WorldWideScience

Sample records for risk prediction tool

  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. 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.

  3. 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

  4. 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...

  5. 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

  6. 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

  7. 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.

  8. 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

  9. 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…

  10. 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...

  11. The East London glaucoma prediction score: web-based validation of glaucoma risk screening tool

    Science.gov (United States)

    Stephen, Cook; Benjamin, Longo-Mbenza

    2013-01-01

    AIM It is difficult for Optometrists and General Practitioners to know which patients are at risk. The East London glaucoma prediction score (ELGPS) is a web based risk calculator that has been developed to determine Glaucoma risk at the time of screening. Multiple risk factors that are available in a low tech environment are assessed to provide a risk assessment. This is extremely useful in settings where access to specialist care is difficult. Use of the calculator is educational. It is a free web based service. Data capture is user specific. METHOD The scoring system is a web based questionnaire that captures and subsequently calculates the relative risk for the presence of Glaucoma at the time of screening. Three categories of patient are described: Unlikely to have Glaucoma; Glaucoma Suspect and Glaucoma. A case review methodology of patients with known diagnosis is employed to validate the calculator risk assessment. RESULTS Data from the patient records of 400 patients with an established diagnosis has been captured and used to validate the screening tool. The website reports that the calculated diagnosis correlates with the actual diagnosis 82% of the time. Biostatistics analysis showed: Sensitivity = 88%; Positive predictive value = 97%; Specificity = 75%. CONCLUSION Analysis of the first 400 patients validates the web based screening tool as being a good method of screening for the at risk population. The validation is ongoing. The web based format will allow a more widespread recruitment for different geographic, population and personnel variables. PMID:23550097

  12. Perioperative Respiratory Adverse Events in Pediatric Ambulatory Anesthesia: Development and Validation of a Risk Prediction Tool.

    Science.gov (United States)

    Subramanyam, Rajeev; Yeramaneni, Samrat; Hossain, Mohamed Monir; Anneken, Amy M; Varughese, Anna M

    2016-05-01

    Perioperative respiratory adverse events (PRAEs) are the most common cause of serious adverse events in children receiving anesthesia. Our primary aim of this study was to develop and validate a risk prediction tool for the occurrence of PRAE from the onset of anesthesia induction until discharge from the postanesthesia care unit in children younger than 18 years undergoing elective ambulatory anesthesia for surgery and radiology. The incidence of PRAE was studied. We analyzed data from 19,059 patients from our department's quality improvement database. The predictor variables were age, sex, ASA physical status, morbid obesity, preexisting pulmonary disorder, preexisting neurologic disorder, and location of ambulatory anesthesia (surgery or radiology). Composite PRAE was defined as the presence of any 1 of the following events: intraoperative bronchospasm, intraoperative laryngospasm, postoperative apnea, postoperative laryngospasm, postoperative bronchospasm, or postoperative prolonged oxygen requirement. Development and validation of the risk prediction tool for PRAE were performed using a split sampling technique to split the database into 2 independent cohorts based on the year when the patient received ambulatory anesthesia for surgery and radiology using logistic regression. A risk score was developed based on the regression coefficients from the validation tool. The performance of the risk prediction tool was assessed by using tests of discrimination and calibration. The overall incidence of composite PRAE was 2.8%. The derivation cohort included 8904 patients, and the validation cohort included 10,155 patients. The risk of PRAE was 3.9% in the development cohort and 1.8% in the validation cohort. Age ≤ 3 years (versus >3 years), ASA physical status II or III (versus ASA physical status I), morbid obesity, preexisting pulmonary disorder, and surgery (versus radiology) significantly predicted the occurrence of PRAE in a multivariable logistic regression

  13. 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...

  14. 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

  15. Mechanisms, Prediction, and Prevention of ACL Injuries: Cut Risk With Three Sharpened and Validated Tools

    Science.gov (United States)

    Hewett, Timothy E.; Myer, Gregory D.; Ford, Kevin R.; Paterno, Mark V.; Quatman, Carmen E.

