting. Equations to predict LBM from simple anthropometric measures, which account for the differing contributions of fat and lean to body weight at different ages and levels of adiposity, would be useful to both human biologists and clinicians. To develop and validate equations to predict LBM in children and adolescents across the entire range of the adiposity spectrum. Dual energy X-ray absorptiometry was used to measure LBM in 836 healthy children (437 females) and linear regression was used to develop sex-specific equations to estimate LBM from height, weight, age, body mass index (BMI) for age z-score and population ancestry. Equations were validated using bootstrapping methods and in a local independent sample of 332 children and in national data collected by NHANES. The mean difference between measured and predicted LBM was - 0.12% (95% limits of agreement - 11.3% to 8.5%) for males and - 0.14% ( - 11.9% to 10.9%) for females. Equations performed equally well across the entire adiposity spectrum, as estimated by BMI z-score. Validation indicated no over-fitting. LBM was predicted within 5% of measured LBM in the validation sample. The equations estimate LBM accurately from simple anthropometric measures.

  9. Water erosion risk prediction in eucalyptus plantations

    Directory of Open Access Journals (Sweden)

    Mayesse Aparecida da Silva

    2014-04-01

    Full Text Available Eucalyptus plantations are normally found in vulnerable ecosystems such as steep slope, soil with low natural fertility and lands that were degraded by agriculture. The objective of this study was to obtain Universal Soil Loss Equation (USLE factors and use them to estimate water erosion risk in regions with eucalyptus planted. The USLE factors were obtained in field plots under natural rainfall in the Rio Doce Basin, MG, Brazil, and the model applied to assess erosion risk using USLE in a Geographic Information System. The study area showed rainfall-runoff erosivity values from 10,721 to 10,642 MJ mm ha-1 h-1 yr-1. Some soils (Latosols had very low erodibility values (2.0 x 10-4 and 1.0 x 10-4t h MJ-1 mm-1, the topographic factor ranged from 0.03 to 10.57 and crop and management factor values obtained for native forest, eucalyptus and planted pasture were 0.09, 0.12 and 0.22, respectively. Water erosion risk estimates for current land use indicated that the areas where should receive more attention were mainly areas with greater topographic factors and those with Cambisols. Planning of forestry activities in this region should consider implementation of other conservation practices beyond those already used, reducing areas with a greater risk of soil erosion and increasing areas with very low risk.

  10. Validity of a population-specific BMR predictive equation for adults from an urban tropical setting.

    Science.gov (United States)

    Wahrlich, Vivian; Teixeira, Tatiana Miliante; Anjos, Luiz Antonio Dos

    2018-02-01

    Basal metabolic rate (BMR) is an important physiologic measure in nutrition research. In many instances it is not measured but estimated by predictive equations. The purpose of this study was to compare measured BMR (BMRm) with estimated BMR (BMRe) obtained by different equations. A convenient sample of 148 (89 women) 20-60 year-old subjects from the metropolitan area of Rio de Janeiro, Brazil participated in the study. BMRm values were measured by an indirect calorimeter and predicted by different equations (Schofield, Henry and Rees, Mifflin-St. Jeor and Anjos. All subjects had their body composition and anthropometric variables also measured. Accuracy of the estimations was established by the percentage of BMRe falling within ±10% of BMRm and bias when the 95% CI of the difference of BMRe and BMRm means did not include zero. Mean BMRm values were 4833.5 (SD 583.3) and 6278.8 (SD 724.0) kJ*day -1 for women and men, respectively. BMRe values were both biased and inaccurate except for values predicted by the Anjos equation. BMR overestimation was approximately 20% for the Schofield equation which was higher comparatively to the Henry and Rees (14.5% and 9.6% for women and men, respectively) and the Mifflin-St. Jeor (approximately 14.0%) equations. BMR estimated by the Anjos equation was unbiased (95% CI = -78.1; 96.3 kJ day -1 for women and -282.6; 30.7 kJ*day -1 for men). Population-specific BMR predictive equations yield unbiased and accurate BMR values in adults from an urban tropical setting. Copyright © 2016 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

  11. Adjustment of equations to predict the metabolizable energy of corn for meat type quails

    Directory of Open Access Journals (Sweden)

    Tiago Junior Pasquetti

    2015-08-01

    Full Text Available The metabolizable energy (ME determination for foods used in quail diets, through metabolism assays, takes time, infrastructure and financial resources, which makes the development of prediction equations based on proximal composition of foods to estimate the ME values of particular interest. The objective of this study was to adjust the prediction equations of metabolizable energy (ME of corn for quail. The chemical compositions of 12 maize varieties were determined and a metabolism assay was carried out in order to determine the apparent metabolizable energy (AME and nitrogen-corrected apparent metabolizable energy (AMEn of these corn varieties. The values of chemical composition, AME and AMEn, converted to dry matter, were used to adjust the prediction equations. The initial adjustment of simple and multiple linear regression of the AME and AMEn was performed using the values of crude protein (CP, ether extract (EE, neutral (NDF and acid (ADF detergent fiber, mineral matter (MM, calcium (Ca and phosphorus (P as regressors (full model. To adjust the prediction equations the statistical procedure of simple and multiple linear regression was used, with the technique of indirect elimination (Backward. There was adjustment of 10 prediction equations, in which five were for AME and another five for AMEn, the R² values of which ranged from 0.20 to 0.75 and from 0.21 to 0.78, respectively. For all adjusted equations, negative correlations for MM were observed, which may be related to its dilutive effect of the gross energy contained in corn. In conclusion, the equations that showed better adjustment were AME= 5605.46 - 385.074CP + 111.648EE + 48.1133NDF + 303.924ADF - 929.931MM (R²= 0.75 and AMEn= 5878.16 - 403.937CP + 81.9618EE + 41.8954NDF + 303.506FDA - 901.621MM (R²= 0.78.

  12. Predicting adolescent's cyberbullying behavior: A longitudinal risk analysis.

    Science.gov (United States)

    Barlett, Christopher P

    2015-06-01

    The current study used the risk factor approach to test the unique and combined influence of several possible risk factors for cyberbullying attitudes and behavior using a four-wave longitudinal design with an adolescent US sample. Participants (N = 96; average age = 15.50 years) completed measures of cyberbullying attitudes, perceptions of anonymity, cyberbullying behavior, and demographics four times throughout the academic school year. Several logistic regression equations were used to test the contribution of these possible risk factors. Results showed that (a) cyberbullying attitudes and previous cyberbullying behavior were important unique risk factors for later cyberbullying behavior, (b) anonymity and previous cyberbullying behavior were valid risk factors for later cyberbullying attitudes, and (c) the likelihood of engaging in later cyberbullying behavior increased with the addition of risk factors. Overall, results show the unique and combined influence of such risk factors for predicting later cyberbullying behavior. Results are discussed in terms of theory. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  13. OR25: Validity of predictive equations for resting energy expenditure for overweight older adults with and without diabetes

    NARCIS (Netherlands)

    Verreijen, A. M.; Garrido, V.; Engberink, M.F.; Memelink, R. G.; Visser, M.; Weijs, P. J.

    2017-01-01

    Rationale: Predictive equations for resting energy expenditure (REE) are used in the treatment of overweight and obesity, but the validity of these equations in overweight older adults is unknown. This study evaluates which predictive REE equation is the best alternative to indirect calorimetry in

  14. Predicting lower body power from vertical jump prediction equations for loaded jump squats at different intensities in men and women.

    Science.gov (United States)

    Wright, Glenn A; Pustina, Andrew A; Mikat, Richard P; Kernozek, Thomas W

    2012-03-01

    The purpose of this study was to determine the efficacy of estimating peak lower body power from a maximal jump squat using 3 different vertical jump prediction equations. Sixty physically active college students (30 men, 30 women) performed jump squats with a weighted bar's applied load of 20, 40, and 60% of body mass across the shoulders. Each jump squat was simultaneously monitored using a force plate and a contact mat. Peak power (PP) was calculated using vertical ground reaction force from the force plate data. Commonly used equations requiring body mass and vertical jump height to estimate PP were applied such that the system mass (mass of body + applied load) was substituted for body mass. Jump height was determined from flight time as measured with a contact mat during a maximal jump squat. Estimations of PP (PP(est)) for each load and for each prediction equation were compared with criterion PP values from a force plate (PP(FP)). The PP(est) values had high test-retest reliability and were strongly correlated to PP(FP) in both men and women at all relative loads. However, only the Harman equation accurately predicted PP(FP) at all relative loads. It can therefore be concluded that the Harman equation may be used to estimate PP of a loaded jump squat knowing the system mass and peak jump height when more precise (and expensive) measurement equipment is unavailable. Further, high reliability and correlation with criterion values suggest that serial assessment of power production across training periods could be used for relative assessment of change by either of the prediction equations used in this study.

  15. Risk prediction of hepatotoxicity in paracetamol poisoning.

    Science.gov (United States)

    Wong, Anselm; Graudins, Andis

    2017-09-01

    Paracetamol (acetaminophen) poisoning is the most common cause of acute liver failure in the developed world. A paracetamol treatment nomogram has been used for over four decades to help determine whether patients will develop hepatotoxicity without acetylcysteine treatment, and thus indicates those needing treatment. Despite this, a small proportion of patients still develop hepatotoxicity. More accurate risk predictors would be useful to increase the early detection of patients with the potential to develop hepatotoxicity despite acetylcysteine treatment. Similarly, there would be benefit in early identification of those with a low likelihood of developing hepatotoxicity, as this group may be safely treated with an abbreviated acetylcysteine regimen. To review the current literature related to risk prediction tools that can be used to identify patients at increased risk of hepatotoxicity. A systematic literature review was conducted using the search terms: "paracetamol" OR "acetaminophen" AND "overdose" OR "toxicity" OR "risk prediction rules" OR "hepatotoxicity" OR "psi parameter" OR "multiplication product" OR "half-life" OR "prothrombin time" OR "AST/ALT (aspartate transaminase/alanine transaminase)" OR "dose" OR "biomarkers" OR "nomogram". The search was limited to human studies without language restrictions, of Medline (1946 to May 2016), PubMed and EMBASE. Original articles pertaining to the theme were identified from January 1974 to May 2016. Of the 13,975 articles identified, 60 were relevant to the review. Paracetamol treatment nomograms: Paracetamol treatment nomograms have been used for decades to help decide the need for acetylcysteine, but rarely used to determine the risk of hepatotoxicity with treatment. Reported paracetamol dose and concentration: A dose ingestion >12 g or serum paracetamol concentration above the treatment thresholds on the paracetamol nomogram are associated with a greater risk of hepatotoxicity. Paracetamol elimination half

  16. Cardiovascular Risk Assessment: A Comparison of the Framingham, PROCAM, and DAD Equations in HIV-Infected Persons

    Directory of Open Access Journals (Sweden)

    Max Weyler Nery

    2013-01-01

    Full Text Available This study aims to estimate the risk of cardiovascular disease (CVD and to assess the agreement between the Framingham, Framingham with aggravating factors, PROCAM, and DAD equations in HIV-infected patients. A cross-sectional study was conducted in an outpatient centre in Brazil. 294 patients older than 19 years were enrolled. Estimates of 10-year cardiovascular risk were calculated. The agreement between the CVD risk equations was assessed using Cohen's kappa coefficient. The participants' mean age was 36.8 years (SD = 10.3, 76.9% were men, and 66.3% were on antiretroviral therapy. 47.8% of the participants had abdominal obesity, 23.1% were current smokers, 20.0% had hypertension, and 2.0% had diabetes. At least one lipid abnormality was detected in 72.8%, and a low HDL-C level was the most common. The majority were classified as having low risk for CV events. The percentage of patients at high risk ranged from 0.4 to 5.7. The PROCAM score placed the lowest proportion of the patients into a high-risk group, and the Framingham equation with aggravating factors placed the highest proportion of patients into the high-risk group. Data concerning the comparability of different tools are informative for estimating the risk of CVD, but accuracy of the outcome predictions should also be considered.

  17. Prediction equations for spirometry in four- to six-year-old children.

    Science.gov (United States)

    França, Danielle Corrêa; Camargos, Paulo Augusto Moreira; Jones, Marcus Herbert; Martins, Jocimar Avelar; Vieira, Bruna da Silva Pinto Pinheiro; Colosimo, Enrico Antônio; de Mendonça, Karla Morganna Pereira Pinto; Borja, Raíssa de Oliveira; Britto, Raquel Rodrigues; Parreira, Verônica Franco

    2016-01-01

    To generate prediction equations for spirometry in 4- to 6-year-old children. Forced vital capacity, forced expiratory volume in 0.5s, forced expiratory volume in one second, peak expiratory flow, and forced expiratory flow at 25-75% of the forced vital capacity were assessed in 195 healthy children residing in the town of Sete Lagoas, state of Minas Gerais, Southeastern Brazil. The least mean squares method was used to derive the prediction equations. The level of significance was established as p<0.05. Overall, 85% of the children succeeded in performing the spirometric maneuvers. In the prediction equation, height was the single predictor of the spirometric variables as follows: forced vital capacity=exponential [(-2.255)+(0.022×height)], forced expiratory volume in 0.5s=exponential [(-2.288)+(0.019×height)], forced expiratory volume in one second=exponential [(-2.767)+(0.026×height)], peak expiratory flow=exponential [(-2.908)+(0.019×height)], and forced expiratory flow at 25-75% of the forced vital capacity=exponential [(-1.404)+(0.016×height)]. Neither age nor weight influenced the regression equations. No significant differences in the predicted values for boys and girls were observed. The predicted values obtained in the present study are comparable to those reported for preschoolers from both Brazil and other countries. Copyright © 2016 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

  18. Are predictive equations for estimating resting energy expenditure accurate in Asian Indian male weightlifters?

    Directory of Open Access Journals (Sweden)

    Mini Joseph

    2017-01-01

    Full Text Available Background: The accuracy of existing predictive equations to determine the resting energy expenditure (REE of professional weightlifters remains scarcely studied. Our study aimed at assessing the REE of male Asian Indian weightlifters with indirect calorimetry and to compare the measured REE (mREE with published equations. A new equation using potential anthropometric variables to predict REE was also evaluated. Materials and Methods: REE was measured on 30 male professional weightlifters aged between 17 and 28 years using indirect calorimetry and compared with the eight formulas predicted by Harris–Benedicts, Mifflin-St. Jeor, FAO/WHO/UNU, ICMR, Cunninghams, Owen, Katch-McArdle, and Nelson. Pearson correlation coefficient, intraclass correlation coefficient, and multiple linear regression analysis were carried out to study the agreement between the different methods, association with anthropometric variables, and to formulate a new prediction equation for this population. Results: Pearson correlation coefficients between mREE and the anthropometric variables showed positive significance with suprailiac skinfold thickness, lean body mass (LBM, waist circumference, hip circumference, bone mineral mass, and body mass. All eight predictive equations underestimated the REE of the weightlifters when compared with the mREE. The highest mean difference was 636 kcal/day (Owen, 1986 and the lowest difference was 375 kcal/day (Cunninghams, 1980. Multiple linear regression done stepwise showed that LBM was the only significant determinant of REE in this group of sportspersons. A new equation using LBM as the independent variable for calculating REE was computed. REE for weightlifters = −164.065 + 0.039 (LBM (confidence interval −1122.984, 794.854]. This new equation reduced the mean difference with mREE by 2.36 + 369.15 kcal/day (standard error = 67.40. Conclusion: The significant finding of this study was that all the prediction equations

  19. Estimating the Accuracy of the Chedoke-McMaster Stroke Assessment Predictive Equations for Stroke Rehabilitation.

    Science.gov (United States)

    Dang, Mia; Ramsaran, Kalinda D; Street, Melissa E; Syed, S Noreen; Barclay-Goddard, Ruth; Stratford, Paul W; Miller, Patricia A

    2011-01-01

    To estimate the predictive accuracy and clinical usefulness of the Chedoke-McMaster Stroke Assessment (CMSA) predictive equations. A longitudinal prognostic study using historical data obtained from 104 patients admitted post cerebrovascular accident was undertaken. Data were abstracted for all patients undergoing rehabilitation post stroke who also had documented admission and discharge CMSA scores. Published predictive equations were used to determine predicted outcomes. To determine the accuracy and clinical usefulness of the predictive model, shrinkage coefficients and predictions with 95% confidence bands were calculated. Complete data were available for 74 patients with a mean age of 65.3±12.4 years. The shrinkage values for the six Impairment Inventory (II) dimensions varied from -0.05 to 0.09; the shrinkage value for the Activity Inventory (AI) was 0.21. The error associated with predictive values was greater than ±1.5 stages for the II dimensions and greater than ±24 points for the AI. This study shows that the large error associated with the predictions (as defined by the confidence band) for the CMSA II and AI limits their clinical usefulness as a predictive measure. Further research to establish predictive models using alternative statistical procedures is warranted.

  20. Estimating the Accuracy of the Chedoke–McMaster Stroke Assessment Predictive Equations for Stroke Rehabilitation

    Science.gov (United States)

    Dang, Mia; Ramsaran, Kalinda D.; Street, Melissa E.; Syed, S. Noreen; Barclay-Goddard, Ruth; Miller, Patricia A.

    2011-01-01

    ABSTRACT Purpose: To estimate the predictive accuracy and clinical usefulness of the Chedoke–McMaster Stroke Assessment (CMSA) predictive equations. Method: A longitudinal prognostic study using historical data obtained from 104 patients admitted post cerebrovascular accident was undertaken. Data were abstracted for all patients undergoing rehabilitation post stroke who also had documented admission and discharge CMSA scores. Published predictive equations were used to determine predicted outcomes. To determine the accuracy and clinical usefulness of the predictive model, shrinkage coefficients and predictions with 95% confidence bands were calculated. Results: Complete data were available for 74 patients with a mean age of 65.3±12.4 years. The shrinkage values for the six Impairment Inventory (II) dimensions varied from −0.05 to 0.09; the shrinkage value for the Activity Inventory (AI) was 0.21. The error associated with predictive values was greater than ±1.5 stages for the II dimensions and greater than ±24 points for the AI. Conclusions: This study shows that the large error associated with the predictions (as defined by the confidence band) for the CMSA II and AI limits their clinical usefulness as a predictive measure. Further research to establish predictive models using alternative statistical procedures is warranted. PMID:22654239

  1. Shoulder dystocia: risk factors, predictability, and preventability.

    Science.gov (United States)

    Mehta, Shobha H; Sokol, Robert J

    2014-06-01

    Shoulder dystocia remains an unpredictable obstetric emergency, striking fear in the hearts of obstetricians both novice and experienced. While outcomes that lead to permanent injury are rare, almost all obstetricians with enough years of practice have participated in a birth with a severe shoulder dystocia and are at least aware of cases that have resulted in significant neurologic injury or even neonatal death. This is despite many years of research trying to understand the risk factors associated with it, all in an attempt primarily to characterize when the risk is high enough to avoid vaginal delivery altogether and prevent a shoulder dystocia, whose attendant morbidities are estimated to be at a rate as high as 16-48%. The study of shoulder dystocia remains challenging due to its generally retrospective nature, as well as dependence on proper identification and documentation. As a result, the prediction of shoulder dystocia remains elusive, and the cost of trying to prevent one by performing a cesarean delivery remains high. While ultimately it is the injury that is the key concern, rather than the shoulder dystocia itself, it is in the presence of an identified shoulder dystocia that occurrence of injury is most common. The majority of shoulder dystocia cases occur without major risk factors. Moreover, even the best antenatal predictors have a low positive predictive value. Shoulder dystocia therefore cannot be reliably predicted, and the only preventative measure is cesarean delivery. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. New methods for fall risk prediction.

    Science.gov (United States)

    Ejupi, Andreas; Lord, Stephen R; Delbaere, Kim

    2014-09-01

    Accidental falls are the leading cause of injury-related death and hospitalization in old age, with over one-third of the older adults experiencing at least one fall or more each year. Because of limited healthcare resources, regular objective fall risk assessments are not possible in the community on a large scale. New methods for fall prediction are necessary to identify and monitor those older people at high risk of falling who would benefit from participating in falls prevention programmes. Technological advances have enabled less expensive ways to quantify physical fall risk in clinical practice and in the homes of older people. Recently, several studies have demonstrated that sensor-based fall risk assessments of postural sway, functional mobility, stepping and walking can discriminate between fallers and nonfallers. Recent research has used low-cost, portable and objective measuring instruments to assess fall risk in older people. Future use of these technologies holds promise for assessing fall risk accurately in an unobtrusive manner in clinical and daily life settings.

  3. Nonparametric predictive pairwise comparison with competing risks

    International Nuclear Information System (INIS)

    Coolen-Maturi, Tahani

    2014-01-01

    In reliability, failure data often correspond to competing risks, where several failure modes can cause a unit to fail. This paper presents nonparametric predictive inference (NPI) for pairwise comparison with competing risks data, assuming that the failure modes are independent. These failure modes could be the same or different among the two groups, and these can be both observed and unobserved failure modes. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. The focus is on the lower and upper probabilities for the event that the lifetime of a future unit from one group, say Y, is greater than the lifetime of a future unit from the second group, say X. The paper also shows how the two groups can be compared based on particular failure mode(s), and the comparison of the two groups when some of the competing risks are combined is discussed

  4. The prediction of the bankruptcy risk

    Directory of Open Access Journals (Sweden)

    Gheorghe DUMITRESCU

    2010-04-01

    Full Text Available The study research results of the bankruptcy risk in the actual economic crisis are very weak. This issue is very important for the economy of every country, no matter what their actual development level.The necessity of bankruptcy risk prediction appears in every company,but also in the related institutions like financial companies, investors, suppliers, customers.The bankruptcy risk made and makes the object of many studies of research that want to identify: the moment of the appearance of the bankruptcy, the factors that compete at the reach of this state, the indicators that express the best this orientation (to the bankruptcy.The threats to the firms impose the knowledge by the managers,permanently of the economic-financial situations, of the vulnerable areas and of those with potential of development. Thus, these must identify and gesture the threats that would stop the fulfillment of the established purposes.

  5. Novel equations to predict body fat percentage of Brazilian professional soccer players: A case study

    Directory of Open Access Journals (Sweden)

    Luiz Fernando Novack

    2014-12-01

    Full Text Available This study analyzed classical and developed novel mathematical models to predict body fat percentage (%BF in professional soccer players from the South Brazilian region using skinfold thicknesses measurement. Skinfolds of thirty one male professional soccer players (age of 21.48 ± 3.38 years, body mass of 79.05 ± 9.48 kg and height of 181.97 ± 8.11 cm were introduced into eight mathematical models from the literature for the prediction of %BF; these results were then compared to Dual-energy X-ray Absorptiometry (DXA. The classical equations were able to account from 65% to 79% of the variation of %BF in DXA. Statistical differences between most of the classical equations (seven of the eight classic equations and DXA were found, rendering their widespread use in this population useless. We developed three new equations for prediction of %BF with skinfolds from: axils, abdomen, thighs and calves. Theses equations accounted for 86.5% of the variation in %BF obtained with DXA.

  6. Method for calculating the variance and prediction intervals for biomass estimates obtained from allometric equations

    CSIR Research Space (South Africa)

    Kirton, A

    2010-08-01

    Full Text Available for calculating the variance and prediction intervals for biomass estimates obtained from allometric equations A KIRTON B SCHOLES S ARCHIBALD CSIR Ecosystem Processes and Dynamics, Natural Resources and the Environment P.O. BOX 395, Pretoria, 0001, South... intervals (confidence intervals for predicted values) for allometric estimates can be obtained using an example of estimating tree biomass from stem diameter. It explains how to deal with relationships which are in the power function form - a common form...

  7. Prediction Equations of Energy Expenditure in Chinese Youth Based on Step Frequency during Walking and Running

    Science.gov (United States)

    Sun, Bo; Liu, Yu; Li, Jing Xian; Li, Haipeng; Chen, Peijie

    2013-01-01

    Purpose: This study set out to examine the relationship between step frequency and velocity to develop a step frequency-based equation to predict Chinese youth's energy expenditure (EE) during walking and running. Method: A total of 173 boys and girls aged 11 to 18 years old participated in this study. The participants walked and ran on a…

  8. Development of equations for predicting Puerto Rican subtropical dry forest biomass and volume

    Science.gov (United States)

    Thomas J. Brandeis; Matthew Delaney; Bernard R. Parresol; Larry Royer

    2006-01-01

    Carbon accounting, forest health monitoring and sustainable management of the subtropical dry forests of Puerto Rico and other Caribbean Islands require an accurate assessment of forest aboveground biomass (AGB) and stem volume. One means of improving assessment accuracy is the development of predictive equations derived from locally collected data. Forest inventory...

  9. Equations for predicting biomass of six introduced tree species, island of Hawaii

    Science.gov (United States)

    Thomas H. Schukrt; Robert F. Strand; Thomas G. Cole; Katharine E. McDuffie

    1988-01-01

    Regression equations to predict total and stem-only above-ground dry biomass for six species (Acacia melanoxylon, Albizio falcataria, Eucalyptus globulus, E. grandis, E. robusta, and E. urophylla) were developed by felling and measuring 2- to 6-year-old...

  10. Mathematical equation for prediction of cat mandibular canal height dimension based on canine tooth width measurement.

    Science.gov (United States)

    Santos, Miguel; Carreira, L Miguel

    2016-06-01

    The present study was performed in a sample of 33 cats and aimed (1) to characterise the mandible height (Mh), mandibular canal height (MCh) and the distance between the interdental alveolar margin and the mandibular canal (dIAM-MC); and (2) to develop a mathematical model for dimension prediction of MCh using the patient's age, weight (Wg) and canine tooth width at the free gingival margin level (wCGM) that was easily accessible during the oral examination. Age, sex, breed, weight, skull type and the wCGM were the recorded variables for each patient. Right and left lateral view skull radiographs were made followed by measurements of the mandible anatomical structures, taken between the third premolar distal root and the fourth premolar proximal root. Results were considered statistically significant for P values <0.05, and statistical analysis was performed using SPSS software. We observed a strong correlation only between wCGM and MCh, and a prediction mathematical model was developed to calculate the MCh, with a standard error of only 0.4 mm. Our study allows a surgeon to establish relationships between a physical parameter, such as wCGM, evaluated in an oral examination, and the mandibular canal, which is a very important anatomical structure to consider in surgical procedures. Ideally, surgeons should always plan their mandible work only after obtaining a final diagnosis achieved through the use of complementary imaging exams, such as intra- and extra-oral radiographs. Thus, this mathematical equation offers an additional tool, providing more information on the relationships between oral anatomical structures, reducing the risk of iatrogenic lesions and promoting patient safety. © ISFM and AAFP 2015.

  11. Risk prediction of major complications in individuals with diabetes: the Atherosclerosis Risk in Communities Study.

    Science.gov (United States)

    Parrinello, C M; Matsushita, K; Woodward, M; Wagenknecht, L E; Coresh, J; Selvin, E

    2016-09-01

    To develop a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in people with diabetes, using demographic and clinical information, and a panel of traditional and non-traditional biomarkers. We included in the study 654 participants in the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study, with diagnosed diabetes (visit 2; 1990-1992). Models included self-reported variables (Model 1), clinical measurements (Model 2), and glycated haemoglobin (Model 3). Model 4 tested the addition of 12 blood-based biomarkers. We compared models using prediction and discrimination statistics. Successive stages of model development improved risk prediction. The C-statistics (95% confidence intervals) of models 1, 2, and 3 were 0.667 (0.64, 0.70), 0.683 (0.65, 0.71), and 0.694 (0.66, 0.72), respectively (p < 0.05 for differences). The addition of three traditional and non-traditional biomarkers [β-2 microglobulin, creatinine-based estimated glomerular filtration rate (eGFR), and cystatin C-based eGFR] to Model 3 significantly improved discrimination (C-statistic = 0.716; p = 0.003) and accuracy of 10-year risk prediction for major complications in people with diabetes (midpoint percentiles of lowest and highest deciles of predicted risk changed from 18-68% to 12-87%). These biomarkers, particularly those of kidney filtration, may help distinguish between people at low versus high risk of long-term major complications. © 2016 John Wiley & Sons Ltd.

  12. Comparison of predictive equations for resting energy expenditure among patients with schizophrenia in Japan

    Directory of Open Access Journals (Sweden)

    Sugawara N

    2014-02-01

    Full Text Available Norio Sugawara,1 Norio Yasui-Furukori,1 Tetsu Tomita,1,2 Hanako Furukori,3 Kazutoshi Kubo,1,4 Taku Nakagami,1,4 Sunao Kaneko1 1Department of Neuropsychiatry, Hirosaki University School of Medicine, Hirosaki, 2Department of Psychiatry, Hirosaki-Aiseikai Hospital, Hirosaki, 3Department of Psychiatry, Kuroishi-Akebono Hospital, Kuroishi, 4Department of Psychiatry, Odate Municipal General Hospital, Odate, Japan Background: Recently, a relationship between obesity and schizophrenia has been reported. The prediction of resting energy expenditure (REE is important to determine the energy expenditure of patients with schizophrenia. However, there is a lack of research concerning the most accurate REE predictive equations among Asian patients with schizophrenia. The purpose of the study reported here was to compare the validity of four REE equations for patients with schizophrenia taking antipsychotics. Methods: For this cross-sectional study, we recruited patients (n=110 who had a Diagnostic and Statistical Manual of Mental Disorders, fourth edition, diagnosis of schizophrenia and were admitted to four psychiatric hospitals. The mean (± standard deviation age of these patients was 45.9±13.2 years. Anthropometric measurements (of height, weight, body mass index were taken at the beginning of the study. REE was measured using indirect calorimetry. Comparisons between the measured and estimated REEs from the four equations (Harris–Benedict, Mifflin–St Jeor, Food and Agriculture Organization/World Health Organization/United Nations University, and Schofield were performed using simple linear regression analysis and Bland–Altman analysis. Results: Significant trends were found between the measured and predicted REEs for all four equations (P<0.001, with the Harris–Benedict equation demonstrating the strongest correlation in both men and women (r=0.617, P<0.001. In all participants, Bland–Altman analysis revealed that the Harris–Benedict and

  13. Predictive equations for lung volumes from computed tomography for size matching in pulmonary transplantation.

    Science.gov (United States)

    Konheim, Jeremy A; Kon, Zachary N; Pasrija, Chetan; Luo, Qingyang; Sanchez, Pablo G; Garcia, Jose P; Griffith, Bartley P; Jeudy, Jean

    2016-04-01

    Size matching for lung transplantation is widely accomplished using height comparisons between donors and recipients. This gross approximation allows for wide variation in lung size and, potentially, size mismatch. Three-dimensional computed tomography (3D-CT) volumetry comparisons could offer more accurate size matching. Although recipient CT scans are universally available, donor CT scans are rarely performed. Therefore, predicted donor lung volumes could be used for comparison to measured recipient lung volumes, but no such predictive equations exist. We aimed to use 3D-CT volumetry measurements from a normal patient population to generate equations for predicted total lung volume (pTLV), predicted right lung volume (pRLV), and predicted left lung volume (pLLV), for size-matching purposes. Chest CT scans of 400 normal patients were retrospectively evaluated. 3D-CT volumetry was performed to measure total lung volume, right lung volume, and left lung volume of each patient, and predictive equations were generated. The fitted model was tested in a separate group of 100 patients. The model was externally validated by comparison of total lung volume with total lung capacity from pulmonary function tests in a subset of those patients. Age, gender, height, and race were independent predictors of lung volume. In the test group, there were strong linear correlations between predicted and actual lung volumes measured by 3D-CT volumetry for pTLV (r = 0.72), pRLV (r = 0.72), and pLLV (r = 0.69). A strong linear correlation was also observed when comparing pTLV and total lung capacity (r = 0.82). We successfully created a predictive model for pTLV, pRLV, and pLLV. These may serve as reference standards and predict donor lung volume for size matching in lung transplantation. Copyright © 2016 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

  14. Prediction of the neutrons subcritical multiplication using the diffusion hybrid equation with external neutron sources

    Energy Technology Data Exchange (ETDEWEB)

    Costa da Silva, Adilson; Carvalho da Silva, Fernando [COPPE/UFRJ, Programa de Engenharia Nuclear, Caixa Postal 68509, 21941-914, Rio de Janeiro (Brazil); Senra Martinez, Aquilino, E-mail: aquilino@lmp.ufrj.br [COPPE/UFRJ, Programa de Engenharia Nuclear, Caixa Postal 68509, 21941-914, Rio de Janeiro (Brazil)

    2011-07-15

    Highlights: > We proposed a new neutron diffusion hybrid equation with external neutron source. > A coarse mesh finite difference method for the adjoint flux and reactivity calculation was developed. > 1/M curve to predict the criticality condition is used. - Abstract: We used the neutron diffusion hybrid equation, in cartesian geometry with external neutron sources to predict the subcritical multiplication of neutrons in a pressurized water reactor, using a 1/M curve to predict the criticality condition. A Coarse Mesh Finite Difference Method was developed for the adjoint flux calculation and to obtain the reactivity values of the reactor. The results obtained were compared with benchmark values in order to validate the methodology presented in this paper.

  15. Prediction of the neutrons subcritical multiplication using the diffusion hybrid equation with external neutron sources

    International Nuclear Information System (INIS)

    Costa da Silva, Adilson; Carvalho da Silva, Fernando; Senra Martinez, Aquilino

    2011-01-01

    Highlights: → We proposed a new neutron diffusion hybrid equation with external neutron source. → A coarse mesh finite difference method for the adjoint flux and reactivity calculation was developed. → 1/M curve to predict the criticality condition is used. - Abstract: We used the neutron diffusion hybrid equation, in cartesian geometry with external neutron sources to predict the subcritical multiplication of neutrons in a pressurized water reactor, using a 1/M curve to predict the criticality condition. A Coarse Mesh Finite Difference Method was developed for the adjoint flux calculation and to obtain the reactivity values of the reactor. The results obtained were compared with benchmark values in order to validate the methodology presented in this paper.

  16. Application of Grounded Theory in Determining Required Elements for IPv6 Risk Assessment Equation

    Directory of Open Access Journals (Sweden)

    Rosli Athirah

    2018-01-01

    Full Text Available The deployment of Internet Protocol version 6 (IPv6 has raised security concerns among the network administrators. Thus, in strengthening the network security, administrator requires an appropriate method to assess the possible risks that occur in their networks. Aware of the needs to calculate risk in IPv6 network, it is essential to an organization to have an equation that is flexible and consider the requirements of the network. However, the existing risk assessment equations do not consider the requirement of the network. Therefore, this paper presents the adaptation of grounded theory to search for elements that are needed to develop IPv6 risk assessment (IRA6 equation. The attack scenarios’ experiments; UDP Flooding, TCP Flooding and Multicast attacks were carried out in different network environment to show how the IPv6 risk assessment equation being used. The result shows that the IRA6 equation is more flexible to be used regardless the network sizes and easier to calculate the risk value compared to the existing risk assessment equations. Hence, network administrators can have a proper decision making and strategic planning for a robust network security.

  17. Validity of Predictive Equations for Resting Energy Expenditure Developed for Obese Patients: Impact of Body Composition Method

    Science.gov (United States)

    Achamrah, Najate; Jésus, Pierre; Grigioni, Sébastien; Rimbert, Agnès; Petit, André; Déchelotte, Pierre; Folope, Vanessa; Coëffier, Moïse

    2018-01-01

    Predictive equations have been specifically developed for obese patients to estimate resting energy expenditure (REE). Body composition (BC) assessment is needed for some of these equations. We assessed the impact of BC methods on the accuracy of specific predictive equations developed in obese patients. REE was measured (mREE) by indirect calorimetry and BC assessed by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). mREE, percentages of prediction accuracy (±10% of mREE) were compared. Predictive equations were studied in 2588 obese patients. Mean mREE was 1788 ± 6.3 kcal/24 h. Only the Müller (BIA) and Harris & Benedict (HB) equations provided REE with no difference from mREE. The Huang, Müller, Horie-Waitzberg, and HB formulas provided a higher accurate prediction (>60% of cases). The use of BIA provided better predictions of REE than DXA for the Huang and Müller equations. Inversely, the Horie-Waitzberg and Lazzer formulas provided a higher accuracy using DXA. Accuracy decreased when applied to patients with BMI ≥ 40, except for the Horie-Waitzberg and Lazzer (DXA) formulas. Müller equations based on BIA provided a marked improvement of REE prediction accuracy than equations not based on BC. The interest of BC to improve REE predictive equations accuracy in obese patients should be confirmed. PMID:29320432

  18. An equation for the prediction of human skin permeability of neutral molecules, ions and ionic species.

    Science.gov (United States)

    Zhang, Keda; Abraham, Michael H; Liu, Xiangli

    2017-04-15

    Experimental values of permeability coefficients, as log K p , of chemical compounds across human skin were collected by carefully screening the literature, and adjusted to 37°C for the effect of temperature. The values of log K p for partially ionized acids and bases were separated into those for their neutral and ionic species, forming a total data set of 247 compounds and species (including 35 ionic species). The obtained log K p values have been regressed against Abraham solute descriptors to yield a correlation equation with R 2 =0.866 and SD=0.432 log units. The equation can provide valid predictions for log K p of neutral molecules, ions and ionic species, with predictive R 2 =0.858 and predictive SD=0.445 log units calculated by the leave-one-out statistics. The predicted log K p values for Na + and Et 4 N + are in good agreement with the observed values. We calculated the values of log K p of ketoprofen as a function of the pH of the donor solution, and found that log K p markedly varies only when ketoprofen is largely ionized. This explains why models that neglect ionization of permeants still yield reasonable statistical results. The effect of skin thickness on log K p was investigated by inclusion of two indicator variables, one for intermediate thickness skin and one for full thickness skin, into the above equation. The newly obtained equations were found to be statistically very close to the above equation. Therefore, the thickness of human skin used makes little difference to the experimental values of log K p . Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Creep-fatigue life prediction method using Diercks equation for Cr-Mo steel

    International Nuclear Information System (INIS)

    Sonoya, Keiji; Nonaka, Isamu; Kitagawa, Masaki

    1990-01-01

    For dealing with the situation that creep-fatigue life properties of materials do not exist, a development of the simple method to predict creep-fatigue life properties is necessary. A method to predict the creep-fatigue life properties of Cr-Mo steels is proposed on the basis of D. Diercks equation which correlates the creep-fatigue lifes of SUS 304 steels under various temperatures, strain ranges, strain rates and hold times. The accuracy of the proposed method was compared with that of the existing methods. The following results were obtained. (1) Fatigue strength and creep rupture strength of Cr-Mo steel are different from those of SUS 304 steel. Therefore in order to apply Diercks equation to creep-fatigue prediction for Cr-Mo steel, the difference of fatigue strength was found to be corrected by fatigue life ratio of both steels and the difference of creep rupture strength was found to be corrected by the equivalent temperature corresponding to equal strength of both steels. (2) Creep-fatigue life can be predicted by the modified Diercks equation within a factor of 2 which is nearly as precise as the accuracy of strain range partitioning method. Required test and analysis procedure of this method are not so complicated as strain range partitioning method. (author)

  20. A Hybrid Ground-Motion Prediction Equation for Earthquakes in Western Alberta

    Science.gov (United States)

    Spriggs, N.; Yenier, E.; Law, A.; Moores, A. O.

    2015-12-01

    Estimation of ground-motion amplitudes that may be produced by future earthquakes constitutes the foundation of seismic hazard assessment and earthquake-resistant structural design. This is typically done by using a prediction equation that quantifies amplitudes as a function of key seismological variables such as magnitude, distance and site condition. In this study, we develop a hybrid empirical prediction equation for earthquakes in western Alberta, where evaluation of seismic hazard associated with induced seismicity is of particular interest. We use peak ground motions and response spectra from recorded seismic events to model the regional source and attenuation attributes. The available empirical data is limited in the magnitude range of engineering interest (M>4). Therefore, we combine empirical data with a simulation-based model in order to obtain seismologically informed predictions for moderate-to-large magnitude events. The methodology is two-fold. First, we investigate the shape of geometrical spreading in Alberta. We supplement the seismic data with ground motions obtained from mining/quarry blasts, in order to gain insights into the regional attenuation over a wide distance range. A comparison of ground-motion amplitudes for earthquakes and mining/quarry blasts show that both event types decay at similar rates with distance and demonstrate a significant Moho-bounce effect. In the second stage, we calibrate the source and attenuation parameters of a simulation-based prediction equation to match the available amplitude data from seismic events. We model the geometrical spreading using a trilinear function with attenuation rates obtained from the first stage, and calculate coefficients of anelastic attenuation and site amplification via regression analysis. This provides a hybrid ground-motion prediction equation that is calibrated for observed motions in western Alberta and is applicable to moderate-to-large magnitude events.

  1. Development of a predictive energy equation for maintenance hemodialysis patients: a pilot study.

    Science.gov (United States)

    Byham-Gray, Laura; Parrott, J Scott; Ho, Wai Yin; Sundell, Mary B; Ikizler, T Alp

    2014-01-01

    The study objectives were to explore the predictors of measured resting energy expenditure (mREE) among a sample of maintenance hemodialysis (MHD) patients, to generate a predictive energy equation (MHDE), and to compare such models to another commonly used predictive energy equation in nutritional care, the Mifflin-St. Jeor equation (MSJE). The study was a retrospective, cross-sectional cohort design conducted at the Vanderbilt University Medical Center. Study subjects were adult MHD patients (N = 67). Data collected from several clinical trials were analyzed using Pearson's correlation and multivariate linear regression procedures. Demographic, anthropometric, clinical, and laboratory data were examined as potential predictors of mREE. Limits of agreement between the MHDE and the MSJE were evaluated using Bland-Altman plots. The a priori α was set at P lean body mass [LBM]) of mREE included (R(2) = 0.489) FFM, ALB, age, and CRP. Two additional models (MHDE-CRP and MHDE-CR) with acceptable predictability (R(2) = 0.460 and R(2) = 0.451) were derived to improve the clinical utility of the developed energy equation (MHDE-LBM). Using Bland-Altman plots, the MHDE over- and underpredicted mREE less often than the MSJE. Predictive models (MHDE) including selective demographic, clinical, and anthropometric data explained less than 50% variance of mREE but had better precision in determining energy requirements for MHD patients when compared with MSJE. Further research is necessary to improve predictive models of mREE in the MHD population and to test its validity and clinical application. Copyright © 2014 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

  2. The contribution of educational class in improving accuracy of cardiovascular risk prediction across European regions

    DEFF Research Database (Denmark)

    Ferrario, Marco M; Veronesi, Giovanni; Chambless, Lloyd E

    2014-01-01

    OBJECTIVE: To assess whether educational class, an index of socioeconomic position, improves the accuracy of the SCORE cardiovascular disease (CVD) risk prediction equation. METHODS: In a pooled analysis of 68 455 40-64-year-old men and women, free from coronary heart disease at baseline, from 47...

  3. Validation of predictive equations for glomerular filtration rate in the Saudi population

    Directory of Open Access Journals (Sweden)

    Al Wakeel Jamal

    2009-01-01

    Full Text Available Predictive equations provide a rapid method of assessing glomerular filtration rate (GFR. To compare the various predictive equations for the measurement of this parameter in the Saudi population, we measured GFR by the Modification of Diet in Renal Disease (MDRD and Cockcroft-Gault formulas, cystatin C, reciprocal of cystatin C, creatinine clearance, reciprocal of creatinine, and inulin clearance in 32 Saudi subjects with different stages of renal disease. We com-pared GFR measured by inulin clearance and the estimated GFR by the equations. The study included 19 males (59.4% and 13 (40.6% females with a mean age of 42.3 ± 15.2 years and weight of 68.6 ± 17.7 kg. The mean serum creatinine was 199 ± 161 μmol/L. The GFR measured by inulin clearance was 50.9 ± 33.5 mL/min, and the estimated by Cockcroft-Gault and by MDRD equations was 56.3 ± 33.3 and 52.8 ± 32.0 mL/min, respectively. The GFR estimated by MDRD revealed the strongest correlation with the measured inulin clearance (r= 0.976, P= 0.0000 followed by the GFR estimated by Cockcroft-Gault, serum cystatin C, and serum creatinine (r= 0.953, P= 0.0000 (r= 0.787, P= 0.0001 (r= -0.678, P= 0.001, respectively. The reciprocal of cystatin C and serum creatinine revealed a correlation coefficient of 0.826 and 0.93, respectively. Cockroft-Gault for-mula overestimated the GFR by 5.40 ± 10.3 mL/min in comparison to the MDRD formula, which exhibited the best correlation with inulin clearance in different genders, age groups, body mass index, renal transplant recipients, chronic kidney disease stages when compared to other GFR predictive equations.

  4. Constructing and predicting solitary pattern solutions for nonlinear time-fractional dispersive partial differential equations

    Science.gov (United States)

    Arqub, Omar Abu; El-Ajou, Ahmad; Momani, Shaher

    2015-07-01

    Building fractional mathematical models for specific phenomena and developing numerical or analytical solutions for these fractional mathematical models are crucial issues in mathematics, physics, and engineering. In this work, a new analytical technique for constructing and predicting solitary pattern solutions of time-fractional dispersive partial differential equations is proposed based on the generalized Taylor series formula and residual error function. The new approach provides solutions in the form of a rapidly convergent series with easily computable components using symbolic computation software. For method evaluation and validation, the proposed technique was applied to three different models and compared with some of the well-known methods. The resultant simulations clearly demonstrate the superiority and potentiality of the proposed technique in terms of the quality performance and accuracy of substructure preservation in the construct, as well as the prediction of solitary pattern solutions for time-fractional dispersive partial differential equations.

  5. Field calibration and modification of scs design equation for predicting length of border under local conditions

    International Nuclear Information System (INIS)

    Choudhary, M.R.; Mustafa, U.S.

    2009-01-01

    Field tests were conducted to calibrate the existing SCS design equation in determining field border length using field data of different field lengths during 2nd and 3rd irrigations under local conditions. A single ring infiltrometer was used to estimate the water movement into and through the irrigated soil profile and in estimating the coefficients of Kostiakov infiltration function. Measurements of the unit discharge and time of advance were carried out during different irrigations on wheat irrigated fields having clay loam soil. The collected field data were used to calibrate the existing SCS design equation developed by USDA for testing its validity under local field conditions. SCS equation was modified further to improve its applicability. Results from the study revealed that the Kostiakov model over predicted the coefficients, which in turn overestimated the water advance length for boarder in the selected field using existing SCS design equation. However, the calibrated SCS design equation after parametric modification produced more satisfactory results encouraging the scientists to make its use at larger scale. (author)

  6. Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations.

    Science.gov (United States)

    Sabounchi, N S; Rahmandad, H; Ammerman, A

    2013-10-01

    Basal metabolic rate (BMR) represents the largest component of total energy expenditure and is a major contributor to energy balance. Therefore, accurately estimating BMR is critical for developing rigorous obesity prevention and control strategies. Over the past several decades, numerous BMR formulas have been developed targeted to different population groups. A comprehensive literature search revealed 248 BMR estimation equations developed using diverse ranges of age, gender, race, fat-free mass, fat mass, height, waist-to-hip ratio, body mass index and weight. A subset of 47 studies included enough detail to allow for development of meta-regression equations. Utilizing these studies, meta-equations were developed targeted to 20 specific population groups. This review provides a comprehensive summary of available BMR equations and an estimate of their accuracy. An accompanying online BMR prediction tool (available at http://www.sdl.ise.vt.edu/tutorials.html) was developed to automatically estimate BMR based on the most appropriate equation after user-entry of individual age, race, gender and weight.

  7. Construction of risk prediction model of type 2 diabetes mellitus based on logistic regression

    Directory of Open Access Journals (Sweden)

    Li Jian

    2017-01-01

    Full Text Available Objective: to construct multi factor prediction model for the individual risk of T2DM, and to explore new ideas for early warning, prevention and personalized health services for T2DM. Methods: using logistic regression techniques to screen the risk factors for T2DM and construct the risk prediction model of T2DM. Results: Male’s risk prediction model logistic regression equation: logit(P=BMI × 0.735+ vegetables × (−0.671 + age × 0.838+ diastolic pressure × 0.296+ physical activity× (−2.287 + sleep ×(−0.009 +smoking ×0.214; Female’s risk prediction model logistic regression equation: logit(P=BMI ×1.979+ vegetables× (−0.292 + age × 1.355+ diastolic pressure× 0.522+ physical activity × (−2.287 + sleep × (−0.010.The area under the ROC curve of male was 0.83, the sensitivity was 0.72, the specificity was 0.86, the area under the ROC curve of female was 0.84, the sensitivity was 0.75, the specificity was 0.90. Conclusion: This study model data is from a compared study of nested case, the risk prediction model has been established by using the more mature logistic regression techniques, and the model is higher predictive sensitivity, specificity and stability.

  8. Validation of the IHE Cohort Model of Type 2 Diabetes and the impact of choice of macrovascular risk equations.

    Directory of Open Access Journals (Sweden)

    Adam Lundqvist

    Full Text Available Health-economic models of diabetes are complex since the disease is chronic, progressive and there are many diabetic complications. External validation of these models helps building trust and satisfies demands from decision makers. We evaluated the external validity of the IHE Cohort Model of Type 2 Diabetes; the impact of using alternative macrovascular risk equations; and compared the results to those from microsimulation models.The external validity of the model was analysed from 12 clinical trials and observational studies by comparing 167 predicted microvascular, macrovascular and mortality outcomes to the observed study outcomes. Concordance was examined using visual inspection of scatterplots and regression-based analysis, where an intercept of 0 and a slope of 1 indicate perfect concordance. Additional subgroup analyses were conducted on 'dependent' vs. 'independent' endpoints and microvascular vs. macrovascular vs. mortality endpoints.Visual inspection indicates that the model predicts outcomes well. The UKPDS-OM1 equations showed almost perfect concordance with observed values (slope 0.996, whereas Swedish NDR (0.952 and UKPDS-OM2 (0.899 had a slight tendency to underestimate. The R2 values were uniformly high (>0.96. There were no major differences between 'dependent' and 'independent' outcomes, nor for microvascular and mortality outcomes. Macrovascular outcomes tended to be underestimated, most so for UKPDS-OM2 and least so for NDR risk equations.External validation indicates that the IHE Cohort Model of Type 2 Diabetes has predictive accuracy in line with microsimulation models, indicating that the trade-off in accuracy using cohort simulation might not be that large. While the choice of risk equations was seen to matter, each were associated with generally reasonable results, indicating that the choice must reflect the specifics of the application. The largest variation was observed for macrovascular outcomes. There, NDR

  9. Developing A New Predictive Dispersion Equation Based on Tidal Average (TA) Condition in Alluvial Estuaries

    Science.gov (United States)

    Anak Gisen, Jacqueline Isabella; Nijzink, Remko C.; Savenije, Hubert H. G.

    2014-05-01

    Dispersion mathematical representation of tidal mixing between sea water and fresh water in The definition of dispersion somehow remains unclear as it is not directly measurable. The role of dispersion is only meaningful if it is related to the appropriate temporal and spatial scale of mixing, which are identified as the tidal period, tidal excursion (longitudinal), width of estuary (lateral) and mixing depth (vertical). Moreover, the mixing pattern determines the salt intrusion length in an estuary. If a physically based description of the dispersion is defined, this would allow the analytical solution of the salt intrusion problem. The objective of this study is to develop a predictive equation for estimating the dispersion coefficient at tidal average (TA) condition, which can be applied in the salt intrusion model to predict the salinity profile for any estuary during different events. Utilizing available data of 72 measurements in 27 estuaries (including 6 recently studied estuaries in Malaysia), regressions analysis has been performed with various combinations of dimensionless parameters . The predictive dispersion equations have been developed for two different locations, at the mouth D0TA and at the inflection point D1TA (where the convergence length changes). Regressions have been carried out with two separated datasets: 1) more reliable data for calibration; and 2) less reliable data for validation. The combination of dimensionless ratios that give the best performance is selected as the final outcome which indicates that the dispersion coefficient is depending on the tidal excursion, tidal range, tidal velocity amplitude, friction and the Richardson Number. A limitation of the newly developed equation is that the friction is generally unknown. In order to compensate this problem, further analysis has been performed adopting the hydraulic model of Cai et. al. (2012) to estimate the friction and depth. Keywords: dispersion, alluvial estuaries, mixing, salt

  10. A predictive group-contribution simplified PC-SAFT equation of state: Application to polymer systems

    DEFF Research Database (Denmark)

    Tihic, Amra; Kontogeorgis, Georgios; von Solms, Nicolas

    2008-01-01

    A group-contribution (GC) method is coupled with the molecular-based perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state (EoS) to predict its characteristic pure compound parameters. The estimation of group contributions for the parameters is based on a parameter...... are the molecular structure of the polymer of interest in terms of functional groups and a single binary interaction parameter for accurate mixture calculations....

  11. Body composition in elderly people: effect of criterion estimates on predictive equations

    International Nuclear Information System (INIS)

    Baumgartner, R.N.; Heymsfield, S.B.; Lichtman, S.; Wang, J.; Pierson, R.N. Jr.

    1991-01-01

    The purposes of this study were to determine whether there are significant differences between two- and four-compartment model estimates of body composition, whether these differences are associated with aqueous and mineral fractions of the fat-free mass (FFM); and whether the differences are retained in equations for predicting body composition from anthropometry and bioelectric resistance. Body composition was estimated in 98 men and women aged 65-94 y by using a four-compartment model based on hydrodensitometry, 3 H 2 O dilution, and dual-photon absorptiometry. These estimates were significantly different from those obtained by using Siri's two-compartment model. The differences were associated significantly (P less than 0.0001) with variation in the aqueous fraction of FFM. Equations for predicting body composition from anthropometry and resistance, when calibrated against two-compartment model estimates, retained these systematic errors. Equations predicting body composition in elderly people should be calibrated against estimates from multicompartment models that consider variability in FFM composition

  12. Equations of prediction for abdominal fat in brown egg-laying hens fed different diets.

    Science.gov (United States)

    Souza, C; Jaimes, J J B; Gewehr, C E

    2017-06-01

    The objective was to use noninvasive measurements to formulate equations for predicting the abdominal fat weight of laying hens in a noninvasive manner. Hens were fed with different diets; the external body measurements of birds were used as regressors. We used 288 Hy-Line Brown laying hens, distributed in a completely randomized design in a factorial arrangement, submitted for 16 wk to 2 metabolizable energy levels (2,550 and 2,800 kcal/kg) and 3 levels of crude protein in the diet (150, 160, and 170 g/kg), totaling 6 treatments, with 48 hens each. Sixteen hens per treatment of 92 wk age were utilized to evaluate body weight, bird length, tarsus and sternum, greater and lesser diameter of the tarsus, and abdominal fat weight, after slaughter. The equations were obtained by using measures evaluated with regressors through simple and multiple linear regression with the stepwise method of indirect elimination (backward), with P abdominal fat as predicted by the equations and observed values for each bird were subjected to Pearson's correlation analysis. The equations generated by energy levels showed coefficients of determination of 0.50 and 0.74 for 2,800 and 2,550 kcal/kg of metabolizable energy, respectively, with correlation coefficients of 0.71 and 0.84, with a highly significant correlation between the calculated and observed values of abdominal fat. For protein levels of 150, 160, and 170 g/kg in the diet, it was possible to obtain coefficients of determination of 0.75, 0.57, and 0.61, with correlation coefficients of 0.86, 0.75, and 0.78, respectively. Regarding the general equation for predicting abdominal fat weight, the coefficient of determination was 0.62; the correlation coefficient was 0.79. The equations for predicting abdominal fat weight in laying hens, based on external measurements of the birds, showed positive coefficients of determination and correlation coefficients, thus allowing researchers to determine abdominal fat weight in vivo.

  13. Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults.

    Science.gov (United States)

    Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

    2015-08-07

    Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  14. Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults

    Science.gov (United States)

    Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

    2015-01-01

    Background Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. Methods and Results We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. PMID:26254303

  15. Genomic Prediction Within and Across Biparental Families: Means and Variances of Prediction Accuracy and Usefulness of Deterministic Equations

    Directory of Open Access Journals (Sweden)

    Pascal Schopp

    2017-11-01

    Full Text Available A major application of genomic prediction (GP in plant breeding is the identification of superior inbred lines within families derived from biparental crosses. When models for various traits were trained within related or unrelated biparental families (BPFs, experimental studies found substantial variation in prediction accuracy (PA, but little is known about the underlying factors. We used SNP marker genotypes of inbred lines from either elite germplasm or landraces of maize (Zea mays L. as parents to generate in silico 300 BPFs of doubled-haploid lines. We analyzed PA within each BPF for 50 simulated polygenic traits, using genomic best linear unbiased prediction (GBLUP models trained with individuals from either full-sib (FSF, half-sib (HSF, or unrelated families (URF for various sizes (Ntrain of the training set and different heritabilities (h2 . In addition, we modified two deterministic equations for forecasting PA to account for inbreeding and genetic variance unexplained by the training set. Averaged across traits, PA was high within FSF (0.41–0.97 with large variation only for Ntrain < 50 and h2 < 0.6. For HSF and URF, PA was on average ∼40–60% lower and varied substantially among different combinations of BPFs used for model training and prediction as well as different traits. As exemplified by HSF results, PA of across-family GP can be very low if causal variants not segregating in the training set account for a sizeable proportion of the genetic variance among predicted individuals. Deterministic equations accurately forecast the PA expected over many traits, yet cannot capture trait-specific deviations. We conclude that model training within BPFs generally yields stable PA, whereas a high level of uncertainty is encountered in across-family GP. Our study shows the extent of variation in PA that must be at least reckoned with in practice and offers a starting point for the design of training sets composed of multiple BPFs.

  16. A temperature rise equation for predicting environmental impact and performance of cooling ponds

    Energy Technology Data Exchange (ETDEWEB)

    Serag-Eldin, M.A. [American Univ. in Cairo, Cairo (Egypt). Dept. of Mechanical Engineering

    2009-07-01

    Cooling ponds are used to cool the condenser water used in large central air-conditioning systems. However, larger cooling loads can often increase pond surface evaporation rates. A temperature-rise energy equation was developed to predict temperature rises in cooling ponds subjected to heating loads. The equation was designed to reduce the need for detailed meteorological data as well as to determine the required surface area and depth of the pond for any given design criteria. Energy equations in the presence and absence of cooling loads were subtracted from each other to determine increases in pond temperature resulting from the cooling load. The energy equations include solar radiation, radiation exchange with sky and surroundings, heat convection from the surface, evaporative cooling, heat conducted to the walls, and rate of change of water temperature. Results of the study suggested that the environmental impact and performance of the cooling pond is a function of temperature only. It was concluded that with the aid of the calculated flow field and temperature distribution, the method can be used to position sprays in order to produce near-uniform pond temperatures. 10 refs., 12 figs.

  17. Are the general equations to predict BMR applicable to patients with anorexia nervosa?

    Science.gov (United States)

    Marra, M; Polito, A; De Filippo, E; Cuzzolaro, M; Ciarapica, D; Contaldo, F; Scalfi, L

    2002-03-01

    To determine whether the general equations to predict basal metabolic rate (BMR) can be reliably applied to female anorectics. Two hundred and thirty-seven female patients with anorexia nervosa (AN) were divided into an adolescent group [n=43, 13-17 yrs, 39.3+/-5.0 kg, body mass index (BMI) (weight/height) 15.5+/-1.8 kg/m2] and a young-adult group (n=194, 18-40 yrs, 40.5+/-6.1 kg, BMI 15.6+/-1.9 kg/m2). BMR values determined by indirect calorimetry were compared with those predicted according to either the WHO/FAO/UNU or the Harris-Benedict general equations, or using the Schebendach correction formula (proposed for adjusting the Harris-Benedict estimates in anorectics). Measured BMR was 3,658+/-665 kJ/day in the adolescent and 3,907+/-760 kJ/day in the young-adult patients. In the adolescent group, the differences between predicted and measured values were (mean+/-SD) 1,466 529 kJ/day (+44+/-21%) for WHO/FAO/UNU, 1,587+/-552 kJ/day (+47+/-23%) for the Harris-Benedict and -20+/-510 kJ/day for the Schebendach (+1+/-13%), while in the young-adult group the corresponding values were 696+/-570 kJ/day (+24+/-24%), 1,252+/-644 kJ/day (+37+/-27%) and -430+/-640 kJ/day (-9+/-16%). The bias was negatively associated with weight and BMI in both groups when using the WHO/FAO/UNU and Harris-Benedict equations, and with age in the young-adult group for the Harris-Benedict and Schebendach equations. The WHO/FAO/UNU and Harris-Benedict equations greatly overestimate BMR in AN. Accurate estimation is to some extent dependent on individual characteristics such as age, weight or BMI. The Schebendach correction formula accurately predicts BMR in female adolescents, but not in young adult women with AN.

  18. Development and validation of equations utilizing lamb vision system output to predict lamb carcass fabrication yields.

    Science.gov (United States)

    Cunha, B C N; Belk, K E; Scanga, J A; LeValley, S B; Tatum, J D; Smith, G C

    2004-07-01

    This study was performed to validate previous equations and to develop and evaluate new regression equations for predicting lamb carcass fabrication yields using outputs from a lamb vision system-hot carcass component (LVS-HCC) and the lamb vision system-chilled carcass LM imaging component (LVS-CCC). Lamb carcasses (n = 149) were selected after slaughter, imaged hot using the LVS-HCC, and chilled for 24 to 48 h at -3 to 1 degrees C. Chilled carcasses yield grades (YG) were assigned on-line by USDA graders and by expert USDA grading supervisors with unlimited time and access to the carcasses. Before fabrication, carcasses were ribbed between the 12th and 13th ribs and imaged using the LVS-CCC. Carcasses were fabricated into bone-in subprimal/primal cuts. Yields calculated included 1) saleable meat yield (SMY); 2) subprimal yield (SPY); and 3) fat yield (FY). On-line (whole-number) USDA YG accounted for 59, 58, and 64%; expert (whole-number) USDA YG explained 59, 59, and 65%; and expert (nearest-tenth) USDA YG accounted for 60, 60, and 67% of the observed variation in SMY, SPY, and FY, respectively. The best prediction equation developed in this trial using LVS-HCC output and hot carcass weight as independent variables explained 68, 62, and 74% of the variation in SMY, SPY, and FY, respectively. Addition of output from LVS-CCC improved predictive accuracy of the equations; the combined output equations explained 72 and 66% of the variability in SMY and SPY, respectively. Accuracy and repeatability of measurement of LM area made with the LVS-CCC also was assessed, and results suggested that use of LVS-CCC provided reasonably accurate (R2 = 0.59) and highly repeatable (repeatability = 0.98) measurements of LM area. Compared with USDA YG, use of the dual-component lamb vision system to predict cut yields of lamb carcasses improved accuracy and precision, suggesting that this system could have an application as an objective means for pricing carcasses in a value

  19. Estimating unknown input parameters when implementing the NGA ground-motion prediction equations in engineering practice

    Science.gov (United States)

    Kaklamanos, James; Baise, Laurie G.; Boore, David M.

    2011-01-01

    The ground-motion prediction equations (GMPEs) developed as part of the Next Generation Attenuation of Ground Motions (NGA-West) project in 2008 are becoming widely used in seismic hazard analyses. However, these new models are considerably more complicated than previous GMPEs, and they require several more input parameters. When employing the NGA models, users routinely face situations in which some of the required input parameters are unknown. In this paper, we present a framework for estimating the unknown source, path, and site parameters when implementing the NGA models in engineering practice, and we derive geometrically-based equations relating the three distance measures found in the NGA models. Our intent is for the content of this paper not only to make the NGA models more accessible, but also to help with the implementation of other present or future GMPEs.

  20. A validated disease specific prediction equation for resting metabolic rate in underweight patients with COPD

    Directory of Open Access Journals (Sweden)

    Anita Nordenson

    2010-09-01

    Full Text Available Anita Nordenson2, Anne Marie Grönberg1,2, Lena Hulthén1, Sven Larsson2, Frode Slinde11Department of Clinical Nutrition, Sahlgrenska Academy at University of Gothenburg, Göteborg, Sweden; 2Department of Internal Medicine/Respiratory Medicine and Allergology, Sahlgrenska Academy at University of Gothenburg, SwedenAbstract: Malnutrition is a serious condition in chronic obstructive pulmonary disease (COPD. Successful dietary intervention calls for calculations of resting metabolic rate (RMR. One disease-specific prediction equation for RMR exists based on mainly male patients. To construct a disease-specific equation for RMR based on measurements in underweight or weight-losing women and men with COPD, RMR was measured by indirect calorimetry in 30 women and 11 men with a diagnosis of COPD and body mass index <21 kg/m2. The following variables, possibly influencing RMR were measured: length, weight, middle upper arm circumference, triceps skinfold, body composition by dual energy x-ray absorptiometry and bioelectrical impedance, lung function, and markers of inflammation. Relations between RMR and measured variables were studied using univariate analysis according to Pearson. Gender and variables that were associated with RMR with a P value <0.15 were included in a forward multiple regression analysis. The best-fit multiple regression equation included only fat-free mass (FFM: RMR (kJ/day = 1856 + 76.0 FFM (kg. To conclude, FFM is the dominating factor influencing RMR. The developed equation can be used for prediction of RMR in underweight COPD patients.Keywords: pulmonary disease, chronic obstructive, basal metabolic rate, malnutrition, body composition

  1. Development of an attrition risk prediction tool.

    Science.gov (United States)

    Fowler, John; Norrie, Peter

    To review lecturers' and students' perceptions of the factors that may lead to attrition from pre-registration nursing and midwifery programmes and to identify ways to reduce the impact of such factors on the student's experience. Comparable attrition rates for nursing and midwifery students across various universities are difficult to monitor accurately; however, estimates that there is approximately a 25% national attrition rate are not uncommon. The financial and human implications of this are significant and worthy of investigation. A study was carried out in one medium-sized UK school of nursing and midwifery, aimed at identifying perceived factors associated with attrition and retention. Thirty-five lecturers were interviewed individually; 605 students completed a questionnaire, and of these, 10 were individually interviewed. Attrition data kept by the student service department were reviewed. Data were collected over an 18-month period in 2007-2008. Regression analysis of the student data identified eight significant predictors. Four of these were 'positive' factors in that they aided student retention and four were 'negative' in that they were associated with students' thoughts of resigning. Student attrition and retention is multifactorial, and, as such, needs to be managed holistically. One aspect of this management could be an attrition risk prediction tool.

  2. Performance prediction of gas turbines by solving a system of non-linear equations

    Energy Technology Data Exchange (ETDEWEB)

    Kaikko, J

    1998-09-01

    This study presents a novel method for implementing the performance prediction of gas turbines from the component models. It is based on solving the non-linear set of equations that corresponds to the process equations, and the mass and energy balances for the engine. General models have been presented for determining the steady state operation of single components. Single and multiple shad arrangements have been examined with consideration also being given to heat regeneration and intercooling. Emphasis has been placed upon axial gas turbines of an industrial scale. Applying the models requires no information of the structural dimensions of the gas turbines. On comparison with the commonly applied component matching procedures, this method incorporates several advantages. The application of the models for providing results is facilitated as less attention needs to be paid to calculation sequences and routines. Solving the set of equations is based on zeroing co-ordinate functions that are directly derived from the modelling equations. Therefore, controlling the accuracy of the results is easy. This method gives more freedom for the selection of the modelling parameters since, unlike for the matching procedures, exchanging these criteria does not itself affect the algorithms. Implicit relationships between the variables are of no significance, thus increasing the freedom for the modelling equations as well. The mathematical models developed in this thesis will provide facilities to optimise the operation of any major gas turbine configuration with respect to the desired process parameters. The computational methods used in this study may also be adapted to any other modelling problems arising in industry. (orig.) 36 refs.

  3. Fast integration-based prediction bands for ordinary differential equation models.

    Science.gov (United States)

    Hass, Helge; Kreutz, Clemens; Timmer, Jens; Kaschek, Daniel

    2016-04-15

    To gain a deeper understanding of biological processes and their relevance in disease, mathematical models are built upon experimental data. Uncertainty in the data leads to uncertainties of the model's parameters and in turn to uncertainties of predictions. Mechanistic dynamic models of biochemical networks are frequently based on nonlinear differential equation systems and feature a large number of parameters, sparse observations of the model components and lack of information in the available data. Due to the curse of dimensionality, classical and sampling approaches propagating parameter uncertainties to predictions are hardly feasible and insufficient. However, for experimental design and to discriminate between competing models, prediction and confidence bands are essential. To circumvent the hurdles of the former methods, an approach to calculate a profile likelihood on arbitrary observations for a specific time point has been introduced, which provides accurate confidence and prediction intervals for nonlinear models and is computationally feasible for high-dimensional models. In this article, reliable and smooth point-wise prediction and confidence bands to assess the model's uncertainty on the whole time-course are achieved via explicit integration with elaborate correction mechanisms. The corresponding system of ordinary differential equations is derived and tested on three established models for cellular signalling. An efficiency analysis is performed to illustrate the computational benefit compared with repeated profile likelihood calculations at multiple time points. The integration framework and the examples used in this article are provided with the software package Data2Dynamics, which is based on MATLAB and freely available at http://www.data2dynamics.org helge.hass@fdm.uni-freiburg.de Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e

  4. Predictive analytics for supply chain collaboration, risk management ...

    African Journals Online (AJOL)

    kirstam

    management, and (2) supply chain risk management predicted financial .... overhead costs, delivery of ever-increasing customer value, flexibility with superior ... risk exposure, relationship longevity, trust and communication are considered as.

  5. Elderly fall risk prediction using static posturography

    Science.gov (United States)

    2017-01-01

    Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP) and medial-lateral (ML) center of pressure (CoP) motion; AP and ML CoP root mean square distance from mean (RMS); and AP, ML, and vector sum magnitude (VSM) CoP velocity were calculated. Romberg Quotients (RQ) were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24), prospective all fallers (42), prospective fallers (22 single, 6 multiple), and prospective non-fallers (47). Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity—0.114 x Eyes Closed Vector Sum Magnitude Velocity—2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity) and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for older

  6. Elderly fall risk prediction using static posturography.

    Science.gov (United States)

    Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan; McIlroy, William E

    2017-01-01

    Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP) and medial-lateral (ML) center of pressure (CoP) motion; AP and ML CoP root mean square distance from mean (RMS); and AP, ML, and vector sum magnitude (VSM) CoP velocity were calculated. Romberg Quotients (RQ) were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24), prospective all fallers (42), prospective fallers (22 single, 6 multiple), and prospective non-fallers (47). Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity-0.114 x Eyes Closed Vector Sum Magnitude Velocity-2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity) and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for older

  7. Elderly fall risk prediction using static posturography.

    Directory of Open Access Journals (Sweden)

    Jennifer Howcroft

    Full Text Available Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP and medial-lateral (ML center of pressure (CoP motion; AP and ML CoP root mean square distance from mean (RMS; and AP, ML, and vector sum magnitude (VSM CoP velocity were calculated. Romberg Quotients (RQ were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24, prospective all fallers (42, prospective fallers (22 single, 6 multiple, and prospective non-fallers (47. Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity-0.114 x Eyes Closed Vector Sum Magnitude Velocity-2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for

  8. Family differences in equations for predicting biomass and leaf area in Douglas-fir (Pseudotsuga menziesii var. menziesii).

    Science.gov (United States)

    J.B. St. Clair

    1993-01-01

    Logarithmic regression equations were developed to predict component biomass and leaf area for an 18-yr-old genetic test of Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco var. menziesii) based on stem diameter or cross-sectional sapwood area. Equations did not differ among open-pollinated families in slope, but intercepts...

  9. Predictive equations for respiratory muscle strength according to international and Brazilian guidelines

    Directory of Open Access Journals (Sweden)

    Isabela M. B. S. Pessoa

    2014-10-01

    Full Text Available Background: The maximum static respiratory pressures, namely the maximum inspiratory pressure (MIP and maximum expiratory pressure (MEP, reflect the strength of the respiratory muscles. These measures are simple, non-invasive, and have established diagnostic and prognostic value. This study is the first to examine the maximum respiratory pressures within the Brazilian population according to the recommendations proposed by the American Thoracic Society and European Respiratory Society (ATS/ERS and the Brazilian Thoracic Association (SBPT. Objective: To establish reference equations, mean values, and lower limits of normality for MIP and MEP for each age group and sex, as recommended by the ATS/ERS and SBPT. Method: We recruited 134 Brazilians living in Belo Horizonte, MG, Brazil, aged 20-89 years, with a normal pulmonary function test and a body mass index within the normal range. We used a digital manometer that operationalized the variable maximum average pressure (MIP/MEP. At least five tests were performed for both MIP and MEP to take into account a possible learning effect. Results: We evaluated 74 women and 60 men. The equations were as follows: MIP=63.27-0.55 (age+17.96 (gender+0.58 (weight, r2 of 34% and MEP= - 61.41+2.29 (age - 0.03(age2+33.72 (gender+1.40 (waist, r2 of 49%. Conclusion: In clinical practice, these equations could be used to calculate the predicted values of MIP and MEP for the Brazilian population.

  10. Phase change predictions for liquid fuel in contact with steel structure using the heat conduction equation

    Energy Technology Data Exchange (ETDEWEB)

    Brear, D.J. [Power Reactor and Nuclear Fuel Development Corp., Oarai, Ibaraki (Japan). Oarai Engineering Center

    1998-01-01

    When liquid fuel makes contact with steel structure the liquid can freeze as a crust and the structure can melt at the surface. The melting and freezing processes that occur can influence the mode of fuel freezing and hence fuel relocation. Furthermore the temperature gradients established in the fuel and steel phases determine the rate at which heat is transferred from fuel to steel. In this memo the 1-D transient heat conduction equations are applied to the case of initially liquid UO{sub 2} brought into contact with solid steel using up-to-date materials properties. The solutions predict criteria for fuel crust formation and steel melting and provide a simple algorithm to determine the interface temperature when one or both of the materials is undergoing phase change. The predicted steel melting criterion is compared with available experimental results. (author)

  11. Phase change predictions for liquid fuel in contact with steel structure using the heat conduction equation

    International Nuclear Information System (INIS)

    Brear, D.J.

    1998-01-01

    When liquid fuel makes contact with steel structure the liquid can freeze as a crust and the structure can melt at the surface. The melting and freezing processes that occur can influence the mode of fuel freezing and hence fuel relocation. Furthermore the temperature gradients established in the fuel and steel phases determine the rate at which heat is transferred from fuel to steel. In this memo the 1-D transient heat conduction equations are applied to the case of initially liquid UO 2 brought into contact with solid steel using up-to-date materials properties. The solutions predict criteria for fuel crust formation and steel melting and provide a simple algorithm to determine the interface temperature when one or both of the materials is undergoing phase change. The predicted steel melting criterion is compared with available experimental results. (author)

  12. Predictive Control of the Blood Glucose Level in Type I Diabetic Patient Using Delay Differential Equation Wang Model.

    Science.gov (United States)

    Esna-Ashari, Mojgan; Zekri, Maryam; Askari, Masood; Khalili, Noushin

    2017-01-01

    Because of increasing risk of diabetes, the measurement along with control of blood sugar has been of great importance in recent decades. In type I diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose leading to low level of glucose. To control blood glucose (BG), the insulin must be injected to the body. This paper proposes a method for BG level regulation in type I diabetes. The control strategy is based on nonlinear model predictive control. The aim of the proposed controller optimized with genetics algorithms is to measure BG level each time and predict it for the next time interval. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. Consequently, this method can decrease the risk of hypoglycemia, a lethal phenomenon in regulating BG level in diabetes caused by a low BG level. Two delay differential equation models, namely Wang model and Enhanced Wang model, are applied as controller model and plant, respectively. The simulation results exhibit an acceptable performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. As a result, if the nutrition of the person decreases instantly, the hypoglycemia will not happen. Furthermore, comparing this method with other works, it was shown that the new method outperforms previous studies.

  13. Development of equations, based on milk intake, to predict starter feed intake of preweaned dairy calves.

    Science.gov (United States)

    Silva, A L; DeVries, T J; Tedeschi, L O; Marcondes, M I

    2018-04-16

    There is a lack of studies that provide models or equations capable of predicting starter feed intake (SFI) for milk-fed dairy calves. Therefore, a multi-study analysis was conducted to identify variables that influence SFI, and to develop equations to predict SFI in milk-fed dairy calves up to 64 days of age. The database was composed of individual data of 176 calves from eight experiments, totaling 6426 daily observations of intake. The information collected from the studies were: birth BW (kg), SFI (kg/day), fluid milk or milk replacer intake (MI; l/day), sex (male or female), breed (Holstein or Holstein×Gyr crossbred) and age (days). Correlations between SFI and the quantitative variables MI, birth BW, metabolic birth BW, fat intake, CP intake, metabolizable energy intake, and age were calculated. Subsequently, data were graphed, and based on a visual appraisal of the pattern of the data, an exponential function was chosen. Data were evaluated using a meta-analysis approach to estimate fixed and random effects of the experiments using nonlinear mixed coefficient statistical models. A negative correlation between SFI and MI was observed (r=-0.39), but age was positively correlated with SFI (r=0.66). No effect of liquid feed source (milk or milk replacer) was observed in developing the equation. Two equations, significantly different for all parameters, were fit to predict SFI for calves that consume less than 5 (SFI5) l/day of milk or milk replacer: ${\\rm SFI}_{{\\,\\lt\\,5}} {\\equals}0.1839_{{\\,\\pm\\,0.0581}} {\\times}{\\rm MI}{\\times}{\\rm exp}^{{\\left( {\\left( {0.0333_{{\\,\\pm\\,0.0021 }} {\\minus}0.0040_{{\\,\\pm\\,0.0011}} {\\times}{\\rm MI}} \\right){\\times}\\left( {{\\rm A}{\\minus}{\\rm }\\left( {0.8302_{{\\,\\pm\\,0.5092}} {\\plus}6.0332_{{\\,\\pm\\,0.3583}} {\\times}{\\rm MI}} \\right)} \\right)} \\right)}} {\\minus}\\left( {0.12{\\times}{\\rm MI}} \\right)$ ; ${\\rm SFI}_{{\\,\\gt\\,5}} {\\equals}0.1225_{{\\,\\pm\\,0.0005 }} {\\times

  14. Predictive Capability of the Compressible MRG Equation for an Explosively Driven Particle with Validation

    Science.gov (United States)

    Garno, Joshua; Ouellet, Frederick; Koneru, Rahul; Balachandar, Sivaramakrishnan; Rollin, Bertrand

    2017-11-01

    An analytic model to describe the hydrodynamic forces on an explosively driven particle is not currently available. The Maxey-Riley-Gatignol (MRG) particle force equation generalized for compressible flows is well-studied in shock-tube applications, and captures the evolution of particle force extracted from controlled shock-tube experiments. In these experiments only the shock-particle interaction was examined, and the effects of the contact line were not investigated. In the present work, the predictive capability of this model is considered for the case where a particle is explosively ejected from a rigid barrel into ambient air. Particle trajectory information extracted from simulations is compared with experimental data. This configuration ensures that both the shock and contact produced by the detonation will influence the motion of the particle. The simulations are carried out using a finite volume, Euler-Lagrange code using the JWL equation of state to handle the explosive products. This work was supported by the U.S. Department of Energy, National Nuclear Security Administration, Advanced Simulation and Computing Program, as a Cooperative Agreement under the Predictive Science Academic Alliance Program,under Contract No. DE-NA0002378.

  15. Prediction equation for estimating total daily energy requirements of special operations personnel.

    Science.gov (United States)

    Barringer, N D; Pasiakos, S M; McClung, H L; Crombie, A P; Margolis, L M

    2018-01-01

    Special Operations Forces (SOF) engage in a variety of military tasks with many producing high energy expenditures, leading to undesired energy deficits and loss of body mass. Therefore, the ability to accurately estimate daily energy requirements would be useful for accurate logistical planning. Generate a predictive equation estimating energy requirements of SOF. Retrospective analysis of data collected from SOF personnel engaged in 12 different SOF training scenarios. Energy expenditure and total body water were determined using the doubly-labeled water technique. Physical activity level was determined as daily energy expenditure divided by resting metabolic rate. Physical activity level was broken into quartiles (0 = mission prep, 1 = common warrior tasks, 2 = battle drills, 3 = specialized intense activity) to generate a physical activity factor (PAF). Regression analysis was used to construct two predictive equations (Model A; body mass and PAF, Model B; fat-free mass and PAF) estimating daily energy expenditures. Average measured energy expenditure during SOF training was 4468 (range: 3700 to 6300) Kcal·d- 1 . Regression analysis revealed that physical activity level ( r  = 0.91; P  plan appropriate feeding regimens to meet SOF nutritional requirements across their mission profile.

  16. Comparison of a Handheld Indirect Calorimetry Device and Predictive Energy Equations Among Individuals on Maintenance Hemodialysis.

    Science.gov (United States)

    Morrow, Ellis A; Marcus, Andrea; Byham-Gray, Laura

    2017-11-01

    Practical methods for determining resting energy expenditure (REE) among individuals on maintenance hemodialysis (MHD) are needed because of the limitations of indirect calorimetry. Two disease-specific predictive energy equations (PEEs) have been developed for this metabolically complex population. The aim of this study was to compare estimated REE (eREE) by PEEs to measured REE (mREE) with a handheld indirect calorimetry device (HICD). A prospective pilot study of adults on MHD (N = 40) was conducted at 2 dialysis clinics in Houston and Texas City, Texas. mREE by an HICD was compared with eREE determined by 6 PEEs using Bland-Altman analysis with a band of acceptable agreement of ±10% of the group mean mREE. Paired t-test and the intraclass correlation coefficient were also used to compare the alternate methods of measuring REE. A priori alpha was set at P Maintenance Hemodialysis Equation-Creatinine version (MHCD-CR) was the most accurate PEE with 52.5% of values within the band of acceptable agreement, followed by the Mifflin-St. Jeor Equation and the Vilar et al. Equation at 45.0% and 42.5%, respectively. When compared with mREE by the HICD, the MHDE-CR was more accurate and precise than other PEEs evaluated; however, this must be interpreted with caution as mREE was consistently lower than eREE from all PEEs. Further research is needed to validate the MHDE-CR and other practical methods for determining REE among individuals on MHD. Copyright © 2017 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

  17. A Structural Equation Model of Risk Perception of Rockfall for Revisit Intention

    OpenAIRE

    Ya-Fen Lee; Yun-Yao Chi

    2014-01-01

    The study aims to explore the relationship between risk perception of rockfall and revisit intention using a Structural Equation Modeling (SEM) analysis. A total of 573 valid questionnaires are collected from travelers to Taroko National Park, Taiwan. The findings show the majority of travelers have the medium perception of rockfall risk, and are willing to revisit the Taroko National Park. The revisit intention to Taroko National Park is influenced by hazardous preferences, willingness-to-pa...

  18. Drug response prediction in high-risk multiple myeloma

    DEFF Research Database (Denmark)

    Vangsted, A J; Helm-Petersen, S; Cowland, J B

    2018-01-01

    from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged...

  19. Risk stratification in upper gastrointestinal bleeding; prediction, prevention and prognosis

    NARCIS (Netherlands)

    de Groot, N.L.

    2013-01-01

    In the first part of this thesis we developed a novel prediction score for predicting upper gastrointestinal (GI) bleeding in both NSAID and low-dose aspirin users. Both for NSAIDs and low-dose aspirin use risk scores were developed by identifying the five most dominant predictors. The risk of upper

  20. Validity of resting energy expenditure predictive equations before and after an energy-restricted diet intervention in obese women.

    Directory of Open Access Journals (Sweden)

    Jonatan R Ruiz

    Full Text Available BACKGROUND: We investigated the validity of REE predictive equations before and after 12-week energy-restricted diet intervention in Spanish obese (30 kg/m(2>BMI<40 kg/m(2 women. METHODS: We measured REE (indirect calorimetry, body weight, height, and fat mass (FM and fat free mass (FFM, dual X-ray absorptiometry in 86 obese Caucasian premenopausal women aged 36.7±7.2 y, before and after (n = 78 women the intervention. We investigated the accuracy of ten REE predictive equations using weight, height, age, FFM and FM. RESULTS: At baseline, the most accurate equation was the Mifflin et al. (Am J Clin Nutr 1990; 51: 241-247 when using weight (bias:-0.2%, P = 0.982, 74% of accurate predictions. This level of accuracy was not reached after the diet intervention (24% accurate prediction. After the intervention, the lowest bias was found with the Owen et al. (Am J Clin Nutr 1986; 44: 1-19 equation when using weight (bias:-1.7%, P = 0.044, 81% accurate prediction, yet it provided 53% accurate predictions at baseline. CONCLUSIONS: There is a wide variation in the accuracy of REE predictive equations before and after weight loss in non-morbid obese women. The results acquire especial relevance in the context of the challenging weight regain phenomenon for the overweight/obese population.

  1. Predictability of cardiovascular risks by psychological measures

    Czech Academy of Sciences Publication Activity Database

    Šolcová, Iva; Kebza, V.

    2008-01-01

    Roč. 23, č. 1 (2008), s. 241-241 ISSN 0887-0446 R&D Projects: GA ČR GA406/06/0747 Institutional research plan: CEZ:AV0Z70250504 Keywords : CVD risks * psychological measures * physiological risks Subject RIV: AN - Psychology

  2. Predicting risk of cancer during HIV infection

    DEFF Research Database (Denmark)

    Borges, Álvaro H; Silverberg, Michael J; Wentworth, Deborah

    2013-01-01

    To investigate the relationship between inflammatory [interleukin-6 (IL-6) and C-reactive protein (CRP)] and coagulation (D-dimer) biomarkers and cancer risk during HIV infection.......To investigate the relationship between inflammatory [interleukin-6 (IL-6) and C-reactive protein (CRP)] and coagulation (D-dimer) biomarkers and cancer risk during HIV infection....

  3. Decision-Making Competence Predicts Domain-Specific Risk Attitudes

    Directory of Open Access Journals (Sweden)

    Joshua eWeller

    2015-05-01

    Full Text Available Decision Making Competence (DMC reflects individual differences in rational responding across several classic behavioral decision-making tasks. Although it has been associated with real-world risk behavior, less is known about the degree to which DMC contributes to specific components of risk attitudes. Utilizing a psychological risk-return framework, we examined the associations between risk attitudes and DMC. Italian community residents (n = 804 completed an online DMC measure, using a subset of the original Adult-DMC battery (A-DMC; Bruine de Bruin, Parker, & Fischhoff, 2007. Participants also completed a self-reported risk attitude measure for three components of risk attitudes (risk-taking, risk perceptions, and expected benefits across six risk domains. Overall, greater performance on the DMC component scales were inversely, albeit modestly, associated with risk-taking tendencies. Structural equation modeling results revealed that DMC was associated with lower perceived expected benefits for all domains. In contrast, its association with perceived risks was more domain-specific. These analyses also revealed stronger indirect effects for the DMC  expected benefits  risk-taking than the DMC  perceived riskrisk-taking path, especially for risk behaviors that may be considered more antisocial in nature. These results suggest that DMC performance differentially impacts specific components of risk attitudes, and may be more strongly related to the evaluation of expected value of the given behavior.

  4. Implementation of the Next Generation Attenuation (NGA) ground-motion prediction equations in Fortran and R

    Science.gov (United States)

    Kaklamanos, James; Boore, David M.; Thompson, Eric M.; Campbell, Kenneth W.

    2010-01-01

    This report presents two methods for implementing the earthquake ground-motion prediction equations released in 2008 as part of the Next Generation Attenuation of Ground Motions (NGA-West, or NGA) project coordinated by the Pacific Earthquake Engineering Research Center (PEER). These models were developed for predicting ground-motion parameters for shallow crustal earthquakes in active tectonic regions (such as California). Of the five ground-motion prediction equations (GMPEs) developed during the NGA project, four models are implemented: the GMPEs of Abrahamson and Silva (2008), Boore and Atkinson (2008), Campbell and Bozorgnia (2008), and Chiou and Youngs (2008a); these models are abbreviated as AS08, BA08, CB08, and CY08, respectively. Since site response is widely recognized as an important influence of ground motions, engineering applications typically require that such effects be modeled. The model of Idriss (2008) is not implemented in our programs because it does not explicitly include site response, whereas the other four models include site response and use the same variable to describe the site condition (VS30). We do not intend to discourage the use of the Idriss (2008) model, but we have chosen to implement the other four NGA models in our programs for those users who require ground-motion estimates for various site conditions. We have implemented the NGA models by using two separate programming languages: Fortran and R (R Development Core Team, 2010). Fortran, a compiled programming language, has been used in the scientific community for decades. R is an object-oriented language and environment for statistical computing that is gaining popularity in the statistical and scientific community. Derived from the S language and environment developed at Bell Laboratories, R is an open-source language that is freely available at http://www.r-project.org/ (last accessed 11 January 2011). In R, the functions for computing the NGA equations can be loaded as an

  5. Global Variance Risk Premium and Forex Return Predictability

    OpenAIRE

    Aloosh, Arash

    2014-01-01

    In a long-run risk model with stochastic volatility and frictionless markets, I express expected forex returns as a function of consumption growth variances and stock variance risk premiums (VRPs)—the difference between the risk-neutral and statistical expectations of market return variation. This provides a motivation for using the forward-looking information available in stock market volatility indices to predict forex returns. Empirically, I find that stock VRPs predict forex returns at a ...

  6. Prediction equation for lower limbs lean soft tissue in circumpubertal boys using anthropometry and biological maturation.

    Directory of Open Access Journals (Sweden)

    João Valente-dos-Santos

    Full Text Available Lean soft tissue (LST, a surrogate of skeletal muscle mass, is largely limited to appendicular body regions. Simple and accurate methods to estimate lower limbs LST are often used in attempts to partition out the influence of body size on performance outputs. The aim of the current study was to develop and cross-validate a new model to predict lower limbs LST in boys aged 10-13 years, using dual-energy X-ray absorptiometry (DXA as the reference method. Total body and segmental (lower limbs composition were assessed with a Hologic Explorer-W QDR DXA scanner in a cross-sectional sample of 75 Portuguese boys (144.8±6.4 cm; 40.2±9.0 kg. Skinfolds were measured at the anterior and posterior mid-thigh, and medial calf. Circumferences were measured at the proximal, mid and distal thigh. Leg length was estimated as stature minus sitting height. Current stature expressed as a percentage of attained predicted mature stature (PMS was used as an estimate of biological maturity status. Backward proportional allometric models were used to identify the model with the best statistical fit: ln (lower limbs LST  = 0.838× ln (body mass +0.476× ln (leg length - 0.135× ln (mid-thigh circumference - 0.053× ln (anterior mid-thigh skinfold - 0.098× ln (medial calf skinfold - 2.680+0.010× (percentage of attained PMS (R = 0.95. The obtained equation was cross-validated using the predicted residuals sum of squares statistics (PRESS method (R2PRESS = 0.90. Deming repression analysis between predicted and current lower limbs LST showed a standard error of estimation of 0.52 kg (95% limits of agreement: 0.77 to -1.27 kg. The new model accurately predicts lower limbs LST in circumpubertal boys.

  7. Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.

    Science.gov (United States)

    Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan

    2018-01-12

    Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.

  8. Prediction of multiphase equilibria in associating fluids by a contact-site quasichemical equation of state

    International Nuclear Information System (INIS)

    Prikhodko, I.V.; Victorov, A.I.; Loos, Th.W.de

    1995-01-01

    A contract-site quasichemical equation of state has been used for the modeling of different kinds of fluid phase equilibria (between a gas phase and one or more liquids) over a wide range of conditions. Among the systems of interest are the ternary mixtures water + alkanols + hydrocarbons (alkanes or alkynes), water + alkanols (or acetone) + CO 2 , water + polyoxyethyleneglycol ethers + heavy alkanes. The model has been applied to describing the thermodynamic properties of the binary subsystems and to predict the phase behavior of the ternary systems. For longer-chain alkanols and hydrocarbons a group-contribution approach is implemented, which allows the modeling when no experimental data are available. The model gives reasonable predictions of phase behavior and the correct trends in the calculated phase diagrams in most cases. The concentrations of associates in liquid and gas phases are estimated by the model and compared with some experimental and computer simulation data. The predictive abilities of the model, its limitations, and possible ways of its improvement are discussed

  9. Testing a 1-D Analytical Salt Intrusion Model and the Predictive Equation in Malaysian Estuaries

    Science.gov (United States)

    Gisen, Jacqueline Isabella; Savenije, Hubert H. G.

    2013-04-01

    Little is known about the salt intrusion behaviour in Malaysian estuaries. Study on this topic sometimes requires large amounts of data especially if a 2-D or 3-D numerical models are used for analysis. In poor data environments, 1-D analytical models are more appropriate. For this reason, a fully analytical 1-D salt intrusion model, based on the theory of Savenije in 2005, was tested in three Malaysian estuaries (Bernam, Selangor and Muar) because it is simple and requires minimal data. In order to achieve that, site surveys were conducted in these estuaries during the dry season (June-August) at spring tide by moving boat technique. Data of cross-sections, water levels and salinity were collected, and then analysed with the salt intrusion model. This paper demonstrates a good fit between the simulated and observed salinity distribution for all three estuaries. Additionally, the calibrated Van der Burgh's coefficient K, Dispersion coefficient D0, and salt intrusion length L, for the estuaries also displayed a reasonable correlations with those calculated from the predictive equations. This indicates that not only is the salt intrusion model valid for the case studies in Malaysia but also the predictive model. Furthermore, the results from this study describe the current state of the estuaries with which the Malaysian water authority in Malaysia can make decisions on limiting water abstraction or dredging. Keywords: salt intrusion, Malaysian estuaries, discharge, predictive model, dispersion

  10. Quantitative accuracy of the simplified strong ion equation to predict serum pH in dogs.

    Science.gov (United States)

    Cave, N J; Koo, S T

    2015-01-01

    Electrochemical approach to the assessment of acid-base states should provide a better mechanistic explanation of the metabolic component than methods that consider only pH and carbon dioxide. Simplified strong ion equation (SSIE), using published dog-specific values, would predict the measured serum pH of diseased dogs. Ten dogs, hospitalized for various reasons. Prospective study of a convenience sample of a consecutive series of dogs admitted to the Massey University Veterinary Teaching Hospital (MUVTH), from which serum biochemistry and blood gas analyses were performed at the same time. Serum pH was calculated (Hcal+) using the SSIE, and published values for the concentration and dissociation constant for the nonvolatile weak acids (Atot and Ka ), and subsequently Hcal+ was compared with the dog's actual pH (Hmeasured+). To determine the source of discordance between Hcal+ and Hmeasured+, the calculations were repeated using a series of substituted values for Atot and Ka . The Hcal+ did not approximate the Hmeasured+ for any dog (P = 0.499, r(2) = 0.068), and was consistently more basic. Substituted values Atot and Ka did not significantly improve the accuracy (r(2) = 0.169 to <0.001). Substituting the effective SID (Atot-[HCO3-]) produced a strong association between Hcal+ and Hmeasured+ (r(2) = 0.977). Using the simplified strong ion equation and the published values for Atot and Ka does not appear to provide a quantitative explanation for the acid-base status of dogs. Efficacy of substituting the effective SID in the simplified strong ion equation suggests the error lies in calculating the SID. Copyright © 2015 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

  11. Using Bronson Equation to Accurately Predict the Dog Brain Weight Based on Body Weight Parameter

    Directory of Open Access Journals (Sweden)

    L. Miguel Carreira

    2016-12-01

    Full Text Available The study used 69 brains (n = 69 from adult dog cadavers, divided by their skull type into three groups, brachi (B, dolicho (D and mesaticephalic (M (n = 23 each, and aimed: (1 to determine whether the Bronson equation may be applied, without reservation, to estimate brain weight (BW in brachy (B, dolicho (D, and mesaticephalic (M dog breeds; and (2 to evaluate which breeds are more closely related to each other in an evolutionary scenario. All subjects were identified by sex, age, breed, and body weight (bw. An oscillating saw was used for a circumferential craniotomy to open the skulls; the brains were removed and weighed using a digital scale. For statistical analysis, p-values < 0.05 were considered significant. The work demonstrated a strong relationship between the observed and predicted BW by using the Bronson equation. It was possible to hypothesize that groups B and D present a greater encephalization level than M breeds, that B and D dog breeds are more closely related to each other than to M, and from the three groups, the D individuals presented the highest brain mass mean.

  12. Seismic Hazard Assessment in Site Evaluation for Nuclear Installations: Ground Motion Prediction Equations and Site Response

    International Nuclear Information System (INIS)

    2016-07-01

    The objective of this publication is to provide the state-of-the-art practice and detailed technical elements related to ground motion evaluation by ground motion prediction equations (GMPEs) and site response in the context of seismic hazard assessments as recommended in IAEA Safety Standards Series No. SSG-9, Seismic Hazards in Site Evaluation for Nuclear Installations. The publication includes the basics of GMPEs, ground motion simulation, selection and adjustment of GMPEs, site characterization, and modelling of site response in order to improve seismic hazard assessment. The text aims at delineating the most important aspects of these topics (including current practices, criticalities and open problems) within a coherent framework. In particular, attention has been devoted to filling conceptual gaps. It is written as a reference text for trained users who are responsible for planning preparatory seismic hazard analyses for siting of all nuclear installations and/or providing constraints for anti-seismic design and retrofitting of existing structures

  13. Sound field prediction of ultrasonic lithotripsy in water with spheroidal beam equations

    International Nuclear Information System (INIS)

    Zhang Lue; Wang Xiang-Da; Liu Xiao-Zhou; Gong Xiu-Fen

    2015-01-01

    With converged shock wave, extracorporeal shock wave lithotripsy (ESWL) has become a preferable way to crush human calculi because of its advantages of efficiency and non-intrusion. Nonlinear spheroidal beam equations (SBE) are employed to illustrate the acoustic wave propagation for transducers with a wide aperture angle. To predict the acoustic field distribution precisely, boundary conditions are obtained for the SBE model of the monochromatic wave when the source is located on the focus of an ESWL transducer. Numerical results of the monochromatic wave propagation in water are analyzed and the influences of half-angle, fundamental frequency, and initial pressure are investigated. According to our results, with optimization of these factors, the pressure focal gain of ESWL can be enhanced and the effectiveness of treatment can be improved. (paper)

  14. Predictive risk factors for persistent postherniotomy pain

    DEFF Research Database (Denmark)

    Aasvang, Eske K; Gmaehle, Eliza; Hansen, Jeanette B

    2010-01-01

    BACKGROUND: Persistent postherniotomy pain (PPP) affects everyday activities in 5-10% of patients. Identification of predisposing factors may help to identify the risk groups and guide anesthetic or surgical procedures in reducing risk for PPP. METHODS: A prospective study was conducted in 464...... patients undergoing open or laparoscopic transabdominal preperitoneal elective groin hernia repair. Primary outcome was identification of risk factors for substantial pain-related functional impairment at 6 months postoperatively assessed by the validated Activity Assessment Scale (AAS). Data on potential...... risk factors for PPP were collected preoperatively (pain from the groin hernia, preoperative AAS score, pain from other body regions, and psychometric assessment). Pain scores were collected on days 7 and 30 postoperatively. Sensory functions including pain response to tonic heat stimulation were...

  15. Lipoprotein metabolism indicators improve cardiovascular risk prediction

    NARCIS (Netherlands)

    Schalkwijk, D.B. van; Graaf, A.A. de; Tsivtsivadze, E.; Parnell, L.D.; Werff-van der Vat, B.J.C. van der; Ommen, B. van; Greef, J. van der; Ordovás, J.M.

    2014-01-01

    Background: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to

  16. Develop mental dyslexia: predicting individual risk

    OpenAIRE

    Thompson, PA; Hulme, C; Nash, HM; Gooch, Deborah; Hayiou-Thomas, E; Snowling, MJ

    2015-01-01

    Background Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited...

  17. Neural network versus activity-specific prediction equations for energy expenditure estimation in children.

    Science.gov (United States)

    Ruch, Nicole; Joss, Franziska; Jimmy, Gerda; Melzer, Katarina; Hänggi, Johanna; Mäder, Urs

    2013-11-01

    The aim of this study was to compare the energy expenditure (EE) estimations of activity-specific prediction equations (ASPE) and of an artificial neural network (ANNEE) based on accelerometry with measured EE. Forty-three children (age: 9.8 ± 2.4 yr) performed eight different activities. They were equipped with one tri-axial accelerometer that collected data in 1-s epochs and a portable gas analyzer. The ASPE and the ANNEE were trained to estimate the EE by including accelerometry, age, gender, and weight of the participants. To provide the activity-specific information, a decision tree was trained to recognize the type of activity through accelerometer data. The ASPE were applied to the activity-type-specific data recognized by the tree (Tree-ASPE). The Tree-ASPE precisely estimated the EE of all activities except cycling [bias: -1.13 ± 1.33 metabolic equivalent (MET)] and walking (bias: 0.29 ± 0.64 MET; P MET) and walking (bias: 0.61 ± 0.72 MET) and underestimated the EE of cycling (bias: -0.90 ± 1.18 MET; P MET, Tree-ASPE: 0.08 ± 0.21 MET) and walking (ANNEE 0.61 ± 0.72 MET, Tree-ASPE: 0.29 ± 0.64 MET) were significantly smaller in the Tree-ASPE than in the ANNEE (P < 0.05). The Tree-ASPE was more precise in estimating the EE than the ANNEE. The use of activity-type-specific information for subsequent EE prediction equations might be a promising approach for future studies.

  18. Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling.

    Science.gov (United States)

    Willis, Michael; Asseburg, Christian; Nilsson, Andreas; Johnsson, Kristina; Kartman, Bernt

    2017-03-01

    Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A 1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R 2 ) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A 1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All

  19. Calibration plots for risk prediction models in the presence of competing risks

    DEFF Research Database (Denmark)

    Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

    2014-01-01

    A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks...... prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves...

  20. Validation of the kidney failure risk equation in European CKD patients

    NARCIS (Netherlands)

    Peeters, M.J.; Zuilen, A.D. van; Brand, A. van den; Bots, M.L.; Blankestijn, P.J.; Wetzels, J.F.M.; Vervoort, G.M.M.; et al.,

    2013-01-01

    BACKGROUND: Patients with chronic kidney disease (CKD) are at risk for progression to kidney failure. Using data of Canadian CKD patients, Tangri et al. recently developed models to predict the progression of CKD stages 3-5 to kidney failure within 5 years. We validated this kidney failure risk

  1. Lean body mass-based standardized uptake value, derived from a predictive equation, might be misleading in PET studies

    International Nuclear Information System (INIS)

    Erselcan, Taner; Turgut, Bulent; Dogan, Derya; Ozdemir, Semra

    2002-01-01

    The standardized uptake value (SUV) has gained recognition in recent years as a semiquantitative evaluation parameter in positron emission tomography (PET) studies. However, there is as yet no consensus on the way in which this index should be determined. One of the confusing factors is the normalisation procedure. Among the proposed anthropometric parameters for normalisation is lean body mass (LBM); LBM has been determined by using a predictive equation in most if not all of the studies. In the present study, we assessed the degree of agreement of various LBM predictive equations with a reference method. Secondly, we evaluated the impact of predicted LBM values on a hypothetical value of 2.5 SUV, normalised to LBM (SUV LBM ), by using various equations. The study population consisted of 153 women, aged 32.3±11.8 years (mean±SD), with a height of 1.61±0.06 m, a weight of 71.1±17.5 kg, a body surface area of 1.77±0.22 m 2 and a body mass index of 27.6±6.9 kg/m 2 . LBM (44.2±6.6 kg) was measured by a dual-energy X-ray absorptiometry (DEXA) method. A total of nine equations from the literature were evaluated, four of them from recent PET studies. Although there was significant correlation between predicted and measured LBM values, 95% limits of agreement determined by the Bland and Altman method showed a wide range of variation in predicted LBM values as compared with DEXA, no matter which predictive equation was used. Moreover, only one predictive equation was not statistically different in the comparison of means (DEXA and predicted LBM values). It was also shown that the predictive equations used in this study yield a wide range of SUV LBM values from 1.78 to 5.16 (29% less or 107% more) for an SUV of 2.5. In conclusion, this study suggests that estimation of LBM by use of a predictive equation may cause substantial error for an individual, and that if LBM is chosen for the SUV normalisation procedure, it should be measured, not predicted. (orig.)

  2. Comparison of Two Creatinine-Based Equations for Predicting Decline in Renal Function in Type 2 Diabetic Patients with Nephropathy in a Korean Population

    Directory of Open Access Journals (Sweden)

    Eun Young Lee

    2013-01-01

    Full Text Available Aim. To compare two creatinine-based estimated glomerular filtration rate (eGFR equations, the chronic kidney disease epidemiology collaboration (CKD-EPI and the modification of diet in renal disease (MDRD, for predicting the risk of CKD progression in type 2 diabetic patients with nephropathy. Methods. A total of 707 type 2 diabetic patients with 24 hr urinary albumin excretion of more than 30 mg/day were retrospectively recruited and traced until doubling of baseline serum creatinine (SCr levels was noted. Results. During the follow-up period (median, 2.4 years, the CKD-EPI equation reclassified 10.9% of all MDRD-estimated subjects: 9.1% to an earlier stage of CKD and 1.8% to a later stage of CKD. Overall, the prevalence of CKD (eGFR < 60 mL/min/1.73 m2 was lowered from 54% to 51.6% by applying the CKD-EPI equation. On Cox-regression analysis, both equations exhibited significant associations with an increased risk for doubling of SCr. However, only the CKD-EPI equation maintained a significant hazard ratio for doubling of SCr in earlier-stage CKD (eGFR ≥ 45 mL/min/1.73 m2, when compared to stage 1 CKD (eGFR ≥ 90 mL/min/1.73 m2. Conclusion. In regard to CKD progression, these results suggest that the CKD-EPI equation might more accurately stratify earlier-stage CKD among type 2 diabetic patients with nephropathy than the MDRD study equation.

  3. Optimal level of continuous positive airway pressure: auto-adjusting titration versus titration with a predictive equation.

    Science.gov (United States)

    Choi, Ji Ho; Jun, Young Joon; Oh, Jeong In; Jung, Jong Yoon; Hwang, Gyu Ho; Kwon, Soon Young; Lee, Heung Man; Kim, Tae Hoon; Lee, Sang Hag; Lee, Seung Hoon

    2013-05-01

    The aims of the present study were twofold. We sought to compare two methods of titrating the level of continuous positive airway pressure (CPAP) - auto-adjusting titration and titration using a predictive equation - with full-night manual titration used as the benchmark. We also investigated the reliability of the two methods in patients with obstructive sleep apnea syndrome (OSAS). Twenty consecutive adult patients with OSAS who had successful, full-night manual and auto-adjusting CPAP titration participated in this study. The titration pressure level was calculated with a previously developed predictive equation based on body mass index and apnea-hypopnea index. The mean titration pressure levels obtained with the manual, auto-adjusting, and predictive equation methods were 9.0 +/- 3.6, 9.4 +/- 3.0, and 8.1 +/- 1.6 cm H2O,respectively. There was a significant difference in the concordance within the range of +/- 2 cm H2O (p = 0.019) between both the auto-adjusting titration and the titration using the predictive equation compared to the full-night manual titration. However, there was no significant difference in the concordance within the range of +/- 1 cm H2O (p > 0.999). When compared to full-night manual titration as the standard method, auto-adjusting titration appears to be more reliable than using a predictive equation for determining the optimal CPAP level in patients with OSAS.