    2017-01-01

    Economic and societal pressures influence modern medical practice to develop and implement prevention strategies. Anterior cruciate ligament (ACL) injury devastates the knee joint leading to short term disability and long term sequelae. Due to the high risk of long term osteoarthritis in all treatment populations following ACL injury, prevention is the only effective intervention for this life-altering disruption in knee health. The “Sequence of Prevention” Model provides a framework to monitor progress towards the ultimate goal of preventing ACL injuries. Utilizing this model, our multidisciplinary collaborative research team has spent the last decade working to delineate injury mechanisms, identify injury risk factors, predict which athletes are at-risk for injury, and develop ACL injury prevention programs. Within this model of injury prevention, modifiable factors (biomechanical and neuromuscular) related to injury mechanisms likely provide the best opportunity for intervention strategies aimed to decrease the risk of ACL injury, particularly in female athletes. Knowledge advancements have led to the development of potential solutions that allow athletes to compete with lowered risk of ACL injury. Design and integration of personalized clinical assessment tools and targeted prevention strategies for athletes at high risk for ACL injury may transform current prevention practices and ultimately significantly reduce ACL injury incidence. This 2016 OREF Clinical Research Award focuses on the authors' work and contributions to the field. The author's acknowledge the many research groups who have contributed to the current state of knowledge in the fields of ACL injury mechanisms, injury risk screening and injury prevention strategies. PMID:27612195

  16. Risk analysis for dengue suitability in Africa using the ArcGIS predictive analysis tools (PA tools).

    Science.gov (United States)

    Attaway, David F; Jacobsen, Kathryn H; Falconer, Allan; Manca, Germana; Waters, Nigel M

    2016-06-01

    Risk maps identifying suitable locations for infection transmission are important for public health planning. Data on dengue infection rates are not readily available in most places where the disease is known to occur. A newly available add-in to Esri's ArcGIS software package, the ArcGIS Predictive Analysis Toolset (PA Tools), was used to identify locations within Africa with environmental characteristics likely to be suitable for transmission of dengue virus. A more accurate, robust, and localized (1 km × 1 km) dengue risk map for Africa was created based on bioclimatic layers, elevation data, high-resolution population data, and other environmental factors that a search of the peer-reviewed literature showed to be associated with dengue risk. Variables related to temperature, precipitation, elevation, and population density were identified as good predictors of dengue suitability. Areas of high dengue suitability occur primarily within West Africa and parts of Central Africa and East Africa, but even in these regions the suitability is not homogenous. This risk mapping technique for an infection transmitted by Aedes mosquitoes draws on entomological, epidemiological, and geographic data. The method could be applied to other infectious diseases (such as Zika) in order to provide new insights for public health officials and others making decisions about where to increase disease surveillance activities and implement infection prevention and control efforts. The ability to map threats to human and animal health is important for tracking vectorborne and other emerging infectious diseases and modeling the likely impacts of climate change. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Colorectal Cancer Risk Assessment Tool

    Science.gov (United States)

    ... 11/12/2014 Risk Calculator About the Tool Colorectal Cancer Risk Factors Download SAS and Gauss Code Page ... Rectal Cancer: Prevention, Genetics, Causes Tests to Detect Colorectal Cancer and Polyps Cancer Risk Prediction Resources Update November ...

  18. Predicting risk and outcomes for frail older adults: an umbrella review of frailty screening tools

    Science.gov (United States)

    Apóstolo, João; Cooke, Richard; Bobrowicz-Campos, Elzbieta; Santana, Silvina; Marcucci, Maura; Cano, Antonio; Vollenbroek-Hutten, Miriam; Germini, Federico; Holland, Carol

    2017-01-01

    EXECUTIVE SUMMARY Background A scoping search identified systematic reviews on diagnostic accuracy and predictive ability of frailty measures in older adults. In most cases, research was confined to specific assessment measures related to a specific clinical model. Objectives To summarize the best available evidence from systematic reviews in relation to reliability, validity, diagnostic accuracy and predictive ability of frailty measures in older adults. Inclusion criteria Population Older adults aged 60 years or older recruited from community, primary care, long-term residential care and hospitals. Index test Available frailty measures in older adults. Reference test Cardiovascular Health Study phenotype model, the Canadian Study of Health and Aging cumulative deficit model, Comprehensive Geriatric Assessment or other reference tests. Diagnosis of interest Frailty defined as an age-related state of decreased physiological reserves characterized by an increased risk of poor clinical outcomes. Types of studies Quantitative systematic reviews. Search strategy A three-step search strategy was utilized to find systematic reviews, available in English, published between January 2001 and October 2015. Methodological quality Assessed by two independent reviewers using the Joanna Briggs Institute critical appraisal checklist for systematic reviews and research synthesis. Data extraction Two independent reviewers extracted data using the standardized data extraction tool designed for umbrella reviews. Data synthesis Data were only presented in a narrative form due to the heterogeneity of included reviews. Results Five reviews with a total of 227,381 participants were included in this umbrella review. Two reviews focused on reliability, validity and diagnostic accuracy; two examined predictive ability for adverse health outcomes; and one investigated validity, diagnostic accuracy and predictive ability. In total, 26 questionnaires and brief assessments and eight frailty