  4. Predicting risk and the emergence of schizophrenia.

    LENUS (Irish Health Repository)

    Clarke, Mary C

    2012-09-01

    This article gives an overview of genetic and environmental risk factors for schizophrenia. The presence of certain molecular, biological, and psychosocial factors at certain points in the life span, has been linked to later development of schizophrenia. All need to be considered in the context of schizophrenia as a lifelong brain disorder. Research interest in schizophrenia is shifting to late childhood\\/early adolescence for screening and preventative measures. This article discusses those environmental risk factors for schizophrenia for which there is the largest evidence base.

  5. Chapter 4. Predicting post-fire erosion and sedimentation risk on a landscape scale

    Science.gov (United States)

    MacDonald, L.H.; Sampson, R.; Brady, D.; Juarros, L.; Martin, Deborah

    2000-01-01

    Historic fire suppression efforts have increased the likelihood of large wildfires in much of the western U.S. Post-fire soil erosion and sedimentation risks are important concerns to resource managers. In this paper we develop and apply procedures to predict post-fire erosion and sedimentation risks on a pixel-, catchment-, and landscape-scale in central and western Colorado.Our model for predicting post-fire surface erosion risk is conceptually similar to the Revised Universal Soil Loss Equation (RUSLE). One key addition is the incorporation of a hydrophobicity risk index (HY-RISK) based on vegetation type, predicted fire severity, and soil texture. Post-fire surface erosion risk was assessed for each 90-m pixel by combining HYRISK, slope, soil erodibility, and a factor representing the likely increase in soil wetness due to removal of the vegetation. Sedimentation risk was a simple function of stream gradient. Composite surface erosion and sedimentation risk indices were calculated and compared across the 72 catchments in the study area.When evaluated on a catchment scale, two-thirds of the catchments had relatively little post-fire erosion risk. Steeper catchments with higher fuel loadings typically had the highest post-fire surface erosion risk. These were generally located along the major north-south mountain chains and, to a lesser extent, in west-central Colorado. Sedimentation risks were usually highest in the eastern part of the study area where a higher proportion of streams had lower gradients. While data to validate the predicted erosion and sedimentation risks are lacking, the results appear reasonable and are consistent with our limited field observations. The models and analytic procedures can be readily adapted to other locations and should provide useful tools for planning and management at both the catchment and landscape scale.

  6. Soil Loss Prediction on Mobile Platform Using Universal Soil-Loss Equation (USLE Model

    Directory of Open Access Journals (Sweden)

    Effendi Rahim Supli

    2017-01-01

    Full Text Available Indirect method for soil loss predictions are plentiful, one of which is Universal soil-loss equation (USLE model. Available technology in mobile applications prompted the authors to develop a tool for calculating soil loss for many land types by transforming the USLE model into smart mobile application. The application is designed by using simple language for calculating each and every factor and lastly summing up the results. Factors that are involved in the calculation of soil loss are namely erosivity, erodibility, slope steepness, length of slope, land cover and conservation measures. The program will also be able to give its judgment for each of the prediction of soil loss rates for each and every possible land uses ranging from very light to very heavy. The application is believed to be useful for land users, students, farmers, planners, companies and government officers. It is shown by conducting usability testing using usability model, which is designed for mobile application. The results showed from 120 respondents that the usability of the system in this study was in “very good” classification, for three characteristics (ease of use, user satisfaction, and learnability. Only attractiveness characteristic that falls into “good” classification.

  7. Risk assessment methodologies for predicting phosphorus losses

    NARCIS (Netherlands)

    Schoumans, O.F.; Chardon, W.J.

    2003-01-01

    Risk assessment parameters are needed to assess the contribution of phosphorus (P) losses from soil to surface water, and the effectiveness of nutrient and land management strategies for the reduction of P loss. These parameters need to take into account the large temporal and spatial variation in P

  8. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  9. Prediction of Banking Systemic Risk Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Shouwei Li

    2013-01-01

    Full Text Available Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.

  10. Evaluation of Equations for Predicting 24-Hour Urinary Sodium Excretion from Casual Urine Samples in Asian Adults.

    Science.gov (United States)

    Whitton, Clare; Gay, Gibson Ming Wei; Lim, Raymond Boon Tar; Tan, Linda Wei Lin; Lim, Wei-Yen; van Dam, Rob M

    2016-08-01

    The collection of 24-h urine samples for the estimation of sodium intake is burdensome, and the utility of spot urine samples in Southeast Asian populations is unclear. We aimed to assess the validity of prediction equations with the use of spot urine concentrations. A sample of 144 Singapore residents of Chinese, Malay, and Indian ethnicity aged 18-79 y were recruited from the Singapore Health 2 Study conducted in 2014. Participants collected urine for 24 h in multiple small bottles on a single day. To determine the optimal collection time for a spot urine sample, a 1-mL sample was taken from a random bottle collected in the morning, afternoon, and evening. Published equations and a newly derived equation were used to predict 24-h sodium excretion from spot urine samples. The mean ± SD concentration of sodium from the 24-h urine sample was 125 ± 53.4 mmol/d, which is equivalent to 7.2 ± 3.1 g salt. Bland-Altman plots showed good agreement at the group level between estimated and actual 24-h sodium excretion, with biases for the morning period of -3.5 mmol (95% CI: -14.8, 7.8 mmol; new equation) and 1.46 mmol (95% CI: -10.0, 13.0 mmol; Intersalt equation). A larger bias of 25.7 mmol (95% CI: 12.2, 39.3 mmol) was observed for the Tanaka equation in the morning period. The prediction accuracy did not differ significantly for spot urine samples collected at different times of the day or at a random time of day (P = 0.11-0.76). This study suggests that the application of both our own newly derived equation and the Intersalt equation to spot urine concentrations may be useful in predicting group means for 24-h sodium excretion in urban Asian populations. © 2016 American Society for Nutrition.

  11. Elderly fall risk prediction using static posturography

    OpenAIRE

    Howcroft, Jennifer; Lemaire, Edward D.; Kofman, Jonathan; McIlroy, William E.

    2017-01-01

    Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed w...

  12. Structural equation models to estimate risk of infection and tolerance to bovine mastitis

    OpenAIRE

    Detilleux, Johann; Theron, Léonard; Duprez, Jean-Noël; Reding, Edouard; Humblet, Marie-France; Planchon, Viviane; Delfosse, Camille; Bertozzi, Carlo; Mainil, Jacques; Hanzen, Christian

    2013-01-01

    Background One method to improve durably animal welfare is to select, as reproducers, animals with the highest ability to resist or tolerate infection. To do so, it is necessary to distinguish direct and indirect mechanisms of resistance and tolerance because selection on these traits is believed to have different epidemiological and evolutionary consequences. Methods We propose structural equation models with latent variables (1) to quantify the latent risk of infection and to identify, amon...

  13. Risk Prediction Models for Incident Heart Failure: A Systematic Review of Methodology and Model Performance.

    Science.gov (United States)

    Sahle, Berhe W; Owen, Alice J; Chin, Ken Lee; Reid, Christopher M

    2017-09-01

    Numerous models predicting the risk of incident heart failure (HF) have been developed; however, evidence of their methodological rigor and reporting remains unclear. This study critically appraises the methods underpinning incident HF risk prediction models. EMBASE and PubMed were searched for articles published between 1990 and June 2016 that reported at least 1 multivariable model for prediction of HF. Model development information, including study design, variable coding, missing data, and predictor selection, was extracted. Nineteen studies reporting 40 risk prediction models were included. Existing models have acceptable discriminative ability (C-statistics > 0.70), although only 6 models were externally validated. Candidate variable selection was based on statistical significance from a univariate screening in 11 models, whereas it was unclear in 12 models. Continuous predictors were retained in 16 models, whereas it was unclear how continuous variables were handled in 16 models. Missing values were excluded in 19 of 23 models that reported missing data, and the number of events per variable was models. Only 2 models presented recommended regression equations. There was significant heterogeneity in discriminative ability of models with respect to age (P prediction models that had sufficient discriminative ability, although few are externally validated. Methods not recommended for the conduct and reporting of risk prediction modeling were frequently used, and resulting algorithms should be applied with caution. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Does the Risk Assessment and Prediction Tool Predict Discharge Disposition After Joint Replacement?

    DEFF Research Database (Denmark)

    Hansen, Viktor J.; Gromov, Kirill; Lebrun, Lauren M

    2015-01-01

    BACKGROUND: Payers of health services and policymakers place a major focus on cost containment in health care. Studies have shown that early planning of discharge is essential in reducing length of stay and achieving financial benefit; tools that can help predict discharge disposition would...... populations is unknown. A low RAPT score is reported to indicate a high risk of needing any form of inpatient rehabilitation after TJA, including short-term nursing facilities. QUESTIONS/PURPOSES: This study attempts (1) to assess predictive accuracy of the RAPT on US patients undergoing total hip and knee....... Based on our findings, the risk categories in our populations should be high risk intermediate risk 7 to 10, and low risk > 10. CONCLUSIONS: The RAPT accurately predicted discharge disposition for high- and low-risk patients in our cohort. Based on our data, intermediate-risk patients should...

  15. Estimation of Resting Energy Expenditure: Validation of Previous and New Predictive Equations in Obese Children and Adolescents.

    Science.gov (United States)

    Acar-Tek, Nilüfer; Ağagündüz, Duygu; Çelik, Bülent; Bozbulut, Rukiye

    2017-08-01

    Accurate estimation of resting energy expenditure (REE) in childrenand adolescents is important to establish estimated energy requirements. The aim of the present study was to measure REE in obese children and adolescents by indirect calorimetry method, compare these values with REE values estimated by equations, and develop the most appropriate equation for this group. One hundred and three obese children and adolescents (57 males, 46 females) between 7 and 17 years (10.6 ± 2.19 years) were recruited for the study. REE measurements of subjects were made with indirect calorimetry (COSMED, FitMatePro, Rome, Italy) and body compositions were analyzed. In females, the percentage of accurate prediction varied from 32.6 (World Health Organization [WHO]) to 43.5 (Molnar and Lazzer). The bias for equations was -0.2% (Kim), 3.7% (Molnar), and 22.6% (Derumeaux-Burel). Kim's (266 kcal/d), Schmelzle's (267 kcal/d), and Henry's equations (268 kcal/d) had the lowest root mean square error (RMSE; respectively 266, 267, 268 kcal/d). The equation that has the highest RMSE values among female subjects was the Derumeaux-Burel equation (394 kcal/d). In males, when the Institute of Medicine (IOM) had the lowest accurate prediction value (12.3%), the highest values were found using Schmelzle's (42.1%), Henry's (43.9%), and Müller's equations (fat-free mass, FFM; 45.6%). When Kim and Müller had the smallest bias (-0.6%, 9.9%), Schmelzle's equation had the smallest RMSE (331 kcal/d). The new specific equation based on FFM was generated as follows: REE = 451.722 + (23.202 * FFM). According to Bland-Altman plots, it has been found out that the new equations are distributed randomly in both males and females. Previously developed predictive equations mostly provided unaccurate and biased estimates of REE. However, the new predictive equations allow clinicians to estimate REE in an obese children and adolescents with sufficient and acceptable accuracy.

  16. Prediction of adiabatic bubbly flows in TRACE using the interfacial area transport equation

    International Nuclear Information System (INIS)

    Talley, J.; Worosz, T.; Kim, S.; Mahaffy, J.; Bajorek, S.; Tien, K.

    2011-01-01

    The conventional thermal-hydraulic reactor system analysis codes utilize a two-field, two-fluid formulation to model two-phase flows. To close this model, static flow regime transition criteria and algebraic relations are utilized to estimate the interfacial area concentration (a i ). To better reflect the continuous evolution of two-phase flow, an experimental version of TRACE is being developed which implements the interfacial area transport equation (IATE) to replace the flow regime based approach. Dynamic estimation of a i is provided through the use of mechanistic models for bubble coalescence and disintegration. To account for the differences in bubble interactions and drag forces, two-group bubble transport is sought. As such, Group 1 accounts for the transport of spherical and distorted bubbles, while Group 2 accounts for the cap, slug, and churn-turbulent bubbles. Based on this categorization, a two-group IATE applicable to the range of dispersed two-phase flows has been previously developed. Recently, a one-group, one-dimensional, adiabatic IATE has been implemented into the TRACE code with mechanistic models accounting for: (1) bubble breakup due to turbulent impact of an eddy on a bubble, (2) bubble coalescence due to random collision driven by turbulent eddies, and (3) bubble coalescence due to the acceleration of a bubble in the wake region of a preceding bubble. To demonstrate the enhancement of the code's capability using the IATE, experimental data for a i , void fraction, and bubble velocity measured by a multi-sensor conductivity probe are compared to both the IATE and flow regime based predictions. In total, 50 air-water vertical co-current upward and downward bubbly flow conditions in pipes with diameters ranging from 2.54 to 20.32 cm are evaluated. It is found that TRACE, using the conventional flow regime relation, always underestimates a i . Moreover, the axial trend of the a i prediction is always quasi-linear because a i in the

  17. PREDICTION OF SURGICAL TREATMENT WITH POUR PERITONITIS QUANTIFYING RISK FACTORS

    Directory of Open Access Journals (Sweden)

    І. К. Churpiy

    2012-11-01

    Full Text Available Explored the possibility of quantitative assessment of risk factors of complications in the treatment of diffuse peritonitis. Highlighted 53 groups of features that are important in predicting the course of diffuse peritonitis. The proposed scheme of defining the risk of clinical course of diffuse peritonitis can quantify the severity of the source of patients and in most cases correctly predict the results of treatment of disease.

  18. Effectiveness of prediction equations in estimating energy expenditure sample of Brazilian and Spanish women with excess body weight

    OpenAIRE

    Lopes Rosado, Eliane; Santiago de Brito, Roberta; Bressan, Josefina; Martínez Hernández, José Alfredo

    2014-01-01

    Objective: To assess the adequacy of predictive equations for estimation of energy expenditure (EE), compared with the EE using indirect calorimetry in a sample of Brazilian and Spanish women with excess body weight Methods: It is a cross-sectional study with 92 obese adult women [26 Brazilian -G1- and 66 Spanish - G2- (aged 20-50)]. Weight and height were evaluated during fasting for the calculation of body mass index and predictive equations. EE was evaluated using the open-circuit indirect...

  19. Predicting child maltreatment: A meta-analysis of the predictive validity of risk assessment instruments.

    Science.gov (United States)

    van der Put, Claudia E; Assink, Mark; Boekhout van Solinge, Noëlle F

    2017-11-01

    Risk assessment is crucial in preventing child maltreatment since it can identify high-risk cases in need of child protection intervention. Despite widespread use of risk assessment instruments in child welfare, it is unknown how well these instruments predict maltreatment and what instrument characteristics are associated with higher levels of predictive validity. Therefore, a multilevel meta-analysis was conducted to examine the predictive accuracy of (characteristics of) risk assessment instruments. A literature search yielded 30 independent studies (N=87,329) examining the predictive validity of 27 different risk assessment instruments. From these studies, 67 effect sizes could be extracted. Overall, a medium significant effect was found (AUC=0.681), indicating a moderate predictive accuracy. Moderator analyses revealed that onset of maltreatment can be better predicted than recurrence of maltreatment, which is a promising finding for early detection and prevention of child maltreatment. In addition, actuarial instruments were found to outperform clinical instruments. To bring risk and needs assessment in child welfare to a higher level, actuarial instruments should be further developed and strengthened by distinguishing risk assessment from needs assessment and by integrating risk assessment with case management. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Obesity Risk Prediction among Women of Upper Egypt: The impact ...

    African Journals Online (AJOL)

    Obesity Risk Prediction among Women of Upper Egypt: The impact of FTO ... with increased obesity risk but there is a lack of association with diabetes. ... (as certain foods or gene therapy) will prevent the percentage of women who is affected ...

  1. Towards renewed health economic simulation of type 2 diabetes: risk equations for first and second cardiovascular events from Swedish register data.

    Directory of Open Access Journals (Sweden)

    Aliasghar Ahmad Kiadaliri

    Full Text Available OBJECTIVE: Predicting the risk of future events is an essential part of health economic simulation models. In pursuit of this goal, the current study aims to predict the risk of developing first and second acute myocardial infarction, heart failure, non-acute ischaemic heart disease, and stroke after diagnosis in patients with type 2 diabetes, using data from the Swedish National Diabetes Register. MATERIAL AND METHODS: Register data on 29,034 patients with type 2 diabetes were analysed over five years of follow up (baseline 2003. To develop and validate the risk equations, the sample was randomly divided into training (75% and test (25% subsamples. The Weibull proportional hazard model was used to estimate the coefficients of the risk equations, and these were validated in both the training and the test samples. RESULTS: In total, 4,547 first and 2,418 second events were observed during the five years of follow up. Experiencing a first event substantially elevated the risk of subsequent events. There were heterogeneities in the effects of covariates within as well as between events; for example, while for females the hazard ratio of having a first acute myocardial infarction was 0.79 (0.70-0.90, the hazard ratio of a second was 1.21 (0.98-1.48. The hazards of second events decreased as the time since first events elapsed. The equations showed adequate calibration and discrimination (C statistics range: 0.70-0.84 in test samples. CONCLUSION: The accuracy of health economic simulation models of type 2 diabetes can be improved by ensuring that they account for the heterogeneous effects of covariates on the risk of first and second cardiovascular events. Thus it is important to extend such models by including risk equations for second cardiovascular events.

  2. Predicting fractional bed load transport rates: Application of the Wilcock‐Crowe equations to a regulated gravel bed river

    Science.gov (United States)

    Gaeuman, David; Andrews, E.D.; Krause, Andreas; Smith, Wes

    2009-01-01

    Bed load samples from four locations in the Trinity River of northern California are analyzed to evaluate the performance of the Wilcock‐Crowe bed load transport equations for predicting fractional bed load transport rates. Bed surface particles become smaller and the fraction of sand on the bed increases with distance downstream from Lewiston Dam. The dimensionless reference shear stress for the mean bed particle size (τ*rm) is largest near the dam, but varies relatively little between the more downstream locations. The relation between τ*rm and the reference shear stresses for other size fractions is constant across all locations. Total bed load transport rates predicted with the Wilcock‐Crowe equations are within a factor of 2 of sampled transport rates for 68% of all samples. The Wilcock‐Crowe equations nonetheless consistently under‐predict the transport of particles larger than 128 mm, frequently by more than an order of magnitude. Accurate prediction of the transport rates of the largest particles is important for models in which the evolution of the surface grain size distribution determines subsequent bed load transport rates. Values of τ*rm estimated from bed load samples are up to 50% larger than those predicted with the Wilcock‐Crowe equations, and sampled bed load transport approximates equal mobility across a wider range of grain sizes than is implied by the equations. Modifications to the Wilcock‐Crowe equation for determining τ*rm and the hiding function used to scale τ*rm to other grain size fractions are proposed to achieve the best fit to observed bed load transport in the Trinity River.

  3. The Role of Risk Aversion in Predicting Individual Behaviours

    OpenAIRE

    Guiso, Luigi; Paiella, Monica

    2004-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers’ decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways cons...

  4. The Role of Risk Aversion in Predicting Individual Behaviour

    OpenAIRE

    Monica Paiella; Luigi Guiso

    2004-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers' decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways cons...

  5. Risk avoidance in sympatric large carnivores: reactive or predictive?

    Science.gov (United States)

    Broekhuis, Femke; Cozzi, Gabriele; Valeix, Marion; McNutt, John W; Macdonald, David W

    2013-09-01

    1. Risks of predation or interference competition are major factors shaping the distribution of species. An animal's response to risk can either be reactive, to an immediate risk, or predictive, based on preceding risk or past experiences. The manner in which animals respond to risk is key in understanding avoidance, and hence coexistence, between interacting species. 2. We investigated whether cheetahs (Acinonyx jubatus), known to be affected by predation and competition by lions (Panthera leo) and spotted hyaenas (Crocuta crocuta), respond reactively or predictively to the risks posed by these larger carnivores. 3. We used simultaneous spatial data from Global Positioning System (GPS) radiocollars deployed on all known social groups of cheetahs, lions and spotted hyaenas within a 2700 km(2) study area on the periphery of the Okavango Delta in northern Botswana. The response to risk of encountering lions and spotted hyaenas was explored on three levels: short-term or immediate risk, calculated as the distance to the nearest (contemporaneous) lion or spotted hyaena, long-term risk, calculated as the likelihood of encountering lions and spotted hyaenas based on their cumulative distributions over a 6-month period and habitat-associated risk, quantified by the habitat used by each of the three species. 4. We showed that space and habitat use by cheetahs was similar to that of lions and, to a lesser extent, spotted hyaenas. However, cheetahs avoided immediate risks by positioning themselves further from lions and spotted hyaenas than predicted by a random distribution. 5. Our results suggest that cheetah spatial distribution is a hierarchical process, first driven by resource acquisition and thereafter fine-tuned by predator avoidance; thus suggesting a reactive, rather than a predictive, response to risk. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

  6. Value of adding the renal pathological score to the kidney failure risk equation in advanced diabetic nephropathy.

    Directory of Open Access Journals (Sweden)

    Masayuki Yamanouchi

    Full Text Available There have been a limited number of biopsy-based studies on diabetic nephropathy, and therefore the clinical importance of renal biopsy in patients with diabetes in late-stage chronic kidney disease (CKD is still debated. We aimed to clarify the renal prognostic value of pathological information to clinical information in patients with diabetes and advanced CKD.We retrospectively assessed 493 type 2 diabetics with biopsy-proven diabetic nephropathy in four centers in Japan. 296 patients with stage 3-5 CKD at the time of biopsy were identified and assigned two risk prediction scores for end-stage renal disease (ESRD: the Kidney Failure Risk Equation (KFRE, a score composed of clinical parameters and the Diabetic Nephropathy Score (D-score, a score integrated pathological parameters of the Diabetic Nephropathy Classification by the Renal Pathology Society (RPS DN Classification. They were randomized 2:1 to development and validation cohort. Hazard Ratios (HR of incident ESRD were reported with 95% confidence interval (CI of the KFRE, D-score and KFRE+D-score in Cox regression model. Improvement of risk prediction with the addition of D-score to the KFRE was assessed using c-statistics, continuous net reclassification improvement (NRI, and integrated discrimination improvement (IDI.During median follow-up of 1.9 years, 194 patients developed ESRD. The cox regression analysis showed that the KFRE,D-score and KFRE+D-score were significant predictors of ESRD both in the development cohort and in the validation cohort. The c-statistics of the D-score was 0.67. The c-statistics of the KFRE was good, but its predictive value was weaker than that in the miscellaneous CKD cohort originally reported (c-statistics, 0.78 vs. 0.90 and was not significantly improved by adding the D-score (0.78 vs. 0.79, p = 0.83. Only continuous NRI was positive after adding the D-score to the KFRE (0.4%; CI: 0.0-0.8%.We found that the predict values of the KFRE and the D

  7. Value of adding the renal pathological score to the kidney failure risk equation in advanced diabetic nephropathy.

    Science.gov (United States)

    Yamanouchi, Masayuki; Hoshino, Junichi; Ubara, Yoshifumi; Takaichi, Kenmei; Kinowaki, Keiichi; Fujii, Takeshi; Ohashi, Kenichi; Mise, Koki; Toyama, Tadashi; Hara, Akinori; Kitagawa, Kiyoki; Shimizu, Miho; Furuichi, Kengo; Wada, Takashi

    2018-01-01

    There have been a limited number of biopsy-based studies on diabetic nephropathy, and therefore the clinical importance of renal biopsy in patients with diabetes in late-stage chronic kidney disease (CKD) is still debated. We aimed to clarify the renal prognostic value of pathological information to clinical information in patients with diabetes and advanced CKD. We retrospectively assessed 493 type 2 diabetics with biopsy-proven diabetic nephropathy in four centers in Japan. 296 patients with stage 3-5 CKD at the time of biopsy were identified and assigned two risk prediction scores for end-stage renal disease (ESRD): the Kidney Failure Risk Equation (KFRE, a score composed of clinical parameters) and the Diabetic Nephropathy Score (D-score, a score integrated pathological parameters of the Diabetic Nephropathy Classification by the Renal Pathology Society (RPS DN Classification)). They were randomized 2:1 to development and validation cohort. Hazard Ratios (HR) of incident ESRD were reported with 95% confidence interval (CI) of the KFRE, D-score and KFRE+D-score in Cox regression model. Improvement of risk prediction with the addition of D-score to the KFRE was assessed using c-statistics, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). During median follow-up of 1.9 years, 194 patients developed ESRD. The cox regression analysis showed that the KFRE,D-score and KFRE+D-score were significant predictors of ESRD both in the development cohort and in the validation cohort. The c-statistics of the D-score was 0.67. The c-statistics of the KFRE was good, but its predictive value was weaker than that in the miscellaneous CKD cohort originally reported (c-statistics, 0.78 vs. 0.90) and was not significantly improved by adding the D-score (0.78 vs. 0.79, p = 0.83). Only continuous NRI was positive after adding the D-score to the KFRE (0.4%; CI: 0.0-0.8%). We found that the predict values of the KFRE and the D-score were

  8. A global earthquake discrimination scheme to optimize ground-motion prediction equation selection

    Science.gov (United States)

    Garcia, Daniel; Wald, David J.; Hearne, Michael

    2012-01-01

    We present a new automatic earthquake discrimination procedure to determine in near-real time the tectonic regime and seismotectonic domain of an earthquake, its most likely source type, and the corresponding ground-motion prediction equation (GMPE) class to be used in the U.S. Geological Survey (USGS) Global ShakeMap system. This method makes use of the Flinn–Engdahl regionalization scheme, seismotectonic information (plate boundaries, global geology, seismicity catalogs, and regional and local studies), and the source parameters available from the USGS National Earthquake Information Center in the minutes following an earthquake to give the best estimation of the setting and mechanism of the event. Depending on the tectonic setting, additional criteria based on hypocentral depth, style of faulting, and regional seismicity may be applied. For subduction zones, these criteria include the use of focal mechanism information and detailed interface models to discriminate among outer-rise, upper-plate, interface, and intraslab seismicity. The scheme is validated against a large database of recent historical earthquakes. Though developed to assess GMPE selection in Global ShakeMap operations, we anticipate a variety of uses for this strategy, from real-time processing systems to any analysis involving tectonic classification of sources from seismic catalogs.

  9. Ground Motion Prediction Equations for the Central and Eastern United States

    Science.gov (United States)

    Seber, D.; Graizer, V.

    2015-12-01

    New ground motion prediction equations (GMPE) G15 model for the Central and Eastern United States (CEUS) is presented. It is based on the modular filter based approach developed by Graizer and Kalkan (2007, 2009) for active tectonic environment in the Western US (WUS). The G15 model is based on the NGA-East database for the horizontal peak ground acceleration and 5%-damped pseudo spectral acceleration RotD50 component (Goulet et al., 2014). In contrast to active tectonic environment the database for the CEUS is not sufficient for creating purely empirical GMPE covering the range of magnitudes and distances required for seismic hazard assessments. Recordings in NGA-East database are sparse and cover mostly range of Mindustry (Vs=2800 m/s). The number of model predictors is limited to a few measurable parameters: moment magnitude M, closest distance to fault rupture plane R, average shear-wave velocity in the upper 30 m of the geological profile VS30, and anelastic attenuation factor Q0. Incorporating anelastic attenuation Q0 as an input parameter allows adjustments based on the regional crustal properties. The model covers the range of magnitudes 4.010 Hz) and is within the range of other models for frequencies lower than 2.5 Hz

  10. Sound field prediction of ultrasonic lithotripsy in water with spheroidal beam equations

    Science.gov (United States)

    Zhang, Lue; Wang, Xiang-Da; Liu, Xiao-Zhou; Gong, Xiu-Fen

    2015-01-01

    With converged shock wave, extracorporeal shock wave lithotripsy (ESWL) has become a preferable way to crush human calculi because of its advantages of efficiency and non-intrusion. Nonlinear spheroidal beam equations (SBE) are employed to illustrate the acoustic wave propagation for transducers with a wide aperture angle. To predict the acoustic field distribution precisely, boundary conditions are obtained for the SBE model of the monochromatic wave when the source is located on the focus of an ESWL transducer. Numerical results of the monochromatic wave propagation in water are analyzed and the influences of half-angle, fundamental frequency, and initial pressure are investigated. According to our results, with optimization of these factors, the pressure focal gain of ESWL can be enhanced and the effectiveness of treatment can be improved. Project supported by the National Basic Research Program of China (Grant Nos. 2012CB921504 and 2011CB707902), the National Natural Science Foundation of China (Grant No. 11274166), the State Key Laboratory of Acoustics, Chinese Academy of Sciences (Grant No. SKLA201401), and the China Postdoctoral Science Foundation (Grant No. 2013M531313).

  11. Predicting soil erosion risk at the Alqueva dam watershed

    OpenAIRE

    Ferreira, Vera; Panagopoulos, Thomas

    2012-01-01

    Soil erosion is serious economic and environmental concern. Assessing soil erosion risk in the Alqueva dam watershed is urgently needed to conserve soil and water resources and prevent the accelerated dam siltation, taking into account the possible land-use changes, due to tourism development, intensification of irrigated farming and biomass production, as well as climate change. A comprehensive methodology that integrates Revised Universal Soil Loss Equation (RUSLE) model and Geographic Info...

  12. Predicting Risk-Mitigating Behaviors From Indecisiveness and Trait Anxiety

    DEFF Research Database (Denmark)

    Mcneill, Ilona M.; Dunlop, Patrick D.; Skinner, Timothy C.

    2016-01-01

    Past research suggests that indecisiveness and trait anxiety may both decrease the likelihood of performing risk-mitigating preparatory behaviors (e.g., preparing for natural hazards) and suggests two cognitive processes (perceived control and worrying) as potential mediators. However, no single...... control over wildfire-related outcomes. Trait anxiety did not uniquely predict preparedness or perceived control, but it did uniquely predict worry, with higher trait anxiety predicting more worrying. Also, worry trended toward uniquely predicting preparedness, albeit in an unpredicted positive direction...

  13. Reference equation for prediction of a total distance during six-minute walk test using Indonesian anthropometrics.

    Science.gov (United States)

    Nusdwinuringtyas, Nury; Widjajalaksmi; Yunus, Faisal; Alwi, Idrus

    2014-04-01

    to develop a reference equation for prediction of the total distance walk using Indonesian anthropometrics of sedentary healthy subjects. Subsequently, the prediction obtained was compared to those calculated by the Caucasian-based Enright prediction equation. the cross-sectional study was conducted among 123 healthy Indonesian adults with sedentary life style (58 male and 65 female subjects in an age range between 18 and 50 years). Heart rate was recorded using Polar with expectation in the sub-maximal zone (120-170 beats per minute). The subjects performed two six-minute walk tests, the first one on a 15-meter track according to the protocol developed by the investigator. The second walk was carried out on Biodex®gait trainer as gold standard. an average total distance of 547±54.24 m was found, not significantly different from the gold standard of 544.72±54.11 m (p>0.05). Multiple regression analysis was performed to develop the new equation. the reference equation for prediction of the total distance using Indonesian anthropometrics is more applicable in Indonesia.

  14. A prediction model of compressor with variable-geometry diffuser based on elliptic equation and partial least squares.

    Science.gov (United States)

    Li, Xu; Yang, Chuanlei; Wang, Yinyan; Wang, Hechun

    2018-01-01

    To achieve a much more extensive intake air flow range of the diesel engine, a variable-geometry compressor (VGC) is introduced into a turbocharged diesel engine. However, due to the variable diffuser vane angle (DVA), the prediction for the performance of the VGC becomes more difficult than for a normal compressor. In the present study, a prediction model comprising an elliptical equation and a PLS (partial least-squares) model was proposed to predict the performance of the VGC. The speed lines of the pressure ratio map and the efficiency map were fitted with the elliptical equation, and the coefficients of the elliptical equation were introduced into the PLS model to build the polynomial relationship between the coefficients and the relative speed, the DVA. Further, the maximal order of the polynomial was investigated in detail to reduce the number of sub-coefficients and achieve acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority of compressor performance prediction, the prediction results of this model were compared with those of the look-up table and back-propagation neural networks (BPNNs). The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than the look-up table and the BPNN methods under the same condition in VGC performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for the VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future.

  15. Predicting of biomass in Brazilian tropical dry forest: a statistical evaluation of generic equations

    Directory of Open Access Journals (Sweden)

    ROBSON B. DE LIMA

    2017-08-01

    Full Text Available ABSTRACT Dry tropical forests are a key component in the global carbon cycle and their biomass estimates depend almost exclusively of fitted equations for multi-species or individual species data. Therefore, a systematic evaluation of statistical models through validation of estimates of aboveground biomass stocks is justifiable. In this study was analyzed the capacity of generic and specific equations obtained from different locations in Mexico and Brazil, to estimate aboveground biomass at multi-species levels and for four different species. Generic equations developed in Mexico and Brazil performed better in estimating tree biomass for multi-species data. For Poincianella bracteosa and Mimosa ophthalmocentra, only the Sampaio and Silva (2005 generic equation was the most recommended. These equations indicate lower tendency and lower bias, and biomass estimates for these equations are similar. For the species Mimosa tenuiflora, Aspidosperma pyrifolium and for the genus Croton the specific regional equations are more recommended, although the generic equation of Sampaio and Silva (2005 is not discarded for biomass estimates. Models considering gender, families, successional groups, climatic variables and wood specific gravity should be adjusted, tested and the resulting equations should be validated at both local and regional levels as well as on the scales of tropics with dry forest dominance.

  16. Predicting of biomass in Brazilian tropical dry forest: a statistical evaluation of generic equations.

    Science.gov (United States)

    Lima, Robson B DE; Alves, Francisco T; Oliveira, Cinthia P DE; Silva, José A A DA; Ferreira, Rinaldo L C

    2017-01-01

    Dry tropical forests are a key component in the global carbon cycle and their biomass estimates depend almost exclusively of fitted equations for multi-species or individual species data. Therefore, a systematic evaluation of statistical models through validation of estimates of aboveground biomass stocks is justifiable. In this study was analyzed the capacity of generic and specific equations obtained from different locations in Mexico and Brazil, to estimate aboveground biomass at multi-species levels and for four different species. Generic equations developed in Mexico and Brazil performed better in estimating tree biomass for multi-species data. For Poincianella bracteosa and Mimosa ophthalmocentra, only the Sampaio and Silva (2005) generic equation was the most recommended. These equations indicate lower tendency and lower bias, and biomass estimates for these equations are similar. For the species Mimosa tenuiflora, Aspidosperma pyrifolium and for the genus Croton the specific regional equations are more recommended, although the generic equation of Sampaio and Silva (2005) is not discarded for biomass estimates. Models considering gender, families, successional groups, climatic variables and wood specific gravity should be adjusted, tested and the resulting equations should be validated at both local and regional levels as well as on the scales of tropics with dry forest dominance.

  17. On the Generalized Maxwell Equations and Their Prediction of Electroscalar Wave

    Directory of Open Access Journals (Sweden)

    Arbab A. I.

    2009-04-01

    Full Text Available We have formulated the basic laws of electromagnetic theory in quaternion form. The formalism shows that Maxwell equations and Lorentz force are derivable from just one quaternion equation that only requires the Lorentz gauge. We proposed a quaternion form of the continuity equation from which we have derived the ordinary continuity equation. We introduce new transformations that produces a scalar wave and generalize the continuity equation to a set of three equations. These equations imply that both current and density are waves. Moreover, we have shown that the current can not cir- culate around a point emanating from it. Maxwell equations are invariant under these transformations. An electroscalar wave propagating with speed of light is derived upon requiring the invariance of the energy conservation equation under the new transforma- tions. The electroscalar wave function is found to be proportional to the electric field component along the charged particle motion. This scalar wave exists with or without considering the Lorentz gauge. We have shown that the electromagnetic fields travel with speed of light in the presence or absence of free charges.

  18. Assessment of the NCHRP abutment scour prediction equations with laboratory and field data

    Science.gov (United States)

    Benedict, Stephen T.

    2014-01-01

    The U.S. Geological Survey, in coopeation with nthe National Cooperative Highway Research Program (NCHRP) is assessing the performance of several abutment-scour predcition equations developed in NCHRP Project 24-15(2) and NCHRP Project 24-20. To accomplish this assssment, 516 laboratory and 329 fiels measurements of abutment scor were complied from selected sources and applied tto the new equations. Results will be used to identify stregths, weaknesses, and limitations of the NCHRP abutment scour equations, providing practical insights for applying the equations. This paper presents some prelimiray findings from the investigation.

  19. Structural equation models to estimate risk of infection and tolerance to bovine mastitis.

    Science.gov (United States)

    Detilleux, Johann; Theron, Léonard; Duprez, Jean-Noël; Reding, Edouard; Humblet, Marie-France; Planchon, Viviane; Delfosse, Camille; Bertozzi, Carlo; Mainil, Jacques; Hanzen, Christian

    2013-03-06

    One method to improve durably animal welfare is to select, as reproducers, animals with the highest ability to resist or tolerate infection. To do so, it is necessary to distinguish direct and indirect mechanisms of resistance and tolerance because selection on these traits is believed to have different epidemiological and evolutionary consequences. We propose structural equation models with latent variables (1) to quantify the latent risk of infection and to identify, among the many potential mediators of infection, the few ones that influence it significantly and (2) to estimate direct and indirect levels of tolerance of animals infected naturally with pathogens. We applied the method to two surveys of bovine mastitis in the Walloon region of Belgium, in which we recorded herd management practices, mastitis frequency, and results of bacteriological analyses of milk samples. Structural equation models suggested that, among more than 35 surveyed herd characteristics, only nine (age, addition of urea in the rations, treatment of subclinical mastitis, presence of dirty liner, cows with hyperkeratotic teats, machine stripping, pre- and post-milking teat disinfection, and housing of milking cows in cubicles) were directly and significantly related to a latent measure of bovine mastitis, and that treatment of subclinical mastitis was involved in the pathway between post-milking teat disinfection and latent mastitis. These models also allowed the separation of direct and indirect effects of bacterial infection on milk productivity. Results suggested that infected cows were tolerant but not resistant to mastitis pathogens. We revealed the advantages of structural equation models, compared to classical models, for dissecting measurements of resistance and tolerance to infectious diseases, here bovine mastitis. Using our method, we identified nine major risk factors that were directly associated with an increased risk of mastitis and suggested that cows were tolerant but

  20. Updating risk prediction tools: a case study in prostate cancer.

    Science.gov (United States)

    Ankerst, Donna P; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J; Feng, Ziding; Sanda, Martin G; Partin, Alan W; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M

    2012-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Enhanced clinical pharmacy service targeting tools: risk-predictive algorithms.

    Science.gov (United States)

    El Hajji, Feras W D; Scullin, Claire; Scott, Michael G; McElnay, James C

    2015-04-01

    This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management (IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index (RAMI). Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized. © 2014 John Wiley & Sons, Ltd.

  2. Sex-specific predictive power of 6-minute walk test in chronic heart failure is not enhanced using percent achieved of published reference equations.

    Science.gov (United States)

    Frankenstein, Lutz; Zugck, Christian; Nelles, Manfred; Schellberg, Dieter; Katus, Hugo; Remppis, Andrew

    2008-04-01

    The 6-minute walk test (6MWT) is an established prognostic tool in chronic heart failure. The strong influence of height, weight, age, and sex on 6MWT distance may be accounted for by using percentage achieved of predicted value rather than uncorrected 6MWT values. The study included 1069 patients (862 men) with a mean age 55.2 +/- 11.7 years and mean left ventricular ejection fraction of 29% +/- 10%, attending the heart failure clinic of the University of Heidelberg between 1995 and 2005. The predictive power and accuracy of 6MWT and achieved percentage values according to all available published equations for mortality and mortality or transplant combined were tested separately for each sex. The percentage values varied largely between equations. For all equations, women in New York Heart Association (NYHA) functional class I had higher values than men. Although the 6MWT significantly discriminated all NYHA classes for both sexes, only 1 equation discriminated all NYHA classes. No significant differences in the area under the receiver operating-characteristic curve were noted between achieved percentage values and 6MWT. Despite strong univariate significance, achieved percentage values did not retain multivariate significance. The 6MWT was independent from N-terminal brain natriuretic propeptide, NYHA, left ventricular ejection fraction, and peak oxygen uptake. We confirmed 6MWT to be a strong and independent risk predictor for both sexes. Because the prognostic power of 6MWT is not enhanced using percentage achieved of published reference equations, we suggest recalibration of these reference values rather than discarding this approach.

  3. Arrhenius equation modeling for the shelf life prediction of tomato paste containing a natural preservative.

    Science.gov (United States)

    Jafari, Seid Mahdi; Ganje, Mohammad; Dehnad, Danial; Ghanbari, Vahid; Hajitabar, Javad

    2017-12-01

    The shelf life of tomato paste with microencapsulated olive leaf extract was compared with that of samples containing a commercial preservative by accelerated shelf life testing. Based on previous studies showing that olive leaf extract as a rich source of phenolic compounds can have antimicrobial properties, application of its encapsulated form to improve the storage stability of tomato paste is proposed here. Regarding total soluble solids, the control and the sample containing 1000 µg g -1 sodium benzoate had the lowest (Q 10  = 1.63) and highest (Q 10  = 1.88) sensitivity to temperature changes respectively; also, the microencapsulated sample containing 1000 µg g -1 encapsulated olive leaf extract (Q 10  = 1.83) followed the sample containing 1000 µg g -1 sodium benzoate in terms of the highest kinetic rates. In the case of consistency, the lowest and highest activation energies (E a ) corresponded to samples containing 1000 µg g -1 non-encapsulated olive leaf extract and 1000 µg g -1 microencapsulated olive leaf extract respectively. Interestingly, samples containing microencapsulated olive leaf extract could maintain the original quality of the tomato paste very well, while those with non-encapsulated olive leaf extract rated the worst performance (among all specimens) in terms of maintaining their quality indices for a long time period. Overall, the shelf life equation was able to predict the consistency index of all tomato paste samples during long-time storage with high precision. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

  4. Ground Motion Prediction Equations for Western Saudi Arabia from a Reference Model

    Science.gov (United States)

    Kiuchi, R.; Mooney, W. D.; Mori, J. J.; Zahran, H. M.; Al-Raddadi, W.; Youssef, S.

    2017-12-01

    Western Saudi Arabia is surrounded by several active seismic zones such as the Red Sea and the Gulf of Aqaba where a destructive magnitude 7.3 event occurred in 1995. Over the last decade, the Saudi Geological Survey (SGS) has deployed a dense seismic network that has made it possible to monitor seismic activity more accurately. For example, the network has detected multiple seismic swarms beneath the volcanic fields in western Saudi Arabia. The most recent damaging event was a M5.7 earthquake that occurred in 2009 at Harrat Lunayyir. In terms of seismic hazard assessment, Zahran et al. (2015; 2016) presented a Probabilistic Seismic Hazard Assessment (PSHA) for western Saudi Arabia that was developed using published Ground Motion Prediction Equations (GMPEs) from areas outside of Saudi Arabia. In this study, we consider 41 earthquakes of M 3.0 - 5.4, recorded on 124 stations of the SGS network, to create a set of 442 peak ground acceleration (PGA) and peak ground velocity (PGV) records with a range of epicentral distances from 3 km to 400 km. We use the GMPE model BSSA14 (Boore et al., 2014) as a reference model to estimate our own best-fitting coefficients from a regression analysis using the events occurred in western Saudi Arabia. For epicentral distances less than 100 km, our best fitting model has different source scaling in comparison with the GMPE of BSSA14 adjusted for the California region. In addition, our model indicates that the peak amplitudes have less attenuation in western Saudi Arabia than in California.

  5. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    the expectations hypothesis (EH) out-ofsample: the forecasts do not add economic value compared to using the average historical excess return as an EH-consistent estimate of constant risk premia. We show that in general statistical signicance does not necessarily translate into economic signicance because EH...... deviations mainly matter at short horizons and standard predictability metrics are not compatible with common measures of economic value. Overall, the EH remains the benchmark for investment decisions and should be considered an economic prior in models of bond risk premia.......This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for bond investors. We show that ane term structure models (ATSMs) estimated by jointly tting yields and bond excess returns capture this predictive information otherwise hidden...

  6. Cull sow knife-separable lean content evaluation at harvest and lean mass content prediction equation development.

    Science.gov (United States)

    Abell, Caitlyn E; Stalder, Kenneth J; Hendricks, Haven B; Fitzgerald, Robert F

    2012-07-01

    The objectives of this study were to develop a prediction equation for carcass knife-separable lean within and across USDA cull sow market weight classes (MWC) and to determine carcass and individual primal cut knife separable lean content from cull sows. There were significant percent lean and fat differences in the primal cuts across USDA MWC. The two lighter USDA MWC had a greater percent carcass lean and lower percent fat compared to the two heavier MWC. In general, hot carcass weight explained the majority of carcass lean variation. Additionally, backfat was a significant variation source when predicting cull sow carcass lean. The findings support using a single lean prediction equation across MWC to assist processors when making cull sow purchasing decisions and determine the mix of animals from various USDA MWC that will meet their needs when making pork products with defined lean:fat content. Copyright © 2012 Elsevier Ltd. All rights reserved.

  7. Validation of a dynamic modulus predictive equation on the basis of spanish asphalt concrete mixtures

    Directory of Open Access Journals (Sweden)

    Mateos, A.

    2015-03-01

    Full Text Available Dynamic modulus is defined as the ratio of peak cyclic stress to peak cyclic strain under harmonic loading. It is one of the most important properties of asphalt mixtures, since it determines the strain response characteristics as a function of loading rate and temperature. Different simplified models exist that can predict this variable from mixture composition and binder rheological data, with Witczak and Hirsh models being the most widely accepted. These models have been evaluated in the present study, on the basis of 352 data points from eight asphalt concrete mixtures that were tested between −5 and 60 °C. A new model is also formulated which improves predictions of the previous ones for Spanish mixtures, even though it is a relatively simple equation that requires very limited binder rheological data compared to Witczak and Hirsch models.El módulo dinámico es la relación entre los picos de tensión y deformación bajo carga armónica. Es una de las propiedades más importantes de las mezclas bituminosas, ya que determina la respuesta deformacional en función de la velocidad de carga y la temperatura. Existen diferentes modelos simplificados que permiten predecir esta variable a partir de la composición de la mezcla y de las características reológicas del betún, siendo los de Witczak y el de Hirsch los más ampliamente aceptados. Dichos modelos han sido evaluados en el presente estudio a partir de 352 puntos procedentes de ocho mezclas tipo hormigón bituminoso que fueron ensayadas entre −5 y 60 °C. Así mismo, se ha formulado un nuevo modelo que mejora las predicciones de los anteriores para las mezclas españolas, aun tratándose de una ecuación relativamente simple que requiere una mínima información reológica del betún en comparación con los modelos de Witczak y Hirsch.

  8. Advanced Time Approach of FW-H Equations for Predicting Noise

    DEFF Research Database (Denmark)

    Haiqing, Si; Yan, Shi; Shen, Wen Zhong

    2013-01-01

    An advanced time approach of Ffowcs Williams-Hawkings (FW-H) acoustic analogy is developed, and the integral equations and integral solution of FW-H acoustic analogy are derived. Compared with the retarded time approach, the transcendental equation need not to be solved in the advanced time...

  9. New Zealand Diabetes Cohort Study cardiovascular risk score for people with Type 2 diabetes: validation in the PREDICT cohort.

    Science.gov (United States)

    Robinson, Tom; Elley, C Raina; Wells, Sue; Robinson, Elizabeth; Kenealy, Tim; Pylypchuk, Romana; Bramley, Dale; Arroll, Bruce; Crengle, Sue; Riddell, Tania; Ameratunga, Shanthi; Metcalf, Patricia; Drury, Paul L

    2012-09-01

    New Zealand (NZ) guidelines recommend treating people for cardiovascular disease (CVD) risk on the basis of five-year absolute risk using a NZ adaptation of the Framingham risk equation. A diabetes-specific Diabetes Cohort Study (DCS) CVD predictive risk model has been developed and validated using NZ Get Checked data. To revalidate the DCS model with an independent cohort of people routinely assessed using PREDICT, a web-based CVD risk assessment and management programme. People with Type 2 diabetes without pre-existing CVD were identified amongst people who had a PREDICT risk assessment between 2002 and 2005. From this group we identified those with sufficient data to allow estimation of CVD risk with the DCS models. We compared the DCS models with the NZ Framingham risk equation in terms of discrimination, calibration, and reclassification implications. Of 3044 people in our study cohort, 1829 people had complete data and therefore had CVD risks calculated. Of this group, 12.8% (235) had a cardiovascular event during the five-year follow-up. The DCS models had better discrimination than the currently used equation, with C-statistics being 0.68 for the two DCS models and 0.65 for the NZ Framingham model. The DCS models were superior to the NZ Framingham equation at discriminating people with diabetes who will have a cardiovascular event. The adoption of a DCS model would lead to a small increase in the number of people with diabetes who are treated with medication, but potentially more CVD events would be avoided.

  10. Vapor-liquid equilibrium prediction with pseudo-cubic equation of state for binary mixtures containing hydrogen, helium, or neon

    Energy Technology Data Exchange (ETDEWEB)

    Kato, M.; Tanaka, H. (Nihon Univ.,Fukushima, (Japan). Faculty of Enineering)

    1990-03-01

    As an equation of state of vapor-liquid equilibrium, an original pseudo-cubic equation of state was previously proposed by the authors of this report and its study is continued. In the present study, new effective critical values of hydrogen, helium and neon were determined empirically from vapor-liquid equilibrium data of literature values against their critical temperatures, critical pressures and critical volumes. The vapor-liquid equilibrium relations of binary system quantum gas mixtures were predicted combining the conventinal pseudo-cubic equation of state and the new effective critical values, and without using binary heteromolecular interaction parameter. The predicted values of hydrogen-ethylene, helium-propane and neon-oxygen systems were compared with literature values. As a result, it was indicated that the vapor-liquid relations of binary system mixtures containing hydrogen, helium and neon can be predicted with favorable accuracy combining the effective critical values and the three parameter pseudo-cubic equation of state. 37 refs., 3 figs., 4 tabs.

  11. Identification of the high risk emergency surgical patient: Which risk prediction model should be used?

    Science.gov (United States)

    Stonelake, Stephen; Thomson, Peter; Suggett, Nigel

    2015-09-01

    National guidance states that all patients having emergency surgery should have a mortality risk assessment calculated on admission so that the 'high risk' patient can receive the appropriate seniority and level of care. We aimed to assess if peri-operative risk scoring tools could accurately calculate mortality and morbidity risk. Mortality risk scores for 86 consecutive emergency laparotomies, were calculated using pre-operative (ASA, Lee index) and post-operative (POSSUM, P-POSSUM and CR-POSSUM) risk calculation tools. Morbidity risk scores were calculated using the POSSUM predicted morbidity and compared against actual morbidity according to the Clavien-Dindo classification. The actual mortality was 10.5%. The average predicted risk scores for all laparotomies were: ASA 26.5%, Lee Index 2.5%, POSSUM 29.5%, P-POSSUM 18.5%, CR-POSSUM 10.5%. Complications occurred following 67 laparotomies (78%). The majority (51%) of complications were classified as Clavien-Dindo grade 2-3 (non-life-threatening). Patients having a POSSUM morbidity risk of greater than 50% developed significantly more life-threatening complications (CD 4-5) compared with those who predicted less than or equal to 50% morbidity risk (P = 0.01). Pre-operative risk stratification remains a challenge because the Lee Index under-predicts and ASA over-predicts mortality risk. Post-operative risk scoring using the CR-POSSUM is more accurate and we suggest can be used to identify patients who require intensive care post-operatively. In the absence of accurate risk scoring tools that can be used on admission to hospital it is not possible to reliably audit the achievement of national standards of care for the 'high-risk' patient.

  12. Predicting disease risk using bootstrap ranking and classification algorithms.

    Directory of Open Access Journals (Sweden)

    Ohad Manor

    Full Text Available Genome-wide association studies (GWAS are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a "black box" in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF, suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.

  13. Investigation on Cardiovascular Risk Prediction Using Physiological Parameters

    Directory of Open Access Journals (Sweden)

    Wan-Hua Lin

    2013-01-01

    Full Text Available Cardiovascular disease (CVD is the leading cause of death worldwide. Early prediction of CVD is urgently important for timely prevention and treatment. Incorporation or modification of new risk factors that have an additional independent prognostic value of existing prediction models is widely used for improving the performance of the prediction models. This paper is to investigate the physiological parameters that are used as risk factors for the prediction of cardiovascular events, as well as summarizing the current status on the medical devices for physiological tests and discuss the potential implications for promoting CVD prevention and treatment in the future. The results show that measures extracted from blood pressure, electrocardiogram, arterial stiffness, ankle-brachial blood pressure index (ABI, and blood glucose carry valuable information for the prediction of both long-term and near-term cardiovascular risk. However, the predictive values should be further validated by more comprehensive measures. Meanwhile, advancing unobtrusive technologies and wireless communication technologies allow on-site detection of the physiological information remotely in an out-of-hospital setting in real-time. In addition with computer modeling technologies and information fusion. It may allow for personalized, quantitative, and real-time assessment of sudden CVD events.