  19. International multicenter tool to predict the risk of nonsentinel node metastases in breast cancer

    DEFF Research Database (Denmark)

    Meretoja, Tuomo J; Leidenius, Marjut H K; Heikkilä, Päivi S

    2012-01-01

    predicting nonsentinel node involvement were identified in logistic regression analysis. A multivariable predictive model was developed and validated by area under the receiver operating characteristics curve (AUC), first internally in 500 additional patients and then externally in 1068 patients from other...... centers. All statistical tests were two-sided. Results Nine tumor- and sentinel node-specific variables were identified as statistically significant factors predicting nonsentinel node involvement in logistic regression analysis. A resulting predictive model applied to the internal validation series...... resulted in an AUC of 0.714 (95% confidence interval [CI] = 0.665 to 0.763). For the external validation series, the AUC was 0.719 (95% CI = 0.689 to 0.750). The model was well calibrated in the external validation series. Conclusions We present a novel, international, multicenter, predictive tool...

  20. Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community.

    Science.gov (United States)

    Fisher, Stacey; Hsu, Amy; Mojaverian, Nassim; Taljaard, Monica; Huyer, Gregory; Manuel, Douglas G; Tanuseputro, Peter

    2017-10-24

    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 modelling techniques and will be used to project dementia prevalence. The derivation cohort will consist of elderly Ontario respondents of the Canadian Community Health Survey (CCHS) (2001, 2003, 2005 and 2007; 18 764 males and 25 288 females). Prespecified predictors include sociodemographic, general health, behavioural, functional and health condition variables. Incident dementia will be identified through individual linkage of survey respondents to population-level administrative healthcare databases (1797 and 3281 events, and 117 795 and 166 573 person-years of follow-up, for males and females, respectively, until 31 March 2014). Using time of first dementia capture as the primary outcome and death as a competing risk, sex-specific proportional hazards regression models will be estimated. The 2008/2009 CCHS survey will be used for validation (approximately 4600 males and 6300 females). Overall calibration and discrimination will be assessed as well as calibration within predefined subgroups of importance to clinicians and policy makers. Research ethics approval has been granted by the Ottawa Health Science Network Research Ethics Board. DemPoRT results will be submitted for publication in peer-review journals and presented at scientific meetings. The algorithm will be assessable online for both population and individual uses. ClinicalTrials.gov NCT03155815, pre-results. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No

  1. Dutch Risk Assessment tools

    NARCIS (Netherlands)

    Venema, A.

    2015-01-01

    The ‘Risico- Inventarisatie- en Evaluatie-instrumenten’ is the name for the Dutch risk assessment (RA) tools. A RA tool can be used to perform a risk assessment including an evaluation of the identified risks. These tools were among the first online risk assessment tools developed in Europe. The

  2. Become the PPUPET Master: Mastering Pressure Ulcer Risk Assessment With the Pediatric Pressure Ulcer Prediction and Evaluation Tool (PPUPET).

    Science.gov (United States)

    Sterken, David J; Mooney, JoAnn; Ropele, Diana; Kett, Alysha; Vander Laan, Karen J

    2015-01-01

    Hospital acquired pressure ulcers (HAPU) are serious, debilitating, and preventable complications in all inpatient populations. Despite evidence of the development of pressure ulcers in the pediatric population, minimal research has been done. Based on observations gathered during quarterly HAPU audits, bedside nursing staff recognized trends in pressure ulcer locations that were not captured using current pressure ulcer risk assessment tools. Together, bedside nurses and nursing leadership created and conducted multiple research studies to investigate the validity and reliability of the Pediatric Pressure Ulcer Prediction and Evaluation Tool (PPUPET). Copyright © 2015 Elsevier Inc. All rights reserved.

  3. Development of a simple tool to predict the risk of postpartum diabetes in women with gestational diabetes mellitus.

    Science.gov (United States)

    Köhler, M; Ziegler, A G; Beyerlein, A

    2016-06-01

    Women with gestational diabetes mellitus (GDM) have an increased risk of diabetes postpartum. We developed a score to predict the long-term risk of postpartum diabetes using clinical and anamnestic variables recorded during or shortly after delivery. Data from 257 GDM women who were prospectively followed for diabetes outcome over 20 years of follow-up were used to develop and validate the risk score. Participants were divided into training and test sets. The risk score was calculated using Lasso Cox regression and divided into four risk categories, and its prediction performance was assessed in the test set. Postpartum diabetes developed in 110 women. The computed training set risk score of 5 × body mass index in early pregnancy (per kg/m(2)) + 132 if GDM was treated with insulin (otherwise 0) + 44 if the woman had a family history of diabetes (otherwise 0) - 35 if the woman lactated (otherwise 0) had R (2) values of 0.23, 0.25, and 0.33 at 5, 10, and 15 years postpartum, respectively, and a C-Index of 0.75. Application of the risk score in the test set resulted in observed risk of postpartum diabetes at 5 years of 11 % for low risk scores ≤140, 29 % for scores 141-220, 64 % for scores 221-300, and 80 % for scores >300. The derived risk score is easy to calculate, allows accurate prediction of GDM-related postpartum diabetes, and may thus be a useful prediction tool for clinicians and general practitioners.

  4. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder.

    Science.gov (United States)

    Perlis, Roy H

    2013-07-01

    Early identification of depressed individuals at high risk for treatment resistance could be helpful in selecting optimal setting and intensity of care. At present, validated tools to facilitate this risk stratification are rarely used in psychiatric practice. Data were drawn from the first two treatment levels of a multicenter antidepressant effectiveness study in major depressive disorder, the STAR*D (Sequenced Treatment Alternatives to Relieve Depression) cohort. This cohort was divided into training, testing, and validation subsets. Only clinical or sociodemographic variables available by or readily amenable to self-report were considered. Multivariate models were developed to discriminate individuals reaching remission with a first or second pharmacological treatment trial from those not reaching remission despite two trials. A logistic regression model achieved an area under the receiver operating characteristic curve exceeding .71 in training, testing, and validation cohorts and maintained good calibration across cohorts. Performance of three alternative models with machine learning approaches--a naïve Bayes classifier and a support vector machine, and a random forest model--was less consistent. Similar performance was observed between more and less severe depression, men and women, and primary versus specialty care sites. A web-based calculator was developed that implements this tool and provides graphical estimates of risk. Risk for treatment resistance among outpatients with major depressive disorder can be estimated with a simple model incorporating baseline sociodemographic and clinical features. Future studies should examine the performance of this model in other clinical populations and its utility in treatment selection or clinical trial design. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  5. Does diagnosis affect the predictive accuracy of risk assessment tools for juvenile offenders: Conduct Disorder and Attention Deficit Hyperactivity Disorder.

    Science.gov (United States)

    Khanna, Dinesh; Shaw, Jenny; Dolan, Mairead; Lennox, Charlotte

    2014-10-01

    Studies have suggested an increased risk of criminality in juveniles if they suffer from co-morbid Attention Deficit Hyperactivity Disorder (ADHD) along with Conduct Disorder. The Structured Assessment of Violence Risk in Youth (SAVRY), the Psychopathy Checklist Youth Version (PCL:YV), and Youth Level of Service/Case Management Inventory (YLS/CMI) have been shown to be good predictors of violent and non-violent re-offending. The aim was to compare the accuracy of these tools to predict violent and non-violent re-offending in young people with co-morbid ADHD and Conduct Disorder and Conduct Disorder only. The sample included 109 White-British adolescent males in secure settings. Results revealed no significant differences between the groups for re-offending. SAVRY factors had better predictive values than PCL:YV or YLS/CMI. Tools generally had better predictive values for the Conduct Disorder only group than the co-morbid group. Possible reasons for these findings have been discussed along with limitations of the study. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  6. Predicting recidivism among adult male child pornography offenders: Development of the Child Pornography Offender Risk Tool (CPORT).