  14. Flood Risk Mapping Using Flow Energy Equation and Geographic Information System

    Directory of Open Access Journals (Sweden)

    pourya Javan

    2013-09-01

    Full Text Available Flooding and its damages are not only found uplift water level in a region. In other words, the depth and speed parameters together have determining the level of flood risk at each point. This subject is visible in flooded plain with low height and high speed of 2 meters per second, which damages are extensive. According to the criteria of having both velocity and flow depth in the governing equation to the flows energy, this equation seems appropriate to analysis in this study. Various methods have been proposed for increase accuracy in flood zoning with different return periods and risks associated with it in land border of river. For example, some of these methods are considered factors such as analysis of past flooding in the area affected by floods, hydrological factors and consideration of hydraulic elements affecting in flood zoning (such as flow velocity. This paper investigates the effect of flood zoning by the energy flow in the areas affected by floods. Also risk due to flood based on energy flow in each section of the river is compared by the proposed graphs of hazard interval and other done flood zoning in this field. In this study, the FORDO river has been selected as the case study. This river is part of the rivers located in the city of QOM KAHAK. The characteristics of river in upstream and downstream are mountain, young and stable and adult, respectively. Also this river in different seasons is exposed the flood damage. The proposed method in this study can be improving recognition accuracy of flood risk in areas affected by flood. Also, this method facilitate the identify parts of the river bed, that is affected by severe flooding, for decision making to improve rivers organizing.

  15. Machine learning application in online lending risk prediction

    OpenAIRE

    Yu, Xiaojiao

    2017-01-01

    Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with various format and size were collected from public website, third-parties and assembled with client's loan application information data. Ensemble machine learning models, random fo...

  16. Predictive risk factors for moderate to severe hyperbilirubinemia

    OpenAIRE

    Gláucia Macedo de Lima; Maria Amélia Sayeg Campos Porto; Arnaldo Prata Barbosa; Antonio José Ledo Alves da Cunha

    2007-01-01

    Objective: to describe predictive factors for severity of neonataljaundice in newborn infants treated at the University Neonatal Clinic,highlighting maternal, obstetric and neonatal factors. Methods: Acohort retrospective study by means of review of medical charts todefine risk factors associated with moderate and severe jaundice.The cohort consisted of newborns diagnosed with indirect neonatalhyperbilirubinemia and submitted to phototherapy. Risk was classifiedas maternal, prenatal, obstetri...

  17. Risk assessment and remedial policy evaluation using predictive modeling

    International Nuclear Information System (INIS)

    Linkov, L.; Schell, W.R.

    1996-01-01

    As a result of nuclear industry operation and accidents, large areas of natural ecosystems have been contaminated by radionuclides and toxic metals. Extensive societal pressure has been exerted to decrease the radiation dose to the population and to the environment. Thus, in making abatement and remediation policy decisions, not only economic costs but also human and environmental risk assessments are desired. This paper introduces a general framework for risk assessment and remedial policy evaluation using predictive modeling. Ecological risk assessment requires evaluation of the radionuclide distribution in ecosystems. The FORESTPATH model is used for predicting the radionuclide fate in forest compartments after deposition as well as for evaluating the efficiency of remedial policies. Time of intervention and radionuclide deposition profile was predicted as being crucial for the remediation efficiency. Risk assessment conducted for a critical group of forest users in Belarus shows that consumption of forest products (berries and mushrooms) leads to about 0.004% risk of a fatal cancer annually. Cost-benefit analysis for forest cleanup suggests that complete removal of organic layer is too expensive for application in Belarus and a better methodology is required. In conclusion, FORESTPATH modeling framework could have wide applications in environmental remediation of radionuclides and toxic metals as well as in dose reconstruction and, risk-assessment

  18. Application of the van der Waals equation of state to polymers .4. Correlation and prediction of lower critical solution temperatures for polymer solutions

    DEFF Research Database (Denmark)

    Goncalves, Ana Saraiva; Kontogeorgis, Georgios; Harismiadis, Vassilis I.

    1996-01-01

    The van der Waals equation of state is used for the correlation and the prediction of the lower critical solution behavior or mixtures including a solvent and a polymer. The equation of state parameters for the polymer are estimated from experimental volumetric data at low pressures. The equation...

  19. The evaporation of oil spills: prediction of equations using distillation data

    International Nuclear Information System (INIS)

    Fingas, M.

    1997-01-01

    The evaporative characteristics of 19 different crude oils and petroleum products were studied . Best-fit equation parameters were determined for percentage loss by time and absolute weight loss. Except in three cases, all oils were found to fit logarithmic curves. The equation constants were correlated with oil distillation data. Relationships enabling calculation of evaporation equations directly from distillation data have been developed. The high correlation of distillation data and evaporation data suggests that the two processes are analogous and that evaporation, like distillation, is largely governed by intrinsic oil properties rather than environmental properties such as boundary-layer factors

  20. THE ROLE OF RISK AVERSION IN PREDICTING INDIVIDUAL BEHAVIOR

    OpenAIRE

    Luigi Guiso; Monica Paiella

    2005-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers� decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways co...

  1. Development of Health Parameter Model for Risk Prediction of CVD Using SVM

    Directory of Open Access Journals (Sweden)

    P. Unnikrishnan

    2016-01-01

    Full Text Available Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared the parameters from the Framingham equation with linear regression analysis to establish the effect of training of the model for the local database. Support vector machine was used to determine the effectiveness of machine learning approach with the Framingham health parameters for risk assessment of cardiovascular disease (CVD. The result shows that while linear model trained using local database was an improvement on Framingham model, SVM based risk assessment model had high sensitivity and specificity of prediction of CVD. This indicates that using the health parameters identified using Framingham study, machine learning approach overcomes the low sensitivity and specificity of Framingham model.

  2. Alternative (G-16v2) Ground Motion Prediction Equations for the Central and Eastern North America

    Science.gov (United States)

    Graizer, V.

    2016-12-01

    Introduced is the ground motion prediction equations model for the Central and Eastern North America that represents an alternative more physically justified approach to ground motion attenuation modeling then previous Graizer (2016) G-16 model. The new model has a bilinear slope of R-1 within 70 km from the fault with a slope of R-0.5 at larger distances corresponding to the geometrical spreading of body and surface waves. The new (G-16v2) model is based in part on the NGA-East database for the horizontal peak ground acceleration and 5%-damped pseudo spectral acceleration (SA) and also on comparisons with the Western U.S. data and ground motion simulations. Based on data, I estimated the average slope of the distance attenuation within the 50-70 km distance from the fault to be -1.0 at most of the frequencies supporting regular geometrical spreading of body waves. Multiple inversions are performed to estimate apparent (combined intrinsic and scattering) attenuation of SA amplitudes from the NGA-East database for incorporation into the model. These estimates demonstrate a difference between seismological Q(f) and the above mentioned attenuation factor that I recommend calling QSA(f). I adjusted previously developed site correction which was based on multiple runs of representative VS30 (time-averaged shear-wave velocity in the upper 30 m) profiles through SHAKE-type equivalent-linear codes. Site amplifications are calculated relative to the hard rock definition used in nuclear industry (VS=2800 m/s). These improvements resulted in a modest reduction in standard deviation in the new G-16v2 relative to the G-16 model. The number of model predictors is limited to a few measurable parameters: moment magnitude M, closest distance to fault rupture plane Rrup, VS30, and apparent attenuation factor QSA(f). The model is applicable for the stable continental regions and covers the following range: 4.0≤M≤8.5, 0≤Rrup≤1000 km, 450≤VS30≤2800 m/s and frequencies 0.1

  3. Does Hofstetter's equation predict the real amplitude of accommodation in children?

    Science.gov (United States)

    Hashemi, Hassan; Nabovati, Payam; Khabazkhoob, Mehdi; Yekta, Abbasali; Emamian, Mohammad Hassan; Fotouhi, Akbar

    2018-01-01

    The aim was to determine the distribution and associated factors of accommodative amplitude (AA) in six- to 12-year-old children and compare the results with those calculated using Hofstetter's formula. In a cross-sectional study in 2015, random sampling was done from urban and rural populations of Shahroud, northern Iran. Participating schoolchildren were examined for manifest, cycloplegic and subjective refraction, as well as uncorrected vision and visual acuity. The AA was measured with Donders' push-up method using a ruler. The near point of convergence (NPC) was also measured. Of the 6,624 selected children, 5,620 participated in the study and after applying the exclusion criteria, the final analyses were done on data from 5,444 schoolchildren. The mean age of the final sample was 9.24 ± 1.71 years (from six to 12 years) and 53.6 per cent (n = 2,919) were boys. Mean measured AA was 14.44 D (95 per cent confidence interval [CI]: 14.33-14.55). In all age groups, the mean measured AA was less than the predicted mean value calculated with the Hofstetter's equation. Mean measured AA was 14.44 D (95 per cent CI: 14.28-14.59) and 14.45 D (95 per cent CI: 14.29-14.6) in boys and girls, respectively (p = 0.926). AA significantly declined with age (coefficient: -0.18, 95 per cent CI: -0.23 to -0.12, p < 0.001). Mean AA in emmetropic, myopic and hyperopic children was 14.31 D, 17.30 D and 14.87 D, respectively. Older age (coefficient = -0.18), living in rural areas (coefficient = -0.48) and NPC (coefficient = 0.47) inversely related with AA and higher AA was associated with a shift of the spherical equivalent refraction toward myopia (coefficient = -0.41). The differences among groups with different types of refractive error and high AA in children with myopia are important findings of this study. The results of the present study suggest that Hofstetter's formula provides inaccurate AA estimates in children and thus, the interpretation of this index requires further

  4. Dynamic Bayesian modeling for risk prediction in credit operations

    DEFF Research Database (Denmark)

    Borchani, Hanen; Martinez, Ana Maria; Masegosa, Andres

    2015-01-01

    Our goal is to do risk prediction in credit operations, and as data is collected continuously and reported on a monthly basis, this gives rise to a streaming data classification problem. Our analysis reveals some practical problems that have not previously been thoroughly analyzed in the context...

  5. Prediction of postpartum blood transfusion – risk factors and recurrence

    DEFF Research Database (Denmark)

    Wikkelsø, Anne J; Hjortøe, Sofie; Gerds, Thomas A

    2014-01-01

    OBJECTIVE: The aim was to find clinically useful risk factors for postpartum transfusion and to assess the joint predictive value in a population of women with a first and second delivery. METHODS: All Danish women with a first and second delivery from January 2001 to September 2009 who gave birt...

  6. Predicting the risk of mineral deficiencies in grazing animals

    African Journals Online (AJOL)

    lambs to mineral supplements can be used to predict risks of deficiency will be demonstrated. In both cases .... between body size and appetite, the onset of lactation or the feeding of ... possible importance of this in the aetiology of milk fever.

  7. Mountain Risks: From Prediction to Management and Governance

    Directory of Open Access Journals (Sweden)

    David Petley

    2015-05-01

    Full Text Available Reviewed: Mountain Risks: From Prediction to Management and Governance. Edited by Theo Van Asch, Jordi Corominas, Stefan Greiving, Jean-Philippe Malet, and Sterlacchini Simone. Dordrecht, The Netherlands: Springer, 2014. xi + 413 pp. US$ 129.00, € 90.00, € 104.00. Also available as an e-book. ISBN 978-94-007-6768-3.

  8. Predicting disease risks from highly imbalanced data using random forest

    Directory of Open Access Journals (Sweden)

    Chakraborty Sounak

    2011-07-01

    Full Text Available Abstract Background We present a method utilizing Healthcare Cost and Utilization Project (HCUP dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare. Methods We employed the National Inpatient Sample (NIS data, which is publicly available through Healthcare Cost and Utilization Project (HCUP, to train random forest classifiers for disease prediction. Since the HCUP data is highly imbalanced, we employed an ensemble learning approach based on repeated random sub-sampling. This technique divides the training data into multiple sub-samples, while ensuring that each sub-sample is fully balanced. We compared the performance of support vector machine (SVM, bagging, boosting and RF to predict the risk of eight chronic diseases. Results We predicted eight disease categories. Overall, the RF ensemble learning method outperformed SVM, bagging and boosting in terms of the area under the receiver operating characteristic (ROC curve (AUC. In addition, RF has the advantage of computing the importance of each variable in the classification process. Conclusions In combining repeated random sub-sampling with RF, we were able to overcome the class imbalance problem and achieve promising results. Using the national HCUP data set, we predicted eight disease categories with an average AUC of 88.79%.

  9. High risk sexual behaviors for HIV among the in-school youth in Swaziland: a structural equation modeling approach.

    Science.gov (United States)

    Sacolo, Hlengiwe Nokuthula; Chung, Min-Huey; Chu, Hsin; Liao, Yuan-Mei; Chen, Chiung-Hua; Ou, Keng-Liang; Chang, Lu-I; Chou, Kuei-Ru

    2013-01-01

    Global efforts in response to the increased prevalence of the human immunodeficiency virus (HIV) are mainly aimed at reducing high risk sexual behaviors among young people. However, knowledge regarding intentions of young people to engage in protective sexual behaviors is still lacking in many countries around the world, especially in Sub-Saharan Africa where prevalence of human immunodeficiency virus is the highest. The objective of this study was to test the theory of planned behavior (TPB) for predicting factors associated with protective sexual behaviors, including sexual abstinence and condom use, among in-school youths aged between 15 and 19 years in Swaziland. This cross-sectional survey was conducted using a anonymous questionnaire. A two-stage stratified and cluster random sampling method was used. Approximately one hundred pupils from each of four schools agreed to participate in the study, providing a total sample size of 403 pupils of which 369 were ultimately included for data analysis. The response rate was 98%. Structural equation modeling was used to analyse hypothesized paths. The TPB model used in this study was effective in predicting protective sexual behavior among Swazi in-school youths, as shown by model fit indices. All hypothesized constructs significantly predicted intentions for abstinence and condom use, except perceived abstinence controls. Subjective norms were the strongest predictors of intention for premarital sexual abstinence; however, perceived controls for condom use were the strongest predictors of intention for condom use. Our findings support application of the model in predicting determinants of condom use and abstinence intentions among Swazi in-school youths.

  10. High risk sexual behaviors for HIV among the in-school youth in Swaziland: a structural equation modeling approach.

    Directory of Open Access Journals (Sweden)

    Hlengiwe Nokuthula Sacolo

    Full Text Available BACKGROUND: Global efforts in response to the increased prevalence of the human immunodeficiency virus (HIV are mainly aimed at reducing high risk sexual behaviors among young people. However, knowledge regarding intentions of young people to engage in protective sexual behaviors is still lacking in many countries around the world, especially in Sub-Saharan Africa where prevalence of human immunodeficiency virus is the highest. The objective of this study was to test the theory of planned behavior (TPB for predicting factors associated with protective sexual behaviors, including sexual abstinence and condom use, among in-school youths aged between 15 and 19 years in Swaziland. METHODS: This cross-sectional survey was conducted using a anonymous questionnaire. A two-stage stratified and cluster random sampling method was used. Approximately one hundred pupils from each of four schools agreed to participate in the study, providing a total sample size of 403 pupils of which 369 were ultimately included for data analysis. The response rate was 98%. Structural equation modeling was used to analyse hypothesized paths. RESULTS: The TPB model used in this study was effective in predicting protective sexual behavior among Swazi in-school youths, as shown by model fit indices. All hypothesized constructs significantly predicted intentions for abstinence and condom use, except perceived abstinence controls. Subjective norms were the strongest predictors of intention for premarital sexual abstinence; however, perceived controls for condom use were the strongest predictors of intention for condom use. CONCLUSIONS: Our findings support application of the model in predicting determinants of condom use and abstinence intentions among Swazi in-school youths.

  11. High Risk Sexual Behaviors for HIV among the In-School Youth in Swaziland: A Structural Equation Modeling Approach

    Science.gov (United States)

    Chu, Hsin; Liao, Yuan-Mei; Chen, Chiung-Hua; Ou, Keng-Liang; Chang, Lu-I; Chou, Kuei-Ru

    2013-01-01

    Background Global efforts in response to the increased prevalence of the human immunodeficiency virus (HIV) are mainly aimed at reducing high risk sexual behaviors among young people. However, knowledge regarding intentions of young people to engage in protective sexual behaviors is still lacking in many countries around the world, especially in Sub-Saharan Africa where prevalence of human immunodeficiency virus is the highest. The objective of this study was to test the theory of planned behavior (TPB) for predicting factors associated with protective sexual behaviors, including sexual abstinence and condom use, among in-school youths aged between 15 and 19 years in Swaziland. Methods This cross-sectional survey was conducted using a anonymous questionnaire. A two-stage stratified and cluster random sampling method was used. Approximately one hundred pupils from each of four schools agreed to participate in the study, providing a total sample size of 403 pupils of which 369 were ultimately included for data analysis. The response rate was 98%. Structural equation modeling was used to analyse hypothesized paths. Results The TPB model used in this study was effective in predicting protective sexual behavior among Swazi in-school youths, as shown by model fit indices. All hypothesized constructs significantly predicted intentions for abstinence and condom use, except perceived abstinence controls. Subjective norms were the strongest predictors of intention for premarital sexual abstinence; however, perceived controls for condom use were the strongest predictors of intention for condom use. Conclusions Our findings support application of the model in predicting determinants of condom use and abstinence intentions among Swazi in-school youths. PMID:23861756

  12. The role of risk propensity in predicting self-employment.

    Science.gov (United States)

    Nieß, Christiane; Biemann, Torsten

    2014-09-01

    This study aims to untangle the role of risk propensity as a predictor of self-employment entry and self-employment survival. More specifically, it examines whether the potentially positive effect of risk propensity on the decision to become self-employed turns curvilinear when it comes to the survival of the business. Building on a longitudinal sample of 4,973 individuals from the German Socio-Economic Panel, we used event history analyses to evaluate the influence of risk propensity on self-employment over a 7-year time period. Results indicated that whereas high levels of risk propensity positively predicted the decision to become self-employed, the relationship between risk propensity and self-employment survival followed an inverted U-shaped curve. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  13. A new equation of state for better liquid density prediction of natural gas systems

    Science.gov (United States)

    Nwankwo, Princess C.

    Equations of state formulations, modifications and applications have remained active research areas since the success of van der Waal's equation in 1873. The need for better reservoir fluid modeling and characterization is of great importance to petroleum engineers who deal with thermodynamic related properties of petroleum fluids at every stage of the petroleum "life span" from its drilling, to production through the wellbore, to transportation, metering and storage. Equations of state methods are far less expensive (in terms of material cost and time) than laboratory or experimental forages and the results are interestingly not too far removed from the limits of acceptable accuracy. In most cases, the degree of accuracy obtained, by using various EOS's, though not appreciable, have been acceptable when considering the gain in time. The possibility of obtaining an equation of state which though simple in form and in use, could have the potential of further narrowing the present existing bias between experimentally determined and popular EOS estimated results spurred the interest that resulted in this study. This research study had as its chief objective, to develop a new equation of state that would more efficiently capture the thermodynamic properties of gas condensate fluids, especially the liquid phase density, which is the major weakness of other established and popular cubic equations of state. The set objective was satisfied by a new semi analytical cubic three parameter equation of state, derived by the modification of the attraction term contribution to pressure of the van der Waal EOS without compromising either structural simplicity or accuracy of estimating other vapor liquid equilibria properties. The application of new EOS to single and multi-component light hydrocarbon fluids recorded far lower error values than does the popular two parameter, Peng-Robinson's (PR) and three parameter Patel-Teja's (PT) equations of state. Furthermore, this research

  14. Personality patterns predict the risk of antisocial behavior in Spanish-speaking adolescents.

    Science.gov (United States)

    Alcázar-Córcoles, Miguel A; Verdejo-García, Antonio; Bouso-Sáiz, José C; Revuelta-Menéndez, Javier; Ramírez-Lira, Ezequiel

    2017-05-01

    There is a renewed interest in incorporating personality variables in criminology theories in order to build models able to integrate personality variables and biological factors with psychosocial and sociocultural factors. The aim of this article is the assessment of personality dimensions that contribute to the prediction of antisocial behavior in adolescents. For this purpose, a sample of adolescents from El Salvador, Mexico, and Spain was obtained. The sample consisted of 1035 participants with a mean age of 16.2. There were 450 adolescents from a forensic population (those who committed a crime) and 585 adolescents from the normal population (no crime committed). All of participants answered personality tests about neuroticism, extraversion, psychoticism, sensation seeking, impulsivity, and violence risk. Principal component analysis of the data identified two independent factors: (i) the disinhibited behavior pattern (PDC), formed by the dimensions of neuroticism, psychoticism, impulsivity and risk of violence; and (ii) the extrovert behavior pattern (PEC), formed by the dimensions of sensation risk and extraversion. Both patterns significantly contributed to the prediction of adolescent antisocial behavior in a logistic regression model which properly classifies a global percentage of 81.9%, 86.8% for non-offense and 72.5% for offense behavior. The classification power of regression equations allows making very satisfactory predictions about adolescent offense commission. Educational level has been classified as a protective factor, while age and gender (male) have been classified as risk factors.

  15. Predicting risk of violence through a self-appraisal questionnaire

    Directory of Open Access Journals (Sweden)

    José Manuel Andreu-Rodríguez

    2016-07-01

    Full Text Available The Self-Appraisal Questionnaire (SAQ is a self-report that predicts the risk of violence and recidivism and provides relevant information about treatment needs for incarcerated populations. The objective of the present study was to evaluate the concurrent and predictive validity of this self-report in Spanish offenders. The SAQ was administered to 276 offenders recruited from several prisons in Madrid (Spain. SAQ total scores presented high levels of internal consistency (alpha = .92. Correlations of the instrument with violence risk instruments were statistically significant and showed a moderate magnitude, indicating a reasonable degree of concurrent validity. The ROC analysis carried out on the SAQ total score revealed an AUC of .80, showing acceptable accuracy discriminating between violent and nonviolent recidivist groups. It is concluded that the SAQ total score is a reliable and valid measure to estimate violence and recidivism risk in Spanish offenders.

  16. Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria

    Directory of Open Access Journals (Sweden)

    Solarin Adewale RT

    2008-05-01

    Full Text Available Abstract Background The control of urinary schistosomiasis in Ogun State, Nigeria remains inert due to lack of reliable data on the geographical distribution of the disease and the population at risk. To help in developing a control programme, delineating areas of risk, geographical information system and remotely sensed environmental images were used to developed predictive risk maps of the probability of occurrence of the disease and quantify the risk for infection in Ogun State, Nigeria. Methods Infection data used were derived from carefully validated morbidity questionnaires among primary school children in 2001–2002, in which school children were asked among other questions if they have experienced "blood in urine" or urinary schistosomiasis. The infection data from 1,092 schools together with remotely sensed environmental data such as rainfall, vegetation, temperature, soil-types, altitude and land cover were analysis using binary logistic regression models to identify environmental features that influence the spatial distribution of the disease. The final regression equations were then used in Arc View 3.2a GIS software to generate predictive risk maps of the distribution of the disease and population at risk in the state. Results Logistic regression analysis shows that the only significant environmental variable in predicting the presence and absence of urinary schistosomiasis in any area of the State was Land Surface Temperature (LST (B = 0.308, p = 0.013. While LST (B = -0.478, p = 0.035, rainfall (B = -0.006, p = 0.0005, ferric luvisols (B = 0.539, p = 0.274, dystric nitosols (B = 0.133, p = 0.769 and pellic vertisols (B = 1.386, p = 0.008 soils types were the final variables in the model for predicting the probability of an area having an infection prevalence equivalent to or more than 50%. The two predictive risk maps suggest that urinary schistosomiasis is widely distributed and occurring in all the Local Government Areas (LGAs

  17. Equations based on anthropometry to predict body fat measured by absorptiometry in schoolchildren and adolescents.

    Science.gov (United States)

    Ortiz-Hernández, Luis; Vega López, A Valeria; Ramos-Ibáñez, Norma; Cázares Lara, L Joana; Medina Gómez, R Joab; Pérez-Salgado, Diana

    To develop and validate equations to estimate the percentage of body fat of children and adolescents from Mexico using anthropometric measurements. A cross-sectional study was carried out with 601 children and adolescents from Mexico aged 5-19 years. The participants were randomly divided into the following two groups: the development sample (n=398) and the validation sample (n=203). The validity of previously published equations (e.g., Slaughter) was also assessed. The percentage of body fat was estimated by dual-energy X-ray absorptiometry. The anthropometric measurements included height, sitting height, weight, waist and arm circumferences, skinfolds (triceps, biceps, subscapular, supra-iliac, and calf), and elbow and bitrochanteric breadth. Linear regression models were estimated with the percentage of body fat as the dependent variable and the anthropometric measurements as the independent variables. Equations were created based on combinations of six to nine anthropometric variables and had coefficients of determination (r 2 ) equal to or higher than 92.4% for boys and 85.8% for girls. In the validation sample, the developed equations had high r 2 values (≥85.6% in boys and ≥78.1% in girls) in all age groups, low standard errors (SE≤3.05% in boys and ≤3.52% in girls), and the intercepts were not different from the origin (p>0.050). Using the previously published equations, the coefficients of determination were lower, and/or the intercepts were different from the origin. The equations developed in this study can be used to assess the percentage of body fat of Mexican schoolchildren and adolescents, as they demonstrate greater validity and lower error compared with previously published equations. Copyright © 2017 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

  18. Predictive performance for population models using stochastic differential equations applied on data from an oral glucose tolerance test

    DEFF Research Database (Denmark)

    Møller, Jonas Bech; Overgaard, R.V.; Madsen, Henrik

    2010-01-01

    Several articles have investigated stochastic differential equations (SDEs) in PK/PD models, but few have quantitatively investigated the benefits to predictive performance of models based on real data. Estimation of first phase insulin secretion which reflects beta-cell function using models of ...... obtained from the glucose tolerance tests. Since, the estimation time of extended models was not heavily increased compared to basic models, the applied method is concluded to have high relevance not only in theory but also in practice....

  19. Risk predictive modelling for diabetes and cardiovascular disease.

    Science.gov (United States)

    Kengne, Andre Pascal; Masconi, Katya; Mbanya, Vivian Nchanchou; Lekoubou, Alain; Echouffo-Tcheugui, Justin Basile; Matsha, Tandi E

    2014-02-01

    Absolute risk models or clinical prediction models have been incorporated in guidelines, and are increasingly advocated as tools to assist risk stratification and guide prevention and treatments decisions relating to common health conditions such as cardiovascular disease (CVD) and diabetes mellitus. We have reviewed the historical development and principles of prediction research, including their statistical underpinning, as well as implications for routine practice, with a focus on predictive modelling for CVD and diabetes. Predictive modelling for CVD risk, which has developed over the last five decades, has been largely influenced by the Framingham Heart Study investigators, while it is only ∼20 years ago that similar efforts were started in the field of diabetes. Identification of predictive factors is an important preliminary step which provides the knowledge base on potential predictors to be tested for inclusion during the statistical derivation of the final model. The derived models must then be tested both on the development sample (internal validation) and on other populations in different settings (external validation). Updating procedures (e.g. recalibration) should be used to improve the performance of models that fail the tests of external validation. Ultimately, the effect of introducing validated models in routine practice on the process and outcomes of care as well as its cost-effectiveness should be tested in impact studies before wide dissemination of models beyond the research context. Several predictions models have been developed for CVD or diabetes, but very few have been externally validated or tested in impact studies, and their comparative performance has yet to be fully assessed. A shift of focus from developing new CVD or diabetes prediction models to validating the existing ones will improve their adoption in routine practice.

  20. Calibration plots for risk prediction models in the presence of competing risks.

    Science.gov (United States)

    Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

    2014-08-15

    A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks such as death due to other causes. For personalized medicine and patient counseling, it is necessary to check that the model is calibrated in the sense that it provides reliable predictions for all subjects. There are three often encountered practical problems when the aim is to display or test if a risk prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves for competing risks models based on jackknife pseudo-values that are combined with a nearest neighborhood smoother and a cross-validation approach to deal with all three problems. Copyright © 2014 John Wiley & Sons, Ltd.

  1. Predicting the effects of ionising radiation on ecosystems by a generic model based on the Lotka-Volterra equations

    International Nuclear Information System (INIS)

    Monte, Luigi

    2009-01-01

    The present work describes a model for predicting the population dynamics of the main components (resources and consumers) of terrestrial ecosystems exposed to ionising radiation. The ecosystem is modelled by the Lotka-Volterra equations with consumer competition. Linear dose-response relationships without threshold are assumed to relate the values of the model parameters to the dose rates. The model accounts for the migration of consumers from areas characterised by different levels of radionuclide contamination. The criteria to select the model parameter values are motivated by accounting for the results of the empirical studies of past decades. Examples of predictions for long-term chronic exposure are reported and discussed.

  2. Providing access to risk prediction tools via the HL7 XML-formatted risk web service.

    Science.gov (United States)

    Chipman, Jonathan; Drohan, Brian; Blackford, Amanda; Parmigiani, Giovanni; Hughes, Kevin; Bosinoff, Phil

    2013-07-01

    Cancer risk prediction tools provide valuable information to clinicians but remain computationally challenging. Many clinics find that CaGene or HughesRiskApps fit their needs for easy- and ready-to-use software to obtain cancer risks; however, these resources may not fit all clinics' needs. The HughesRiskApps Group and BayesMendel Lab therefore developed a web service, called "Risk Service", which may be integrated into any client software to quickly obtain standardized and up-to-date risk predictions for BayesMendel tools (BRCAPRO, MMRpro, PancPRO, and MelaPRO), the Tyrer-Cuzick IBIS Breast Cancer Risk Evaluation Tool, and the Colorectal Cancer Risk Assessment Tool. Software clients that can convert their local structured data into the HL7 XML-formatted family and clinical patient history (Pedigree model) may integrate with the Risk Service. The Risk Service uses Apache Tomcat and Apache Axis2 technologies to provide an all Java web service. The software client sends HL7 XML information containing anonymized family and clinical history to a Dana-Farber Cancer Institute (DFCI) server, where it is parsed, interpreted, and processed by multiple risk tools. The Risk Service then formats the results into an HL7 style message and returns the risk predictions to the originating software client. Upon consent, users may allow DFCI to maintain the data for future research. The Risk Service implementation is exemplified through HughesRiskApps. The Risk Service broadens the availability of valuable, up-to-date cancer risk tools and allows clinics and researchers to integrate risk prediction tools into their own software interface designed for their needs. Each software package can collect risk data using its own interface, and display the results using its own interface, while using a central, up-to-date risk calculator. This allows users to choose from multiple interfaces while always getting the latest risk calculations. Consenting users contribute their data for future

  3. Time of Concentration equations: the role of morphometric uncertainties in flood risk analysis and management

    Science.gov (United States)

    Martins, Luciano; Díez-Herrero, Andrés; Bodoque, Jose M.; Bateira, Carlos

    2016-04-01

    The perception of flood risk by the responsible authorities on the flood management disasters and mitigation strategies should be based on an overall evaluation of the uncertainties associated with the procedures for risk assessment and mapping production. This contribution presents the results of the development of mapping evaluation of the time of concentration (tc). This parameter reflects the time-space at which a watershed responds to rainfall events and is the most frequently utilized time parameter, and is of great importance in many hydrologic analysis. Accurate estimates of the tc are very important, for instance, if tc is under-estimated, the result is an over-estimated peak discharge and vice versa, resulting significant variations on the flooded areas, and could have important consequences in terms of the land use and occupation of territory, as management's own flood risk. The methology used evaluate 20 different empirical, semi-empirical and kinematics equations of tc calculation, due to different cartographic scales (1:200000; 1:100000; 1:25000; LIDAR 5x5m &1x1m) in in two hydrographic basins with distinct dimensions and geomorphological characteristics, located in the Gredos Mountain range (Spain). The results suggest that the changes in the cartographic scale, has not influence as significant as one might expect. The most important variations occur in the characteristics of the fequations, use different morphometricparameters in the calculations. Some just are based on geomorphological criteria and other magnify the hydraulic characteristics of the channels, resulting in very different tc values. However, we highlighting the role of cartographic scale particularly in the application of semi-empirical equations that take into account changes in land use and occupation. In this case, the determination of parameters, such as flow coefficient, curve number and roughness coefficient are very sensitive to cartographic scale. Sensitivity analysis

  4. The Reliability and Predictive Validity of the Stalking Risk Profile.

    Science.gov (United States)

    McEwan, Troy E; Shea, Daniel E; Daffern, Michael; MacKenzie, Rachel D; Ogloff, James R P; Mullen, Paul E

    2018-03-01

    This study assessed the reliability and validity of the Stalking Risk Profile (SRP), a structured measure for assessing stalking risks. The SRP was administered at the point of assessment or retrospectively from file review for 241 adult stalkers (91% male) referred to a community-based forensic mental health service. Interrater reliability was high for stalker type, and moderate-to-substantial for risk judgments and domain scores. Evidence for predictive validity and discrimination between stalking recidivists and nonrecidivists for risk judgments depended on follow-up duration. Discrimination was moderate (area under the curve = 0.66-0.68) and positive and negative predictive values good over the full follow-up period ( Mdn = 170.43 weeks). At 6 months, discrimination was better than chance only for judgments related to stalking of new victims (area under the curve = 0.75); however, high-risk stalkers still reoffended against their original victim(s) 2 to 4 times as often as low-risk stalkers. Implications for the clinical utility and refinement of the SRP are discussed.

  5. B(E2) ↑ (01+ -> 21+) predictions for even–even nuclei in the differential equation model

    International Nuclear Information System (INIS)

    Nayak, R.C.; Pattnaik, S.

    2015-01-01

    We use the recently developed differential equation model (DEM) for the reduced electric quadrupole transition probability B(E2)↑ for the transition from the ground to the first 2 + state for predicting its values for a wide range of even–even nuclides almost throughout the nuclear landscape from Neon to Californium. This is made possible as the principal equation in the model, namely, the differential equation connecting the B(E2)↑ value of a given even–even nucleus with its derivatives with respect to the neutron and proton numbers, provides two different recursion relations, each connecting three different neighboring even–even nuclei from lower- to higher-mass numbers and vice versa. These relations are primarily responsible in extrapolating from known to unknown terrain of the B(E2)↑-landscape and thereby facilitate the predictions throughout. As a result, we have succeeded in predicting its hitherto unknown value for the adjacent 251 isotopes lying on either side of the known B(E2)↑ database. (author)

  6. Reliability of CKD-EPI predictive equation in estimating chronic kidney disease prevalence in the Croatian endemic nephropathy area.

    Science.gov (United States)

    Fuček, Mirjana; Dika, Živka; Karanović, Sandra; Vuković Brinar, Ivana; Premužić, Vedran; Kos, Jelena; Cvitković, Ante; Mišić, Maja; Samardžić, Josip; Rogić, Dunja; Jelaković, Bojan

    2018-02-15

    Chronic kidney disease (CKD) is a significant public health problem and it is not possible to precisely predict its progression to terminal renal failure. According to current guidelines, CKD stages are classified based on the estimated glomerular filtration rate (eGFR) and albuminuria. Aims of this study were to determine the reliability of predictive equation in estimation of CKD prevalence in Croatian areas with endemic nephropathy (EN), compare the results with non-endemic areas, and to determine if the prevalence of CKD stages 3-5 was increased in subjects with EN. A total of 1573 inhabitants of the Croatian Posavina rural area from 6 endemic and 3 non-endemic villages were enrolled. Participants were classified according to the modified criteria of the World Health Organization for EN. Estimated GFR was calculated using Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI). The results showed a very high CKD prevalence in the Croatian rural area (19%). CKD prevalence was significantly higher in EN then in non EN villages with the lowest eGFR value in diseased subgroup. eGFR correlated significantly with the diagnosis of EN. Kidney function assessment using CKD-EPI predictive equation proved to be a good marker in differentiating the study subgroups, remained as one of the diagnostic criteria for EN.

  7. GPS-VTEC near the magnetic equator during a high solar activity year: Observations and IRI predictions

    International Nuclear Information System (INIS)

    Ezquer, R.G.; Mosert, M.; Brunini, C.; Meza, A.; Cabrera, M.A.; Araoz, L.; Radicella, S.M.

    2002-01-01

    The validity of International Reference Ionosphere model to predict the vertical electron content (VTEC) over Arequipa (-16.5, 289.0; geoma. Lat.: - 5.1), station placed near the magnetic equator, is checked. VTEC measurements obtained with GPS satellite signals during year 2000 are considered. These data correspond to equinoxes and solstices. The results are similar to those obtained for the southern peak of the equatorial anomaly in previous work. In some cases, good VTEC predictions have been observed for hours of maximum ionisation. Overestimation for nighttime, sunrise and sunset hours were observed. The disagreements between predictions and measurements could arise because peak characteristics or the shape of the N profile, or both, are not well predicted. More studies including ionosonde measurements would be useful. (author)

  8. Predicting impacts of climate change on Fasciola hepatica risk.

    Science.gov (United States)

    Fox, Naomi J; White, Piran C L; McClean, Colin J; Marion, Glenn; Evans, Andy; Hutchings, Michael R

    2011-01-10

    Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  9. Predicting impacts of climate change on Fasciola hepatica risk.

    Directory of Open Access Journals (Sweden)

    Naomi J Fox

    2011-01-01

    Full Text Available Fasciola hepatica (liver fluke is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

  10. Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data

    International Nuclear Information System (INIS)

    Hormuth II, David A; Weis, Jared A; Barnes, Stephanie L; Miga, Michael I; Yankeelov, Thomas E; Rericha, Erin C; Quaranta, Vito

    2015-01-01

    Reaction–diffusion models have been widely used to model glioma growth. However, it has not been shown how accurately this model can predict future tumor status using model parameters (i.e., tumor cell diffusion and proliferation) estimated from quantitative in vivo imaging data. To this end, we used in silico studies to develop the methods needed to accurately estimate tumor specific reaction–diffusion model parameters, and then tested the accuracy with which these parameters can predict future growth. The analogous study was then performed in a murine model of glioma growth. The parameter estimation approach was tested using an in silico tumor ‘grown’ for ten days as dictated by the reaction–diffusion equation. Parameters were estimated from early time points and used to predict subsequent growth. Prediction accuracy was assessed at global (total volume and Dice value) and local (concordance correlation coefficient, CCC) levels. Guided by the in silico study, rats (n = 9) with C6 gliomas, imaged with diffusion weighted magnetic resonance imaging, were used to evaluate the model’s accuracy for predicting in vivo tumor growth. The in silico study resulted in low global (tumor volume error 0.92) and local (CCC values >0.80) level errors for predictions up to six days into the future. The in vivo study showed higher global (tumor volume error >11.7%, Dice <0.81) and higher local (CCC <0.33) level errors over the same time period. The in silico study shows that model parameters can be accurately estimated and used to accurately predict future tumor growth at both the global and local scale. However, the poor predictive accuracy in the experimental study suggests the reaction–diffusion equation is an incomplete description of in vivo C6 glioma biology and may require further modeling of intra-tumor interactions including segmentation of (for example) proliferative and necrotic regions. (paper)

  11. Resting energy expenditure prediction in recreational athletes of 18-35 years: confirmation of Cunningham equation and an improved weight-based alternative.

    Science.gov (United States)

    ten Haaf, Twan; Weijs, Peter J M

    2014-01-01

    Resting energy expenditure (REE) is expected to be higher in athletes because of their relatively high fat free mass (FFM). Therefore, REE predictive equation for recreational athletes may be required. The aim of this study was to validate existing REE predictive equations and to develop a new recreational athlete specific equation. 90 (53 M, 37 F) adult athletes, exercising on average 9.1 ± 5.0 hours a week and 5.0 ± 1.8 times a week, were included. REE was measured using indirect calorimetry (Vmax Encore n29), FFM and FM were measured using air displacement plethysmography. Multiple linear regression analysis was used to develop a new FFM-based and weight-based REE predictive equation. The percentage accurate predictions (within 10% of measured REE), percentage bias, root mean square error and limits of agreement were calculated. Results: The Cunningham equation and the new weight-based equation REE(kJ / d) = 49.940* weight(kg) + 2459.053* height(m) - 34.014* age(y) + 799.257* sex(M = 1,F = 0) + 122.502 and the new FFM-based equation REE(kJ / d) = 95.272*FFM(kg) + 2026.161 performed equally well. De Lorenzo's equation predicted REE less accurate, but better than the other generally used REE predictive equations. Harris-Benedict, WHO, Schofield, Mifflin and Owen all showed less than 50% accuracy. For a population of (Dutch) recreational athletes, the REE can accurately be predicted with the existing Cunningham equation. Since body composition measurement is not always possible, and other generally used equations fail, the new weight-based equation is advised for use in sports nutrition.

  12. A Coupled System of Integrodifferential Equations Arising in Liquidity Risk Model

    International Nuclear Information System (INIS)

    Pham, Huyen; Tankov, Peter

    2009-01-01

    We study the mathematical aspects of the portfolio/consumption choice problem in a market model with liquidity risk introduced in (Pham and Tankov, Math. Finance, 2006, to appear). In this model, the investor can trade and observe stock prices only at exogenous Poisson arrival times. He may also consume continuously from his cash holdings, and his goal is to maximize his expected utility from consumption. This is a mixed discrete/continuous time stochastic control problem, nonstandard in the literature. We show how the dynamic programming principle leads to a coupled system of Integro-Differential Equations (IDE), and we prove an analytic characterization of this control problem by adapting the concept of viscosity solutions. This coupled system of IDE may be numerically solved by a decoupling algorithm, and this is the topic of a companion paper (Pham and Tankov, Math. Finance, 2006, to appear)

  13. Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool.

    Science.gov (United States)

    Veeravagu, Anand; Li, Amy; Swinney, Christian; Tian, Lu; Moraff, Adrienne; Azad, Tej D; Cheng, Ivan; Alamin, Todd; Hu, Serena S; Anderson, Robert L; Shuer, Lawrence; Desai, Atman; Park, Jon; Olshen, Richard A; Ratliff, John K

    2017-07-01

    OBJECTIVE The ability to assess the risk of adverse events based on known patient factors and comorbidities would provide more effective preoperative risk stratification. Present risk assessment in spine surgery is limited. An adverse event prediction tool was developed to predict the risk of complications after spine surgery and tested on a prospective patient cohort. METHODS The spinal Risk Assessment Tool (RAT), a novel instrument for the assessment of risk for patients undergoing spine surgery that was developed based on an administrative claims database, was prospectively applied to 246 patients undergoing 257 spinal procedures over a 3-month period. Prospectively collected data were used to compare the RAT to the Charlson Comorbidity Index (CCI) and the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator. Study end point was occurrence and type of complication after spine surgery. RESULTS The authors identified 69 patients (73 procedures) who experienced a complication over the prospective study period. Cardiac complications were most common (10.2%). Receiver operating characteristic (ROC) curves were calculated to compare complication outcomes using the different assessment tools. Area under the curve (AUC) analysis showed comparable predictive accuracy between the RAT and the ACS NSQIP calculator (0.670 [95% CI 0.60-0.74] in RAT, 0.669 [95% CI 0.60-0.74] in NSQIP). The CCI was not accurate in predicting complication occurrence (0.55 [95% CI 0.48-0.62]). The RAT produced mean probabilities of 34.6% for patients who had a complication and 24% for patients who did not (p = 0.0003). The generated predicted values were stratified into low, medium, and high rates. For the RAT, the predicted complication rate was 10.1% in the low-risk group (observed rate 12.8%), 21.9% in the medium-risk group (observed 31.8%), and 49.7% in the high-risk group (observed 41.2%). The ACS NSQIP calculator consistently

  14. Machine learning derived risk prediction of anorexia nervosa.

    Science.gov (United States)

    Guo, Yiran; Wei, Zhi; Keating, Brendan J; Hakonarson, Hakon

    2016-01-20

    Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

  15. Predictive risk factors for moderate to severe hyperbilirubinemia

    Directory of Open Access Journals (Sweden)

    Gláucia Macedo de Lima

    2007-12-01

    Full Text Available Objective: to describe predictive factors for severity of neonataljaundice in newborn infants treated at the University Neonatal Clinic,highlighting maternal, obstetric and neonatal factors. Methods: Acohort retrospective study by means of review of medical charts todefine risk factors associated with moderate and severe jaundice.The cohort consisted of newborns diagnosed with indirect neonatalhyperbilirubinemia and submitted to phototherapy. Risk was classifiedas maternal, prenatal, obstetric and neonatal factors; risk estimationwas based on the odds ratio (95% confidence interval; a bi-variantmultivariate regression logistic analysis was applied to variables forp < 0.1. Results: Of 818 babies born during the studied period, 94(11% had jaundice prior to hospital discharge. Phototherapy was usedon 69 (73% patients. Predictive factors for severity were multiparity;prolonged rupture of membranes, dystocia, cephalohematoma, a lowApgar score, prematurity and small-for-date babies. Following birth,breastfeeding, sepsis, Rh incompatibility, and jaundice presentingbefore the third day of life were associated with an increased risk ofhyperbilirubinemia and the need for therapy. Conclusion: Other thanthose characteristics that are singly associated with phototherapy,we concluded that multiparity, presumed neonatal asphyxia, low birthweight and infection are the main predictive factors leading to moderateand severe jaundice in newborn infants in our neonatal unit.

  16. A risk prediction model for xerostomia: a retrospective cohort study.

    Science.gov (United States)

    Villa, Alessandro; Nordio, Francesco; Gohel, Anita

    2016-12-01

    We investigated the prevalence of xerostomia in dental patients and built a xerostomia risk prediction model by incorporating a wide range of risk factors. Socio-demographic data, past medical history, self-reported dry mouth and related symptoms were collected retrospectively from January 2010 to September 2013 for all new dental patients. A logistic regression framework was used to build a risk prediction model for xerostomia. External validation was performed using an independent data set to test the prediction power. A total of 12 682 patients were included in this analysis (54.3%, females). Xerostomia was reported by 12.2% of patients. The proportion of people reporting xerostomia was higher among those who were taking more medications (OR = 1.11, 95% CI = 1.08-1.13) or recreational drug users (OR = 1.4, 95% CI = 1.1-1.9). Rheumatic diseases (OR = 2.17, 95% CI = 1.88-2.51), psychiatric diseases (OR = 2.34, 95% CI = 2.05-2.68), eating disorders (OR = 2.28, 95% CI = 1.55-3.36) and radiotherapy (OR = 2.00, 95% CI = 1.43-2.80) were good predictors of xerostomia. For the test model performance, the ROC-AUC was 0.816 and in the external validation sample, the ROC-AUC was 0.799. The xerostomia risk prediction model had high accuracy and discriminated between high- and low-risk individuals. Clinicians could use this model to identify the classes of medications and systemic diseases associated with xerostomia. © 2015 John Wiley & Sons A/S and The Gerodontology Association. Published by John Wiley & Sons Ltd.

  17. Lung cancer in never smokers Epidemiology and risk prediction models

    Science.gov (United States)

    McCarthy, William J.; Meza, Rafael; Jeon, Jihyoun; Moolgavkar, Suresh

    2012-01-01

    In this chapter we review the epidemiology of lung cancer incidence and mortality among never smokers/ nonsmokers and describe the never smoker lung cancer risk models used by CISNET modelers. Our review focuses on those influences likely to have measurable population impact on never smoker risk, such as secondhand smoke, even though the individual-level impact may be small. Occupational exposures may also contribute importantly to the population attributable risk of lung cancer. We examine the following risk factors in this chapter: age, environmental tobacco smoke, cooking fumes, ionizing radiation including radon gas, inherited genetic susceptibility, selected occupational exposures, preexisting lung disease, and oncogenic viruses. We also compare the prevalence of never smokers between the three CISNET smoking scenarios and present the corresponding lung cancer mortality estimates among never smokers as predicted by a typical CISNET model. PMID:22882894

  18. Cumulative risk hypothesis: Predicting and preventing child maltreatment recidivism.

    Science.gov (United States)

    Solomon, David; Åsberg, Kia; Peer, Samuel; Prince, Gwendolyn

    2016-08-01

    Although Child Protective Services (CPS) and other child welfare agencies aim to prevent further maltreatment in cases of child abuse and neglect, recidivism is common. Having a better understanding of recidivism predictors could aid in preventing additional instances of maltreatment. A previous study identified two CPS interventions that predicted recidivism: psychotherapy for the parent, which was related to a reduced risk of recidivism, and temporary removal of the child from the parent's custody, which was related to an increased recidivism risk. However, counter to expectations, this previous study did not identify any other specific risk factors related to maltreatment recidivism. For the current study, it was hypothesized that (a) cumulative risk (i.e., the total number of risk factors) would significantly predict maltreatment recidivism above and beyond intervention variables in a sample of CPS case files and that (b) therapy for the parent would be related to a reduced likelihood of recidivism. Because it was believed that the relation between temporary removal of a child from the parent's custody and maltreatment recidivism is explained by cumulative risk, the study also hypothesized that that the relation between temporary removal of the child from the parent's custody and recidivism would be mediated by cumulative risk. After performing a hierarchical logistic regression analysis, the first two hypotheses were supported, and an additional predictor, psychotherapy for the child, also was related to reduced chances of recidivism. However, Hypothesis 3 was not supported, as risk did not significantly mediate the relation between temporary removal and recidivism. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Predicting risk and human reliability: a new approach

    International Nuclear Information System (INIS)

    Duffey, R.; Ha, T.-S.

    2009-01-01

    Learning from experience describes human reliability and skill acquisition, and the resulting theory has been validated by comparison against millions of outcome data from multiple industries and technologies worldwide. The resulting predictions were used to benchmark the classic first generation human reliability methods adopted in probabilistic risk assessments. The learning rate, probabilities and response times are also consistent with the existing psychological models for human learning and error correction. The new approach also implies a finite lower bound probability that is not predicted by empirical statistical distributions that ignore the known and fundamental learning effects. (author)

  20. Conductive sapwood area prediction from stem and canopy areas - allometric equations of Kalahari trees, Botswana

    NARCIS (Netherlands)

    Lubczynski, M.W.; Chavarro-Rincon, D.C.; Rossiter, David

    2017-01-01

    Conductive sapwood (xylem) area (Ax) of all trees in a given forested area is the main factor contributing to spatial tree transpiration. One hundred ninety-five trees of 9 species in the Kalahari region of Botswana were felled, stained, cut into discs, and measured to develop allometric equations

  1. Estimated GFR (eGFR by prediction equation in staging of chronic kidney disease compared to gamma camera GFR

    Directory of Open Access Journals (Sweden)

    Mohammad Masum Alam

    2016-07-01

    Full Text Available Background: Glomerular filtration rate is an effective tool for diagnosis and staging of chronic kidney disease. The effect ofrenal insufficiency by different method of this tool among patients with CKD is controversial.Objective: The objec­tive of this study was to evaluate the performance of eGFR in staging of CKD compared to gamma camera based GFR.Methods: This cross sectional analytical study was conducted in the Department of Biochemistry Bangabandhu Sheikh Mujib Medical University (BSMMU with the collaboration with National Institute of Nuclear Medicine and Allied Sciences, BSMMU during the period of January 2011 to December 2012. Gama camera based GFR was estimated from DTP A reno gram and eGFR was estimated by three prediction equations. Comparison was done by Bland Altman agree­ment test to see the agreement on the measurement of GFR between three equation based eGFR method and gama camera based GFR method. Staging comparison was done by Kappa analysis to see the agreement between the stages identified by those different methods.Results: Bland-Altman agreement analysis between GFR measured by gamma camera, CG equation ,CG equation corrected by BSA and MDRD equation shows statistically significant. CKD stages determined by CG GFR, CG GFR corrected by BSA , MDRD GFR and gamma camera based GFR was compared by Kappa statistical analysis .The kappa value was 0.66, 0.77 and 0.79 respectively.Conclusions: This study findings suggest that GFR estimation by MDRD equation in CKD patients shows good agreement with gamma camera based GFR and for staging of CKD patients, eGFR by MDRD formula may be used as very effective tool in Bangladeshi population.

  2. Validity of Miles Equation in Predicting Propellant Slosh Damping in Baffled Tanks at Variable Slosh Amplitude

    Science.gov (United States)

    Yang, H. Q.; West, Jeff

    2018-01-01

    Determination of slosh damping is a very challenging task as there is no analytical solution. The damping physics involves the vorticity dissipation which requires the full solution of the nonlinear Navier-Stokes equations. As a result, previous investigations were mainly carried out by extensive experiments. A systematical study is needed to understand the damping physics of baffled tanks, to identify the difference between the empirical Miles equation and experimental measurements, and to develop new semi-empirical relations to better represent the real damping physics. The approach of this study is to use Computational Fluid Dynamics (CFD) technology to shed light on the damping mechanisms of a baffled tank. First, a 1-D Navier-Stokes equation representing different length scales and time scales in the baffle damping physics is developed and analyzed. Loci-STREAM-VOF, a well validated CFD solver developed at NASA MSFC, is applied to study the vorticity field around a baffle and around the fluid-gas interface to highlight the dissipation mechanisms at different slosh amplitudes. Previous measurement data is then used to validate the CFD damping results. The study found several critical parameters controlling fluid damping from a baffle: local slosh amplitude to baffle thickness (A/t), surface liquid depth to tank radius (d/R), local slosh amplitude to baffle width (A/W); and non-dimensional slosh frequency. The simulation highlights three significant damping regimes where different mechanisms dominate. The study proves that the previously found discrepancies between Miles equation and experimental measurement are not due to the measurement scatter, but rather due to different damping mechanisms at various slosh amplitudes. The limitations on the use of Miles equation are discussed based on the flow regime.

  3. Risk prediction of cardiovascular death based on the QTc interval

    DEFF Research Database (Denmark)

    Nielsen, Jonas B; Graff, Claus; Rasmussen, Peter V

    2014-01-01

    electrocardiograms from 173 529 primary care patients aged 50-90 years were collected during 2001-11. The Framingham formula was used for heart rate-correction of the QT interval. Data on medication, comorbidity, and outcomes were retrieved from administrative registries. During a median follow-up period of 6......AIMS: Using a large, contemporary primary care population we aimed to provide absolute long-term risks of cardiovascular death (CVD) based on the QTc interval and to test whether the QTc interval is of value in risk prediction of CVD on an individual level. METHODS AND RESULTS: Digital...