    Science.gov (United States)

    Seto, Michael C; Eke, Angela W

    2015-08-01

    In this study, we developed a structured risk checklist, the Child Pornography Offender Risk Tool (CPORT), to predict any sexual recidivism among adult male offenders with a conviction for child pornography offenses. We identified predictors of sexual recidivism using a 5-year fixed follow-up analysis from a police case file sample of 266 adult male child pornography offenders in the community after their index offense. In our 5-year follow-up, 29% committed a new offense, and 11% committed a new sexual offense, with 3% committing a new contact sexual offense against a child and 9% committing a new child pornography offense. The CPORT items comprised younger offender age, any prior criminal history, any contact sexual offending, any failure on conditional release, indication of sexual interest in child pornography material or prepubescent or pubescent children, more boy than girl content in child pornography, and more boy than girl content in other child depictions. The CPORT was significantly associated with any sexual recidivism, with moderate predictive accuracy, and thus has promise in the risk assessment of adult male child pornography offenders with further cross-validation. (c) 2015 APA, all rights reserved).

  7. Primary Sclerosing Cholangitis Risk Estimate Tool (PREsTo) Predicts Outcomes in PSC: A Derivation & Validation Study Using Machine Learning.

    Science.gov (United States)

    Eaton, John E; Vesterhus, Mette; McCauley, Bryan M; Atkinson, Elizabeth J; Schlicht, Erik M; Juran, Brian D; Gossard, Andrea A; LaRusso, Nicholas F; Gores, Gregory J; Karlsen, Tom H; Lazaridis, Konstantinos N

    2018-05-09

    Improved methods are needed to risk stratify and predict outcomes in patients with primary sclerosing cholangitis (PSC). Therefore, we sought to derive and validate a new prediction model and compare its performance to existing surrogate markers. The model was derived using 509 subjects from a multicenter North American cohort and validated in an international multicenter cohort (n=278). Gradient boosting, a machine based learning technique, was used to create the model. The endpoint was hepatic decompensation (ascites, variceal hemorrhage or encephalopathy). Subjects with advanced PSC or cholangiocarcinoma at baseline were excluded. The PSC risk estimate tool (PREsTo) consists of 9 variables: bilirubin, albumin, serum alkaline phosphatase (SAP) times the upper limit of normal (ULN), platelets, AST, hemoglobin, sodium, patient age and the number of years since PSC was diagnosed. Validation in an independent cohort confirms PREsTo accurately predicts decompensation (C statistic 0.90, 95% confidence interval (CI) 0.84-0.95) and performed well compared to MELD score (C statistic 0.72, 95% CI 0.57-0.84), Mayo PSC risk score (C statistic 0.85, 95% CI 0.77-0.92) and SAP statistic 0.65, 95% CI 0.55-0.73). PREsTo continued to be accurate among individuals with a bilirubin statistic 0.90, 95% CI 0.82-0.96) and when the score was re-applied at a later course in the disease (C statistic 0.82, 95% CI 0.64-0.95). PREsTo accurately predicts hepatic decompensation in PSC and exceeds the performance among other widely available, noninvasive prognostic scoring systems. This article is protected by copyright. All rights reserved. © 2018 by the American Association for the Study of Liver Diseases.

  8. A Screening Tool Using Five Risk Factors Was Developed for Fall-Risk Prediction in Chinese Community-Dwelling Elderly Individuals.

    Science.gov (United States)

    Kang, Li; Chen, Xiaoyu; Han, Peipei; Ma, Yixuan; Jia, Liye; Fu, Liyuan; Yu, Hairui; Wang, Lu; Hou, Lin; Yu, Xing; An, Zongyang; Wang, Xuetong; Li, Lu; Zhang, Yuanyuan; Zhao, Peng; Guo, Qi