  4. Risk determination after an acute myocardial infarction: review of 3 clinical risk prediction tools.

    Science.gov (United States)

    Scruth, Elizabeth Ann; Page, Karen; Cheng, Eugene; Campbell, Michelle; Worrall-Carter, Linda

    2012-01-01

    The objective of the study was to provide comprehensive information for the clinical nurse specialist (CNS) on commonly used clinical prediction (risk assessment) tools used to estimate risk of a secondary cardiac or noncardiac event and mortality in patients undergoing primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI). The evolution and widespread adoption of primary PCI represent major advances in the treatment of acute myocardial infarction, specifically STEMI. The American College of Cardiology and the American Heart Association have recommended early risk stratification for patients presenting with acute coronary syndromes using several clinical risk scores to identify patients' mortality and secondary event risk after PCI. Clinical nurse specialists are integral to any performance improvement strategy. Their knowledge and understandings of clinical prediction tools will be essential in carrying out important assessment, identifying and managing risk in patients who have sustained a STEMI, and enhancing discharge education including counseling on medications and lifestyle changes. Over the past 2 decades, risk scores have been developed from clinical trials to facilitate risk assessment. There are several risk scores that can be used to determine in-hospital and short-term survival. This article critiques the most common tools: the Thrombolytic in Myocardial Infarction risk score, the Global Registry of Acute Coronary Events risk score, and the Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications risk score. The importance of incorporating risk screening assessment tools (that are important for clinical prediction models) to guide therapeutic management of patients cannot be underestimated. The ability to forecast secondary risk after a STEMI will assist in determining which patients would require the most aggressive level of treatment and monitoring postintervention including

  5. Nonparametric predictive inference for combined competing risks data

    International Nuclear Information System (INIS)

    Coolen-Maturi, Tahani; Coolen, Frank P.A.

    2014-01-01

    The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies

  6. Improving default risk prediction using Bayesian model uncertainty techniques.

    Science.gov (United States)

    Kazemi, Reza; Mosleh, Ali

    2012-11-01

    Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

  7. Concordance between hypoxic challenge testing and predictive equations for hypoxic flight assessment in chronic obstructive pulmonary disease patients prior to air travel

    Directory of Open Access Journals (Sweden)

    Mohie Aldeen Abd Alzaher Khalifa

    2016-10-01

    Conclusions: The present study supports on-HCT as a reliable, on-invasive and continuous methods determining the requirement for in-flight O2 are relatively constant. Predictive equations considerably overestimate the need for in-flight O2 compared to hypoxic inhalation test. Predictive equations are cheap, readily available methods of flight assessment, but this study shows poor agreement between their predictions and the measured individual hypoxic responses during HCT.

  8. Risk prediction, safety analysis and quantitative probability methods - a caveat

    International Nuclear Information System (INIS)

    Critchley, O.H.

    1976-01-01

    Views are expressed on the use of quantitative techniques for the determination of value judgements in nuclear safety assessments, hazard evaluation, and risk prediction. Caution is urged when attempts are made to quantify value judgements in the field of nuclear safety. Criteria are given the meaningful application of reliability methods but doubts are expressed about their application to safety analysis, risk prediction and design guidances for experimental or prototype plant. Doubts are also expressed about some concomitant methods of population dose evaluation. The complexities of new designs of nuclear power plants make the problem of safety assessment more difficult but some possible approaches are suggested as alternatives to the quantitative techniques criticized. (U.K.)

  9. Predictions of space radiation fatality risk for exploration missions.

    Science.gov (United States)

    Cucinotta, Francis A; To, Khiet; Cacao, Eliedonna

    2017-05-01

    In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits. Copyright © 2017 The Committee on Space Research (COSPAR

  10. Predicting epidemic risk from past temporal contact data.

    Directory of Open Access Journals (Sweden)

    Eugenio Valdano

    2015-03-01

    Full Text Available Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system's functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system's pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node's loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node's epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.

  11. Viscoplastic equations incorporated into a finite element model to predict deformation behavior of irradiated reduced activation ferritic/martensitic steel

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yuanyuan, E-mail: 630wyy@163.com [Key Laboratory of Materials Modification by Laser, Ion and Electron Beams (Ministry of Education), Dalian University of Technology, Dalian 116024 (China); Zhao, Jijun, E-mail: zhaojj@dlut.edu.cn [Key Laboratory of Materials Modification by Laser, Ion and Electron Beams (Ministry of Education), Dalian University of Technology, Dalian 116024 (China); Zhang, Chi [Key Laboratory of Advanced Materials of Ministry of Education, School of Materials Science and Engineering, Tsinghua University, Beijing 100084 (China)

    2017-05-15

    Highlights: • The initial internal variable in the Anand model is modified by considering both temperature and irradiation dose. • The tensile stress-strain response is examined and analyzed under different temperatures and irradiation doses. • Yield strengths are predicted as functions of strain rate, temperature and irradiation dose. - Abstract: The viscoplastic equations with a modified initial internal variable are implemented into the finite element code to investigate stress-strain response and irradiation hardening of the materials under increased temperature and at different levels of irradiated dose. We applied this model to Mod 9Cr-1Mo steel. The predicted results are validated by the experimentally measured data. Furthermore, they show good agreement with the previous data from a constitutive crystal plasticity model in account of dislocation and interstitial loops. Three previous hardening models for predicting the yield strength of the material are discussed and compared with our simulation results.

  12. The organization of irrational beliefs in posttraumatic stress symptomology: testing the predictions of REBT theory using structural equation modelling.

    Science.gov (United States)

    Hyland, Philip; Shevlin, Mark; Adamson, Gary; Boduszek, Daniel

    2014-01-01

    This study directly tests a central prediction of rational emotive behaviour therapy (REBT) that has received little empirical attention regarding the core and intermediate beliefs in the development of posttraumatic stress symptoms. A theoretically consistent REBT model of posttraumatic stress disorder (PTSD) was examined using structural equation modelling techniques among a sample of 313 trauma-exposed military and law enforcement personnel. The REBT model of PTSD provided a good fit of the data, χ(2) = 599.173, df = 356, p depreciation beliefs. Results were consistent with the predictions of REBT theory and provides strong empirical support that the cognitive variables described by REBT theory are critical cognitive constructs in the prediction of PTSD symptomology. © 2013 Wiley Periodicals, Inc.

  13. How to make predictions about future infectious disease risks

    Science.gov (United States)

    Woolhouse, Mark

    2011-01-01

    Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models. PMID:21624924

  14. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    2016-01-01

    evaluation. More specifically, the model mostly generates positive (negative) economic value during times of high (low) macroeconomic uncertainty. Overall, the expectations hypothesis remains a useful benchmark for investment decisions in bond markets, especially in low uncertainty states.......This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for investors. We propose a novel estimation strategy for affine term structure models that jointly fits yields and bond excess returns, thereby capturing predictive information...... otherwise hidden to standard estimations. The model predicts excess returns with high regression R2s and high forecast accuracy but cannot outperform the expectations hypothesis out-of-sample in terms of economic value, showing a general contrast between statistical and economic metrics of forecast...

  15. Predicted cancer risks induced by computed tomography examinations during childhood, by a quantitative risk assessment approach.

    Science.gov (United States)

    Journy, Neige; Ancelet, Sophie; Rehel, Jean-Luc; Mezzarobba, Myriam; Aubert, Bernard; Laurier, Dominique; Bernier, Marie-Odile

    2014-03-01

    The potential adverse effects associated with exposure to ionizing radiation from computed tomography (CT) in pediatrics must be characterized in relation to their expected clinical benefits. Additional epidemiological data are, however, still awaited for providing a lifelong overview of potential cancer risks. This paper gives predictions of potential lifetime risks of cancer incidence that would be induced by CT examinations during childhood in French routine practices in pediatrics. Organ doses were estimated from standard radiological protocols in 15 hospitals. Excess risks of leukemia, brain/central nervous system, breast and thyroid cancers were predicted from dose-response models estimated in the Japanese atomic bomb survivors' dataset and studies of medical exposures. Uncertainty in predictions was quantified using Monte Carlo simulations. This approach predicts that 100,000 skull/brain scans in 5-year-old children would result in eight (90 % uncertainty interval (UI) 1-55) brain/CNS cancers and four (90 % UI 1-14) cases of leukemia and that 100,000 chest scans would lead to 31 (90 % UI 9-101) thyroid cancers, 55 (90 % UI 20-158) breast cancers, and one (90 % UI risks without exposure). Compared to background risks, radiation-induced risks would be low for individuals throughout life, but relative risks would be highest in the first decades of life. Heterogeneity in the radiological protocols across the hospitals implies that 5-10 % of CT examinations would be related to risks 1.4-3.6 times higher than those for the median doses. Overall excess relative risks in exposed populations would be 1-10 % depending on the site of cancer and the duration of follow-up. The results emphasize the potential risks of cancer specifically from standard CT examinations in pediatrics and underline the necessity of optimization of radiological protocols.

  16. Predictive Modelling Risk Calculators and the Non Dialysis Pathway.

    Science.gov (United States)

    Robins, Jennifer; Katz, Ivor

    2013-04-16

    This guideline will review the current prediction models and survival/mortality scores available for decision making in patients with advanced kidney disease who are being considered for a non-dialysis treatment pathway. Risk prediction is gaining increasing attention with emerging literature suggesting improved patient outcomes through individualised risk prediction (1). Predictive models help inform the nephrologist and the renal palliative care specialists in their discussions with patients and families about suitability or otherwise of dialysis. Clinical decision making in the care of end stage kidney disease (ESKD) patients on a non-dialysis treatment pathway is currently governed by several observational trials (3). Despite the paucity of evidence based medicine in this field, it is becoming evident that the survival advantages associated with renal replacement therapy in these often elderly patients with multiple co-morbidities and limited functional status may be negated by loss of quality of life (7) (6), further functional decline (5, 8), increased complications and hospitalisations. This article is protected by copyright. All rights reserved.

  17. On the Use of Linearized Euler Equations in the Prediction of Jet Noise

    Science.gov (United States)

    Mankbadi, Reda R.; Hixon, R.; Shih, S.-H.; Povinelli, L. A.

    1995-01-01

    Linearized Euler equations are used to simulate supersonic jet noise generation and propagation. Special attention is given to boundary treatment. The resulting solution is stable and nearly free from boundary reflections without the need for artificial dissipation, filtering, or a sponge layer. The computed solution is in good agreement with theory and observation and is much less CPU-intensive as compared to large-eddy simulations.

  18. Equations for predicting biomass in 2- to 6-year-old Eucalyptus saligna in Hawaii

    Science.gov (United States)

    Craig D. Whitesell; Susan C. Miyasaka; Robert F. Strand; Thomas H. Schubert; Katharine E. McDuffie

    1988-01-01

    Eucalyptus saligna trees grown in short-rotation plantations on the island of Hawaii were measured, harvested, and weighed to provide data for developing regression equations using non-destructive stand measurements. Regression analysis of the data from 190 trees in the 2.0- to 3.5-year range and 96 trees in the 4- to 6-year range related stem-only...

  19. Equation of state for dense nucleonic matter from metamodeling. II. Predictions for neutron star properties

    Science.gov (United States)

    Margueron, Jérôme; Hoffmann Casali, Rudiney; Gulminelli, Francesca

    2018-02-01

    Employing recently proposed metamodeling for the nucleonic matter equation of state, we analyze neutron star global properties such as masses, radii, momentum of inertia, and others. The impact of the uncertainty on empirical parameters on these global properties is analyzed in a Bayesian statistical approach. Physical constraints, such as causality and stability, are imposed on the equation of state and different hypotheses for the direct Urca (dUrca) process are investigated. In addition, only metamodels with maximum masses above 2 M⊙ are selected. Our main results are the following: the equation of state exhibits a universal behavior against the dUrca hypothesis under the condition of charge neutrality and β equilibrium; neutron stars, if composed exclusively of nucleons and leptons, have a radius of 12.7 ±0.4 km for masses ranging from 1 up to 2 M⊙ ; a small radius lower than 11 km is very marginally compatible with our present knowledge of the nuclear empirical parameters; and finally, the most important empirical parameters which are still affected by large uncertainties and play an important role in determining the radius of neutrons stars are the slope and curvature of the symmetry energy (Lsym and Ksym) and, to a lower extent, the skewness parameters (Qsat /sym).

  20. Prediction of health risks from accidents: A comprehensive assessment methodology

    International Nuclear Information System (INIS)

    MacFarlane, D.R.; Yuan, Y.C.

    1992-01-01

    We have developed two computer programs to predict radiation risks to individuals and/or the collective population from exposures to accidental releases of radioactive materials. When used together, these two codes provide a consistent, comprehensive tool to estimate not only the risks to specific individuals but also the distribution of risks in the exposed population and the total number of individuals within a specific level of risk. Prompt and latent fatalities are estimated for the exposed population, and from these, the risk to an average individual can be derived. Uncertainty in weather conditions is considered by estimating both the ''median'' and the ''maximum'' population doses based on the frequency distribution of wind speeds and stabilities for a given site. The importance of including all dispersible particles (particles smaller than about 100 μm) for dose and health risk analyses from nonfiltered releases for receptor locations within about 10 km from a release has been investigated. The dose contribution of the large particles (> 10 μm) has been shown to be substantially greater than those from the small particles for the dose receptors in various release and exposure conditions. These conditions include, particularly, elevated releases, strong wind weather, and exposure pathways associated with ground-deposited material over extended periods of time

  1. Predicting risk behaviors: development and validation of a diagnostic scale.

    Science.gov (United States)

    Witte, K; Cameron, K A; McKeon, J K; Berkowitz, J M

    1996-01-01

    The goal of this study was to develop and validate the Risk Behavior Diagnosis (RBD) Scale for use by health care providers and practitioners interested in promoting healthy behaviors. Theoretically guided by the Extended Parallel Process Model (EPPM; a fear appeal theory), the RBD scale was designed to work in conjunction with an easy-to-use formula to determine which types of health risk messages would be most appropriate for a given individual or audience. Because some health risk messages promote behavior change and others backfire, this type of scale offers guidance to practitioners on how to develop the best persuasive message possible to motivate healthy behaviors. The results of the study demonstrate the RBD scale to have a high degree of content, construct, and predictive validity. Specific examples and practical suggestions are offered to facilitate use of the scale for health practitioners.

  2. A Numerical Comparison of Soave Redlich Kwong and Peng-Robinson Equations of State for Predicting Hydrocarbons’ Thermodynamic Properties

    Directory of Open Access Journals (Sweden)

    B. Hussain

    2018-02-01

    Full Text Available Mixture phase equilibrium and thermodynamic properties have a significant role in industry. Numerical analysis of flash calculation generates an appropriate solution for the problem. In this research, a comparison of Soave Redlich Kwong (SRK and Peng-Robinson (PR equations of state predicting the thermodynamic properties of a mixture of hydrocarbon and related compounds in a critical region at phase equilibrium is performed. By applying mathematical modeling of both equations of states, the behavior of binary gases mixtures is monitored. The numerical analysis of isothermal flash calculations is applied to study the pressure behavior with volume and mole fraction. The approach used in this research shows considerable convergence with experimental results available in the literature.

  3. Children’s Experiences of Maternal Incarceration-Specific Risks: Predictions to Psychological Maladaptation

    Science.gov (United States)

    Dallaire, Danielle H.; Zeman, Janice L.; Thrash, Todd M.

    2014-01-01

    Children of incarcerated mothers are at increased risk for social and emotional difficulties, yet few studies have investigated potential mechanisms of risk within this population. This research simultaneously examined the association of children’s experience of incarceration-specific risk factors (e.g., witness mother’s arrest) and environmental risks (e.g., low educational attainment) to children’s psychological maladaptation using a multi-informant design and a latent variable analytic approach. Participants were 117 currently incarcerated mothers (64.1% African American), their 151 children (53.6% boys, M age =9.8 years, range =6–12 years, 61.7% African American), and the 118 caregivers (74.8% female, 61.9% grandparents, 62.2% African American) of the children. Mothers, children, and caregivers each provided accounts of children’s experiences related to maternal incarceration and children’s internalizing and externalizing behavior problems. Mothers and caregivers each supplied information about 10 environmental risk factors. Findings from structural equation modeling indicate that children’s incarceration-specific risk experiences predict internalizing and externalizing behavior problems whereas the influence of environmental risks was negligible. Follow-up analyses examining the contribution of specific risks indicate that significant predictors differ by reporter and separate into effects of family incarceration history and direct experiences of maternal incarceration. Incarceration-specific experiences place children at higher risk for maladjustment than exposure to general environmental risk factors. These findings indicate the need to critically examine children’s exposure to experiences related to maternal incarceration and family incarceration history to help to clarify the multifaceted stressor of maternal incarceration. PMID:24871820

  4. Development of a single-frequency bioimpedance prediction equation for fat-free mass in an adult Indigenous Australian population.

    Science.gov (United States)

    Hughes, J T; Maple-Brown, L J; Piers, L S; Meerkin, J; O'Dea, K; Ward, L C

    2015-01-01

    To describe the development of a single-frequency bioimpedance prediction equation for fat-free mass (FFM) suitable for adult Aboriginal and Torres Strait Islander peoples with and without diabetes or indicators of chronic kidney disease (CKD). FFM was measured by whole-body dual-energy X-ray absorptiometry in 147 adult Indigenous Australians. Height, weight, body circumference and resistance were also measured. Adults with and without diabetes and indicators of CKD were examined. A random split sample with internal cross-validation approach was used to predict and subsequently validate FFM using resistance, height, weight, age and gender against measured FFM. Among 147 adults with a median body mass index of 31 kg/m(2), the final model of FFM was FFM (kg)=0.432 (height, cm(2)/resistance, ohm)-0.086 (age, years)+0.269 (weight, kg)-6.422 (if female)+16.429. Adjusted R(2) was 0.94 and the root mean square error was 3.33 kg. The concordance was high (rc=0.97) between measured and predicted FFM across a wide range of FFM (31-85 kg). In the context of the high burden of diabetes and CKD among adult Indigenous Australians, this new equation for FFM was both accurate and precise and based on easily acquired variables (height, weight, age, gender and resistance) among a heterogeneous adult cohort.

  5. A Novel Risk Scoring System Reliably Predicts Readmission Following Pancreatectomy

    Science.gov (United States)

    Valero, Vicente; Grimm, Joshua C.; Kilic, Arman; Lewis, Russell L.; Tosoian, Jeffrey J.; He, Jin; Griffin, James; Cameron, John L.; Weiss, Matthew J.; Vollmer, Charles M.; Wolfgang, Christopher L.

    2015-01-01

    Background Postoperative readmissions have been proposed by Medicare as a quality metric and may impact provider reimbursement. Since readmission following pancreatectomy is common, we sought to identify factors associated with readmission in order to establish a predictive risk scoring system (RSS). Study Design A retrospective analysis of 2,360 pancreatectomies performed at nine, high-volume pancreatic centers between 2005 and 2011 was performed. Forty-five factors strongly associated with readmission were identified. To derive and validate a RSS, the population was randomly divided into two cohorts in a 4:1 fashion. A multivariable logistic regression model was constructed and scores were assigned based on the relative odds ratio of each independent predictor. A composite Readmission After Pancreatectomy (RAP) score was generated and then stratified to create risk groups. Results Overall, 464 (19.7%) patients were readmitted within 90-days. Eight pre- and postoperative factors, including prior myocardial infarction (OR 2.03), ASA Class ≥ 3 (OR 1.34), dementia (OR 6.22), hemorrhage (OR 1.81), delayed gastric emptying (OR 1.78), surgical site infection (OR 3.31), sepsis (OR 3.10) and short length of stay (OR 1.51), were independently predictive of readmission. The 32-point RAP score generated from the derivation cohort was highly predictive of readmission in the validation cohort (AUC 0.72). The low (0-3), intermediate (4-7) and high risk (>7) groups correlated to 11.7%, 17.5% and 45.4% observed readmission rates, respectively (preadmission following pancreatectomy. Identification of patients with increased risk of readmission using the RAP score will allow efficient resource allocation aimed to attenuate readmission rates. It also has potential to serve as a new metric for comparative research and quality assessment. PMID:25797757

  6. Prediction of tension-type headache risk in adolescents

    Directory of Open Access Journals (Sweden)

    K. A. Stepanchenko

    2016-08-01

    Full Text Available Tension-type headache is the actual problem of adolescent neurology, which is associated with the prevalence of the disease, the tendency of the disease to the chronic course and a negative impact on performance in education, work capacity and quality of patients’ life. The aim. To develop a method for prediction of tension-type headache occurrence in adolescents. Materials and methods. 2342 adolescent boys and girls at the age of 13-17 years in schools of Kharkiv were examined. We used questionnaire to identify the headache. A group of adolescents with tension-type headache - 1430 people (61.1% was selected. The control group included 246 healthy adolescents. Possible risk factors for tension-type headache formation were divided into 4 groups: genetic, biomedical, psychosocial and social. Mathematical prediction of tension-type headache risk in adolescents was performed using the method of intensive indicators normalization of E.N. Shigan, which was based on probabilistic Bayesian’s method. The result was presented in the form of prognostic coefficients. Results. The most informative risk factors for tension-type headache development were the diseases, from which the teenager suffered after 1 year (sleep disorders, gastrointestinal diseases, autonomic disorders in the family history, traumatic brain injury, physical inactivity, poor adaptation of the patient in the kindergarten and school, stresses. Diagnostic scale has been developed to predict the risk of tension-type headache. It includes 23 prognostic factors with their gradation and meaning of integrated risk indicator, depending on individual factor strength influence. The risk of tension-type headache development ranged from 25,27 to 81,43 values of prognostic coefficient (low probability (25,27-43,99, the average probability (43,99-62,71 and high probability (62,71- 81,43. Conclusion. The study of tension-type headache risk factors, which were obtained by using an assessed and

  7. Modified Gompertz equation for electrotherapy murine tumor growth kinetics: predictions and new hypotheses

    International Nuclear Information System (INIS)

    Cabrales, Luis E Bergues; Mateus, Miguel A O'Farril; Brooks, Soraida C Acosta; Palencia, Fabiola Suárez; Zamora, Lisset Ortiz; Quevedo, María C Céspedes; Seringe, Sarah Edward; Cuitié, Vladimir Crombet; Cabrales, Idelisa Bergues; González, Gustavo Sierra; Nava, Juan J Godina; Aguilera, Andrés Ramírez; Joa, Javier A González; Ciria, Héctor M Camué; González, Maraelys Morales; Salas, Miriam Fariñas; Jarque, Manuel Verdecia; González, Tamara Rubio

    2010-01-01

    Electrotherapy effectiveness at different doses has been demonstrated in preclinical and clinical studies; however, several aspects that occur in the tumor growth kinetics before and after treatment have not yet been revealed. Mathematical modeling is a useful instrument that can reveal some of these aspects. The aim of this paper is to describe the complete growth kinetics of unperturbed and perturbed tumors through use of the modified Gompertz equation in order to generate useful insight into the mechanisms that underpin this devastating disease. The complete tumor growth kinetics for control and treated groups are obtained by interpolation and extrapolation methods with different time steps, using experimental data of fibrosarcoma Sa-37. In the modified Gompertz equation, a delay time is introduced to describe the tumor's natural history before treatment. Different graphical strategies are used in order to reveal new information in the complete kinetics of this tumor type. The first stage of complete tumor growth kinetics is highly non linear. The model, at this stage, shows different aspects that agree with those reported theoretically and experimentally. Tumor reversibility and the proportionality between regions before and after electrotherapy are demonstrated. In tumors that reach partial remission, two antagonistic post-treatment processes are induced, whereas in complete remission, two unknown antitumor mechanisms are induced. The modified Gompertz equation is likely to lead to insights within cancer research. Such insights hold promise for increasing our understanding of tumors as self-organizing systems and, the possible existence of phase transitions in tumor growth kinetics, which, in turn, may have significant impacts both on cancer research and on clinical practice

  8. Empirical equations for the prediction of PGA and pseudo spectral accelerations using Iranian strong-motion data

    Science.gov (United States)

    Zafarani, H.; Luzi, Lucia; Lanzano, Giovanni; Soghrat, M. R.

    2018-01-01

    A recently compiled, comprehensive, and good-quality strong-motion database of the Iranian earthquakes has been used to develop local empirical equations for the prediction of peak ground acceleration (PGA) and 5%-damped pseudo-spectral accelerations (PSA) up to 4.0 s. The equations account for style of faulting and four site classes and use the horizontal distance from the surface projection of the rupture plane as a distance measure. The model predicts the geometric mean of horizontal components and the vertical-to-horizontal ratio. A total of 1551 free-field acceleration time histories recorded at distances of up to 200 km from 200 shallow earthquakes (depth < 30 km) with moment magnitudes ranging from Mw 4.0 to 7.3 are used to perform regression analysis using the random effects algorithm of Abrahamson and Youngs (Bull Seism Soc Am 82:505-510, 1992), which considers between-events as well as within-events errors. Due to the limited data used in the development of previous Iranian ground motion prediction equations (GMPEs) and strong trade-offs between different terms of GMPEs, it is likely that the previously determined models might have less precision on their coefficients in comparison to the current study. The richer database of the current study allows improving on prior works by considering additional variables that could not previously be adequately constrained. Here, a functional form used by Boore and Atkinson (Earthquake Spect 24:99-138, 2008) and Bindi et al. (Bull Seism Soc Am 9:1899-1920, 2011) has been adopted that allows accounting for the saturation of ground motions at close distances. A regression has been also performed for the V/H in order to retrieve vertical components by scaling horizontal spectra. In order to take into account epistemic uncertainty, the new model can be used along with other appropriate GMPEs through a logic tree framework for seismic hazard assessment in Iran and Middle East region.

  9. Predictive equations for lumbar spine loads in load-dependent asymmetric one- and two-handed lifting activities.

    Science.gov (United States)

    Arjmand, N; Plamondon, A; Shirazi-Adl, A; Parnianpour, M; Larivière, C

    2012-07-01

    Asymmetric lifting activities are associated with low back pain. A finite element biomechanical model is used to estimate spinal loads during one- and two-handed asymmetric static lifting activities. Model input variables are thorax flexion angle, load magnitude as well as load sagittal and lateral positions while response variables are L4-L5 and L5-S1 disc compression and shear forces. A number of levels are considered for each input variable and all their possible combinations are introduced into the model. Robust yet user-friendly predictive equations that relate model responses to its inputs are established. Predictive equations with adequate goodness-of-fit (R(2) ranged from ~94% to 99%, P≤0.001) that relate spinal loads to task (input) variables are established. Contour plots are used to identify combinations of task variable levels that yield spine loads beyond the recommended limits. The effect of uncertainties in the measurements of asymmetry-related inputs on spinal loads is studied. A number of issues regarding the NIOSH asymmetry multiplier are discussed and it is concluded that this multiplier should depend on the trunk posture and be defined in terms of the load vertical and horizontal positions. Due to an imprecise adjustment of the handled load magnitude this multiplier inadequately controls the biomechanical loading of the spine. Ergonomists and bioengineers, faced with the dilemma of using either complex but more accurate models on one hand or less accurate but simple models on the other hand, have hereby easy-to-use predictive equations that quantify spinal loads under various occupational tasks. Copyright © 2011 Elsevier Ltd. All rights reserved.

  10. Degradation kinetics and assessment of the prediction equation of indigestible fraction of neutral detergent fiber from agroindustrial byproducts

    Directory of Open Access Journals (Sweden)

    José Gilson Louzada Regadas Filho

    2011-09-01

    Full Text Available This study aimed at estimating the kinetic parameters of ruminal degradation of neutral detergent fiber from agroindustrial byproducts of cashew (pulp and cashew nut, passion fruit, melon, pineapple, West Indian cherry, grape, annatto and coconut through the gravimetric technique of nylon bag, and to evaluate the prediction equation of indigestible fraction of neutral detergent fiber suggested by the Cornell Net Carbohydrate and Protein System. Samples of feed crushed to 2 mm were placed in 7 × 14 cm nylon bags with porosity of 50 µm in a ratio of 20 g DM/cm² and incubated in duplicate in the rumen of a heifer at 0, 3, 6, 9, 12, 16, 24, 36, 48, 72, 96 and 144 hours. The incubation residues were analyzed for NDF content and evaluated by a non-linear logistic model. The evaluation process of predicting the indigestible fraction of NDF was carried out through adjustment of linear regression models between predicted and observed values. There was a wide variation in the degradation parameters of NDF among byproducts. The degradation rate of NDF ranged from 0.0267 h-1 to 0.0971 h-1 for grape and West Indian cherry, respectively. The potentially digestible fraction of NDF ranged from 4.17 to 90.67%, respectively, for melon and coconut byproducts. The CNCPS equation was sensitive to predict the indigestible fraction of neutral detergent fiber of the byproducts. However, due to the high value of the mean squared error of prediction, such estimates are very variable; hence the most suitable would be estimation by biological methods.

  11. Predictive cytogenetic biomarkers for colorectal neoplasia in medium risk patients.

    Science.gov (United States)

    Ionescu, E M; Nicolaie, T; Ionescu, M A; Becheanu, G; Andrei, F; Diculescu, M; Ciocirlan, M

    2015-01-01

    DNA damage and chromosomal alterations in peripheral lymphocytes parallels DNA mutations in tumor tissues. The aim of our study was to predict the presence of neoplastic colorectal lesions by specific biomarkers in "medium risk" individuals (age 50 to 75, with no personal or family of any colorectal neoplasia). We designed a prospective cohort observational study including patients undergoing diagnostic or opportunistic screening colonoscopy. Specific biomarkers were analyzed for each patient in peripheral lymphocytes - presence of micronuclei (MN), nucleoplasmic bridges (NPB) and the Nuclear Division Index (NDI) by the cytokinesis-blocked micronucleus assay (CBMN). Of 98 patients included, 57 were "medium risk" individuals. MN frequency and NPB presence were not significantly different in patients with neoplastic lesions compared to controls. In "medium risk" individuals, mean NDI was significantly lower for patients with any neoplastic lesions (adenomas and adenocarcinomas, AUROC 0.668, p 00.5), for patients with advanced neoplasia (advanced adenoma and adenocarcinoma, AUROC 0.636 p 0.029) as well as for patients with adenocarcinoma (AUROC 0.650, p 0.048), for each comparison with the rest of the population. For a cut-off of 1.8, in "medium risk" individuals, an NDI inferior to that value may predict any neoplastic lesion with a sensitivity of 97.7%, an advanced neoplastic lesion with a sensitivity of 97% and adenocarcinoma with a sensitivity of 94.4%. NDI score may have a role as a colorectal cancer-screening test in "medium risk" individuals. DNA = deoxyribonucleic acid; CRC = colorectal cancer; EU = European Union; WHO = World Health Organization; FOBT = fecal occult blood test; CBMN = cytokinesis-blocked micronucleus assay; MN = micronuclei; NPB = nucleoplasmic bridges; NDI = Nuclear Division Index; FAP = familial adenomatous polyposis; HNPCC = hereditary non-polypoid colorectal cancer; IBD = inflammatory bowel diseases; ROC = receiver operating

  12. Direct Prediction of Cricondentherm and Cricondenbar Coordinates of Natural Gas Mixtures using Cubic Equation of State

    Science.gov (United States)

    Taraf, R.; Behbahani, R.; Moshfeghian, Mahmood

    2008-12-01

    A numerical algorithm is presented for direct calculation of the cricondenbar and cricondentherm coordinates of natural gas mixtures of known composition based on the Michelsen method. In the course of determination of these coordinates, the equilibrium mole fractions at these points are also calculated. In this algorithm, the property of the distance from the free energy surfaces to a tangent plane in equilibrium condition is added to saturation calculation as an additional criterion. An equation of state (EoS) was needed to calculate all required properties. Therefore, the algorithm was tested with Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), and modified Nasrifar-Moshfeghian (MNM) equations of state. For different EoSs, the impact of the binary interaction coefficient ( k ij) was studied. The impact of initial guesses for temperature and pressure was also studied. The convergence speed and the accuracy of the results of this new algorithm were compared with experimental data and the results obtained from other methods and simulation softwares such as Hysys, Aspen Plus, and EzThermo.

  13. Effect of creatinine assay calibration on glomerular filtration rate prediction by MDRD equation

    Directory of Open Access Journals (Sweden)

    Débora Spessatto

    2009-01-01

    Full Text Available Background: The evaluation of renal function should be performed with glomerular filtration rate (GFR estimation employing the Modification of Diet in Renal Disease (MDRD study equation, which includes age, gender, ethnicity and serum creatinine. However, creatinine methods require traceability with standardized methods. Objective: To analyse the impact of creatinine calibration on MDRD calculated GFR. Methods: 140 samples of plasma with creatinine values <2,0 mg/dl were analysed by Jaffé’s reaction with Creatinina Modular P (Roche ®; method A; reference and Creatinina Advia 1650 (Bayer ®; method B; non-standardized. The results with the different methods were compared and aligned with standardized method through a conversion formula. MDRD GFR was estimated. Results: Values were higher for method B (1.03 ± 0.29 vs. 0.86 ± 0.32 mg/dl, P<0.001. This difference declined when methods were aligned with the equation y=1.07x -0.249, and the aligned values were 0,9 ± 0,31 mg/dl. Non-traceable creatinine methods misclassificaed chronic kidney disease in 10% more (false positive. This disagreement disappeared after the regression alignment. Conclusion: Creatinine method calibration has a large impact over the final results of serum creatinine and GFR. The alignment of the non-standardized results through conversion formulas is a reasonable alternative to harmonize serum creatinine results while waiting for the full implementation of international  standardization programs.

  14. Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis. Pc Behavior Prediction Models

    Science.gov (United States)

    Vallejo, J.J.; Hejduk, M.D.; Stamey, J. D.

    2015-01-01

    Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

  15. Threat and error management for anesthesiologists: a predictive risk taxonomy

    Science.gov (United States)

    Ruskin, Keith J.; Stiegler, Marjorie P.; Park, Kellie; Guffey, Patrick; Kurup, Viji; Chidester, Thomas

    2015-01-01

    Purpose of review Patient care in the operating room is a dynamic interaction that requires cooperation among team members and reliance upon sophisticated technology. Most human factors research in medicine has been focused on analyzing errors and implementing system-wide changes to prevent them from recurring. We describe a set of techniques that has been used successfully by the aviation industry to analyze errors and adverse events and explain how these techniques can be applied to patient care. Recent findings Threat and error management (TEM) describes adverse events in terms of risks or challenges that are present in an operational environment (threats) and the actions of specific personnel that potentiate or exacerbate those threats (errors). TEM is a technique widely used in aviation, and can be adapted for the use in a medical setting to predict high-risk situations and prevent errors in the perioperative period. A threat taxonomy is a novel way of classifying and predicting the hazards that can occur in the operating room. TEM can be used to identify error-producing situations, analyze adverse events, and design training scenarios. Summary TEM offers a multifaceted strategy for identifying hazards, reducing errors, and training physicians. A threat taxonomy may improve analysis of critical events with subsequent development of specific interventions, and may also serve as a framework for training programs in risk mitigation. PMID:24113268

  16. Empirical models based on the universal soil loss equation fail to predict sediment discharges from Chesapeake Bay catchments.

    Science.gov (United States)

    Boomer, Kathleen B; Weller, Donald E; Jordan, Thomas E

    2008-01-01

    The Universal Soil Loss Equation (USLE) and its derivatives are widely used for identifying watersheds with a high potential for degrading stream water quality. We compared sediment yields estimated from regional application of the USLE, the automated revised RUSLE2, and five sediment delivery ratio algorithms to measured annual average sediment delivery in 78 catchments of the Chesapeake Bay watershed. We did the same comparisons for another 23 catchments monitored by the USGS. Predictions exceeded observed sediment yields by more than 100% and were highly correlated with USLE erosion predictions (Pearson r range, 0.73-0.92; p USLE estimates (r = 0.87; p USLE model did not change the results. In ranked comparisons between observed and predicted sediment yields, the models failed to identify catchments with higher yields (r range, -0.28-0.00; p > 0.14). In a multiple regression analysis, soil erodibility, log (stream flow), basin shape (topographic relief ratio), the square-root transformed proportion of forest, and occurrence in the Appalachian Plateau province explained 55% of the observed variance in measured suspended sediment loads, but the model performed poorly (r(2) = 0.06) at predicting loads in the 23 USGS watersheds not used in fitting the model. The use of USLE or multiple regression models to predict sediment yields is not advisable despite their present widespread application. Integrated watershed models based on the USLE may also be unsuitable for making management decisions.

  17. Age-Predicted Maximal Heart Rate in Recreational Marathon Runners: A Cross-Sectional Study on Fox's and Tanaka's Equations

    Science.gov (United States)

    Nikolaidis, Pantelis T.; Rosemann, Thomas; Knechtle, Beat

    2018-01-01

    Age-based prediction equations of maximal heart rate (HRmax), such as the popular formulas Fox's 220-age, or Tanaka's 208-0.7 × age, have been widely used in various populations. Surprisingly, so far these equations have not been validated in marathon runners, despite the importance of the role of HRmax for training purposes in endurance running. The aim of the present study was to examine the validity of Fox and Tanaka equations in a large sample of women and men recreational marathon runners. Participants (n = 180, age 43.2 ± 8.5 years, VO2max 46.8 mL/min/kg, finishers in at least one marathon during the last year) performed a graded exercise test on a treadmill, where HRmax was measured. Measured HRmax correlated largely with age in the total sample (r = −0.50, p marathon runners. In addition, exercise physiologists and sport scientists should consider the observed differences among various assessment methods when performing exercise testing or prescribing training program relying on HR. PMID:29599724

  18. Age-Predicted Maximal Heart Rate in Recreational Marathon Runners: A Cross-Sectional Study on Fox's and Tanaka's Equations.

    Science.gov (United States)

    Nikolaidis, Pantelis T; Rosemann, Thomas; Knechtle, Beat

    2018-01-01

    Age-based prediction equations of maximal heart rate (HR max ), such as the popular formulas Fox's 220-age, or Tanaka's 208-0.7 × age, have been widely used in various populations. Surprisingly, so far these equations have not been validated in marathon runners, despite the importance of the role of HR max for training purposes in endurance running. The aim of the present study was to examine the validity of Fox and Tanaka equations in a large sample of women and men recreational marathon runners. Participants ( n = 180, age 43.2 ± 8.5 years, VO 2max 46.8 mL/min/kg, finishers in at least one marathon during the last year) performed a graded exercise test on a treadmill, where HR max was measured. Measured HR max correlated largely with age in the total sample ( r = -0.50, p marathon runners. In addition, exercise physiologists and sport scientists should consider the observed differences among various assessment methods when performing exercise testing or prescribing training program relying on HR.

  19. NGA-West 2 Equations for predicting PGA, PGV, and 5%-Damped PSA for shallow crustal earthquakes

    Science.gov (United States)

    Boore, David M.; Stewart, Jon P.; Seyhan, Emel; Atkinson, Gail M.

    2013-01-01

    We provide ground-motion prediction equations for computing medians and standard deviations of average horizontal component intensity measures (IMs) for shallow crustal earthquakes in active tectonic regions. The equations were derived from a global database with M 3.0–7.9 events. We derived equations for the primary M- and distance-dependence of the IMs after fixing the VS30-based nonlinear site term from a parallel NGA-West 2 study. We then evaluated additional effects using mixed effects residuals analysis, which revealed no trends with source depth over the M range of interest, indistinct Class 1 and 2 event IMs, and basin depth effects that increase and decrease long-period IMs for depths larger and smaller, respectively, than means from regional VS30-depth relations. Our aleatory variability model captures decreasing between-event variability with M, as well as within-event variability that increases or decreases with M depending on period, increases with distance, and decreases for soft sites.

  20. NGA-West2 equations for predicting vertical-component PGA, PGV, and 5%-damped PSA from shallow crustal earthquakes

    Science.gov (United States)

    Stewart, Jonathan P.; Boore, David M.; Seyhan, Emel; Atkinson, Gail M.

    2016-01-01

    We present ground motion prediction equations (GMPEs) for computing natural log means and standard deviations of vertical-component intensity measures (IMs) for shallow crustal earthquakes in active tectonic regions. The equations were derived from a global database with M 3.0–7.9 events. The functions are similar to those for our horizontal GMPEs. We derive equations for the primary M- and distance-dependence of peak acceleration, peak velocity, and 5%-damped pseudo-spectral accelerations at oscillator periods between 0.01–10 s. We observe pronounced M-dependent geometric spreading and region-dependent anelastic attenuation for high-frequency IMs. We do not observe significant region-dependence in site amplification. Aleatory uncertainty is found to decrease with increasing magnitude; within-event variability is independent of distance. Compared to our horizontal-component GMPEs, attenuation rates are broadly comparable (somewhat slower geometric spreading, faster apparent anelastic attenuation), VS30-scaling is reduced, nonlinear site response is much weaker, within-event variability is comparable, and between-event variability is greater.

  1. Predicting elastic properties of porous fluid-filled rocks by inverting the BGG equation: Applications to seismic and borehole data

    International Nuclear Information System (INIS)

    Benson, A.K.; Wu, J.

    2000-01-01

    Two of the needed elastic parameters for predicting velocities in porous, fluid-filled rocks, the bulk modulus of the empty, porous rock and the shear modulus of the rock, are very difficult to obtain in situ. A novel modeling approach is developed by inverting the Biot-Geertsma-Gassmann (BGG) and shear-wave equations to generate values for the bulk and shear moduli, respectively, by using available velocity and porosity data obtained from borehole logs and/or cores from water/brine-saturated rocks. These values of bulk and shear moduli, along with reasonable in-situ estimates of rock-matrix and fluid parameters generated from the Batzle-Wang formulation, are then used to predict compressional and shear-wave velocities, compressional-shear wave ratios, and reflection coefficients at the interfaces between host rocks and fluid-saturated rocks, either fully or partially saturated with hydrocarbons or water, as a function of depth and/or porosity

  2. Adolescent expectations of early death predict adult risk behaviors.

    Directory of Open Access Journals (Sweden)

    Quynh C Nguyen

    Full Text Available Only a handful of public health studies have investigated expectations of early death among adolescents. Associations have been found between these expectations and risk behaviors in adolescence. However, these beliefs may not only predict worse adolescent outcomes, but worse trajectories in health with ties to negative outcomes that endure into young adulthood. The objectives of this study were to investigate perceived chances of living to age 35 (Perceived Survival Expectations, PSE as a predictor of suicidal ideation, suicide attempt and substance use in young adulthood. We examined the predictive capacity of PSE on future suicidal ideation/attempt after accounting for sociodemographics, depressive symptoms, and history of suicide among family and friends to more fully assess its unique contribution to suicide risk. We investigated the influence of PSE on legal and illegal substance use and varying levels of substance use. We utilized the National Longitudinal Study of Adolescent Health (Add Health initiated in 1994-95 among 20,745 adolescents in grades 7-12 with follow-up interviews in 1996 (Wave II, 2001-02 (Wave III and 2008 (Wave IV; ages 24-32. Compared to those who were almost certain of living to age 35, perceiving a 50-50 or less chance of living to age 35 at Waves I or III predicted suicide attempt and ideation as well as regular substance use (i.e., exceeding daily limits for moderate drinking; smoking ≥ a pack/day; and using illicit substances other than marijuana at least weekly at Wave IV. Associations between PSE and detrimental adult outcomes were particularly strong for those reporting persistently low PSE at both Waves I and III. Low PSE at Wave I or Wave III was also related to a doubling and tripling, respectively, of death rates in young adulthood. Long-term and wide-ranging ties between PSE and detrimental outcomes suggest these expectations may contribute to identifying at-risk youth.

  3. Predicting cognitive function of the Malaysian elderly: a structural equation modelling approach.

    Science.gov (United States)

    Foong, Hui Foh; Hamid, Tengku Aizan; Ibrahim, Rahimah; Haron, Sharifah Azizah; Shahar, Suzana

    2018-01-01

    The aim of this study was to identify the predictors of elderly's cognitive function based on biopsychosocial and cognitive reserve perspectives. The study included 2322 community-dwelling elderly in Malaysia, randomly selected through a multi-stage proportional cluster random sampling from Peninsular Malaysia. The elderly were surveyed on socio-demographic information, biomarkers, psychosocial status, disability, and cognitive function. A biopsychosocial model of cognitive function was developed to test variables' predictive power on cognitive function. Statistical analyses were performed using SPSS (version 15.0) in conjunction with Analysis of Moment Structures Graphics (AMOS 7.0). The estimated theoretical model fitted the data well. Psychosocial stress and metabolic syndrome (MetS) negatively predicted cognitive function and psychosocial stress appeared as a main predictor. Socio-demographic characteristics, except gender, also had significant effects on cognitive function. However, disability failed to predict cognitive function. Several factors together may predict cognitive function in the Malaysian elderly population, and the variance accounted for it is large enough to be considered substantial. Key factor associated with the elderly's cognitive function seems to be psychosocial well-being. Thus, psychosocial well-being should be included in the elderly assessment, apart from medical conditions, both in clinical and community setting.

  4. Investigating the Predictive Role of Authenticity on Subjective Vitality with Structural Equation Modelling

    Science.gov (United States)

    Akin, Umran; Akin, Ahmet

    2014-01-01

    Authenticity is a basic personality characteristic that has an important influence on both the psychological and social lives of individuals. Subjective vitality also assumes a facilitative role regarding positive mental health indicators. Therefore, the purpose of this study is to investigate the predictive role of authenticity on subjective…

  5. The "polyenviromic risk score": Aggregating environmental risk factors predicts conversion to psychosis in familial high-risk subjects.

    Science.gov (United States)

    Padmanabhan, Jaya L; Shah, Jai L; Tandon, Neeraj; Keshavan, Matcheri S

    2017-03-01

    Young relatives of individuals with schizophrenia (i.e. youth at familial high-risk, FHR) are at increased risk of developing psychotic disorders, and show higher rates of psychiatric symptoms, cognitive and neurobiological abnormalities than non-relatives. It is not known whether overall exposure to environmental risk factors increases risk of conversion to psychosis in FHR subjects. Subjects consisted of a pilot longitudinal sample of 83 young FHR subjects. As a proof of principle, we examined whether an aggregate score of exposure to environmental risk factors, which we term a 'polyenviromic risk score' (PERS), could predict conversion to psychosis. The PERS combines known environmental risk factors including cannabis use, urbanicity, season of birth, paternal age, obstetric and perinatal complications, and various types of childhood adversity, each weighted by its odds ratio for association with psychosis in the literature. A higher PERS was significantly associated with conversion to psychosis in young, familial high-risk subjects (OR=1.97, p=0.009). A model combining the PERS and clinical predictors had a sensitivity of 27% and specificity of 96%. An aggregate index of environmental risk may help predict conversion to psychosis in FHR subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Melanoma risk prediction using a multilocus genetic risk score in the Women's Health Initiative cohort.

    Science.gov (United States)

    Cho, Hyunje G; Ransohoff, Katherine J; Yang, Lingyao; Hedlin, Haley; Assimes, Themistocles; Han, Jiali; Stefanick, Marcia; Tang, Jean Y; Sarin, Kavita Y

    2018-07-01

    Single-nucleotide polymorphisms (SNPs) associated with melanoma have been identified though genome-wide association studies. However, the combined impact of these SNPs on melanoma development remains unclear, particularly in postmenopausal women who carry a lower melanoma risk. We examine the contribution of a combined polygenic risk score on melanoma development in postmenopausal women. Genetic risk scores were calculated using 21 genome-wide association study-significant SNPs. Their combined effect on melanoma development was evaluated in 19,102 postmenopausal white women in the clinical trial and observational study arms of the Women's Health Initiative dataset. Compared to the tertile of weighted genetic risk score with the lowest genetic risk, the women in the tertile with the highest genetic risk were 1.9 times more likely to develop melanoma (95% confidence interval 1.50-2.42). The incremental change in c-index from adding genetic risk scores to age were 0.075 (95% confidence interval 0.041-0.109) for incident melanoma. Limitations include a lack of information on nevi count, Fitzpatrick skin type, family history of melanoma, and potential reporting and selection bias in the Women's Health Initiative cohort. Higher genetic risk is associated with increased melanoma prevalence and incidence in postmenopausal women, but current genetic information may have a limited role in risk prediction when phenotypic information is available. Copyright © 2018 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

  7. Competition, Innovation, Risk-Taking, and Profitability in the Chinese Banking Sector: An Empirical Analysis Based on Structural Equation Modeling

    Directory of Open Access Journals (Sweden)

    Ti Hu

    2016-01-01

    Full Text Available We introduce a new perspective to systematically investigate the cause-and-effect relationships among competition, innovation, risk-taking, and profitability in the Chinese banking industry. Our hypotheses are tested by the structural equation modeling (SEM, and the empirical results show that (i risk-taking is positively related to profitability; (ii innovation positively affects both risk-taking and profitability, and the effect of innovation on profitability works both directly and indirectly; (iii competition negatively affects risk-taking but positively affects both innovation and profitability, and the effects of competition on risk-taking and profitability work both directly and indirectly; (iv there is a cascading relationship among market competition and bank innovation, risk-taking, and profitability.

  8. Elastic band prediction equations for combined free-weight and elastic band bench presses and squats.

    Science.gov (United States)

    Shoepe, Todd C; Ramirez, David A; Almstedt, Hawley C

    2010-01-01

    Elastic bands added to traditional free-weight techniques have become a part of suggested training routines in recent years. Because of the variable loading patterns of elastic bands (i.e., greater stretch produces greater resistance), it is necessary to quantify the exact loading patterns of bands to identify the volume and intensity of training. The purpose of this study was to determine the length vs. tension properties of multiple sizes of a set of commonly used elastic bands to quantify the resistance that would be applied to free-weight plus elastic bench presses (BP) and squats (SQ). Five elastic bands of varying thickness were affixed to an overhead support beam. Dumbbells of varying weights were progressively added to the free end while the linear deformation was recorded with each subsequent weight increment. The resistance was plotted as a factor of linear deformation, and best-fit nonlinear logarithmic regression equations were then matched to the data. For both the BP and SQ loading conditions and all band thicknesses tested, R values were greater than 0.9623. These data suggest that differences in load exist as a result of the thickness of the elastic band, attachment technique, and type of exercise being performed. Facilities should adopt their own form of loading quantification to match their unique set of circumstances when acquiring, researching, and implementing elastic band and free-weight exercises into the training programs.

  9. The Dirac equation in electronic structure calculations: Accurate evaluation of DFT predictions for actinides

    International Nuclear Information System (INIS)

    Wills, John M.; Mattsson, Ann E.

    2012-01-01

    Brooks, Johansson, and Skriver, using the LMTO-ASA method and considerable insight, were able to explain many of the ground state properties of the actinides. In the many years since this work was done, electronic structure calculations of increasing sophistication have been applied to actinide elements and compounds, attempting to quantify the applicability of DFT to actinides and actinide compounds and to try to incorporate other methodologies (i.e. DMFT) into DFT calculations. Through these calculations, the limits of both available density functionals and ad hoc methodologies are starting to become clear. However, it has also become clear that approximations used to incorporate relativity are not adequate to provide rigorous tests of the underlying equations of DFT, not to mention ad hoc additions. In this talk, we describe the result of full-potential LMTO calculations for the elemental actinides, comparing results obtained with a full Dirac basis with those obtained from scalar-relativistic bases, with and without variational spin-orbit. This comparison shows that the scalar relativistic treatment of actinides does not have sufficient accuracy to provide a rigorous test of theory and that variational spin-orbit introduces uncontrolled errors in the results of electronic structure calculations on actinide elements.

  10. An Integrated and Interdisciplinary Model for Predicting the Risk of Injury and Death in Future Earthquakes.

    Science.gov (United States)

    Shapira, Stav; Novack, Lena; Bar-Dayan, Yaron; Aharonson-Daniel, Limor

    2016-01-01

    A comprehensive technique for earthquake-related casualty estimation remains an unmet challenge. This study aims to integrate risk factors related to characteristics of the exposed population and to the built environment in order to improve communities' preparedness and response capabilities and to mitigate future consequences. An innovative model was formulated based on a widely used loss estimation model (HAZUS) by integrating four human-related risk factors (age, gender, physical disability and socioeconomic status) that were identified through a systematic review and meta-analysis of epidemiological data. The common effect measures of these factors were calculated and entered to the existing model's algorithm using logistic regression equations. Sensitivity analysis was performed by conducting a casualty estimation simulation in a high-vulnerability risk area in Israel. the integrated model outcomes indicated an increase in the total number of casualties compared with the prediction of the traditional model; with regard to specific injury levels an increase was demonstrated in the number of expected fatalities and in the severely and moderately injured, and a decrease was noted in the lightly injured. Urban areas with higher populations at risk rates were found more vulnerable in this regard. The proposed model offers a novel approach that allows quantification of the combined impact of human-related and structural factors on the results of earthquake casualty modelling. Investing efforts in reducing human vulnerability and increasing resilience prior to an occurrence of an earthquake could lead to a possible decrease in the expected number of casualties.

  11. Fat-free mass prediction equations for bioelectric impedance analysis compared to dual energy X-ray absorptiometry in obese adolescents: a validation study.

    Science.gov (United States)

    Hofsteenge, Geesje H; Chinapaw, Mai J M; Weijs, Peter J M

    2015-10-15

    In clinical practice, patient friendly methods to assess body composition in obese adolescents are needed. Therefore, the bioelectrical impedance analysis (BIA) related fat-free mass (FFM) prediction equations (FFM-BIA) were evaluated in obese adolescents (age 11-18 years) compared to FFM measured by dual-energy x-ray absorptiometry (FFM-DXA) and a new population specific FFM-BIA equation is developed. After an overnight fast, the subjects attended the outpatient clinic. After measuring height and weight, a full body scan by dual-energy x-ray absorptiometry (DXA) and a BIA measurement was performed. Thirteen predictive FFM-BIA equations based on weight, height, age, resistance, reactance and/or impedance were systematically selected and compared to FFM-DXA. Accuracy of FFM-BIA equations was evaluated by the percentage adolescents predicted within 5% of FFM-DXA measured, the mean percentage difference between predicted and measured values (bias) and the Root Mean Squared prediction Error (RMSE). Multiple linear regression was conducted to develop a new BIA equation. Validation was based on 103 adolescents (60% girls), age 14.5 (sd1.7) years, weight 94.1 (sd15.6) kg and FFM-DXA of 56.1 (sd9.8) kg. The percentage accurate estimations varied between equations from 0 to 68%; bias ranged from -29.3 to +36.3% and RMSE ranged from 2.8 to 12.4 kg. An alternative prediction equation was developed: FFM = 0.527 * H(cm)(2)/Imp + 0.306 * weight - 1.862 (R(2) = 0.92, SEE = 2.85 kg). Percentage accurate prediction was 76%. Compared to DXA, the Gray equation underestimated the FFM with 0.4 kg (55.7 ± 8.3), had an RMSE of 3.2 kg, 63% accurate prediction and the smallest bias of (-0.1%). When split by sex, the Gray equation had the narrowest range in accurate predictions, bias, and RMSE. For the assessment of FFM with BIA, the Gray-FFM equation appears to be the most accurate, but 63% is still not at an acceptable accuracy level for obese adolescents. The new equation appears to

  12. Alternative Testing Methods for Predicting Health Risk from Environmental Exposures

    Directory of Open Access Journals (Sweden)

    Annamaria Colacci

    2014-08-01

    Full Text Available Alternative methods to animal testing are considered as promising tools to support the prediction of toxicological risks from environmental exposure. Among the alternative testing methods, the cell transformation assay (CTA appears to be one of the most appropriate approaches to predict the carcinogenic properties of single chemicals, complex mixtures and environmental pollutants. The BALB/c 3T3 CTA shows a good degree of concordance with the in vivo rodent carcinogenesis tests. Whole-genome transcriptomic profiling is performed to identify genes that are transcriptionally regulated by different kinds of exposures. Its use in cell models representative of target organs may help in understanding the mode of action and predicting the risk for human health. Aiming at associating the environmental exposure to health-adverse outcomes, we used an integrated approach including the 3T3 CTA and transcriptomics on target cells, in order to evaluate the effects of airborne particulate matter (PM on toxicological complex endpoints. Organic extracts obtained from PM2.5 and PM1 samples were evaluated in the 3T3 CTA in order to identify effects possibly associated with different aerodynamic diameters or airborne chemical components. The effects of the PM2.5 extracts on human health were assessed by using whole-genome 44 K oligo-microarray slides. Statistical analysis by GeneSpring GX identified genes whose expression was modulated in response to the cell treatment. Then, modulated genes were associated with pathways, biological processes and diseases through an extensive biological analysis. Data derived from in vitro methods and omics techniques could be valuable for monitoring the exposure to toxicants, understanding the modes of action via exposure-associated gene expression patterns and to highlight the role of genes in key events related to adversity.

  13. Mapping Erosion Risk in California's Rangelands Using the Universal Soil Loss Equation (USLE)

    Science.gov (United States)

    Salls, W. B.; O'Geen, T. T.

    2015-12-01

    Soil loss constitutes a multi-faceted problem for agriculture: in addition to reducing soil fertility and crop yield, it compromises downstream water quality. Sediment itself is a major issue for aquatic ecosystems, but also serves as a vector for transporting nutrients, pesticides, and pathogens. Rangelands are thought to be a contributor to water quality degradation in California, particularly in the northern Coast Range. Though total maximum daily loads (TMDLs) have been imposed in some watersheds, and countless rangeland water quality outreach activities have been conducted, the connection between grazing intensity recommendations and changes in water quality is poorly understood at the state level. This disconnect gives rise to poorly informed regulations and discourages adoption of best management practices by ranchers. By applying the Universal Soil Loss Equation (USLE) at a statewide scale, we highlighted areas most prone to erosion. We also investigated how two different grazing intensity scenarios affect modeled soil loss. Geospatial data layers representing the USLE parameters—rainfall erosivity, soil erodibility, slope length and steepness, and cover—were overlaid to model annual soil loss. Monitored suspended sediment data from a small North Coast watershed with grazing as the predominant land use was used to validate the model. Modeled soil loss values were nearly one order of magnitude higher than monitored values; average soil loss feeding the downstream-most site was modeled at 0.329 t ha-1 yr-1, whereas storm-derived sediment passing the site over two years was calculated to be 0.037 t ha-1 yr-1. This discrepancy may stem from the fact that the USLE models detached sediment, whereas stream monitoring reflects sediment detached and subsequently transported to the waterway. Preliminary findings from the statewide map support the concern that the North Coast is particularly at risk given its combination of intense rain, erodible soils, and

  14. Value of multiple risk factors in predicting coronary artery disease

    International Nuclear Information System (INIS)

    Zhu Zhengbin; Zhang Ruiyan; Zhang Qi; Yang Zhenkun; Hu Jian; Zhang Jiansheng; Shen Weifeng

    2008-01-01

    Objective: This study sought to assess the relationship between correlative comprehension risk factors and coronary arterial disease and to build up a simple mathematical model to evaluate the extension of coronary artery lesion in patients with stable angina. Methods: A total of 1024 patients with chest pain who underwent coronary angiography were divided into CAD group(n=625)and control group(n=399) based on at least one significant coronary artery narrowing more than 50% in diameter. Independent risk factors for CAD were evaluated and multivariate logistic regression model and receiver-operating characteristic(ROC) curves were used to estimate the independent influence factor for CAD and built up a simple formula for clinical use. Results: Multivariate regression analysis revealed that UACR > 7.25 μg/mg(OR=3.6; 95% CI 2.6-4.9; P 20 mmol/L(OR=3.2; 95% CI 2.3-4.4; P 2 (OR=2.3; 95% CI 1.4-3.8; P 2.6 mmol/L (OR 2.141; 95% CI 1.586-2.890; P 7.25 μg/mg + 1.158 x hsCRP > 20 mmol/L + 0.891 GFR 2 + 0.831 x LVEF 2.6 mmol/L + 0.676 x smoking history + 0.594 x male + 0.459 x diabetes + 0.425 x hypertension). Area under the curve was 0.811 (P < 0.01), and the optimal probability value for predicting severe stage of CAD was 0.977 (sensitivity 49.0%, specificity 92.7% ). Conclusions: Risk factors including renal insufficiency were the main predictors for CAD. The logistic regression model is the non-invasive method of choice for predicting the extension of coronary artery lesion in patients with stable agiana. (authors)

  15. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer

    NARCIS (Netherlands)

    Petersen, Japke F.; Stuiver, Martijn M.; Timmermans, Adriana J.; Chen, Amy; Zhang, Hongzhen; O'Neill, James P.; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T.; Koch, Wayne; van den Brekel, Michiel W. M.

    2017-01-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442

  16. Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks.

    Science.gov (United States)

    Coiera, Enrico; Wang, Ying; Magrabi, Farah; Concha, Oscar Perez; Gallego, Blanca; Runciman, William

    2014-05-21

    Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models. Patients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria. There was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known 'weekend-effect' where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94). Risks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual's hospitalization. Combining disease specific models with such time varying- estimates appears to

  17. Linking spring phenology with mechanistic models of host movement to predict disease transmission risk

    Science.gov (United States)

    Merkle, Jerod A.; Cross, Paul C.; Scurlock, Brandon M.; Cole, Eric K.; Courtemanch, Alyson B.; Dewey, Sarah R.; Kauffman, Matthew J.

    2018-01-01

    Disease models typically focus on temporal dynamics of infection, while often neglecting environmental processes that determine host movement. In many systems, however, temporal disease dynamics may be slow compared to the scale at which environmental conditions alter host space-use and accelerate disease transmission.Using a mechanistic movement modelling approach, we made space-use predictions of a mobile host (elk [Cervus Canadensis] carrying the bacterial disease brucellosis) under environmental conditions that change daily and annually (e.g., plant phenology, snow depth), and we used these predictions to infer how spring phenology influences the risk of brucellosis transmission from elk (through aborted foetuses) to livestock in the Greater Yellowstone Ecosystem.Using data from 288 female elk monitored with GPS collars, we fit step selection functions (SSFs) during the spring abortion season and then implemented a master equation approach to translate SSFs into predictions of daily elk distribution for five plausible winter weather scenarios (from a heavy snow, to an extreme winter drought year). We predicted abortion events by combining elk distributions with empirical estimates of daily abortion rates, spatially varying elk seroprevelance and elk population counts.Our results reveal strong spatial variation in disease transmission risk at daily and annual scales that is strongly governed by variation in host movement in response to spring phenology. For example, in comparison with an average snow year, years with early snowmelt are predicted to have 64% of the abortions occurring on feedgrounds shift to occurring on mainly public lands, and to a lesser extent on private lands.Synthesis and applications. Linking mechanistic models of host movement with disease dynamics leads to a novel bridge between movement and disease ecology. Our analysis framework offers new avenues for predicting disease spread, while providing managers tools to proactively mitigate

  18. Violence risk prediction. Clinical and actuarial measures and the role of the Psychopathy Checklist.

    Science.gov (United States)

    Dolan, M; Doyle, M

    2000-10-01

    Violence risk prediction is a priority issue for clinicians working with mentally disordered offenders. To review the current status of violence risk prediction research. Literature search (Medline). Key words: violence, risk prediction, mental disorder. Systematic/structured risk assessment approaches may enhance the accuracy of clinical prediction of violent outcomes. Data on the predictive validity of available clinical risk assessment tools are based largely on American and North American studies and further validation is required in British samples. The Psychopathy Checklist appears to be a key predictor of violent recidivism in a variety of settings. Violence risk prediction is an inexact science and as such will continue to provoke debate. Clinicians clearly need to be able to demonstrate the rationale behind their decisions on violence risk and much can be learned from recent developments in research on violence risk prediction.

  19. Fast and General Method To Predict the Physicochemical Properties of Druglike Molecules Using the Integral Equation Theory of Molecular Liquids.

    Science.gov (United States)

    Palmer, David S; Mišin, Maksim; Fedorov, Maxim V; Llinas, Antonio

    2015-09-08

    We report a method to predict physicochemical properties of druglike molecules using a classical statistical mechanics based solvent model combined with machine learning. The RISM-MOL-INF method introduced here provides an accurate technique to characterize solvation and desolvation processes based on solute-solvent correlation functions computed by the 1D reference interaction site model of the integral equation theory of molecular liquids. These functions can be obtained in a matter of minutes for most small organic and druglike molecules using existing software (RISM-MOL) (Sergiievskyi, V. P.; Hackbusch, W.; Fedorov, M. V. J. Comput. Chem. 2011, 32, 1982-1992). Predictions of caco-2 cell permeability and hydration free energy obtained using the RISM-MOL-INF method are shown to be more accurate than the state-of-the-art tools for benchmark data sets. Due to the importance of solvation and desolvation effects in biological systems, it is anticipated that the RISM-MOL-INF approach will find many applications in biophysical and biomedical property prediction.

  20. Young Children’s Risk-Taking: Mothers’ Authoritarian Parenting Predicts Risk-Taking by Daughters but Not Sons

    OpenAIRE

    Wood, Erin E.; Kennison, Shelia M.

    2017-01-01

    We investigated how mothers’ parenting behaviors and personal characteristics were related to risk-taking by young children. We tested contrasting predictions from evolutionary and social role theories with the former predicting higher risk-taking by boys compared to girls and the latter predicting that mothers would influence children’s gender role development with risk-taking occurring more in children parented with higher levels of harshness (i.e., authoritarian parenting style). In our st...

  1. Validation of the Spanish equation to predict the lean meat percentage of pig carcasses with the Fat-O-Meat'er

    NARCIS (Netherlands)

    Font I Furnols, M.; Engel, B.; Gispert, M.

    2004-01-01

    In Spain the lean percentage of pig carcasses is predicted objectively with the Fat-O-Meat'er. Changes in the pig population can affect the accuracy of a prediction formula. The aim of this study was to see whether the present Spanish equation for the Fat-O-Meat'er, that was established after a

  2. The Human Bathtub: Safety and Risk Predictions Including the Dynamic Probability of Operator Errors

    International Nuclear Information System (INIS)

    Duffey, Romney B.; Saull, John W.

    2006-01-01

    Reactor safety and risk are dominated by the potential and major contribution for human error in the design, operation, control, management, regulation and maintenance of the plant, and hence to all accidents. Given the possibility of accidents and errors, now we need to determine the outcome (error) probability, or the chance of failure. Conventionally, reliability engineering is associated with the failure rate of components, or systems, or mechanisms, not of human beings in and interacting with a technological system. The probability of failure requires a prior knowledge of the total number of outcomes, which for any predictive purposes we do not know or have. Analysis of failure rates due to human error and the rate of learning allow a new determination of the dynamic human error rate in technological systems, consistent with and derived from the available world data. The basis for the analysis is the 'learning hypothesis' that humans learn from experience, and consequently the accumulated experience defines the failure rate. A new 'best' equation has been derived for the human error, outcome or failure rate, which allows for calculation and prediction of the probability of human error. We also provide comparisons to the empirical Weibull parameter fitting used in and by conventional reliability engineering and probabilistic safety analysis methods. These new analyses show that arbitrary Weibull fitting parameters and typical empirical hazard function techniques cannot be used to predict the dynamics of human errors and outcomes in the presence of learning. Comparisons of these new insights show agreement with human error data from the world's commercial airlines, the two shuttle failures, and from nuclear plant operator actions and transient control behavior observed in transients in both plants and simulators. The results demonstrate that the human error probability (HEP) is dynamic, and that it may be predicted using the learning hypothesis and the minimum

  3. Comparison of Ground Motion Prediction Equations (GMPE) for Chile and Canada With Recent Chilean Megathust Earthquakes

    Science.gov (United States)

    Herrera, C.; Cassidy, J. F.; Dosso, S. E.

    2017-12-01

    The ground shaking assessment allows quantifying the hazards associated with the occurrence of earthquakes. Chile and western Canada are two areas that have experienced, and are susceptible to imminent large crustal, in-slab and megathrust earthquakes that can affect the population significantly. In this context, we compare the current GMPEs used in the 2015 National Building Code of Canada and the most recent GMPEs calculated for Chile, with observed accelerations generated by four recent Chilean megathrust earthquakes (MW ≥ 7.7) that have occurred during the past decade, which is essential to quantify how well current models predict observations of major events.We collected the 3-component waveform data of more than 90 stations from the Centro Sismologico Nacional and the Universidad de Chile, and processed them by removing the trend and applying a band-pass filter. Then, for each station, we obtained the Peak Ground Acceleration (PGA), and by using a damped response spectra, we calculated the Pseudo Spectral Acceleration (PSA). Finally, we compared those observations with the most recent Chilean and Canadian GMPEs. Given the lack of geotechnical information for most of the Chilean stations, we also used a new method to obtain the VS30 by inverting the H/V ratios using a trans-dimensional Bayesian inversion, which allows us to improve the correction of observations according to soil conditions.As expected, our results show a good fit between observations and the Chilean GMPEs, but we observe that although the shape of the Canadian GMPEs is coherent with the distribution of observations, in general they under predict the observations for PGA and PSA at shorter periods for most of the considered earthquakes. An example of this can be seen in the attached figure for the case of the 2014 Iquique earthquake.These results present important implications related to the hazards associated to large earthquakes, especially for western Canada, where the probability of a

  4. The utility of absolute risk prediction using FRAX® and Garvan Fracture Risk Calculator in daily practice.

    Science.gov (United States)

    van Geel, Tineke A C M; Eisman, John A; Geusens, Piet P; van den Bergh, Joop P W; Center, Jacqueline R; Dinant, Geert-Jan

    2014-02-01

    There are two commonly used fracture risk prediction tools FRAX(®) and Garvan Fracture Risk Calculator (GARVAN-FRC). The objective of this study was to investigate the utility of these tools in daily practice. A prospective population-based 5-year follow-up study was conducted in ten general practice centres in the Netherlands. For the analyses, the FRAX(®) and GARVAN-FRC 10-year absolute risks (FRAX(®) does not have 5-year risk prediction) for all fractures were used. Among 506 postmenopausal women aged ≥60 years (mean age: 67.8±5.8 years), 48 (9.5%) sustained a fracture during follow-up. Both tools, using BMD values, distinguish between women who did and did not fracture (10.2% vs. 6.8%, respectively for FRAX(®) and 32.4% vs. 39.1%, respectively for GARVAN-FRC, pbetter for women who sustained a fracture (higher sensitivity) and FRAX(®) for women who did not sustain a fracture (higher specificity). Similar results were obtained using age related cut off points. The discriminant value of both models is at least as good as models used in other medical conditions; hence they can be used to communicate the fracture risk to patients. However, given differences in the estimated risks between FRAX(®) and GARVAN-FRC, the significance of the absolute risk must be related to country-specific recommended intervention thresholds to inform the patient. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  5. Implications of next generation attenuation ground motion prediction equations for site coefficients used in earthquake resistant design

    Science.gov (United States)

    Borcherdt, Roger D.

    2014-01-01

    Proposals are developed to update Tables 11.4-1 and 11.4-2 of Minimum Design Loads for Buildings and Other Structures published as American Society of Civil Engineers Structural Engineering Institute standard 7-10 (ASCE/SEI 7–10). The updates are mean next generation attenuation (NGA) site coefficients inferred directly from the four NGA ground motion prediction equations used to derive the maximum considered earthquake response maps adopted in ASCE/SEI 7–10. Proposals include the recommendation to use straight-line interpolation to infer site coefficients at intermediate values of (average shear velocity to 30-m depth). The NGA coefficients are shown to agree well with adopted site coefficients at low levels of input motion (0.1 g) and those observed from the Loma Prieta earthquake. For higher levels of input motion, the majority of the adopted values are within the 95% epistemic-uncertainty limits implied by the NGA estimates with the exceptions being the mid-period site coefficient, Fv, for site class D and the short-period coefficient, Fa, for site class C, both of which are slightly less than the corresponding 95% limit. The NGA data base shows that the median value  of 913 m/s for site class B is more typical than 760 m/s as a value to characterize firm to hard rock sites as the uniform ground condition for future maximum considered earthquake response ground motion estimates. Future updates of NGA ground motion prediction equations can be incorporated easily into future adjustments of adopted site coefficients using procedures presented herein. 

  6. Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers

    DEFF Research Database (Denmark)

    Kuchenbaecker, Karoline B; McGuffog, Lesley; Barrowdale, Daniel

    2017-01-01

    Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic ...... risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management....

  7. Uncertainty estimation and risk prediction in air quality

    International Nuclear Information System (INIS)

    Garaud, Damien

    2011-01-01

    This work is about uncertainty estimation and risk prediction in air quality. Firstly, we build a multi-model ensemble of air quality simulations which can take into account all uncertainty sources related to air quality modeling. Ensembles of photochemical simulations at continental and regional scales are automatically generated. Then, these ensemble are calibrated with a combinatorial optimization method. It selects a sub-ensemble which is representative of uncertainty or shows good resolution and reliability for probabilistic forecasting. This work shows that it is possible to estimate and forecast uncertainty fields related to ozone and nitrogen dioxide concentrations or to improve the reliability of threshold exceedance predictions. The approach is compared with Monte Carlo simulations, calibrated or not. The Monte Carlo approach appears to be less representative of the uncertainties than the multi-model approach. Finally, we quantify the observational error, the representativeness error and the modeling errors. The work is applied to the impact of thermal power plants, in order to quantify the uncertainty on the impact estimates. (author) [fr

  8. Cardiovascular risk prediction: the old has given way to the new but at what risk-benefit ratio?

    Directory of Open Access Journals (Sweden)

    Yeboah J

    2014-10-01

    Full Text Available Joseph Yeboah Heart and Vascular Center of Excellence, Wake Forest University School of Medicine, Winston-Salem, NC, USA Abstract: The ultimate goal of cardiovascular risk prediction is to identify individuals in the population to whom the application or administration of current proven lifestyle modifications and medicinal therapies will result in reduction in cardiovascular disease events and minimal adverse effects (net benefit to society. The use of cardiovascular risk prediction tools dates back to 1976 when the Framingham coronary heart disease risk score was published. Since then a lot of novel risk markers have been identified and other cardiovascular risk prediction tools have been developed to either improve or replace the Framingham Risk Score (FRS. In 2013, the new atherosclerotic cardiovascular disease risk estimator was published by the American College of Cardiology and the American Heart Association to replace the FRS for cardiovascular risk prediction. It is too soon to know the performance of the new atherosclerotic cardiovascular disease risk estimator. The risk-benefit ratio for preventive therapy (lifestyle modifications, statin +/− aspirin based on cardiovascular disease risk assessed using the FRS is unknown but it was assumed to be a net benefit. Should we also assume the risk-benefit ratio for the new atherosclerotic cardiovascular disease risk estimator is also a net benefit? Keywords: risk prediction, prevention, cardiovascular disease

  9. Predicting infection risk of airborne foot-and-mouth disease.

    Science.gov (United States)

    Schley, David; Burgin, Laura; Gloster, John

    2009-05-06

    Foot-and-mouth disease is a highly contagious disease of cloven-hoofed animals, the control and eradication of which is of significant worldwide socio-economic importance. The virus may spread by direct contact between animals or via fomites as well as through airborne transmission, with the latter being the most difficult to control. Here, we consider the risk of infection to flocks or herds from airborne virus emitted from a known infected premises. We show that airborne infection can be predicted quickly and with a good degree of accuracy, provided that the source of virus emission has been determined and reliable geo-referenced herd data are available. A simple model provides a reliable tool for estimating risk from known sources and for prioritizing surveillance and detection efforts. The issue of data information management systems was highlighted as a lesson to be learned from the official inquiry into the UK 2007 foot-and-mouth outbreak: results here suggest that the efficacy of disease control measures could be markedly improved through an accurate livestock database incorporating flock/herd size and location, which would enable tactical as well as strategic modelling.

  10. Regression Trees Identify Relevant Interactions: Can This Improve the Predictive Performance of Risk Adjustment?

    Science.gov (United States)

    Buchner, Florian; Wasem, Jürgen; Schillo, Sonja

    2017-01-01

    Risk equalization formulas have been refined since their introduction about two decades ago. Because of the complexity and the abundance of possible interactions between the variables used, hardly any interactions are considered. A regression tree is used to systematically search for interactions, a methodologically new approach in risk equalization. Analyses are based on a data set of nearly 2.9 million individuals from a major German social health insurer. A two-step approach is applied: In the first step a regression tree is built on the basis of the learning data set. Terminal nodes characterized by more than one morbidity-group-split represent interaction effects of different morbidity groups. In the second step the 'traditional' weighted least squares regression equation is expanded by adding interaction terms for all interactions detected by the tree, and regression coefficients are recalculated. The resulting risk adjustment formula shows an improvement in the adjusted R 2 from 25.43% to 25.81% on the evaluation data set. Predictive ratios are calculated for subgroups affected by the interactions. The R 2 improvement detected is only marginal. According to the sample level performance measures used, not involving a considerable number of morbidity interactions forms no relevant loss in accuracy. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  11. Validity of impedance-based equations for the prediction of total body water as measured by deuterium dilution in African women

    International Nuclear Information System (INIS)

    Dioum, Aissatou S.; Cisse, Aita; Wade, Salimata; Gartner, Agnes; Delpeuch, Francis; Maire, Bernard; Schutz, Yves

    2005-01-01

    Background: Little information is available on the validity of simple and indirect body-composition methods in non-Western populations. Equations for predicting body composition are population- specific, and body composition differs between blacks and whites. Objective:Wetestedthehypothesisthatthevalidityofequationsfor predicting total body water (TBW) from bioelectrical impedance analysis measurements is likely to depend on the racial background of the group from which the equations were derived. Design: The hypothesis was tested by comparing, in 36 African women, TBW values measured by deuterium dilution with those predicted by 23 equations developed in white, African American, or African subjects. These cross-validations in our African sample were also compared, whenever possible, with results from other studies in black subjects. Results: Errors in predicting TBW showed acceptable values (1.3- 1.9 kg) in all cases, whereas a large range of bias (0.2-6.1 kg) was observed independently of the ethnic origin of the sample from which the equations were derived. Three equations (2 from whites and 1 from blacks) showed nonsignificant bias and could be used in Africans. In all other cases, we observed either an overestimation or under estimation of TBW with variable bias values, regardless of racial background, yielding no clear trend for validity as a function of ethnic origin. Conclusions: The findings of this cross-validation study emphasize the need for further fundamental research to explore the causes of the poor validity of TBW prediction equations across populations rather than the need to develop new prediction equations for use in Africa. (Authors)

  12. Risk score to predict gastrointestinal bleeding after acute ischemic stroke.

    Science.gov (United States)

    Ji, Ruijun; Shen, Haipeng; Pan, Yuesong; Wang, Penglian; Liu, Gaifen; Wang, Yilong; Li, Hao; Singhal, Aneesh B; Wang, Yongjun

    2014-07-25

    Gastrointestinal bleeding (GIB) is a common and often serious complication after stroke. Although several risk factors for post-stroke GIB have been identified, no reliable or validated scoring system is currently available to predict GIB after acute stroke in routine clinical practice or clinical trials. In the present study, we aimed to develop and validate a risk model (acute ischemic stroke associated gastrointestinal bleeding score, the AIS-GIB score) to predict in-hospital GIB after acute ischemic stroke. The AIS-GIB score was developed from data in the China National Stroke Registry (CNSR). Eligible patients in the CNSR were randomly divided into derivation (60%) and internal validation (40%) cohorts. External validation was performed using data from the prospective Chinese Intracranial Atherosclerosis Study (CICAS). Independent predictors of in-hospital GIB were obtained using multivariable logistic regression in the derivation cohort, and β-coefficients were used to generate point scoring system for the AIS-GIB. The area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively. A total of 8,820, 5,882, and 2,938 patients were enrolled in the derivation, internal validation and external validation cohorts. The overall in-hospital GIB after AIS was 2.6%, 2.3%, and 1.5% in the derivation, internal, and external validation cohort, respectively. An 18-point AIS-GIB score was developed from the set of independent predictors of GIB including age, gender, history of hypertension, hepatic cirrhosis, peptic ulcer or previous GIB, pre-stroke dependence, admission National Institutes of Health stroke scale score, Glasgow Coma Scale score and stroke subtype (Oxfordshire). The AIS-GIB score showed good discrimination in the derivation (0.79; 95% CI, 0.764-0.825), internal (0.78; 95% CI, 0.74-0.82) and external (0.76; 95% CI, 0.71-0.82) validation cohorts

  13. A genetic risk score combining ten psoriasis risk loci improves disease prediction.

    Directory of Open Access Journals (Sweden)

    Haoyan Chen

    2011-04-01

    Full Text Available Psoriasis is a chronic, immune-mediated skin disease affecting 2-3% of Caucasians. Recent genetic association studies have identified multiple psoriasis risk loci; however, most of these loci contribute only modestly to disease risk. In this study, we investigated whether a genetic risk score (GRS combining multiple loci could improve psoriasis prediction. Two approaches were used: a simple risk alleles count (cGRS and a weighted (wGRS approach. Ten psoriasis risk SNPs were genotyped in 2815 case-control samples and 858 family samples. We found that the total number of risk alleles in the cases was significantly higher than in controls, mean 13.16 (SD 1.7 versus 12.09 (SD 1.8, p = 4.577×10(-40. The wGRS captured considerably more risk than any SNP considered alone, with a psoriasis OR for high-low wGRS quartiles of 10.55 (95% CI 7.63-14.57, p = 2.010×10(-65. To compare the discriminatory ability of the GRS models, receiver operating characteristic curves were used to calculate the area under the curve (AUC. The AUC for wGRS was significantly greater than for cGRS (72.0% versus 66.5%, p = 2.13×10(-8. Additionally, the AUC for HLA-C alone (rs10484554 was equivalent to the AUC for all nine other risk loci combined (66.2% versus 63.8%, p = 0.18, highlighting the dominance of HLA-C as a risk locus. Logistic regression revealed that the wGRS was significantly associated with two subphenotypes of psoriasis, age of onset (p = 4.91×10(-6 and family history (p = 0.020. Using a liability threshold model, we estimated that the 10 risk loci account for only 11.6% of the genetic variance in psoriasis. In summary, we found that a GRS combining 10 psoriasis risk loci captured significantly more risk than any individual SNP and was associated with early onset of disease and a positive family history. Notably, only a small fraction of psoriasis heritability is captured by the common risk variants identified to date.

  14. Genetic risk prediction and neurobiological understanding of alcoholism.

    Science.gov (United States)

    Levey, D F; Le-Niculescu, H; Frank, J; Ayalew, M; Jain, N; Kirlin, B; Learman, R; Winiger, E; Rodd, Z; Shekhar, A; Schork, N; Kiefer, F; Kiefe, F; Wodarz, N; Müller-Myhsok, B; Dahmen, N; Nöthen, M; Sherva, R; Farrer, L; Smith, A H; Kranzler, H R; Rietschel, M; Gelernter, J; Niculescu, A B

    2014-05-20

    We have used a translational Convergent Functional Genomics (CFG) approach to discover genes involved in alcoholism, by gene-level integration of genome-wide association study (GWAS) data from a German alcohol dependence cohort with other genetic and gene expression data, from human and animal model studies, similar to our previous work in bipolar disorder and schizophrenia. A panel of all the nominally significant P-value SNPs in the top candidate genes discovered by CFG  (n=135 genes, 713 SNPs) was used to generate a genetic  risk prediction score (GRPS), which showed a trend towards significance (P=0.053) in separating  alcohol dependent individuals from controls in an independent German test cohort. We then validated and prioritized our top findings from this discovery work, and subsequently tested them in three independent cohorts, from two continents. A panel of all the nominally significant P-value single-nucleotide length polymorphisms (SNPs) in the top candidate genes discovered by CFG (n=135 genes, 713 SNPs) were used to generate a Genetic Risk Prediction Score (GRPS), which showed a trend towards significance (P=0.053) in separating alcohol-dependent individuals from controls in an independent German test cohort. In order to validate and prioritize the key genes that drive behavior without some of the pleiotropic environmental confounds present in humans, we used a stress-reactive animal model of alcoholism developed by our group, the D-box binding protein (DBP) knockout mouse, consistent with the surfeit of stress theory of addiction proposed by Koob and colleagues. A much smaller panel (n=11 genes, 66 SNPs) of the top CFG-discovered genes for alcoholism, cross-validated and prioritized by this stress-reactive animal model showed better predictive ability in the independent German test cohort (P=0.041). The top CFG scoring gene for alcoholism from the initial discovery step, synuclein alpha (SNCA) remained the top gene after the stress

  15. The association between risk perception and behavior. Results of structural equation modelling

    International Nuclear Information System (INIS)

    Rundmo, T.; Hestad, H.

    1998-01-01

    Full text of publication follows: there are at least four hypotheses about the association between risk perception and behaviour. The first one is that perceived risk caused by risk exposure may awake a person's 'attention' towards the potentially-hazardous risk source, and hence cause safer behaviour. The second hypothesis is that the affective reactions may be seen as reactions to stress, i.e. a strain factor, which may reduce a person's ability to cope with risk and therefore cause enhanced risk behaviour. The third hypothesis is that risk judgements and risk behaviours are both dependent variables which do not affect each other. Consequently, a positive correlation between perceived risk and behaviour may be caused by the fact that perception and behaviour both are affected by the same predictor variables. Risk perception contain a rational as well as an emotional 'component'. The fourth hypothesis is that rational judgements of risk and emotional reactions to risk may be related differently to risk behaviour. In the present study the rational judgements and the affective reactions are split and different measures are aimed at determining both the components. The core aim of this paper is to determine whether or not they are differently related to risk behaviour. The results are based on a self-completion questionnaire survey which was carried out among employees (n=814) at 14 plants within the industrial company Norsk Hydro in 1997 and 1998. The plants belonged to several divisions within the company. The results showed that the rational and emotional components of risk perception were related differently to risk behaviour. The rational risk judgements had a moderate direct as well as an indirect effect on risk behaviour. The affective reactions to risk were caused by the probability judgements and did not affect risk behaviour. (authors)

  16. The association between risk perception and behavior. Results of structural equation modelling

    Energy Technology Data Exchange (ETDEWEB)

    Rundmo, T.; Hestad, H. [Norwegian University of Science and Technology, Dragvoll (Norway). Dept. of Psychology

    1998-07-01

    Full text of publication follows: there are at least four hypotheses about the association between risk perception and behaviour. The first one is that perceived risk caused by risk exposure may awake a person's 'attention' towards the potentially-hazardous risk source, and hence cause safer behaviour. The second hypothesis is that the affective reactions may be seen as reactions to stress, i.e. a strain factor, which may reduce a person's ability to cope with risk and therefore cause enhanced risk behaviour. The third hypothesis is that risk judgements and risk behaviours are both dependent variables which do not affect each other. Consequently, a positive correlation between perceived risk and behaviour may be caused by the fact that perception and behaviour both are affected by the same predictor variables. Risk perception contain a rational as well as an emotional 'component'. The fourth hypothesis is that rational judgements of risk and emotional reactions to risk may be related differently to risk behaviour. In the present study the rational judgements and the affective reactions are split and different measures are aimed at determining both the components. The core aim of this paper is to determine whether or not they are differently related to risk behaviour. The results are based on a self-completion questionnaire survey which was carried out among employees (n=814) at 14 plants within the industrial company Norsk Hydro in 1997 and 1998. The plants belonged to several divisions within the company. The results showed that the rational and emotional components of risk perception were related differently to risk behaviour. The rational risk judgements had a moderate direct as well as an indirect effect on risk behaviour. The affective reactions to risk were caused by the probability judgements and did not affect risk behaviour. (authors)

  17. Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

    Science.gov (United States)

    Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

    2014-05-01

    Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. Do sophisticated epistemic beliefs predict meaningful learning? Findings from a structural equation model of undergraduate biology learning

    Science.gov (United States)

    Lee, Silvia Wen-Yu; Liang, Jyh-Chong; Tsai, Chin-Chung

    2016-10-01

    This study investigated the relationships among college students' epistemic beliefs in biology (EBB), conceptions of learning biology (COLB), and strategies of learning biology (SLB). EBB includes four dimensions, namely 'multiple-source,' 'uncertainty,' 'development,' and 'justification.' COLB is further divided into 'constructivist' and 'reproductive' conceptions, while SLB represents deep strategies and surface learning strategies. Questionnaire responses were gathered from 303 college students. The results of the confirmatory factor analysis and structural equation modelling showed acceptable model fits. Mediation testing further revealed two paths with complete mediation. In sum, students' epistemic beliefs of 'uncertainty' and 'justification' in biology were statistically significant in explaining the constructivist and reproductive COLB, respectively; and 'uncertainty' was statistically significant in explaining the deep SLB as well. The results of mediation testing further revealed that 'uncertainty' predicted surface strategies through the mediation of 'reproductive' conceptions; and the relationship between 'justification' and deep strategies was mediated by 'constructivist' COLB. This study provides evidence for the essential roles some epistemic beliefs play in predicting students' learning.

  19. Efficacy of the theory of planned behavior in predicting breastfeeding: Meta-analysis and structural equation modeling.

    Science.gov (United States)

    Guo, J L; Wang, T F; Liao, J Y; Huang, C M

    2016-02-01

    This study assessed the applicability and efficacy of the theory of planned behavior (TPB) in predicting breastfeeding. The TPB assumes a rational approach for engaging in various behaviors, and has been used extensively for explaining health behavior. However, most studies have tested the effectiveness of TPB constructs in predicting how people perform actions for their own benefit rather than performing behaviors that are beneficial to others, such as breastfeeding infants. A meta-analysis approach could help clarify the breastfeeding practice to promote breastfeeding. This study used meta-analytic procedures. We searched for studies to include in our analysis, examining those published between January 1, 1990 and December 31, 2013 in PubMed, Medline, CINAHL, ProQuest, and Mosby's Index. We also reviewed journals with a history of publishing breastfeeding studies and searched reference lists for potential articles to include. Ten studies comprising a total of 2694 participants were selected for analysis. These studies yielded 10 effect sizes from the TPB, which ranged from 0.20 to 0.59. Structural equation model analysis using the pooled correlation matrix enabled us to determine the relative coefficients among TPB constructs. Attitude, subjective norms, and perceived behavioral control were all significant predictors of breastfeeding intention, whereas intention was a strong predictor of breastfeeding behavior. Perceived behavioral control reached a borderline level of significance to breastfeeding behavior. Theoretical and empirical implications are discussed from the perspective of evidence-based practice. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Comparison of predictive equations and measured resting energy expenditure among obese youth attending a pediatric healthy weight clinic: one size does not fit all.

    Science.gov (United States)

    Henes, Sarah T; Cummings, Doyle M; Hickner, Robert C; Houmard, Joseph A; Kolasa, Kathryn M; Lazorick, Suzanne; Collier, David N

    2013-10-01

    The Academy of Nutrition and Dietetics recommends the use of indirect calorimetry for calculating caloric targets for weight loss in obese youth. Practitioners typically use predictive equations since indirect calorimetry is often not available. The objective of this study was to compare measured resting energy expenditure (MREE) with that estimated using published predictive equations in obese pediatric patients. Youth aged 7 to 18 years (n = 80) who were referred to a university-based healthy weight clinic and who were greater than the 95th percentile BMI for age and gender participated. MREE was measured via a portable indirect calorimeter. Predicted energy expenditure (pEE) was estimated using published equations including those commonly used in children. pEE was compared to the MREE for each subject. Absolute mean difference between MREE and pEE, mean percentage accuracy, and mean error were determined. Mean percentage accuracy of pEE compared with MREE varied widely, with the Harris-Benedict, Lazzer, and Molnar equations providing the greatest accuracy (65%, 61%, and 60%, respectively). Mean differences between MREE and equation-estimated caloric targets varied from 197.9 kcal/day to 307.7 kcal/day. The potential to either overestimate or underestimate calorie needs in the clinical setting is significant when comparing EE derived from predictive equations with that measured using portable indirect calorimetry. While our findings suggest that the Harris-Benedict equation has improved accuracy relative to other equations in severely obese youth, the potential for error remains sufficiently great to suggest that indirect calorimetry is preferred.

  1. Using a topographic index to distribute variable source area runoff predicted with the SCS curve-number equation

    Science.gov (United States)

    Lyon, Steve W.; Walter, M. Todd; Gérard-Marchant, Pierre; Steenhuis, Tammo S.

    2004-10-01

    Because the traditional Soil Conservation Service curve-number (SCS-CN) approach continues to be used ubiquitously in water quality models, new application methods are needed that are consistent with variable source area (VSA) hydrological processes in the landscape. We developed and tested a distributed approach for applying the traditional SCS-CN equation to watersheds where VSA hydrology is a dominant process. Predicting the location of source areas is important for watershed planning because restricting potentially polluting activities from runoff source areas is fundamental to controlling non-point-source pollution. The method presented here used the traditional SCS-CN approach to predict runoff volume and spatial extent of saturated areas and a topographic index, like that used in TOPMODEL, to distribute runoff source areas through watersheds. The resulting distributed CN-VSA method was applied to two subwatersheds of the Delaware basin in the Catskill Mountains region of New York State and one watershed in south-eastern Australia to produce runoff-probability maps. Observed saturated area locations in the watersheds agreed with the distributed CN-VSA method. Results showed good agreement with those obtained from the previously validated soil moisture routing (SMR) model. When compared with the traditional SCS-CN method, the distributed CN-VSA method predicted a similar total volume of runoff, but vastly different locations of runoff generation. Thus, the distributed CN-VSA approach provides a physically based method that is simple enough to be incorporated into water quality models, and other tools that currently use the traditional SCS-CN method, while still adhering to the principles of VSA hydrology.

  2. Comparing the measured basal metabolic rates in patients with chronic disorders of consciousness to the estimated basal metabolic rate calculated from common predictive equations.

    Science.gov (United States)

    Xiao, Guizhen; Xie, Qiuyou; He, Yanbin; Wang, Ziwen; Chen, Yan; Jiang, Mengliu; Ni, Xiaoxiao; Wang, Qinxian; Murong, Min; Guo, Yequn; Qiu, Xiaowen; Yu, Ronghao

    2017-10-01

    Accurately predicting the basal metabolic rate (BMR) of patients in a vegetative state (VS) or minimally conscious state (MCS) is critical to proper nutritional therapy, but commonly used equations have not been shown to be accurate. Therefore, we compared the BMR measured by indirect calorimetry (IC) to BMR values estimated using common predictive equations in VS and MCS patients. Body composition variables were measured using the bioelectric impedance analysis (BIA) technique. BMR was measured by IC in 82 patients (64 men and 18 women) with VS or MCS. Patients were classified by body mass index as underweight (BMR was estimated for each group using the Harris-Benedict (H-B), Schofield, or Cunningham equations and compared to the measured BMR using Bland-Altman analyses. For the underweight group, there was a significant difference between the measured BMR values and the estimated BMR values calculated using the H-B, Schofield, and Cunningham equations (p BMR values estimated using the H-B and Cunningham equations were different significantly from the measured BMR (p BMR in the normal-weight group. The Schofield equation showed the best concordance (only 41.5%) with the BMR values measured by IC. None of the commonly used equations to estimate BMR were suitable for the VS or MCS populations. Indirect calorimetry is the preferred way to avoid either over or underestimate of BMR values. Copyright © 2016. Published by Elsevier Ltd.

  3. Risk approximation in decision making: approximative numeric abilities predict advantageous decisions under objective risk.

    Science.gov (United States)

    Mueller, Silke M; Schiebener, Johannes; Delazer, Margarete; Brand, Matthias

    2018-01-22

    Many decision situations in everyday life involve mathematical considerations. In decisions under objective risk, i.e., when explicit numeric information is available, executive functions and abilities to handle exact numbers and ratios are predictors of objectively advantageous choices. Although still debated, exact numeric abilities, e.g., normative calculation skills, are assumed to be related to approximate number processing skills. The current study investigates the effects of approximative numeric abilities on decision making under objective risk. Participants (N = 153) performed a paradigm measuring number-comparison, quantity-estimation, risk-estimation, and decision-making skills on the basis of rapid dot comparisons. Additionally, a risky decision-making task with exact numeric information was administered, as well as tasks measuring executive functions and exact numeric abilities, e.g., mental calculation and ratio processing skills, were conducted. Approximative numeric abilities significantly predicted advantageous decision making, even beyond the effects of executive functions and exact numeric skills. Especially being able to make accurate risk estimations seemed to contribute to superior choices. We recommend approximation skills and approximate number processing to be subject of future investigations on decision making under risk.

  4. Malignancy risk prediction for primary jejunum-ileal tumors

    Directory of Open Access Journals (Sweden)

    MARQUES Ruy Garcia

    2000-01-01

    Full Text Available This work is aimed at identifying factors associated with primary jejunum-ileal tumors malignancy, defining a prediction model with sensitivity, specificity and accuracy to distinguish malign from benign neoplasms. These tumors are rare, have highly unspecific presentation and, frequently, are diagnosed late. We reviewed the charts of 42 patients with primary jejunum-ileal tumors treated in the Department of General Surgery of Rio de Janeiro State University Hospital, Rio de Janeiro, RJ, Brazil, from 1969 to 1998. We performed bivariate analyses, based on chi² test, searching associations between tumors malignancy and demographic and clinical variables. Then logistic regression was employed to consider the independent effect of variables previously identified on malignancy risk. The malign tumors included 11 adenocarcinomas, 7 leiomyosarcomas, 5 carcinoids and 4 lymphomas; the benign tumors included 10 leiomyomas, 2 hamartomas, and single cases of adenoma, multiple neurilemoma and choristoma. The bivariate analyses indicated the association between malignancy and palpable abdominal mass (P = 0.003, period from signs and symptoms onset to diagnosis (P = 0.016, anemia (P = 0.020, anorexia (P = 0.003, abdominal pain (P = 0.031, weight loss (P = 0.001, nausea and vomit (P = 0.094, and intestinal obstruction (P = 0.066; no association with patients demographic characteristics were found. In the final logistic regression model, weight loss, anemia and intestinal obstruction were statistically associated with the dependent variable of interest. Based only on three variables -- weight loss, anemia and intestinal obstruction -- the model defined was able to predict primary jejunum-ileal tumors malignancy with sensitivity of 85.2%, specificity of 80.0%, and accuracy of 83.3%.

  5. Age-Predicted Maximal Heart Rate in Recreational Marathon Runners: A Cross-Sectional Study on Fox's and Tanaka's Equations

    Directory of Open Access Journals (Sweden)

    Pantelis T. Nikolaidis

    2018-03-01

    Full Text Available Age-based prediction equations of maximal heart rate (HRmax, such as the popular formulas Fox's 220-age, or Tanaka's 208-0.7 × age, have been widely used in various populations. Surprisingly, so far these equations have not been validated in marathon runners, despite the importance of the role of HRmax for training purposes in endurance running. The aim of the present study was to examine the validity of Fox and Tanaka equations in a large sample of women and men recreational marathon runners. Participants (n = 180, age 43.2 ± 8.5 years, VO2max 46.8 mL/min/kg, finishers in at least one marathon during the last year performed a graded exercise test on a treadmill, where HRmax was measured. Measured HRmax correlated largely with age in the total sample (r = −0.50, p < 0.001, women (r = −0.60, p < 0.001 and men (r = −0.53, p < 0.001. In women, a large main effect of method on HRmax (p = 0.001, η2 = 0.294 was shown with measured HRmax lower than Fox-HRmax (−4.8 bpm; −8.4, −1.3 and Tanaka-HRmax (−4.9 bpm; −8.1, −1.8. In men, a moderate effect of assessment method on HRmax was found (p = 0.001, η2 = 0.066 with measured HRmax higher than Fox-HRmax (+2.8; 1.0, 4.6, Tanaka-HRmax higher than Fox-HRmax (+1.2; 0.7, 1.7. Based on these findings, it was concluded that Fox and Tanaka' formulas overestimated HRmax by ~5 bpm in women, whereas Fox underestimated HRmax in men by ~3 bpm. Thus, we recommend the further use of Tanaka's formula in men marathon runners. In addition, exercise physiologists and sport scientists should consider the observed differences among various assessment methods when performing exercise testing or prescribing training program relying on HR.

  6. Predictive Accuracy of a Cardiovascular Disease Risk Prediction Model in Rural South India – A Community Based Retrospective Cohort Study

    Directory of Open Access Journals (Sweden)

    Farah N Fathima

    2015-03-01

    Full Text Available Background: Identification of individuals at risk of developing cardiovascular diseases by risk stratification is the first step in primary prevention. Aims & Objectives: To assess the five year risk of developing a cardiovascular event from retrospective data and to assess the predictive accuracy of the non laboratory based National Health and Nutrition Examination Survey (NHANES risk prediction model among individuals in a rural South Indian population. Materials & Methods: A community based retrospective cohort study was conducted in three villages where risk stratification was done for all eligible adults aged between 35-74 years at the time of initial assessment using the NHANES risk prediction charts. Household visits were made after a period of five years by trained doctors to determine cardiovascular outcomes. Results: 521 people fulfilled the eligibility criteria of whom 486 (93.3% could be traced after five years. 56.8% were in low risk, 36.6% were in moderate risk and 6.6% were in high risk categories. 29 persons (5.97% had had cardiovascular events over the last five years of which 24 events (82.7% were nonfatal and five (17.25% were fatal. The mean age of the people who developed cardiovascular events was 57.24 ± 9.09 years. The odds ratios for the three levels of risk showed a linear trend with the odds ratios for the moderate risk and high risk category being 1.35 and 1.94 respectively with the low risk category as baseline. Conclusion: The non laboratory based NHANES charts did not accurately predict the occurrence of cardiovascular events in any of the risk categories.

  7. Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking.

    Directory of Open Access Journals (Sweden)

    Ian C Scott

    Full Text Available The improved characterisation of risk factors for rheumatoid arthritis (RA suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA. Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls; UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls. HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG. Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001; ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively.

  8. Indoor Tanning and the MC1R Genotype: Risk Prediction for Basal Cell Carcinoma Risk in Young People

    OpenAIRE

    Molinaro, Annette M.; Ferrucci, Leah M.; Cartmel, Brenda; Loftfield, Erikka; Leffell, David J.; Bale, Allen E.; Mayne, Susan T.

    2015-01-01

    Basal cell carcinoma (BCC) incidence is increasing, particularly in young people, and can be associated with significant morbidity and treatment costs. To identify young individuals at risk of BCC, we assessed existing melanoma or overall skin cancer risk prediction models and built a novel risk prediction model, with a focus on indoor tanning and the melanocortin 1 receptor gene, MC1R. We evaluated logistic regression models among 759 non-Hispanic whites from a case-control study of patients...

  9. Basal Metabolic Rate of Adolescent Modern Pentathlon Athletes: Agreement between Indirect Calorimetry and Predictive Equations and the Correlation with Body Parameters.

    Directory of Open Access Journals (Sweden)

    Luiz Lannes Loureiro

    Full Text Available The accurate estimative of energy needs is crucial for an optimal physical performance among athletes and the basal metabolic rate (BMR equations often are not well adjusted for adolescent athletes requiring the use of specific methods, such as the golden standard indirect calorimetry (IC. Therefore, we had the aim to analyse the agreement between the BMR of adolescents pentathletes measured by IC and estimated by commonly used predictive equations.Twenty-eight athletes (17 males and 11 females were evaluated for BMR, using IC and the predictive equations Harris and Benedict (HB, Cunningham (CUN, Henry and Rees (HR and FAO/WHO/UNU (FAO. Body composition was obtained using DXA and sexual maturity data were retrieved through validated questionnaires. The correlations among anthropometric variables an IC were analysed by T-student test and ICC, while the agreement between IC and the predictive equations was analysed according to Bland and Altman and by survival-agreement plotting.The whole sample average BMR measured by IC was significantly different from the estimated by FAO (p<0.05. Adjusting data by gender FAO and HR equations were statistically different from IC (p <0.05 among males, while female differed only for the HR equation (p <0.05.The FAO equation underestimated athletes' BMR when compared with IC (T Test. When compared to the golden standard IC, using Bland and Altman, ICC and Survival-Agreement, the equations underestimated the energy needs of adolescent pentathlon athletes up to 300kcal/day. Therefore, they should be used with caution when estimating individual energy requirements in such populations.

  10. Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 1. Digestibility of fiber, fat, protein, and nonfiber carbohydrate.

    Science.gov (United States)

    White, R R; Roman-Garcia, Y; Firkins, J L; VandeHaar, M J; Armentano, L E; Weiss, W P; McGill, T; Garnett, R; Hanigan, M D

    2017-05-01

    Evaluation of ration balancing systems such as the National Research Council (NRC) Nutrient Requirements series is important for improving predictions of animal nutrient requirements and advancing feeding strategies. This work used a literature data set (n = 550) to evaluate predictions of total-tract digested neutral detergent fiber (NDF), fatty acid (FA), crude protein (CP), and nonfiber carbohydrate (NFC) estimated by the NRC (2001) dairy model. Mean biases suggested that the NRC (2001) lactating cow model overestimated true FA and CP digestibility by 26 and 7%, respectively, and under-predicted NDF digestibility by 16%. All NRC (2001) estimates had notable mean and slope biases and large root mean squared prediction error (RMSPE), and concordance (CCC) ranged from poor to good. Predicting NDF digestibility with independent equations for legumes, corn silage, other forages, and nonforage feeds improved CCC (0.85 vs. 0.76) compared with the re-derived NRC (2001) equation form (NRC equation with parameter estimates re-derived against this data set). Separate FA digestion coefficients were derived for different fat supplements (animal fats, oils, and other fat types) and for the basal diet. This equation returned improved (from 0.76 to 0.94) CCC compared with the re-derived NRC (2001) equation form. Unique CP digestibility equations were derived for forages, animal protein feeds, plant protein feeds, and other feeds, which improved CCC compared with the re-derived NRC (2001) equation form (0.74 to 0.85). New NFC digestibility coefficients were derived for grain-specific starch digestibilities, with residual organic matter assumed to be 98% digestible. A Monte Carlo cross-validation was performed to evaluate repeatability of model fit. In this procedure, data were randomly subsetted 500 times into derivation (60%) and evaluation (40%) data sets, and equations were derived using the derivation data and then evaluated against the independent evaluation data. Models

  11. Genetic risk prediction and neurobiological understanding of alcoholism

    Science.gov (United States)

    Levey, D F; Le-Niculescu, H; Frank, J; Ayalew, M; Jain, N; Kirlin, B; Learman, R; Winiger, E; Rodd, Z; Shekhar, A; Schork, N; Kiefe, F; Wodarz, N; Müller-Myhsok, B; Dahmen, N; Nöthen, M; Sherva, R; Farrer, L; Smith, A H; Kranzler, H R; Rietschel, M; Gelernter, J; Niculescu, A B

    2014-01-01

    We have used a translational Convergent Functional Genomics (CFG) approach to discover genes involved in alcoholism, by gene-level integration of genome-wide association study (GWAS) data from a German alcohol dependence cohort with other genetic and gene expression data, from human and animal model studies, similar to our previous work in bipolar disorder and schizophrenia. A panel of all the nominally significant P-value SNPs in the top candidate genes discovered by CFG  (n=135 genes, 713 SNPs) was used to generate a genetic  risk prediction score (GRPS), which showed a trend towards significance (P=0.053) in separating  alcohol dependent individuals from controls in an independent German test cohort. We then validated and prioritized our top findings from this discovery work, and subsequently tested them in three independent cohorts, from two continents. In order to validate and prioritize the key genes that drive behavior without some of the pleiotropic environmental confounds present in humans, we used a stress-reactive animal model of alcoholism developed by our group, the D-box binding protein (DBP) knockout mouse, consistent with the surfeit of stress theory of addiction proposed by Koob and colleagues. A much smaller panel (n=11 genes, 66 SNPs) of the top CFG-discovered genes for alcoholism, cross-validated and prioritized by this stress-reactive animal model showed better predictive ability in the independent German test cohort (P=0.041). The top CFG scoring gene for alcoholism from the initial discovery step, synuclein alpha (SNCA) remained the top gene after the stress-reactive animal model cross-validation. We also tested this small panel of genes in two other independent test cohorts from the United States, one with alcohol dependence (P=0.00012) and one with alcohol abuse (a less severe form of alcoholism; P=0.0094). SNCA by itself was able to separate alcoholics from controls in the alcohol-dependent cohort (P=0.000013) and the alcohol abuse

  12. Development of Response Spectral Ground Motion Prediction Equations from Empirical Models for Fourier Spectra and Duration of Ground Motion

    Science.gov (United States)

    Bora, S. S.; Scherbaum, F.; Kuehn, N. M.; Stafford, P.; Edwards, B.

    2014-12-01

    In a probabilistic seismic hazard assessment (PSHA) framework, it still remains a challenge to adjust ground motion prediction equations (GMPEs) for application in different seismological environments. In this context, this study presents a complete framework for the development of a response spectral GMPE easily adjustable to different seismological conditions; and which does not suffer from the technical problems associated with the adjustment in response spectral domain. Essentially, the approach consists of an empirical FAS (Fourier Amplitude Spectrum) model and a duration model for ground motion which are combined within the random vibration theory (RVT) framework to obtain the full response spectral ordinates. Additionally, FAS corresponding to individual acceleration records are extrapolated beyond the frequency range defined by the data using the stochastic FAS model, obtained by inversion as described in Edwards & Faeh, (2013). To that end, an empirical model for a duration, which is tuned to optimize the fit between RVT based and observed response spectral ordinate, at each oscillator frequency is derived. Although, the main motive of the presented approach was to address the adjustability issues of response spectral GMPEs; comparison, of median predicted response spectra with the other regional models indicate that presented approach can also be used as a stand-alone model. Besides that, a significantly lower aleatory variability (σbrands it to a potentially viable alternative to the classical regression (on response spectral ordinates) based GMPEs for seismic hazard studies in the near future. The dataset used for the presented analysis is a subset of the recently compiled database RESORCE-2012 across Europe, Middle East and the Mediterranean region.

  13. Application-driven ground motion prediction equation for seismic hazard assessments in non-cratonic moderate-seismicity areas

    Science.gov (United States)

    Bindi, D.; Cotton, F.; Kotha, S. R.; Bosse, C.; Stromeyer, D.; Grünthal, G.

    2017-09-01

    We present a ground motion prediction equation (GMPE) for probabilistic seismic hazard assessments (PSHA) in low-to-moderate seismicity areas, such as Germany. Starting from the NGA-West2 flat-file (Ancheta et al. in Earthquake Spectra 30:989-1005, 2014), we develop a model tailored to the hazard application in terms of data selection and implemented functional form. In light of such hazard application, the GMPE is derived for hypocentral distance (along with the Joyner-Boore one), selecting recordings at sites with vs30 ≥ 360 m/s, distances within 300 km, and magnitudes in the range 3 to 8 (being 7.4 the maximum magnitude for the PSHA in the target area). Moreover, the complexity of the considered functional form is reflecting the availability of information in the target area. The median predictions are compared with those from the NGA-West2 models and with one recent European model, using the Sammon's map constructed for different scenarios. Despite the simplification in the functional form, the assessed epistemic uncertainty in the GMPE median is of the order of those affecting the NGA-West2 models for the magnitude range of interest of the hazard application. On the other hand, the simplification of the functional form led to an increment of the apparent aleatory variability. In conclusion, the GMPE developed in this study is tailored to the needs for applications in low-to-moderate seismic areas and for short return periods (e.g., 475 years); its application in studies where the hazard is involving magnitudes above 7.4 and for long return periods is not advised.

  14. Prediction of transient mechanical response of type 316 stainless steel cladding using an equation-of-state approach

    International Nuclear Information System (INIS)

    Wire, G.L.; Cannon, N.S.; Johnson, G.D.

    1979-01-01

    Correlation of short-term mechanical properties of breeder reactor core component materials play an important role in design and safety analysis. A description of the short-term high strain-rate flow properties for 20% CW 316 SS was developed using a mechanical equation-of-state approach developed by Hart. The stress strain-rate relationship was developed from tensile yield strength data over the temperature range 427-871 0 C. The description, developed for constant structure or hardness, was then combined with simplified work hardening and recovery models to predict response of unirradiated 20% CW 316 SS over loading paths important to breeder reactor cladding. The advantage of the method is that it provides a description of mechanical response under a wide range of loading conditions, yet the formulation is simple in form with a single structure parameter used to describe material structure changes. The method is also shown to be applicable to neutron irradiated 316 SS. This implies that while neutron irradiation can change the hardness and ductility of 316 SS, the basic flow law is unchanged by irradiation. (Auth.)

  15. A Statistical Test for Identifying the Number of Creep Regimes When Using the Wilshire Equations for Creep Property Predictions

    Science.gov (United States)

    Evans, Mark

    2016-12-01

    A new parametric approach, termed the Wilshire equations, offers the realistic potential of being able to accurately lift materials operating at in-service conditions from accelerated test results lasting no more than 5000 hours. The success of this approach can be attributed to a well-defined linear relationship that appears to exist between various creep properties and a log transformation of the normalized stress. However, these linear trends are subject to discontinuities, the number of which appears to differ from material to material. These discontinuities have until now been (1) treated as abrupt in nature and (2) identified by eye from an inspection of simple graphical plots of the data. This article puts forward a statistical test for determining the correct number of discontinuities present within a creep data set and a method for allowing these discontinuities to occur more gradually, so that the methodology is more in line with the accepted view as to how creep mechanisms evolve with changing test conditions. These two developments are fully illustrated using creep data sets on two steel alloys. When these new procedures are applied to these steel alloys, not only do they produce more accurate and realistic looking long-term predictions of the minimum creep rate, but they also lead to different conclusions about the mechanisms determining the rates of creep from those originally put forward by Wilshire.

  16. Predicting organic food consumption: A meta-analytic structural equation model based on the theory of planned behavior.

    Science.gov (United States)

    Scalco, Andrea; Noventa, Stefano; Sartori, Riccardo; Ceschi, Andrea

    2017-05-01

    During the last decade, the purchase of organic food within a sustainable consumption context has gained momentum. Consequently, the amount of research in the field has increased, leading in some cases to discrepancies regarding both methods and results. The present review examines those works that applied the theory of planned behavior (TPB; Ajzen, 1991) as a theoretical framework in order to understand and predict consumers' motivation to buy organic food. A meta-analysis has been conducted to assess the strength of the relationships between attitude, subjective norms, perceived behavioral control, and intention, as well as between intention and behavior. Results confirm the major role played by individual attitude in shaping buying intention, followed by subjective norms and perceived behavioral control. Intention-behavior shows a large effect size, few studies however explicitly reported such an association. Furthermore, starting from a pooled correlation matrix, a meta-analytic structural equation model has been applied to jointly evaluate the strength of the relationships among the factors of the original model. Results suggest the robustness of the TPB model. In addition, mediation analysis indicates a potential direct effect from subjective norms to individual attitude in the present context. Finally, some issues regarding methodological aspects of the application of the TPB within the context of organic food are discussed for further research developments. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Basal Metabolic Rate of Adolescent Modern Pentathlon Athletes: Agreement between Indirect Calorimetry and Predictive Equations and the Correlation with Body Parameters

    Science.gov (United States)

    Loureiro, Luiz Lannes; Fonseca, Sidnei; Castro, Natalia Gomes Casanova de Oliveira e; dos Passos, Renata Baratta; Porto, Cristiana Pedrosa Melo; Pierucci, Anna Paola Trindade Rocha

    2015-01-01

    Purpose The accurate estimative of energy needs is crucial for an optimal physical performance among athletes and the basal metabolic rate (BMR) equations often are not well adjusted for adolescent athletes requiring the use of specific methods, such as the golden standard indirect calorimetry (IC). Therefore, we had the aim to analyse the agreement between the BMR of adolescents pentathletes measured by IC and estimated by commonly used predictive equations. Methods Twenty-eight athletes (17 males and 11 females) were evaluated for BMR, using IC and the predictive equations Harris and Benedict (HB), Cunningham (CUN), Henry and Rees (HR) and FAO/WHO/UNU (FAO). Body composition was obtained using DXA and sexual maturity data were retrieved through validated questionnaires. The correlations among anthropometric variables an IC were analysed by T-student test and ICC, while the agreement between IC and the predictive equations was analysed according to Bland and Altman and by survival-agreement plotting. Results The whole sample average BMR measured by IC was significantly different from the estimated by FAO (pBMR when compared with IC (T Test). When compared to the golden standard IC, using Bland and Altman, ICC and Survival-Agreement, the equations underestimated the energy needs of adolescent pentathlon athletes up to 300kcal/day. Therefore, they should be used with caution when estimating individual energy requirements in such populations. PMID:26569101

  18. Morphometry Predicts Early GFR Change in Primary Proteinuric Glomerulopathies: A Longitudinal Cohort Study Using Generalized Estimating Equations.

    Directory of Open Access Journals (Sweden)

    Kevin V Lemley

    Full Text Available Most predictive models of kidney disease progression have not incorporated structural data. If structural variables have been used in models, they have generally been only semi-quantitative.We examined the predictive utility of quantitative structural parameters measured on the digital images of baseline kidney biopsies from the NEPTUNE study of primary proteinuric glomerulopathies. These variables were included in longitudinal statistical models predicting the change in estimated glomerular filtration rate (eGFR over up to 55 months of follow-up.The participants were fifty-six pediatric and adult subjects from the NEPTUNE longitudinal cohort study who had measurements made on their digital biopsy images; 25% were African-American, 70% were male and 39% were children; 25 had focal segmental glomerular sclerosis, 19 had minimal change disease, and 12 had membranous nephropathy. We considered four different sets of candidate predictors, each including four quantitative structural variables (for example, mean glomerular tuft area, cortical density of patent glomeruli and two of the principal components from the correlation matrix of six fractional cortical areas-interstitium, atrophic tubule, intact tubule, blood vessel, sclerotic glomerulus, and patent glomerulus along with 13 potentially confounding demographic and clinical variables (such as race, age, diagnosis, and baseline eGFR, quantitative proteinuria and BMI. We used longitudinal linear models based on these 17 variables to predict the change in eGFR over up to 55 months. All 4 models had a leave-one-out cross-validated R2 of about 62%.Several combinations of quantitative structural variables were significantly and strongly associated with changes in eGFR. The structural variables were generally stronger than any of the confounding variables, other than baseline eGFR. Our findings suggest that quantitative assessment of diagnostic renal biopsies may play a role in estimating the baseline

  19. Value of routine blood tests for prediction of mortality risk in hip fracture patients

    DEFF Research Database (Denmark)

    Mosfeldt, Mathias; Pedersen, Ole Birger Vesterager; Riis, Troels

    2012-01-01

    There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission.......There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission....

  20. Predictive performance for population models using stochastic differential equations applied on data from an oral glucose tolerance test.

    Science.gov (United States)

    Møller, Jonas B; Overgaard, Rune V; Madsen, Henrik; Hansen, Torben; Pedersen, Oluf; Ingwersen, Steen H

    2010-02-01

    Several articles have investigated stochastic differential equations (SDEs) in PK/PD models, but few have quantitatively investigated the benefits to predictive performance of models based on real data. Estimation of first phase insulin secretion which reflects beta-cell function using models of the OGTT is a difficult problem in need of further investigation. The present work aimed at investigating the power of SDEs to predict the first phase insulin secretion (AIR (0-8)) in the IVGTT based on parameters obtained from the minimal model of the OGTT, published by Breda et al. (Diabetes 50(1):150-158, 2001). In total 174 subjects underwent both an OGTT and a tolbutamide modified IVGTT. Estimation of parameters in the oral minimal model (OMM) was performed using the FOCE-method in NONMEM VI on insulin and C-peptide measurements. The suggested SDE models were based on a continuous AR(1) process, i.e. the Ornstein-Uhlenbeck process, and the extended Kalman filter was implemented in order to estimate the parameters of the models. Inclusion of the Ornstein-Uhlenbeck (OU) process caused improved description of the variation in the data as measured by the autocorrelation function (ACF) of one-step prediction errors. A main result was that application of SDE models improved the correlation between the individual first phase indexes obtained from OGTT and AIR (0-8) (r = 0.36 to r = 0.49 and r = 0.32 to r = 0.47 with C-peptide and insulin measurements, respectively). In addition to the increased correlation also the properties of the indexes obtained using the SDE models more correctly assessed the properties of the first phase indexes obtained from the IVGTT. In general it is concluded that the presented SDE approach not only caused autocorrelation of errors to decrease but also improved estimation of clinical measures obtained from the glucose tolerance tests. Since, the estimation time of extended models was not heavily increased compared to basic models, the applied method

  1. Fat-free mass prediction equations for bioelectric impedance analysis compared to dual energy X-ray absorptiometry in obese adolescents: a validation study

    NARCIS (Netherlands)

    Hofsteenge, G.H.; Chin A Paw, M.J.M.; Weijs, P.J.M.

    2015-01-01

    Background: In clinical practice, patient friendly methods to assess body composition in obese adolescents are needed. Therefore, the bioelectrical impedance analysis (BIA) related fat-free mass (FFM) prediction equations (FFM-BIA) were evaluated in obese adolescents (age 11-18 years) compared to

  2. Accuracy of Prediction Equations to Assess Percentage of Body Fat in Children and Adolescents with Down Syndrome Compared to Air Displacement Plethysmography

    Science.gov (United States)

    Gonzalez-Aguero, A.; Vicente-Rodriguez, G.; Ara, I.; Moreno, L. A.; Casajus, J. A.

    2011-01-01

    To determine the accuracy of the published percentage body fat (%BF) prediction equations (Durnin et al., Johnston et al., Brook and Slaughter et al.) from skinfold thickness compared to air displacement plethysmography (ADP) in children and adolescents with Down syndrome (DS). Twenty-eight children and adolescents with DS (10-20 years old; 12…

  3. Driving Strategic Risk Planning With Predictive Modelling For Managerial Accounting

    DEFF Research Database (Denmark)

    Nielsen, Steen; Pontoppidan, Iens Christian

    for managerial accounting and shows how it can be used to determine the impact of different types of risk assessment input parameters on the variability of important outcome measures. The purpose is to: (i) point out the theoretical necessity of a stochastic risk framework; (ii) present a stochastic framework......Currently, risk management in management/managerial accounting is treated as deterministic. Although it is well-known that risk estimates are necessarily uncertain or stochastic, until recently the methodology required to handle stochastic risk-based elements appear to be impractical and too...... mathematical. The ultimate purpose of this paper is to “make the risk concept procedural and analytical” and to argue that accountants should now include stochastic risk management as a standard tool. Drawing on mathematical modelling and statistics, this paper methodically develops risk analysis approach...

  4. Young Children’s Risk-Taking: Mothers’ Authoritarian Parenting Predicts Risk-Taking by Daughters but Not Sons

    Directory of Open Access Journals (Sweden)

    Erin E. Wood

    2017-01-01

    Full Text Available We investigated how mothers’ parenting behaviors and personal characteristics were related to risk-taking by young children. We tested contrasting predictions from evolutionary and social role theories with the former predicting higher risk-taking by boys compared to girls and the latter predicting that mothers would influence children’s gender role development with risk-taking occurring more in children parented with higher levels of harshness (i.e., authoritarian parenting style. In our study, mothers reported their own gender roles and parenting styles as well as their children’s risk-taking and activities related to gender roles. The results were only partially consistent with the two theories, as the amount of risk-taking by sons and daughters did not differ significantly and risk-taking by daughters, but not sons, was positively related to mothers’ use of the authoritarian parenting style and the girls’ engagement in masculine activities. Risk-taking by sons was not predicted by any combination of mother-related variables. Overall, mothers who were higher in femininity used more authoritative and less authoritarian parenting styles. Theoretical implications as well as implications for predicting and reducing children’s risk-taking are discussed.

  5. Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk.

    Science.gov (United States)

    Glynn, Robert J; Colditz, Graham A; Tamimi, Rulla M; Chen, Wendy Y; Hankinson, Susan E; Willett, Walter W; Rosner, Bernard

    2017-08-01

    A breast cancer risk prediction rule previously developed by Rosner and Colditz has reasonable predictive ability. We developed a re-fitted version of this model, based on more than twice as many cases now including women up to age 85, and further extended it to a model that distinguished risk factor prediction of tumors with different estrogen/progesterone receptor status. We compared the calibration and discriminatory ability of the original, the re-fitted, and the type-specific models. Evaluation used data from the Nurses' Health Study during the period 1980-2008, when 4384 incident invasive breast cancers occurred over 1.5 million person-years. Model development used two-thirds of study subjects and validation used one-third. Predicted risks in the validation sample from the original and re-fitted models were highly correlated (ρ = 0.93), but several parameters, notably those related to use of menopausal hormone therapy and age, had different estimates. The re-fitted model was well-calibrated and had an overall C-statistic of 0.65. The extended, type-specific model identified several risk factors with varying associations with occurrence of tumors of different receptor status. However, this extended model relative to the prediction of any breast cancer did not meaningfully reclassify women who developed breast cancer to higher risk categories, nor women remaining cancer free to lower risk categories. The re-fitted Rosner-Colditz model has applicability to risk prediction in women up to age 85, and its discrimination is not improved by consideration of varying associations across tumor subtypes.

  6. The potential of large studies for building genetic risk prediction models

    Science.gov (United States)

    NCI scientists have developed a new paradigm to assess hereditary risk prediction in common diseases, such as prostate cancer. This genetic risk prediction concept is based on polygenic analysis—the study of a group of common DNA sequences, known as singl

  7. The Efficacy of Violence Prediction: A Meta-Analytic Comparison of Nine Risk Assessment Tools

    Science.gov (United States)

    Yang, Min; Wong, Stephen C. P.; Coid, Jeremy

    2010-01-01

    Actuarial risk assessment tools are used extensively to predict future violence, but previous studies comparing their predictive accuracies have produced inconsistent findings as a result of various methodological issues. We conducted meta-analyses of the effect sizes of 9 commonly used risk assessment tools and their subscales to compare their…

  8. Models for predicting turnover of residential aged care nurses: a structural equation modelling analysis of secondary data.

    Science.gov (United States)

    Gao, Fengsong; Newcombe, Peter; Tilse, Cheryl; Wilson, Jill; Tuckett, Anthony

    2014-09-01

    Nurse turnover in the residential aged care industry is a pressing issue. Researchers have shown ongoing interest in exploring how the factors that are amendable to change in aged care policy, regulation and funding and in organizational procedures (e.g. job demands, coping resources and psychological health of nurses) impact on turnover. However, the findings are mixed. This study tested two theoretical models of turnover to examine the structural relationships among job demands, coping resources, psychological health and turnover of residential aged care nurses. Although many previous studies operationalized turnover as intention to leave, the present study investigated actual turnover by following up with the same individuals over time, and thus provided more accurate predictive models of turnover behaviour. The sample, 239 Australian residential aged care nurses, came from the Nurses and Midwives e-cohort Study. Job demands, coping resources, and psychological health were measured using standardized instruments. Structural equation modelling was used to test the measurement and structural models. Controlling for a number of workforce and individual characteristics, coping resources (measured by job control, supervisor support, and co-worker support) were negatively and directly associated with turnover. Additionally, the findings supported the Job Demand-Control-Support model in that higher coping resources and lower job demands (indicated by psychological demands, physical demands, and effort) were related to better psychological health (measured by vitality, social functioning, role emotional, and mental health), and higher job demands were related to lower coping resources. Findings suggest that aged care policy makers and service providers might consider increasing coping resources available to nurses and minimizing job demands of care work to reduce turnover and improve nurses' psychological health. Moreover, findings from this Australian study may provide

  9. Predictive Risk Factors in the Treatment of Gestational Diabetes Mellitus

    Directory of Open Access Journals (Sweden)

    Lebriz Hale Aktun

    2015-01-01

    Full Text Available Objective This study aims to investigate predictive risk factors in the treatment of gestational diabetes mellitus (GDM. Patients and Methods A total of 256 pregnant women who underwent 75 g oral glucose tolerance test (OGTT during 24–28 weeks of pregnancy were included according to the World Health Organization criteria. Demographic characteristics of the patients, including age, parity, family history of diabetes, body weight before pregnancy, and body weight at the diagnosis of GDM, were recorded. Fasting insulin and hemoglobin A1c (HbA1c values at the time of diagnosis were evaluated. The patients were divided into two groups: those requiring insulin treatment (insulin group, n = 89 and those receiving diet therapy (diet group, n = 167 during pregnancy according to the American Diabetes Association recommendations. Results A total of 34.76% of the pregnant women with GDM required insulin treatment. The mean age of these patients was significantly higher compared to the diet group (34.9 ± 0.6 years vs. 31.9 ± 0.6 years; P = 0.004. Body mass index before pregnancy was also significantly higher in the insulin group than that in the diet group (32 ± 0.9 kg/m 2 vs. 29 ± 0.7 kg/m 2 ; P = 0.004. Fasting blood glucose (FBG during OGTT was 105.6 ± 2.1 mg/dL and 96.7 ± 1.1 mg/dL in the insulin group and diet group, respectively ( P < 0.001. There was no significant difference in fasting plasma glucose during OGTT between the groups ( P = 0.069, while plasma glucose at two hours was 161.1 ± 6.8 mg/dL in the insulin group and 145.1 ± 3.7 mg/dL in the diet group ( P = 0.027. At the time of diagnosis, HbA1c values were significantly higher in the insulin group compared to the diet group (5.3 ± 0.1 vs. 4.9 ± 0.1; P = 0.001. There was no significant difference in FBG and homeostasis model assessment-insulin resistance values between the groups ( P = 0.908, P = 0.073. Conclusion Our study results suggest that age, family history of diabetes, body

  10. On the Relationship between Fourier and Response Spectra: Implications for the Adjustment of Empirical Ground-Motion Prediction Equations (GMPEs)

    Science.gov (United States)

    Bora, Sanjay; Scherbaum, Frank; Kuehn, Nicolas; Stafford, Peter

    2016-04-01

    Often, scaling of response spectral amplitudes, (e.g., spectral acceleration) obtained from empirical ground motion prediction equations (GMPEs), with respect to commonly used seismological parameters such as magnitude, distance and site condition is assumed/referred to be representing a similar scaling of Fourier spectral amplitudes. For instance, the distance scaling of response spectral amplitudes is related with the geometrical spreading of seismic waves. Such comparison of scaling of response spectral amplitudes with that of corresponding Fourier spectral amplitudes is motivated by that, the functional forms of response spectral GMPEs are often derived using the concepts borrowed from Fourier spectral modeling of ground motion. As these GMPEs are subsequently calibrated with empirical observations, this may not appear to pose any major problems in the prediction of ground motion for a particular earthquake scenario. However, the assumption that the Fourier spectral concepts persist for response spectra can lead to undesirable consequences when it comes to the adjustment of response spectral GMPEs to represent conditions not covered in the original empirical data set. In this context, a couple of important questions arise, e.g., what are the distinctions and/or similarities between Fourier and response spectra of ground-motions? And, if they are different, then what is the mechanism responsible for such differences and how do adjustments that are made to FAS manifest in response spectra? We explore the relationship between the Fourier and response spectrum of ground motion by using random vibration theory (RVT). With a simple Brune (1970, 1971) source model, RVT-generated acceleration spectra for a fixed magnitude and distance scenario are used. The RVT analyses reveal that the scaling of low oscillator-frequency response spectral ordinates can be treated as being equivalent to the scaling of the corresponding Fourier spectral ordinates. However, the high

  11. Predictive risk modelling under different data access scenarios: who is identified as high risk and for how long?

    Science.gov (United States)

    Johnson, Tracy L; Kaldor, Jill; Sutherland, Kim; Humphries, Jacob; Jorm, Louisa R; Levesque, Jean-Frederic

    2018-01-01

    Objective This observational study critically explored the performance of different predictive risk models simulating three data access scenarios, comparing: (1) sociodemographic and clinical profiles; (2) consistency in high-risk designation across models; and (3) persistence of high-risk status over time. Methods Cross-sectional health survey data (2006–2009) for more than 260 000 Australian adults 45+ years were linked to longitudinal individual hospital, primary care, pharmacy and mortality data. Three risk models predicting acute emergency hospitalisations were explored, simulating conditions where data are accessed through primary care practice management systems, or through hospital-based electronic records, or through a hypothetical ‘full’ model using a wider array of linked data. High-risk patients were identified using different risk score thresholds. Models were reapplied monthly for 24 months to assess persistence in high-risk categorisation. Results The three models displayed similar statistical performance. Three-quarters of patients in the high-risk quintile from the ‘full’ model were also identified using the primary care or hospital-based models, with the remaining patients differing according to age, frailty, multimorbidity, self-rated health, polypharmacy, prior hospitalisations and imminent mortality. The use of higher risk prediction thresholds resulted in lower levels of agreement in high-risk designation across models and greater morbidity and mortality in identified patient populations. Persistence of high-risk status varied across approaches according to updated information on utilisation history, with up to 25% of patients reassessed as lower risk within 1 year. Conclusion/implications Small differences in risk predictors or risk thresholds resulted in comparatively large differences in who was classified as high risk and for how long. Pragmatic predictive risk modelling design decisions based on data availability or projected

  12. Commentary on Holmes et al. (2007): resolving the debate on when extinction risk is predictable.

    Science.gov (United States)

    Ellner, Stephen P; Holmes, Elizabeth E

    2008-08-01

    We reconcile the findings of Holmes et al. (Ecology Letters, 10, 2007, 1182) that 95% confidence intervals for quasi-extinction risk were narrow for many vertebrates of conservation concern, with previous theory predicting wide confidence intervals. We extend previous theory, concerning the precision of quasi-extinction estimates as a function of population dynamic parameters, prediction intervals and quasi-extinction thresholds, and provide an approximation that specifies the prediction interval and threshold combinations where quasi-extinction estimates are precise (vs. imprecise). This allows PVA practitioners to define the prediction interval and threshold regions of safety (low risk with high confidence), danger (high risk with high confidence), and uncertainty.

  13. Creation of a predictive equation to estimate fat-free mass and the ratio of fat-free mass to skeletal size using morphometry in lean working farm dogs.

    Science.gov (United States)

    Leung, Y M; Cave, N J; Hodgson, B A S

    2018-06-27

    To develop an equation that accurately estimates fat-free mass (FFM) and the ratio of FFM to skeletal size or mass, using morphometric measurements in lean working farm dogs, and to examine the association between FFM derived from body condition score (BCS) and FFM measured using isotope dilution. Thirteen Huntaway and seven Heading working dogs from sheep and beef farms in the Waikato region of New Zealand were recruited based on BCS (BCS 4) using a nine-point scale. Bodyweight, BCS, and morphometric measurements (head length and circumference, body length, thoracic girth, and fore and hind limb length) were recorded for each dog, and body composition was measured using an isotopic dilution technique. A new variable using morphometric measurements, termed skeletal size, was created using principal component analysis. Models for predicting FFM, leanST (FFM minus skeletal mass) and ratios of FFM and leanST to skeletal size or mass were generated using multiple linear regression analysis. Mean FFM of the 20 dogs, measured by isotope dilution, was 22.1 (SD 4.4) kg and the percentage FFM of bodyweight was 87.0 (SD 5.0)%. Median BCS was 3.0 (min 1, max 6). Bodyweight, breed, age and skeletal size or mass were associated with measured FFM (pFFM and measured FFM (R 2 =0.96), and for the ratio of predicted FFM to skeletal size and measured values (R 2 =0.99). Correlation coefficients were higher for the ratio FFM and leanST to skeletal size than for ratios using skeletal mass. There was a positive correlation between BCS-derived fat mass as a percentage of bodyweight and fat mass percentage determined using isotope dilution (R 2 =0.65). As expected, the predictive equation was accurate in estimating FFM when tested on the same group of dogs used to develop the equation. The significance of breed, independent of skeletal size, in predicting FFM indicates that individual breed formulae may be required. Future studies that apply these equations on a greater population of

  14. Predictive value of updating Framingham risk scores with novel risk markers in the U.S. general population.

    Directory of Open Access Journals (Sweden)

    Bart S Ferket

    Full Text Available BACKGROUND: According to population-based cohort studies CT coronary calcium score (CTCS, carotid intima-media thickness (cIMT, high-sensitivity C- reactive protein (CRP, and ankle-brachial index (ABI are promising novel risk markers for improving cardiovascular risk assessment. Their impact in the U.S. general population is however uncertain. Our aim was to estimate the predictive value of four novel cardiovascular risk markers for the U.S. general population. METHODS AND FINDINGS: Risk profiles, CRP and ABI data of 3,736 asymptomatic subjects aged 40 or older from the National Health and Nutrition Examination Survey (NHANES 2003-2004 exam were used along with predicted CTCS and cIMT values. For each subject, we calculated 10-year cardiovascular risks with and without each risk marker. Event rates adjusted for competing risks were obtained by microsimulation. We assessed the impact of updated 10-year risk scores by reclassification and C-statistics. In the study population (mean age 56±11 years, 48% male, 70% (80% were at low (<10%, 19% (14% at intermediate (≥10-<20%, and 11% (6% at high (≥20% 10-year CVD (CHD risk. Net reclassification improvement was highest after updating 10-year CVD risk with CTCS: 0.10 (95%CI 0.02-0.19. The C-statistic for 10-year CVD risk increased from 0.82 by 0.02 (95%CI 0.01-0.03 with CTCS. Reclassification occurred most often in those at intermediate risk: with CTCS, 36% (38% moved to low and 22% (30% to high CVD (CHD risk. Improvements with other novel risk markers were limited. CONCLUSIONS: Only CTCS appeared to have significant incremental predictive value in the U.S. general population, especially in those at intermediate risk. In future research, cost-effectiveness analyses should be considered for evaluating novel cardiovascular risk assessment strategies.

  15. Predictive Value of Updating Framingham Risk Scores with Novel Risk Markers in the U.S. General Population

    Science.gov (United States)

    Hunink, M. G. Myriam; Agarwal, Isha; Kavousi, Maryam; Franco, Oscar H.; Steyerberg, Ewout W.; Max, Wendy; Fleischmann, Kirsten E.

    2014-01-01

    Background According to population-based cohort studies CT coronary calcium score (CTCS), carotid intima-media thickness (cIMT), high-sensitivity C- reactive protein (CRP), and ankle-brachial index (ABI) are promising novel risk markers for improving cardiovascular risk assessment. Their impact in the U.S. general population is however uncertain. Our aim was to estimate the predictive value of four novel cardiovascular risk markers for the U.S. general population. Methods and Findings Risk profiles, CRP and ABI data of 3,736 asymptomatic subjects aged 40 or older from the National Health and Nutrition Examination Survey (NHANES) 2003–2004 exam were used along with predicted CTCS and cIMT values. For each subject, we calculated 10-year cardiovascular risks with and without each risk marker. Event rates adjusted for competing risks were obtained by microsimulation. We assessed the impact of updated 10-year risk scores by reclassification and C-statistics. In the study population (mean age 56±11 years, 48% male), 70% (80%) were at low (risk. Net reclassification improvement was highest after updating 10-year CVD risk with CTCS: 0.10 (95%CI 0.02–0.19). The C-statistic for 10-year CVD risk increased from 0.82 by 0.02 (95%CI 0.01–0.03) with CTCS. Reclassification occurred most often in those at intermediate risk: with CTCS, 36% (38%) moved to low and 22% (30%) to high CVD (CHD) risk. Improvements with other novel risk markers were limited. Conclusions Only CTCS appeared to have significant incremental predictive value in the U.S. general population, especially in those at intermediate risk. In future research, cost-effectiveness analyses should be considered for evaluating novel cardiovascular risk assessment strategies. PMID:24558385

  16. Relationships among sexual self-concept, sexual risk cognition and sexual communication in adolescents: a structural equation model.

    Science.gov (United States)

    Lou, Jiunn-Horng; Chen, Sheng-Hwang; Li, Ren-Hau; Yu, Hsing-Yi

    2011-06-01

    The purpose of this study was to test a model of sexual self-concept and sexual risk cognition affecting sexual communication in Taiwanese adolescents. Parent-adolescent sexual communication has been shown to influence adolescent sexual behaviour. Self-concept is an important predictor of human behaviour, especially sexual behaviour. Few researchers have assessed sexual self-concept in adolescents, despite its clear relevance to understanding adolescent sexual behaviour. A cross-sectional survey with convenience sampling was used in this study. In 2009, data were collected by questionnaire from 748 adolescent students at a junior college in Taiwan. The results revealed that the postulated model fits the data from this study well. Sexual self-concept significantly predicts sexual risk cognition and sexual communication. Sexual risk cognition significantly predicts sexual communication and has an intervening effect on the relationship between sexual self-concept and sexual communication. Sexual risk cognition is important in explaining sexuality in adolescents. Sexual self-concept has both direct and indirect effects on sexual communication. Our findings provide concrete directions for school educators in developing sexual health programmes to increase adolescent sexual self-concept and sexual communication with their parents. Future sexual health programmes about sexual self-concept and sexual risk cognition must add for increasing adolescent's sexual communication with their parents. © 2010 Blackwell Publishing Ltd.

  17. An extension of the theory of planned behavior to predict pedestrians' violating crossing behavior using structural equation modeling.

    Science.gov (United States)

    Zhou, Hongmei; Romero, Stephanie Ballon; Qin, Xiao

    2016-10-01

    This paper aimed to examine pedestrians' self-reported violating crossing behavior intentions by applying the theory of planned behavior (TPB). We studied the behavior intentions regarding instrumental attitude, subjective norm, perceived behavioral control, the three basic components of TPB, and extended the theory by adding new factors including descriptive norm, perceived risk and conformity tendency to evaluate their respective impacts on pedestrians' behavior intentions. A questionnaire presented with a scenario that pedestrians crossed the road violating the pedestrian lights at an intersection was designed, and the survey was conducted in Dalian, China. Based on the 260 complete and valid responses, reliability and validity of the data for each question was evaluated. The data were then analyzed by using the structural equation modeling (SEM). The results showed that people had a negative attitude toward the behavior of violating road-crossing rules; they perceived social influences from their family and friends; and they believed that this kind of risky behavior would potentially harm them in a traffic accident. The results also showed that instrumental attitude and subjective norm were significant in the basic TPB model. After adding descriptive norm, subjective norm was no more significant. Other models showed that conformity tendency was a strong predictor, indicating that the presence of other pedestrians would influence behavioral intention. The findings could help to design more effective interventions and safety campaigns, such as changing people's attitude toward this violation behavior, correcting the social norms, increasing their safety awareness, etc. in order to reduce pedestrians' road crossing violations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Negative Affectivity Predicts Lower Quality of Life and Metabolic Control in Type 2 Diabetes Patients: A Structural Equation Modeling Approach

    Directory of Open Access Journals (Sweden)

    Chiara Conti

    2017-05-01

    Full Text Available Introduction: It is essential to consider the clinical assessment of psychological aspects in patients with Diabetes Mellitus (DM, in order to prevent potentially adverse self-management care behaviors leading to diabetes-related complications, including declining levels of Quality of Life (QoL and negative metabolic control.Purpose: In the framework of Structural Equation Modeling (SEM, the specific aim of this study is to evaluate the influence of distressed personality factors as Negative Affectivity (NA and Social Inhibition (SI on diabetes-related clinical variables (i.e., QoL and glycemic control.Methods: The total sample consists of a clinical sample, including 159 outpatients with Type 2 Diabetes Mellitus (T2DM, and a control group composed of 102 healthy respondents. All participants completed the following self- rating scales: The Type D Scale (DS14 and the World Health Organization QoL Scale (WHOQOLBREF. Furthermore, the participants of the clinical group were assessed for HbA1c, disease duration, and BMI. The observed covariates were BMI, gender, and disease duration, while HbA1c was considered an observed variable.Results: SEM analysis revealed significant differences between groups in regards to the latent construct of NA and the Environmental dimension of QoL. For the clinical sample, SEM showed that NA had a negative impact on both QoL dimensions and metabolic control.Conclusions: Clinical interventions aiming to improve medication adherence in patients with T2DM should include the psychological evaluation of Type D Personality traits, by focusing especially on its component of NA as a significant risk factor leading to negative health outcomes.

  19. Risk of Rare Disasters, Euler Equation Errors and the Performance of the C-CAPM

    DEFF Research Database (Denmark)

    Posch, Olaf; Schrimpf, Andreas

    pricing errors in the C-CAPM. We also show (analytically and in a Monte Carlo study) that implausible estimates of risk aversion and time preference are not puzzling in this framework and emerge as a result of rational pricing errors. While this bias essentially removes the pricing error...

  20. Testing the Predictive Validity of the Hendrich II Fall Risk Model.

    Science.gov (United States)

    Jung, Hyesil; Park, Hyeoun-Ae

    2018-03-01

    Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.

  1. Comparison between frailty index of deficit accumulation and fracture risk assessment tool (FRAX) in prediction of risk of fractures.

    Science.gov (United States)

    Li, Guowei; Thabane, Lehana; Papaioannou, Alexandra; Adachi, Jonathan D

    2015-08-01

    A frailty index (FI) of deficit accumulation could quantify and predict the risk of fractures based on the degree of frailty in the elderly. We aimed to compare the predictive powers between the FI and the fracture risk assessment tool (FRAX) in predicting risk of major osteoporotic fracture (hip, upper arm or shoulder, spine, or wrist) and hip fracture, using the data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) 3-year Hamilton cohort. There were 3985 women included in the study, with the mean age of 69.4 years (standard deviation [SD] = 8.89). During the follow-up, there were 149 (3.98%) incident major osteoporotic fractures and 18 (0.48%) hip fractures reported. The FRAX and FI were significantly related to each other. Both FRAX and FI significantly predicted risk of major osteoporotic fracture, with a hazard ratio (HR) of 1.03 (95% confidence interval [CI]: 1.02-1.05) and 1.02 (95% CI: 1.01-1.04) for per-0.01 increment for the FRAX and FI respectively. The HRs were 1.37 (95% CI: 1.19-1.58) and 1.26 (95% CI: 1.12-1.42) for an increase of per-0.10 (approximately one SD) in the FRAX and FI respectively. Similar discriminative ability of the models was found: c-index = 0.62 for the FRAX and c-index = 0.61 for the FI. When cut-points were chosen to trichotomize participants into low-risk, medium-risk and high-risk groups, a significant increase in fracture risk was found in the high-risk group (HR = 2.04, 95% CI: 1.36-3.07) but not in the medium-risk group (HR = 1.23, 95% CI: 0.82-1.84) compared with the low-risk women for the FI, while for FRAX the medium-risk (HR = 2.00, 95% CI: 1.09-3.68) and high-risk groups (HR = 2.61, 95% CI: 1.48-4.58) predicted risk of major osteoporotic fracture significantly only when survival time exceeded 18months (550 days). Similar findings were observed for hip fracture and in sensitivity analyses. In conclusion, the FI is comparable with FRAX in the prediction of risk of future fractures, indicating that

  2. Dynamics of Variance Risk Premia, Investors' Sentiment and Return Predictability

    DEFF Research Database (Denmark)

    Rombouts, Jerome V.K.; Stentoft, Lars; Violante, Francesco

    We develop a joint framework linking the physical variance and its risk neutral expectation implying variance risk premia that are persistent, appropriately reacting to changes in level and variability of the variance and naturally satisfying the sign constraint. Using option market data and real...... events and only marginally by the premium associated with normal price fluctuations....

  3. Risk Prediction Models for Other Cancers or Multiple Sites

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing other multiple cancers over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  4. Evaluating and predicting overall process risk using event logs

    NARCIS (Netherlands)

    Pika, A.; Van Der Aalst, W.M.P.; Wynn, M.T.; Fidge, C.J.; Ter Hofstede, A.H.M.

    2016-01-01

    Companies standardise and automate their business processes in order to improve process efficiency and minimise operational risks. However, it is difficult to eliminate all process risks during the process design stage due to the fact that processes often run in complex and changeable environments

  5. Genetically Predicted Body Mass Index and Breast Cancer Risk

    DEFF Research Database (Denmark)

    Guo, Yan; Warren Andersen, Shaneda; Shu, Xiao-Ou

    2016-01-01

    BACKGROUND: Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or enviro...

  6. Resistance training and predicted risk of coronary heart disease in ...

    African Journals Online (AJOL)

    The purpose of this study was to determine the impact of resistance training, designed to prevent the development of coronary heart disease (CHD) based on the Framingham Risk Assessment (FRA) score. Twenty-five healthy sedentary men with low CHD risk were assigned to participate in a 16-week (three days per week) ...

  7. Application of cardiovascular disease risk prediction models and the relevance of novel biomarkers to risk stratification in Asian Indians.

    Science.gov (United States)

    Kanjilal, S; Rao, V S; Mukherjee, M; Natesha, B K; Renuka, K S; Sibi, K; Iyengar, S S; Kakkar, Vijay V

    2008-01-01

    The increasing pressure on health resources has led to the emergence of risk assessment as an essential tool in the management of cardiovascular disease (CVD). Concern exists regarding the validity of their generalization to all populations. Existing risk scoring models do not incorporate emerging 'novel' risk factors. In this context, the aim of the study was to examine the relevance of British, European, and Framingham predictive CVD risk scores to the asymptomatic high risk Indian population. Blood samples drawn from the participants were analyzed for various 'traditional' and 'novel' biomarkers, and their CVD risk factor profiling was also done. The Framingham model defined only 5% of the study cohort to be at high risk, which appears to be an underestimation of CVD risk in this genetically predisposed population. These subjects at high risk had significantly elevated levels of lipid, pro-inflammatory, pro-thrombotic, and serological markers. It is more relevant to develop risk predictive scores for application to the Indian population. This study substantiates the argument that alternative approaches to risk stratification are required in order to make them more adaptable and applicable to different populations with varying risk factor and disease patterns.

  8. Predicting reattendance at a high-risk breast cancer clinic.

    Science.gov (United States)

    Ormseth, Sarah R; Wellisch, David K; Aréchiga, Adam E; Draper, Taylor L

    2015-10-01

    The research about follow-up patterns of women attending high-risk breast-cancer clinics is sparse. This study sought to profile daughters of breast-cancer patients who are likely to return versus those unlikely to return for follow-up care in a high-risk clinic. Our investigation included 131 patients attending the UCLA Revlon Breast Center High Risk Clinic. Predictor variables included age, computed breast-cancer risk, participants' perceived personal risk, clinically significant depressive symptomatology (CES-D score ≥ 16), current level of anxiety (State-Trait Anxiety Inventory), and survival status of participants' mothers (survived or passed away from breast cancer). A greater likelihood of reattendance was associated with older age (adjusted odds ratio [AOR] = 1.07, p = 0.004), computed breast-cancer risk (AOR = 1.10, p = 0.017), absence of depressive symptomatology (AOR = 0.25, p = 0.009), past psychiatric diagnosis (AOR = 3.14, p = 0.029), and maternal loss to breast cancer (AOR = 2.59, p = 0.034). Also, an interaction was found between mother's survival and perceived risk (p = 0.019), such that reattendance was associated with higher perceived risk among participants whose mothers survived (AOR = 1.04, p = 0.002), but not those whose mothers died (AOR = 0.99, p = 0.685). Furthermore, a nonlinear inverted "U" relationship was observed between state anxiety and reattendance (p = 0.037); participants with moderate anxiety were more likely to reattend than those with low or high anxiety levels. Demographic, medical, and psychosocial factors were found to be independently associated with reattendance to a high-risk breast-cancer clinic. Explication of the profiles of women who may or may not reattend may serve to inform the development and implementation of interventions to increase the likelihood of follow-up care.

  9. From the lab - Predicting Autism in High-Risk Infants | NIH MedlinePlus the Magazine

    Science.gov (United States)

    ... High-Risk Infants Follow us Photo: iStock Predicting Autism in High-Risk Infants AN NIH-SUPPORTED STUDY ... high-risk, 6-month-old infants will develop autism spectrum disorder by age 2. Such a tool ...

  10. A comparison of the predictive properties of nine sex offender risk assessment instruments

    NARCIS (Netherlands)

    Smid, W.J.; Kamphuis, J.H.; Wever, E.C.; van Beek, D.J.

    2014-01-01

    Sex offender treatment is most effective when tailored to risk-need-responsivity principles, which dictate that treatment levels should match risk levels as assessed by structured risk assessment instruments. The predictive properties, missing values, and interrater agreement of the scores of 9

  11. Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction.

    Science.gov (United States)

    Luo, Gang

    2016-01-01

    Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. For the champion machine learning model of the competition, our method explained prediction results for 87.4 % of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. Our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.

  12. Risk Prediction of New Adjacent Vertebral Fractures After PVP for Patients with Vertebral Compression Fractures: Development of a Prediction Model

    International Nuclear Information System (INIS)

    Zhong, Bin-Yan; He, Shi-Cheng; Zhu, Hai-Dong; Wu, Chun-Gen; Fang, Wen; Chen, Li; Guo, Jin-He; Deng, Gang; Zhu, Guang-Yu; Teng, Gao-Jun

    2017-01-01

    PurposeWe aim to determine the predictors of new adjacent vertebral fractures (AVCFs) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs) and to construct a risk prediction score to estimate a 2-year new AVCF risk-by-risk factor condition.Materials and MethodsPatients with OVCFs who underwent their first PVP between December 2006 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included in this study. In training cohort, we assessed the independent risk predictors and developed the probability of new adjacent OVCFs (PNAV) score system using the Cox proportional hazard regression analysis. The accuracy of this system was then validated in both training and validation cohorts by concordance (c) statistic.Results421 patients (training cohort: n = 256; validation cohort: n = 165) were included in this study. In training cohort, new AVCFs after the first PVP treatment occurred in 33 (12.9%) patients. The independent risk factors were intradiscal cement leakage and preexisting old vertebral compression fracture(s). The estimated 2-year absolute risk of new AVCFs ranged from less than 4% in patients with neither independent risk factors to more than 45% in individuals with both factors.ConclusionsThe PNAV score is an objective and easy approach to predict the risk of new AVCFs.

  13. Risk Prediction of New Adjacent Vertebral Fractures After PVP for Patients with Vertebral Compression Fractures: Development of a Prediction Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhong, Bin-Yan; He, Shi-Cheng; Zhu, Hai-Dong [Southeast University, Department of Radiology, Medical School, Zhongda Hospital (China); Wu, Chun-Gen [Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Department of Diagnostic and Interventional Radiology (China); Fang, Wen; Chen, Li; Guo, Jin-He; Deng, Gang; Zhu, Guang-Yu; Teng, Gao-Jun, E-mail: gjteng@vip.sina.com [Southeast University, Department of Radiology, Medical School, Zhongda Hospital (China)

    2017-02-15

    PurposeWe aim to determine the predictors of new adjacent vertebral fractures (AVCFs) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs) and to construct a risk prediction score to estimate a 2-year new AVCF risk-by-risk factor condition.Materials and MethodsPatients with OVCFs who underwent their first PVP between December 2006 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included in this study. In training cohort, we assessed the independent risk predictors and developed the probability of new adjacent OVCFs (PNAV) score system using the Cox proportional hazard regression analysis. The accuracy of this system was then validated in both training and validation cohorts by concordance (c) statistic.Results421 patients (training cohort: n = 256; validation cohort: n = 165) were included in this study. In training cohort, new AVCFs after the first PVP treatment occurred in 33 (12.9%) patients. The independent risk factors were intradiscal cement leakage and preexisting old vertebral compression fracture(s). The estimated 2-year absolute risk of new AVCFs ranged from less than 4% in patients with neither independent risk factors to more than 45% in individuals with both factors.ConclusionsThe PNAV score is an objective and easy approach to predict the risk of new AVCFs.

  14. Prostate Cancer Predictive Simulation Modelling, Assessing the Risk Technique (PCP-SMART): Introduction and Initial Clinical Efficacy Evaluation Data Presentation of a Simple Novel Mathematical Simulation Modelling Method, Devised to Predict the Outcome of Prostate Biopsy on an Individual Basis.

    Science.gov (United States)

    Spyropoulos, Evangelos; Kotsiris, Dimitrios; Spyropoulos, Katherine; Panagopoulos, Aggelos; Galanakis, Ioannis; Mavrikos, Stamatios

    2017-02-01

    We developed a mathematical "prostate cancer (PCa) conditions simulating" predictive model (PCP-SMART), from which we derived a novel PCa predictor (prostate cancer risk determinator [PCRD] index) and a PCa risk equation. We used these to estimate the probability of finding PCa on prostate biopsy, on an individual basis. A total of 371 men who had undergone transrectal ultrasound-guided prostate biopsy were enrolled in the present study. Given that PCa risk relates to the total prostate-specific antigen (tPSA) level, age, prostate volume, free PSA (fPSA), fPSA/tPSA ratio, and PSA density and that tPSA ≥ 50 ng/mL has a 98.5% positive predictive value for a PCa diagnosis, we hypothesized that correlating 2 variables composed of 3 ratios (1, tPSA/age; 2, tPSA/prostate volume; and 3, fPSA/tPSA; 1 variable including the patient's tPSA and the other, a tPSA value of 50 ng/mL) could operate as a PCa conditions imitating/simulating model. Linear regression analysis was used to derive the coefficient of determination (R 2 ), termed the PCRD index. To estimate the PCRD index's predictive validity, we used the χ 2 test, multiple logistic regression analysis with PCa risk equation formation, calculation of test performance characteristics, and area under the receiver operating characteristic curve analysis using SPSS, version 22 (P regression revealed the PCRD index as an independent PCa predictor, and the formulated risk equation was 91% accurate in predicting the probability of finding PCa. On the receiver operating characteristic analysis, the PCRD index (area under the curve, 0.926) significantly (P < .001) outperformed other, established PCa predictors. The PCRD index effectively predicted the prostate biopsy outcome, correctly identifying 9 of 10 men who were eventually diagnosed with PCa and correctly ruling out PCa for 9 of 10 men who did not have PCa. Its predictive power significantly outperformed established PCa predictors, and the formulated risk equation

  15. Cardiovascular risk prediction in chronic kidney disease patients

    Directory of Open Access Journals (Sweden)

    Santiago Cedeño Mora

    2017-05-01

    Conclusion: The cardiovascular risk scores (FRS-CVD and ASCVD [AHA/ACC 2013] can estimate the probability of atherosclerotic cardiovascular events in patients with CKD regardless of renal function, albuminuria and previous cardiovascular events.

  16. Using Simulated Ground Motions to Constrain Near-Source Ground Motion Prediction Equations in Areas Experiencing Induced Seismicity

    Science.gov (United States)

    Bydlon, S. A.; Dunham, E. M.

    2016-12-01

    Recent increases in seismic activity in historically quiescent areas such as Oklahoma, Texas, and Arkansas, including large, potentially induced events such as the 2011 Mw 5.6 Prague, OK, earthquake, have spurred the need for investigation into expected ground motions associated with these seismic sources. The neoteric nature of this seismicity increase corresponds to a scarcity of ground motion recordings within 50 km of earthquakes Mw 3.0 and greater, with increasing scarcity at larger magnitudes. Gathering additional near-source ground motion data will help better constraints on regional ground motion prediction equations (GMPEs) and will happen over time, but this leaves open the possibility of damaging earthquakes occurring before potential ground shaking and seismic hazard in these areas are properly understood. To aid the effort of constraining near-source GMPEs associated with induced seismicity, we integrate synthetic ground motion data from simulated earthquakes into the process. Using the dynamic rupture and seismic wave propagation code waveqlab3d, we perform verification and validation exercises intended to establish confidence in simulated ground motions for use in constraining GMPEs. We verify the accuracy of our ground motion simulator by performing the PEER/SCEC layer-over-halfspace comparison problem LOH.1 Validation exercises to ensure that we are synthesizing realistic ground motion data include comparisons to recorded ground motions for specific earthquakes in target areas of Oklahoma between Mw 3.0 and 4.0. Using a 3D velocity structure that includes a 1D structure with additional small-scale heterogeneity, the properties of which are based on well-log data from Oklahoma, we perform ground motion simulations of small (Mw 3.0 - 4.0) earthquakes using point moment tensor sources. We use the resulting synthetic ground motion data to develop GMPEs for small earthquakes in Oklahoma. Preliminary results indicate that ground motions can be amplified

  17. Accuracy and precision of the CKD-EPI and MDRD predictive equations compared with glomerular filtration rate measured by inulin clearance in a Saudi population.

    Science.gov (United States)

    Al-Wakeel, Jamal Saleh

    2016-01-01

    Predictive equations for estimating glomerular filtration rate (GFR) in different clinical conditions should be validated by comparing with the measurement of GFR using inulin clearance, a highly accurate measure of GFR. Our aim was to validate the Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) equations by comparing it to the GFR measured using inulin clearance in chronic kidney disease (CKD) patients. Cross-sectional study performed in adult Saudi patients with CKD. King Saud University Affiliated Hospital, Riyadh, Saudi Arabia in 2014. We compared GFR measured by inulin clearance with the estimated GFR calculated using CKD-EPI and MDRD predictive formulas. Correlation, bias, precision and accuracy between the estimated GFR and inulin clearance. Comparisons were made in 31 participants (23 CKD and 8 transplanted), including 19 males (mean age 42.2 [15] years and weight 68.7 [18] kg). GFR using inulin was 51.54 (33.8) mL/min/1.73 m2 in comparison to inulin clearance, the GFR by the predictive equations was: CKD-EPI creatinine 52.6 (34.4) mL/ min/1.73 m2 (P=.490), CKD-EPI cystatin C 41.39 (30.30) mL/min/1.73 m2 (P=.002), CKD creatinine-cystatin C 45.03 (30.9) mL/min/1.73 m2 (P=.004) and MDRD GFR 48.35 (31.5) mL/min/1.73 m2 (P=.028) (statistical comparisons vs inulin). Bland-Altman plots demonstrated that GFR estimated by the CKD-EPI creatinine was the most accurate compared with inulin clearance, having a mean difference (estimated bias) and limits of agreement of -1.1 (15.6,-17.7). By comparison the mean differences for predictive equations were: CKD-EPI cystatin C 10.2 (43.7,-23.4), CKD creatinine-cystatin C 6.5 (29.3,-16.3) and MDRD 3.2 (18.3,-11.9). except for CKD-EPI creatinine, all of the equations underestimated GFR in comparison with inulin clearance. When compared with inulin clearance, the CKD-EPI creatinine equation is the most accurate, precise and least biased equation for estimation of GFR

  18. Uranium Chemical and Radiological Risk Assessment for Freshwater Ecosystems Receiving Ore Mining Releases: Principles, Equations and Parameters

    International Nuclear Information System (INIS)

    Beaugelin-Seiller, K.; Garnier-Laplace, J.; Gilbin, R.; Adam, C.

    2008-01-01

    Uranium is an element that has the solely characteristic to behave as significant hazard both from a chemical and radiological point of view. Exclusively of natural occurrence, its distribution into the environment may be influenced by human activities, such as nuclear fuel cycle, military use of depleted uranium, or coal and phosphate fertilizer use, which finally may impact freshwater ecosystems. Until now, the associated environmental impact and risk assessments were conducted separately. We propose here to apply the same methodology to evaluate the ecological risk due to potential chemotoxicity and radiotoxicity of uranium. This methodology is articulated into the classical four steps (EC, 2003: problem formulation, effect and exposure analysis, risk characterisation). The problem formulation dealt both with uranium viewed as a chemical element and as the three isotopes 234, 235 and 238 of uranium and their main daughters. Then, the exposure analysis of non-human species was led on the basis of a common conceptual model of the fluxes occurring in freshwater ecosystems. No-effect values for the ecosystem were derived using the same effect data treatment in parallel. A Species Sensitivity Distribution was fitted: (1) to the ecotoxicity data sets illustrating uranium chemotoxicity and allowing the estimation of a Predicted-No-Effect-Concentration for uranium in water expressed in μg/L; (2) to radiotoxicity effect data as it was done within the ERICA project, allowing the estimation of a Predicted No-Effect-Dose-Rate (in μGy·h -1 ). Two methods were then applied to characterize the risk to the ecosystem: a screening method using the risk quotient approach, involving for the radiological aspect back calculation of the water limiting concentration from the PNEDR for each isotope taken into account and a probabilistic risk assessment. A former uranium ore mining case-study will help in demonstrating the application of the whole methodology

  19. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.

    Directory of Open Access Journals (Sweden)

    Yiming Hu

    2017-06-01

    Full Text Available Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

  20. Multimethod prediction of child abuse risk in an at-risk sample of male intimate partner violence offenders.

    Science.gov (United States)

    Rodriguez, Christina M; Gracia, Enrique; Lila, Marisol

    2016-10-01

    The vast majority of research on child abuse potential has concentrated on women demonstrating varying levels of risk of perpetrating physical child abuse. In contrast, the current study considered factors predictive of physical child abuse potential in a group of 70 male intimate partner violence offenders, a group that would represent a likely high risk group. Elements of Social Information Processing theory were evaluated, including pre-existing schemas of empathy, anger, and attitudes approving of parent-child aggression considered as potential moderators of negative attributions of child behavior. To lend methodological rigor, the study also utilized multiple measures and multiple methods, including analog tasks, to predict child abuse risk. Contrary to expectations, findings did not support the role of anger independently predicting child abuse risk in this sample of men. However, preexisting beliefs approving of parent-child aggression, lower empathy, and more negative child behavior attributions independently predicted abuse potential; in addition, greater anger, poorer empathy, and more favorable attitudes toward parent-child aggression also exacerbated men's negative child attributions to further elevate their child abuse risk. Future work is encouraged to consider how factors commonly considered in women parallel or diverge from those observed to elevate child abuse risk in men of varying levels of risk. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study.

    Science.gov (United States)

    Seyednasrollah, Fatemeh; Mäkelä, Johanna; Pitkänen, Niina; Juonala, Markus; Hutri-Kähönen, Nina; Lehtimäki, Terho; Viikari, Jorma; Kelly, Tanika; Li, Changwei; Bazzano, Lydia; Elo, Laura L; Raitakari, Olli T

    2017-06-01

    Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity. © 2017 American Heart Association, Inc.

  2. Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery

    DEFF Research Database (Denmark)

    Meretoja, Tuomo J; Andersen, Kenneth Geving; Bruce, Julie

    2017-01-01

    are missing. The aim was to develop a clinically applicable risk prediction tool. Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity......), high body mass index ( P = .039), axillary lymph node dissection ( P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day ( P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC......-AUC, 0.740) cohorts. At the 20% risk level, the model had 32.8% and 47.4% sensitivity and 94.4% and 82.4% specificity in the Danish and Scottish cohorts, respectively. Conclusion Our validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen...

  3. The role of acculturation and family functioning in predicting HIV risk behaviors among Hispanic delinquent youth.

    Science.gov (United States)

    Farrelly, Colleen; Cordova, David; Huang, Shi; Estrada, Yannine; Prado, Guillermo

    2013-06-01

    The present study examined the relationship between Berry's acculturation typology and HIV risk behaviors and whether family functioning mediated any such effects. A total of 235 high risk Hispanic adolescents were categorized into one of Berry's four acculturation typologies through the use of cut-off scores on measures of Hispanicism and Americanism. Structural equation modeling was used to examine the effects of acculturation typology on HIV risk behaviors and the indirect effects of acculturation typology on HIV risk behaviors through family functioning. Acculturation typology was related to HIV risk behaviors. Family functioning partially mediated the effects of acculturation typology on the HIV risk behavior outcomes. These findings suggest that both Americanism and Hispanicism play an important role in the etiology of HIV risk behaviors among Hispanic youth and that both, along with family functioning, are important to consider when designing preventive interventions for this population.

  4. The Use of the Framingham Equation to Predict Myocardial Infarctions in HIV-infected Patients: Comparison with Observed Events in the D:A:D Study

    DEFF Research Database (Denmark)

    Law, MG; Friis-Møller, Nina; El-Sadr, WM

    2006-01-01

    and predicted rates of MI increased in a parallel fashion with increased CART duration, suggesting that the observed increase in risk of MI may at least in part be explained by CART-induced changes in conventional risk factors. These findings provide guidance in terms of choosing lifestyle or therapeutic...

  5. Update on breast cancer risk prediction and prevention.

    Science.gov (United States)

    Sestak, Ivana; Cuzick, Jack

    2015-02-01

    Breast cancer is the most common cancer in women worldwide. This review will focus on current prevention strategies for women at high risk. The identification of women who are at high risk of developing breast cancer is key to breast cancer prevention. Recent findings have shown that the inclusion of breast density and a panel of low-penetrance genetic polymorphisms can improve risk estimation compared with previous models. Preventive therapy with aromatase inhibitors has produced large reductions in breast cancer incidence in postmenopausal women. Tamoxifen confers long-term protection and is the only proven preventive treatment for premenopausal women. Several other agents, including metformin, bisphosphonates, aspirin and statins, have been found to be effective in nonrandomized settings. There are many options for the prevention of oestrogen-positive breast cancer, in postmenopausal women who can be given a selective oestrogen receptor modulator or an aromatase inhibitor. It still remains unclear how to prevent oestrogen-negative breast cancer, which occurs more often in premenopausal women. Identification of women at high risk of the disease is crucial, and the inclusion of breast density and a panel of genetic polymorphisms, which individually have low penetrance, can improve risk assessment.

  6. A structural equation modelling approach to explore the role of B vitamins and immune markers in lung cancer risk.

    Science.gov (United States)

    Baltar, Valéria Troncoso; Xun, Wei W; Johansson, Mattias; Ferrari, Pietro; Chuang, Shu-Chun; Relton, Caroline; Ueland, Per Magne; Midttun, Øivind; Slimani, Nadia; Jenab, Mazda; Clavel-Chapelon, Françoise; Boutron-Ruault, Marie-Christine; Fagherazzi, Guy; Kaaks, Rudolf; Rohrmann, Sabine; Boeing, Heiner; Weikert, Cornelia; Bueno-de-Mesquita, Bas; Boshuizen, Hendriek; van Gils, Carla H; Onland-Moret, N Charlotte; Agudo, Antonio; Barricarte, Aurelio; Navarro, Carmen; Rodríguez, Laudina; Castaño, José Maria Huerta; Larrañaga, Nerea; Khaw, Kay-Tee; Wareham, Nick; Allen, Naomi E; Crowe, Francesca; Gallo, Valentina; Norat, Teresa; Krogh, Vittorio; Masala, Giovanna; Panico, Salvatore; Sacerdote, Carlotta; Tumino, Rosario; Trichopoulou, Antonia; Lagiou, Pagona; Trichopoulos, Dimitrios; Rasmuson, Torgny; Hallmans, Göran; Roswall, Nina; Tjønneland, Anne; Riboli, Elio; Brennan, Paul; Vineis, Paolo

    2013-08-01

    The one-carbon metabolism (OCM) is considered key in maintaining DNA integrity and regulating gene expression, and may be involved in the process of carcinogenesis. Several B-vitamins and amino acids have been implicated in lung cancer risk, via the OCM directly as well as immune system activation. However it is unclear whether these factors act independently or through complex mechanisms. The current study applies structural equations modelling (SEM) to further disentangle the mechanisms involved in lung carcinogenesis. SEM allows simultaneous estimation of linear relations where a variable can be the outcome in one equation and the predictor in another, as well as allowing estimation using latent variables (factors estimated by correlation matrix). A large number of biomarkers have been analysed from 891 lung cancer cases and 1,747 controls nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Four putative mechanisms in the OCM and immunity were investigated in relation to lung cancer risk: methionine-homocysteine metabolism, folate cycle, transsulfuration, and mechanisms involved in inflammation and immune activation, all adjusted for tobacco exposure. The hypothesized SEM model confirmed a direct and protective effect for factors representing methionine-homocysteine metabolism (p = 0.020) and immune activation (p = 0.021), and an indirect protective effect of folate cycle (p = 0.019), after adjustment for tobacco smoking. In conclusion, our results show that in the investigation of the involvement of the OCM, the folate cycle and immune system in lung carcinogenesis, it is important to consider complex pathways (by applying SEM) rather than the effects of single vitamins or nutrients (e.g. using traditional multiple regression). In our study SEM were able to suggest a greater role of the methionine-homocysteine metabolism and immune activation over other potential mechanisms.

  7. Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

    Science.gov (United States)

    King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin

    2011-01-01

    Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

  8. Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

    Directory of Open Access Journals (Sweden)

    Michael King

    Full Text Available Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL for the development of hazardous drinking in safe drinkers.A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women.69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873. The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51. External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846 and Hedge's g of 0.68 (95% CI 0.57, 0.78.The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

  9. Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

    Science.gov (United States)

    Luo, Wei; Tran, Truyen; Berk, Michael; Venkatesh, Svetha

    2016-01-01

    Background Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk. Objective The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data. Methods We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC). Results The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians. Conclusions This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk. PMID:27400764

  10. Maternal smoking predicts the risk of spontaneous abortion

    DEFF Research Database (Denmark)

    Nielsen, Ann; Hannibal, Charlotte Gerd; Lindekilde, Bodil Eriksen

    2006-01-01

    BACKGROUND: Few studies have examined smoking prior to pregnancy and the occurrence of spontaneous abortion, as most studies have addressed the risk of spontaneous abortion in relation to smoking during pregnancy. However, results are not entirely consistent. The aim of the present study...... was to assess the risk of spontaneous abortion considering smoking prior to pregnancy. METHODS: We performed a nested case-control study using prospective data from a population-based cohort comprising 11,088 women aged 20-29 years. From this cohort, women who experienced either a spontaneous abortion (n=343......) or who gave birth (n=1,578) during follow-up were selected. Associations between self-reported smoking at enrollment and subsequent spontaneous abortion were analyzed by means of multiple logistic regression. RESULTS: The risk of spontaneous abortion in relation to pre-pregnancy smoking showed a clear...

  11. In-treatment stroke volume predicts cardiovascular risk in hypertension

    DEFF Research Database (Denmark)

    Lønnebakken, Mai T; Gerdts, Eva; Boman, Kurt

    2011-01-01

    , the prespecified primary study endpoint, was assessed in Cox regression analysis using data from baseline and annual follow-up visits in 855 patients during 4.8 years of randomized losartan-based or atenolol-based treatment in the Losartan Intervention For Endpoint reduction in hypertension (LIFE) echocardiography...... with higher risk of cardiovascular events {hazard ratio 1.69 per 1 SD (6 ml/m2.04) lower stroke volume [95% confidence interval (CI) 1.35–2.11], P secondary model also independent of stress-corrected midwall shortening......, hence, adds information on cardiovascular risk in treated hypertensive patients beyond assessment of left ventricular structure alone....

  12. Risk-informed prediction of feeder end of life

    International Nuclear Information System (INIS)

    Jyrkama, M.; Pandey, M.

    2011-01-01

    The operating life of feeder piping is negatively impacted by flow accelerated corrosion (FAC). In this study, an assessment of a large set of inspection data reveals that FAC in feeders is a relatively stationary process, with variability only at the local scale. Given the added uncertainty from inspection coverage, a new method for estimating the thinning rate and feeder EOL is developed using a probabilistic approach. The results of the study illustrate the benefits of the methodology in supporting risk-informed decision making at the station by quantifying the present and incremental risk in the feeder system over time. (author)

  13. Risk-informed prediction of feeder end of life

    Energy Technology Data Exchange (ETDEWEB)

    Jyrkama, M.; Pandey, M. [Univ. of Waterloo, Ontario (Canada)

    2011-07-01

    The operating life of feeder piping is negatively impacted by flow accelerated corrosion (FAC). In this study, an assessment of a large set of inspection data reveals that FAC in feeders is a relatively stationary process, with variability only at the local scale. Given the added uncertainty from inspection coverage, a new method for estimating the thinning rate and feeder EOL is developed using a probabilistic approach. The results of the study illustrate the benefits of the methodology in supporting risk-informed decision making at the station by quantifying the present and incremental risk in the feeder system over time. (author)

  14. Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

    Science.gov (United States)

    Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

    2015-01-01

    Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

  15. Rate equation for creatine kinase predicts the in vivo reaction velocity: 31P NMR surface coil studies in brain, heart, and skeletal muscle of the living rat

    International Nuclear Information System (INIS)

    Bittl, J.A.; DeLayre, J.; Ingwall, J.S.

    1987-01-01

    Brain, heart, and skeletal muscle contain four different creatine kinase isozymes and various concentrations of substrates for the creatine kinase reaction. To identify if the velocity of the creatine kinase reaction under cellular conditions is regulated by enzyme activity and substrate concentrations as predicted by the rate equation, the authors used 31 P NMR and spectrophotometric techniques to measure reaction velocity, enzyme content, isozyme distribution, and concentrations of substrates in brain, heart, and skeletal muscle of living rat under basal or resting conditions. The total tissue activity of creatine kinase in the direction of MgATP synthesis provided an estimate for V/sub max/ and exceeded the NMR-determined in vivo reaction velocities by an order of magnitude. The isozyme composition varied among the three tissues: >99% BB for brain; 14% MB, 61% MM, and 25% mitochondrial for heart; and 98% MM and 2% mitochondrial for skeletal muscle. The NMR-determined reaction velocities agreed with predicted values from the creatine kinase rate equation. The concentrations of free creatine and cytosolic MgADP, being less than or equal to the dissociation constants for each isozyme, were dominant terms in the creatine kinase rate equation for predicting the in vivo reaction velocity. Thus, they observed that the velocity of the creatine kinase reaction is regulated by total tissue enzyme activity and by the concentrations of creatine and MgADP in a manner that is independent of isozyme distribution

  16. Polygenic risk predicts obesity in both white and black young adults.

    Directory of Open Access Journals (Sweden)

    Benjamin W Domingue

    Full Text Available To test transethnic replication of a genetic risk score for obesity in white and black young adults using a national sample with longitudinal data.A prospective longitudinal study using the National Longitudinal Study of Adolescent Health Sibling Pairs (n = 1,303. Obesity phenotypes were measured from anthropometric assessments when study members were aged 18-26 and again when they were 24-32. Genetic risk scores were computed based on published genome-wide association study discoveries for obesity. Analyses tested genetic associations with body-mass index (BMI, waist-height ratio, obesity, and change in BMI over time.White and black young adults with higher genetic risk scores had higher BMI and waist-height ratio and were more likely to be obese compared to lower genetic risk age-peers. Sibling analyses revealed that the genetic risk score was predictive of BMI net of risk factors shared by siblings. In white young adults only, higher genetic risk predicted increased risk of becoming obese during the study period. In black young adults, genetic risk scores constructed using loci identified in European and African American samples had similar predictive power.Cumulative information across the human genome can be used to characterize individual level risk for obesity. Measured genetic risk accounts for only a small amount of total variation in BMI among white and black young adults. Future research is needed to identify modifiable environmental exposures that amplify or mitigate genetic risk for elevated BMI.

  17. Polygenic risk predicts obesity in both white and black young adults.

    Science.gov (United States)

    Domingue, Benjamin W; Belsky, Daniel W; Harris, Kathleen Mullan; Smolen, Andrew; McQueen, Matthew B; Boardman, Jason D

    2014-01-01

    To test transethnic replication of a genetic risk score for obesity in white and black young adults using a national sample with longitudinal data. A prospective longitudinal study using the National Longitudinal Study of Adolescent Health Sibling Pairs (n = 1,303). Obesity phenotypes were measured from anthropometric assessments when study members were aged 18-26 and again when they were 24-32. Genetic risk scores were computed based on published genome-wide association study discoveries for obesity. Analyses tested genetic associations with body-mass index (BMI), waist-height ratio, obesity, and change in BMI over time. White and black young adults with higher genetic risk scores had higher BMI and waist-height ratio and were more likely to be obese compared to lower genetic risk age-peers. Sibling analyses revealed that the genetic risk score was predictive of BMI net of risk factors shared by siblings. In white young adults only, higher genetic risk predicted increased risk of becoming obese during the study period. In black young adults, genetic risk scores constructed using loci identified in European and African American samples had similar predictive power. Cumulative information across the human genome can be used to characterize individual level risk for obesity. Measured genetic risk accounts for only a small amount of total variation in BMI among white and black young adults. Future research is needed to identify modifiable environmental exposures that amplify or mitigate genetic risk for elevated BMI.

  18. Reliability of blood pressure measurement and cardiovascular risk prediction

    NARCIS (Netherlands)

    van der Hoeven, N.V.

    2016-01-01

    High blood pressure is one of the leading risk factors for cardiovascular disease, but difficult to reliably assess because there are many factors which can influence blood pressure including stress, exercise or illness. The first part of this thesis focuses on possible ways to improve the

  19. Genetic liability for schizophrenia predicts risk of immune disorders

    NARCIS (Netherlands)

    Stringer, Sven; Kahn, René S.; de Witte, Lot D.; Ophoff, Roel A.; Derks, Eske M.

    2014-01-01

    Schizophrenia patients and their parents have an increased risk of immune disorders compared to population controls and their parents. This may be explained by genetic overlap in the pathogenesis of both types of disorders. The purpose of this study was to investigate the genetic overlap between

  20. Genetic liability for schizophrenia predicts risk of immune disorders

    NARCIS (Netherlands)

    Stringer, Sven; Kahn, René S; de Witte, Lot D; Ophoff, Roel A; Derks, Eske M

    2014-01-01

    BACKGROUND: Schizophrenia patients and their parents have an increased risk of immune disorders compared to population controls and their parents. This may be explained by genetic overlap in the pathogenesis of both types of disorders. The purpose of this study was to investigate the genetic overlap

  1. Androidal fat dominates in predicting cardiometabolic risk in postmenopausal women

    Science.gov (United States)

    We hypothesized that soy isoflavones would attenuate the anticipated increase in androidal fat mass in postmenopausal women during the 36-month treatment, and thereby favorably modify the circulating cardiometabolic risk factors: triacylglycerol, LDLC, HDL-C, glucose, insulin, uric acid, C-reactive ...

  2. Self-Esteem and Future Orientation Predict Adolescents' Risk Engagement

    Science.gov (United States)

    Jackman, Danielle M.; MacPhee, David

    2017-01-01

    This study's purpose was to examine the relations among future orientation, self-esteem, and later adolescent risk behaviors, and to compare two mediational models involving self-esteem versus future orientation as mediators. An ethnically diverse sample of 12- to 14-year-olds (N = 862, 54% female, 53% ethnic minority) was assessed longitudinally.…

  3. Psychological Factors Predicting Risk-Taking Propensity of Poultry ...

    African Journals Online (AJOL)

    Using a correlational design, the following measures were used: Performance Failure Appraisal Inventory (PFAI), Need for achievement scale, Locus of Control Behaviour and Risk-taking propensity. Results indicate a significant joint influence of perceived of fear of failure, achievement motivation, locus of control, age and ...

  4. Optimization of agricultural field workability predictions for improved risk management

    Science.gov (United States)

    Risks introduced by weather variability are key considerations in agricultural production. The sensitivity of agriculture to weather variability is of special concern in the face of climate change. In particular, the availability of workable days is an important consideration in agricultural practic...

  5. Prediction of Risk Behaviors in HIV-infected Patients Based on Family Functioning: The Mediating Roles of Lifestyle and Risky Decision Making

    Directory of Open Access Journals (Sweden)

    Fariba Ebrahim Babaei

    2017-09-01

    Full Text Available Background and Objective: Risk behaviors are more common in the HIV-positive patients than that in the general population. These behaviors are affected by various factors, such as biological, familial, and social determinants, peer group, media, and lifestyle. Low family functioning is one of the important factors predicting risk behaviors. Regarding this, the present study aimed to investigate the role of family functioning in predicting risk behaviors in the HIV-infected patients based on the mediating roles of risky decision making and lifestyle. Materials and Methods: This descriptive correlational study was conducted on 147 HIV-positive patients selected through convenience sampling technique. The data were collected using the health promoting lifestyle profile-2 (HPLP-2, family adaptability and cohesion scale IV (FACES-IV, balloon analogue risk task (BART, and risk behavior assessment in social situation. The data were analyzed using structural equation modeling method in LISREL 8.8 software. Results: According to the results, there was an indirect relationship between family functioning and risk behaviors. Furthermore, family functioning both directly and indirectly affected the risk behaviors through two mediators of lifestyle and risky decision making. Conclusion: As the findings indicated, family functioning directly contributed to risk behaviors. Moreover, this variable indirectly affected risk behaviors through the mediating roles of risky decision making and lifestyle. Consequently, the future studies should focus more deeply on family functioning role in the risk behaviors of the HIV-infected patients.

  6. Development and Validation of a Prediction Model to Estimate Individual Risk of Pancreatic Cancer.

    Science.gov (United States)

    Yu, Ami; Woo, Sang Myung; Joo, Jungnam; Yang, Hye-Ryung; Lee, Woo Jin; Park, Sang-Jae; Nam, Byung-Ho

    2016-01-01

    There is no reliable screening tool to identify people with high risk of developing pancreatic cancer even though pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea. The goal of this study was to develop an individualized risk prediction model that can be used to screen for asymptomatic pancreatic cancer in Korean men and women. Gender-specific risk prediction models for pancreatic cancer were developed using the Cox proportional hazards model based on an 8-year follow-up of a cohort study of 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996-1997. The performance of the models was evaluated with respect to their discrimination and calibration ability based on the C-statistic and Hosmer-Lemeshow type χ2 statistic. A total of 1,634 (0.13%) men and 561 (0.10%) women were newly diagnosed with pancreatic cancer. Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the risk prediction model for men. Height, BMI, fasting glucose, urine glucose, smoking, and drinking habit were included in the risk prediction model for women. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. The risk prediction model exhibited good discrimination and calibration ability, and in external validation it had excellent prediction ability. Gender-specific risk prediction models for pancreatic cancer were developed and validated for the first time. The prediction models will be a useful tool for detecting high-risk individuals who may benefit from increased surveillance for pancreatic cancer.

  7. Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Jae Kwon Kim

    2017-01-01

    Full Text Available Background. Of the machine learning techniques used in predicting coronary heart disease (CHD, neural network (NN is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC curve of the proposed model (0.749 ± 0.010 was larger than the Framingham risk score (FRS (0.393 ± 0.010. Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

  8. Toward Malaria Risk Prediction in Afghanistan Using Remote Sensing

    Science.gov (United States)

    Safi, N.; Adimi, F.; Soebiyanto, R. P.; Kiang, R. K.

    2010-01-01

    Malaria causes more than one million deaths every year worldwide, with most of the mortality in Sub-Saharan Africa. It is also a significant public health concern in Afghanistan, with approximately 60% of the population, or nearly 14 million people, living in a malaria-endemic area. Malaria transmission has been shown to be dependent on a number of environmental and meteorological variables. For countries in the tropics and the subtropics, rainfall is normally the most important variable, except for regions with high altitude where temperature may also be important. Afghanistan s diverse landscape contributes to the heterogeneous malaria distribution. Understanding the environmental effects on malaria transmission is essential to the effective control of malaria in Afghanistan. Provincial malaria data gathered by Health Posts in 23 provinces during 2004-2007 are used in this study. Remotely sensed geophysical parameters, including precipitation from TRMM, and surface temperature and vegetation index from MODIS are used to derive the empirical relationship between malaria cases and these geophysical parameters. Both neural network methods and regression analyses are used to examine the environmental dependency of malaria transmission. And the trained models are used for predicting future transmission. While neural network methods are intrinsically more adaptive for nonlinear relationship, the regression approach lends itself in providing statistical significance measures. Our results indicate that NDVI is the strongest predictor. This reflects the role of irrigation, instead of precipitation, in Afghanistan for agricultural production. The second strongest prediction is surface temperature. Precipitation is not shown as a significant predictor, contrary to other malarious countries in the tropics or subtropics. With the regression approach, the malaria time series are modelled well, with average R2 of 0.845. For cumulative 6-month prediction of malaria cases, the

  9. A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

    Science.gov (United States)

    Xie, Jiang; Liu, Yan; Zeng, Xu; Zhang, Wu; Mei, Zhen

    2017-07-01

    An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

  10. Development of equations to predict the influence of floor space on average daily gain, average daily feed intake and gain : feed ratio of finishing pigs.

    Science.gov (United States)

    Flohr, J R; Dritz, S S; Tokach, M D; Woodworth, J C; DeRouchey, J M; Goodband, R D

    2018-05-01

    Floor space allowance for pigs has substantial effects on pig growth and welfare. Data from 30 papers examining the influence of floor space allowance on the growth of finishing pigs was used in a meta-analysis to develop alternative prediction equations for average daily gain (ADG), average daily feed intake (ADFI) and gain : feed ratio (G : F). Treatment means were compiled in a database that contained 30 papers for ADG and 28 papers for ADFI and G : F. The predictor variables evaluated were floor space (m2/pig), k (floor space/final BW0.67), Initial BW, Final BW, feed space (pigs per feeder hole), water space (pigs per waterer), group size (pigs per pen), gender, floor type and study length (d). Multivariable general linear mixed model regression equations were used. Floor space treatments within each experiment were the observational and experimental unit. The optimum equations to predict ADG, ADFI and G : F were: ADG, g=337.57+(16 468×k)-(237 350×k 2)-(3.1209×initial BW (kg))+(2.569×final BW (kg))+(71.6918×k×initial BW (kg)); ADFI, g=833.41+(24 785×k)-(388 998×k 2)-(3.0027×initial BW (kg))+(11.246×final BW (kg))+(187.61×k×initial BW (kg)); G : F=predicted ADG/predicted ADFI. Overall, the meta-analysis indicates that BW is an important predictor of ADG and ADFI even after computing the constant coefficient k, which utilizes final BW in its calculation. This suggests including initial and final BW improves the prediction over using k as a predictor alone. In addition, the analysis also indicated that G : F of finishing pigs is influenced by floor space allowance, whereas individual studies have concluded variable results.

  11. An updated prediction model of the global risk of cardiovascular disease in HIV-positive persons

    DEFF Research Database (Denmark)

    Friis-Møller, Nina; Ryom, Lene; Smith, Colette

    2016-01-01

    ,663 HIV-positive persons from 20 countries in Europe and Australia, who were free of CVD at entry into the Data-collection on Adverse Effects of Anti-HIV Drugs (D:A:D) study. Cox regression models (full and reduced) were developed that predict the risk of a global CVD endpoint. The predictive performance...... significantly predicted risk more accurately than the recalibrated Framingham model (Harrell's c-statistic of 0.791, 0.783 and 0.766 for the D:A:D full, D:A:D reduced, and Framingham models respectively; p models also more accurately predicted five-year CVD-risk for key prognostic subgroups...... to quantify risk and to guide preventive care....

  12. Nonvisible tumors on multiparametric magnetic resonance imaging does not predict low-risk prostate cancer

    Directory of Open Access Journals (Sweden)

    Seung Hwan Lee

    2015-12-01

    Conclusions: Even though cancer foci were not visualized by postbiopsy MRI, the pathological tumor volumes and extent of GS upgrading were relatively high. Therefore, nonvisible tumors by multiparametric MRI do not appear to be predictive of low-risk PCA.

  13. A Prediction Rule for Risk Stratification of Incidentally Discovered Gallstones

    DEFF Research Database (Denmark)

    Shabanzadeh, Daniel Mønsted; Sørensen, Lars Tue; Jørgensen, Torben

    2016-01-01

    % confidence interval [CI], 1.45-3.69), acute cholecystitis (HR, 9.49; 95% CI, 2.05-43.92), and uncomplicated events (HR, 2.55; 95% CI, 1.38-4.71), including cholecystectomy (HR, 2.69; 95% CI, 1.29-5.60). Multiple stones were associated with all events (HR, 1.68; 95% CI, 1.00-2.81), complicated events (HR, 2...... the highest risk for events (HR, 11.05; 95% CI, 3.76-32.44; unadjusted absolute risk, 0.0235 events/person-years). CONCLUSIONS: Fewer than 20% of subjects with gallstones develop clinical events. Larger, multiple, and older gallstones are associated with events. Further studies are needed to confirm...

  14. Impairment of executive function and attention predicts onset of affective disorder in healthy high-risk twins

    DEFF Research Database (Denmark)

    Vinberg, Maj; Miskowiak, Kamilla W; Kessing, Lars Vedel

    2013-01-01

    To investigate whether measures of cognitive function can predict onset of affective disorder in individuals at heritable risk.......To investigate whether measures of cognitive function can predict onset of affective disorder in individuals at heritable risk....

  15. Potential ecological risk assessment and predicting zinc accumulation in soils

    OpenAIRE

    Baran, Agnieszka; Wieczorek, Jerzy; Mazurek, Ryszard; Urbański, Krzysztof; Klimkowicz-Pawlas, Agnieszka

    2017-01-01

    The aims of this study were to investigate zinc content in the studied soils; evaluate the efficiency of geostatistics in presenting spatial variability of zinc in the soils; assess bioavailable forms of zinc in the soils and to assess soil–zinc binding ability; and to estimate the potential ecological risk of zinc in soils. The study was conducted in southern Poland, in the Malopolska Province. This area is characterized by a great diversity of geological structures and types of land use and...

  16. A simple risk scoring system for prediction of relapse after inpatient alcohol treatment.

    Science.gov (United States)

    Pedersen, Mads Uffe; Hesse, Morten

    2009-01-01

    Predicting relapse after alcoholism treatment can be useful in targeting patients for aftercare services. However, a valid and practical instrument for predicting relapse risk does not exist. Based on a prospective study of alcoholism treatment, we developed the Risk of Alcoholic Relapse Scale (RARS) using items taken from the Addiction Severity Index and some basic demographic information. The RARS was cross-validated using two non-overlapping samples, and tested for its ability to predict relapse across different models of treatment. The RARS predicted relapse to drinking within 6 months after alcoholism treatment in both the original and the validation sample, and in a second validation sample it predicted admission to new treatment 3 years after treatment. The RARS can identify patients at high risk of relapse who need extra aftercare and support after treatment.

  17. Maternal risk factors predicting child physical characteristics and dysmorphology in fetal alcohol syndrome and partial fetal alcohol syndrome.

    Science.gov (United States)

    May, Philip A; Tabachnick, Barbara G; Gossage, J Phillip; Kalberg, Wendy O; Marais, Anna-Susan; Robinson, Luther K; Manning, Melanie; Buckley, David; Hoyme, H Eugene

    2011-12-01

    Previous research in South Africa revealed very high rates of fetal alcohol syndrome (FAS), of 46-89 per 1000 among young children. Maternal and child data from studies in this community summarize the multiple predictors of FAS and partial fetal alcohol syndrome (PFAS). Sequential regression was employed to examine influences on child physical characteristics and dysmorphology from four categories of maternal traits: physical, demographic, childbearing, and drinking. Then, a structural equation model (SEM) was constructed to predict influences on child physical characteristics. Individual sequential regressions revealed that maternal drinking measures were the most powerful predictors of a child's physical anomalies (R² = .30, p < .001), followed by maternal demographics (R² = .24, p < .001), maternal physical characteristics (R²=.15, p < .001), and childbearing variables (R² = .06, p < .001). The SEM utilized both individual variables and the four composite categories of maternal traits to predict a set of child physical characteristics, including a total dysmorphology score. As predicted, drinking behavior is a relatively strong predictor of child physical characteristics (β = 0.61, p < .001), even when all other maternal risk variables are included; higher levels of drinking predict child physical anomalies. Overall, the SEM model explains 62% of the variance in child physical anomalies. As expected, drinking variables explain the most variance. But this highly controlled estimation of multiple effects also reveals a significant contribution played by maternal demographics and, to a lesser degree, maternal physical and childbearing variables. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  18. Risk factors and visual fatigue of baggage X-ray security screeners: a structural equation modelling analysis.

    Science.gov (United States)

    Yu, Rui-Feng; Yang, Lin-Dong; Wu, Xin

    2017-05-01

    This study identified the risk factors influencing visual fatigue in baggage X-ray security screeners and estimated the strength of correlations between those factors and visual fatigue using structural equation modelling approach. Two hundred and five X-ray security screeners participated in a questionnaire survey. The result showed that satisfaction with the VDT's physical features and the work environment conditions were negatively correlated with the intensity of visual fatigue, whereas job stress and job burnout had direct positive influences. The path coefficient between the image quality of VDT and visual fatigue was not significant. The total effects of job burnout, job stress, the VDT's physical features and the work environment conditions on visual fatigue were 0.471, 0.469, -0.268 and -0.251 respectively. These findings indicated that both extrinsic factors relating to VDT and workplace environment and psychological factors including job burnout and job stress should be considered in the workplace design and work organisation of security screening tasks to reduce screeners' visual fatigue. Practitioner Summary: This study identified the risk factors influencing visual fatigue in baggage X-ray security screeners and estimated the strength of correlations between those factors and visual fatigue. The findings were of great importance to the workplace design and the work organisation of security screening tasks to reduce screeners' visual fatigue.

  19. Predicting women's alcohol risk-taking while abroad.

    Science.gov (United States)

    Smith, Gabie; Klein, Sarah

    2010-05-01

    Numerous studies have examined risk factors that are associated with heavy alcohol use; however, much of this research has not addressed factors that specifically relate to women's alcohol use. The current study has extended the previous literature on women's alcohol-use behavior by examining factors associated with risky drinking in young women traveling abroad (n = 55). Using a pretest-posttest design, we examined the influence of disinhibition sensation-seeking and endorsement of social enhancement alcohol expectancies in relation to participation in risky alcohol use while abroad for three weeks. Analyses confirmed that disinhibition sensation-seeking and social enhancement alcohol expectancies were associated with participation in risky alcohol-use behaviors while abroad (controlling for alcohol-use at the pretest). Analysis of qualitative data reinforced the importance of social facilitation in women's alcohol risk-taking. Participants' qualitative data also emphasized characteristics of situational disinhibition relating to travel as well as culturally-specific motivations for alcohol-use behaviors. Further research examining women's personal need for disinhibition and the role of situational disinhibition in motivating alcohol risk-taking is warranted. In addition, the current findings suggest that interventions focusing on the connections between alcohol use and enhancement of social relationships and the potential isolating effects of non-use are necessary.

  20. Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

    OpenAIRE

    Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

    2012-01-01

    Abstract Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). Methods A ...

  1. PREDICTION OF SURGICAL TREATMENT WITH POUR PERITONITIS TAKING INTO ACCOUNT QUANTIFYING RISK FACTORS

    Directory of Open Access Journals (Sweden)

    І. К. Churpiy

    2012-11-01

    Full Text Available There was investigated the possibility of quantitative assessment of risk factors of complications in the treatment of diffuse peritonitis. There were ditermined 70 groups of features that are important in predicting the course of diffuse peritonitis. The proposed scheme is the definition of risk clinical course of diffuse peritonitis can quantify the severity of the original patients and in most cases is correctly to predict the results of treatment of disease.

  2. Environmental risk prediction and emergency plan for liquid ammonia leakage fault

    International Nuclear Information System (INIS)

    He Zhanfei; Lian Guoxi; Zhang Yuntao; Sun Juan; Du Juan

    2014-01-01

    Taking liquid ammonia storage in a uranium production process as an example, a multi-puff Gassian model was used to predict and analyze the environmental risk under the scenario of the maximum credible accident as well as the most unfavorable weather conditions. According to the results of prediction, the suggestions for safety evacuation and evacuation way were made, thus providing theoretical basis and technical guideline for uranium mine making risk management and contingency plan. (authors)

  3. Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries

    DEFF Research Database (Denmark)

    Ueda, Peter; Woodward, Mark; Lu, Yuan

    2017-01-01

    BACKGROUND: Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and ...

  4. Perceived extrinsic mortality risk and reported effort in looking after health: testing a behavioral ecological prediction.

    Science.gov (United States)

    Pepper, Gillian V; Nettle, Daniel

    2014-09-01

    Socioeconomic gradients in health behavior are pervasive and well documented. Yet, there is little consensus on their causes. Behavioral ecological theory predicts that, if people of lower socioeconomic position (SEP) perceive greater personal extrinsic mortality risk than those of higher SEP, they should disinvest in their future health. We surveyed North American adults for reported effort in looking after health, perceived extrinsic and intrinsic mortality risks, and measures of SEP. We examined the relationships between these variables and found that lower subjective SEP predicted lower reported health effort. Lower subjective SEP was also associated with higher perceived extrinsic mortality risk, which in turn predicted lower reported health effort. The effect of subjective SEP on reported health effort was completely mediated by perceived extrinsic mortality risk. Our findings indicate that perceived extrinsic mortality risk may be a key factor underlying SEP gradients in motivation to invest in future health.

  5. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

    Science.gov (United States)

    Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

    2018-05-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

  6. A utility/cost analysis of breast cancer risk prediction algorithms

    Science.gov (United States)

    Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

    2016-03-01

    Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

  7. Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community

    OpenAIRE

    Fisher, Stacey; Hsu, Amy; Mojaverian, Nassim; Taljaard, Monica; Huyer, Gregory; Manuel, Douglas G; Tanuseputro, Peter

    2017-01-01

    Introduction The burden of disease from dementia is a growing global concern as incidence increases dramatically with age, and average life expectancy has been increasing around the world. Planning for an ageing population requires reliable projections of dementia prevalence; however, existing population projections are simple and have poor predictive accuracy. The Dementia Population Risk Tool (DemPoRT) will predict incidence of dementia in the population setting using multivariable modellin...

  8. The Cumulative Lifting Index (CULI) for the Revised NIOSH Lifting Equation: Quantifying Risk for Workers With Job Rotation.

    Science.gov (United States)

    Garg, Arun; Kapellusch, Jay M

    2016-08-01

    The objectives were to: (a) develop a continuous frequency multiplier (FM) for the Revised NIOSH Lifting Equation (RNLE) as a function of lifting frequency and duration of a lifting task, and (b) describe the Cumulative Lifting Index (CULI), a methodology for estimating physical exposure to workers with job rotation. The existing FM for the RNLE (FME) does not differentiate between task duration >2 hr and <8 hr, which makes quantifying physical exposure to workers with job rotation difficult and presents challenges to job designers. Using the existing FMs for 1, 2, and 8 hr of task durations, we developed a continuous FM (FMP) that extends to 12 hr per day. We simulated 157,500 jobs consisting of two tasks each and, using different combinations of Frequency Independent Lifting Index, lifting frequency and duration of lifting. Biomechanical stresses were estimated using the CULI, time-weighted average (TWA), and peak exposure. The median difference between FME and FMP was ±1% (range: 0%-15%). Compared to CULI, TWA underestimated risk of low-back pain (LBP) for 18% to 30% of jobs, and peak exposure for an assumed 8-hr work shift overestimated risk of LBP for 20% to 25% of jobs. Peak task exposure showed 90% agreement with CULI but ignored one of two tasks. The CULI partially addressed the underestimation of physical exposure using the TWA approach and overestimation of exposure using the peak-exposure approach. The proposed FM and CULI may provide more accurate physical exposure estimates, and therefore estimated risk of LBP, for workers with job rotation. © 2016, Human Factors and Ergonomics Society.

  9. Potential ecological risk assessment and predicting zinc accumulation in soils.

    Science.gov (United States)

    Baran, Agnieszka; Wieczorek, Jerzy; Mazurek, Ryszard; Urbański, Krzysztof; Klimkowicz-Pawlas, Agnieszka

    2018-02-01

    The aims of this study were to investigate zinc content in the studied soils; evaluate the efficiency of geostatistics in presenting spatial variability of zinc in the soils; assess bioavailable forms of zinc in the soils and to assess soil-zinc binding ability; and to estimate the potential ecological risk of zinc in soils. The study was conducted in southern Poland, in the Malopolska Province. This area is characterized by a great diversity of geological structures and types of land use and intensity of industrial development. The zinc content was affected by soil factors, and the type of land use (arable lands, grasslands, forests, wastelands). A total of 320 soil samples were characterized in terms of physicochemical properties (texture, pH, organic C content, total and available Zn content). Based on the obtained data, assessment of the ecological risk of zinc was conducted using two methods: potential ecological risk index and hazard quotient. Total Zn content in the soils ranged from 8.27 to 7221 mg kg -1 d.m. Based on the surface semivariograms, the highest variability of zinc in the soils was observed from northwest to southeast. The point sources of Zn contamination were located in the northwestern part of the area, near the mining-metallurgical activity involving processing of zinc and lead ores. These findings were confirmed by the arrangement of semivariogram surfaces and bivariate Moran's correlation coefficients. The content of bioavailable forms of zinc was between 0.05 and 46.19 mg kg -1 d.m. (0.01 mol dm -3 CaCl 2 ), and between 0.03 and 71.54 mg kg -1 d.m. (1 mol dm -3 NH 4 NO 3 ). Forest soils had the highest zinc solubility, followed by arable land, grassland and wasteland. PCA showed that organic C was the key factor to control bioavailability of zinc in the soils. The extreme, very high and medium zinc accumulation was found in 69% of studied soils. There is no ecological risk of zinc to living organisms in the study area, and in 90

  10. Liver function tests and risk prediction of incident type 2 diabetes : evaluation in two independent cohorts

    NARCIS (Netherlands)

    Abbasi, Ali; Bakker, Stephan J. L.; Corpeleijn, Eva; van der A, Daphne L.; Gansevoort, Ron T.; Gans, Rijk O. B.; Peelen, Linda M.; van der Schouw, Yvonne T.; Stolk, Ronald P.; Navis, Gerjan; Spijkerman, Annemieke M. W.; Beulens, Joline W. J.

    2012-01-01

    Background: Liver function tests might predict the risk of type 2 diabetes. An independent study evaluating utility of these markers compared with an existing prediction model is yet lacking. Methods and Findings: We performed a case-cohort study, including random subcohort (6.5%) from 38,379

  11. Predicting nosocomial lower respiratory tract infections by a risk index based system

    NARCIS (Netherlands)

    Chen, Yong; Shan, Xue; Zhao, Jingya; Han, Xuelin; Tian, Shuguang; Chen, Fangyan; Su, Xueting; Sun, Yansong; Huang, Liuyu; Grundmann, Hajo; Wang, Hongyuan; Han, Li

    2017-01-01

    Although belonging to one of the most common type of nosocomial infection, there was currently no simple prediction model for lower respiratory tract infections (LRTIs). This study aims to develop a risk index based system for predicting nosocomial LRTIs based on data from a large point-prevalence

  12. The risk of severe postoperative pain: Modification and validation of a clinical prediction rule

    NARCIS (Netherlands)

    Janssen, Kristel J. M.; Kalkman, Cor J.; Grobbee, Diederick E.; Bonsel, Gouke J.; Moons, Karel G. M.; Vergouwe, Yvonne

    2008-01-01

    BACKGROUND: Recently, a prediction rule was developed to preoperatively predict the risk of severe pain in the first postoperative hour in surgical inpatients. We aimed to modify the rule to enhance its use in both surgical inpatients and outpatients (ambulatory patients). Subsequently, we

  13. Using cognitive status to predict crash risk: blazing new trails?

    Science.gov (United States)

    Staplin, Loren; Gish, Kenneth W; Sifrit, Kathy J

    2014-02-01

    A computer-based version of an established neuropsychological paper-and-pencil assessment tool, the Trail-Making Test, was applied with approximately 700 drivers aged 70 years and older in offices of the Maryland Motor Vehicle Administration. This was a volunteer sample that received a small compensation for study participation, with an assurance that their license status would not be affected by the results. Analyses revealed that the study sample was representative of Maryland older drivers with respect to age and indices of prior driving safety. The relationship between drivers' scores on the Trail-Making Test and prospective crash experience was analyzed using a new outcome measure that explicitly takes into account error responses as well as correct responses, the error-compensated completion time. For the only reliable predictor of crash risk, Trail-Making Test Part B, this measure demonstrated a modest gain in specificity and was a more significant predictor of future safety risk than the simple time-to-completion measure. Improved specificity and the potential for autonomous test administration are particular advantages of this measure for use with large populations, in settings such as health care or driver licensing. © 2013.

  14. The importance of virulence prediction and gene networks in microbial risk assessment

    DEFF Research Database (Denmark)

    Wassenaar, Gertrude Maria; Gamieldien, Junaid; Shatkin, JoAnne

    2007-01-01

    For microbial risk assessment, it is necessary to recognize and predict Virulence of bacterial pathogens, including their ability to contaminate foods. Hazard characterization requires data on strain variability regarding virulence and survival during food processing. Moreover, information...... and characterization of microbial hazards, including emerging pathogens, in the context of microbial risk assessment....

  15. Clinical prediction of fall risk and white matter abnormalities: a diffusion tensor imaging study

    Science.gov (United States)

    The Tinetti scale is a simple clinical tool designed to predict risk of falling by focusing on gait and stance impairment in elderly persons. Gait impairment is also associated with white matter (WM) abnormalities. Objective: To test the hypothesis that elderly subjects at risk for falling, as deter...

  16. Significant increase of Echinococcus multilocularis prevalencein foxes, but no increased predicted risk for humans

    NARCIS (Netherlands)

    Maas, M.; Dam-Deisz, W.D.C.; Roon, van A.M.; Takumi, K.; Giessen, van der J.W.B.

    2014-01-01

    The emergence of the zoonotic tapeworm Echinococcus multilocularis, causative agent ofalveolar echinococcosis (AE), poses a public health risk. A previously designed risk mapmodel predicted a spread of E. multilocularis and increasing numbers of alveolar echinococ-cosis patients in the province of

  17. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction

    DEFF Research Database (Denmark)

    Paige, Ellie; Barrett, Jessica; Pennells, Lisa

    2017-01-01

    The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data...

  18. School Violence in Taiwan: Examining How Western Risk Factors Predict School Violence in an Asian Culture

    Science.gov (United States)

    Chen, Ji-Kang; Astor, Ron Avi

    2010-01-01

    The current study explores whether theorized risk factors in Western countries can be used to predict school violence perpetration in an Asian cultural context. The study examines the associations between risk factors and school violence perpetration in Taiwan. Data were obtained from a nationally representative sample of 14,022 students from…

  19. [Predicting value of 2014 European guidelines risk prediction model for sudden cardiac death (HCM Risk-SCD) in Chinese patients with hypertrophic cardiomyopathy].

    Science.gov (United States)

    Li, W X; Liu, L W; Wang, J; Zuo, L; Yang, F; Kang, N; Lei, C H

    2017-12-24

    Objective: To evaluate the predicting value of the 2014 European Society of Cardiology (ESC) guidelines risk prediction model for sudden cardiac death (HCM Risk-SCD) in Chinese patients with hypertrophic cardiomyopathy (HCM), and to explore the predictors of adverse cardiovascular events in Chinese HCM patients. Methods: The study population consisted of a consecutive 207 HCM patients admitted in our center from October 2014 to October 2016. All patients were followed up to March 2017. The 5-year SCD probability of each patient was estimated using HCM Risk-SCD model based on electrocardiogram, echocardiography and cardiac magnetic resonance (CMR) examination results. The primary, second, and composite endpoints were recorded. The primary endpoint included SCD and appropriate ICD therapy, identical to the HCM Risk-SCD endpoint. The second endpoint included acute myocardial infarction, hospitalization for heart failure, thrombus embolism and end-stage HCM. The composite endpoint was either the primary or the second endpoint. Patients were divided into the 3 categories according to 5-year SCD probability assessed by HCM Risk-SCD model: low risk grouprisk group ≥4% torisk group≥6%. Results: (1) Prevalence of endpoints: All 207 HCM patients completed the follow-up (350 (230, 547) days). During follow-up, 8 (3.86%) patients reached the primary endpoints (3 cases of SCD, 3 cases of survival after defibrillation, and 2 cases of appropriate ICD discharge); 21 (10.14%) patients reached the second endpoints (1 case of acute myocardial infarction, 16 cases of heart failure hospitalization, 2 cases of thromboembolism, and 2 cases of end-stage HCM). (2) Predicting value of HCM Risk-SCD model: Patients with primary endpoints had higher prevalence of syncope and intermediate-high risk of 5-year SCD, as compared to those without primary endpoints (both Pvalue of HCM Risk-SCD model: The low risk group included 122 patients (59%), the intermediate risk group 42 (20%), and the

  20. Predictive Local Composition Models for Solid/Liquid Equilibrium in n-Alkane Systems: Wilson Equation for Multicomponent Systems

    DEFF Research Database (Denmark)

    Coutinho, João A.P.; Stenby, Erling Halfdan

    1996-01-01

    The predictive local composition model is applied to multicomponent hydrocarbon systems with long-chain n-alkanes as solutes. The results show that it can successfully be extended to highorder systems and accurately predict the solid appearance temperature, also known as cloud point, in solutions...

  1. A risk score to predict type 2 diabetes mellitus in an elderly Spanish Mediterranean population at high cardiovascular risk.

    Directory of Open Access Journals (Sweden)

    Marta Guasch-Ferré

    Full Text Available INTRODUCTION: To develop and test a diabetes risk score to predict incident diabetes in an elderly Spanish Mediterranean population at high cardiovascular risk. MATERIALS AND METHODS: A diabetes risk score was derived from a subset of 1381 nondiabetic individuals from three centres of the PREDIMED study (derivation sample. Multivariate Cox regression model ß-coefficients were used to weigh each risk factor. PREDIMED-personal Score included body-mass-index, smoking status, family history of type 2 diabetes, alcohol consumption and hypertension as categorical variables; PREDIMED-clinical Score included also high blood glucose. We tested the predictive capability of these scores in the DE-PLAN-CAT cohort (validation sample. The discrimination of Finnish Diabetes Risk Score (FINDRISC, German Diabetes Risk Score (GDRS and our scores was assessed with the area under curve (AUC. RESULTS: The PREDIMED-clinical Score varied from 0 to 14 points. In the subset of the PREDIMED study, 155 individuals developed diabetes during the 4.75-years follow-up. The PREDIMED-clinical score at a cutoff of ≥6 had sensitivity of 72.2%, and specificity of 72.5%, whereas AUC was 0.78. The AUC of the PREDIMED-clinical Score was 0.66 in the validation sample (sensitivity = 85.4%; specificity = 26.6%, and was significantly higher than the FINDRISC and the GDRS in both the derivation and validation samples. DISCUSSION: We identified classical risk factors for diabetes and developed the PREDIMED-clinical Score to determine those individuals at high risk of developing diabetes in elderly individuals at high cardiovascular risk. The predictive capability of the PREDIMED-clinical Score was significantly higher than the FINDRISC and GDRS, and also used fewer items in the questionnaire.

  2. Predicting risk for disciplinary action by a state medical board.

    Science.gov (United States)

    Cardarelli, Roberto; Licciardone, John C; Ramirez, Gilbert

    2004-01-01

    Disciplinary actions taken against physicians in the United States have been increasing over the last decade, yet the factors that place physicians at risk have not been well identified. The objective of this study is to identify predictors of physician disciplinary action. This case-control study used data from the Texas State Board of Medical Examiners from January 1989 through December 1998. Characteristics of disciplined physicians and predictors of disciplinary action for all violations and by type of violation were the main outcome descriptors. Years in practice, black physicians, and osteopathic graduates were positive predictors for disciplinary action. In contrast, female physicians, international medical graduates, and Hispanic and Asian physicians were less likely to receive disciplinary action compared with male, US allopathic, and white physicians, respectively. Most specialists, except psychiatrists and obstetrician-gynecologists, were less likely to be disciplined than were family practitioners, whereas general practitioners were more likely to be disciplined. More studies are needed to corroborate these findings.