    2018-01-22

    The objective of this study was to determine falls risk profiles to derive a falls risk prediction score and establish a simple and practical clinical screening tool for Chinese community-dwelling elderly individuals. This was a prospective cohort study (n = 619) among adults aged 60 years and older. Falls were ascertained at a 1-year follow-up appointment. Sociodemographic information, medical history, and physical performance data were collected. The mean age was 67.4 years; 57.7% were women. Female sex (odds ratios [ORs] 1.82; 95% confidence interval [95% CI] 1.17-2.82), diabetes (OR 2.13; 95% CI 1.13-3.98), a Timed Up and Go Test (TUGT) ≥10.49 seconds (OR 1.51; 95% CI 1.23-1.94), a history of falls (OR 3.15; 95% CI 1.72-5.79), and depression (Geriatric Depression Scale [GDS] ≥11, OR 2.51; 95% CI 1.36-4.63) were the strongest predictors. These predictors were used to establish a risk score. The area under the curve of the score was 0.748. From a clinical point of view, the most appropriate cutoff value was 7 (97.5% specificity, 70.7% positive predictive value, and 83.6% negative predictive value). For this cutoff, the fraction correctly classified was 82.5%. A cutoff score of 7 derived from a risk assessment tool using four risk factors (gender, falls history, diabetes, and depression) and the TUGT may be used in Chinese community-dwelling elderly individuals as an initial step to screen those at low risk for falls.

  9. The Identification of Seniors at Risk screening tool is useful for predicting acute readmissions

    DEFF Research Database (Denmark)

    Rosted, Elizabeth; Schultz, Martin; Dynesen, Helle

    2014-01-01

    . Patients ≥ 65 years treated during a 14-day period were included. Their mean age was 78 years. Screening with the Identification of Seniors at Risk (ISAR) was performed (n = 198) by the Mobile Geriatric Team (MGT). The patients' medical journals were assessed retrospectively by the SG to determine any need...

  10. Cross-cultural adaptation of the STRATIFY tool in detecting and predicting risk of falling.

    Science.gov (United States)

    Enríquez de Luna-Rodríguez, Margarita; Aranda-Gallardo, Marta; Canca-Sánchez, José Carlos; Vazquez-Blanco, M José; Moya-Suárez, Ana Belén; Morales-Asencio, José Miguel

    To adapt to Spanish language the STRATIFY tool for clinical use in the Spanish-speaking World. A multicenter, 2 care settings cross-sectional study cultural adaptation study in acute care hospitals and nursing homes was performed in Andalusia during 2014. The adaptation process was divided into 4 stages: translation, back-translation, equivalence between the 2 back-translations and piloting of the Spanish version, thus obtaining the final version. The validity of appearance, content validity and the time required to complete the scale were taken into account. For analysis, the median, central tendency and dispersion of scores, the interquartile range, and the interquartile deviation for the possible variability in responses it was calculated. Content validity measured by content validity index reached a profit of 1. For the validity aspect the clarity and comprehensibility of the questions were taken into account. Of the 5 questions of the instrument, 2 had a small disagreement solved with the introduction of an explanatory phrase to achieve conceptual equivalence. Median both questions were equal or superior to 5. The average time for completion of the scale was less than 3 minutes. The process of adaptation to Spanish of STRATIFY has led to a semantic version and culturally equivalent to the original for easy filling and understanding for use in the Spanish-speaking world. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

  11. Youth Actuarial Risk Assessment Tool (Y-ARAT): The development of an actuarial risk assessment instrument for predicting general offense recidivism on the basis of police records.

    Science.gov (United States)

    van der Put, Claudia E

    2014-06-01

    Estimating the risk for recidivism is important for many areas of the criminal justice system. In the present study, the Youth Actuarial Risk Assessment Tool (Y-ARAT) was developed for juvenile offenders based solely on police records, with the aim to estimate the risk of general recidivism among large groups of juvenile offenders by police officers without clinical expertise. On the basis of the Y-ARAT, juvenile offenders are classified into five risk groups based on (combinations of) 10 variables including different types of incidents in which the juvenile was a suspect, total number of incidents in which the juvenile was a suspect, total number of other incidents, total number of incidents in which co-occupants at the youth's address were suspects, gender, and age at first incident. The Y-ARAT was developed on a sample of 2,501 juvenile offenders and validated on another sample of 2,499 juvenile offenders, showing moderate predictive accuracy (area under the receiver-operating-characteristic curve = .73), with little variation between the construction and validation sample. The predictive accuracy of the Y-ARAT was considered sufficient to justify its use as a screening instrument for the police. © The Author(s) 2013.

  12. Simulation for Prediction of Entry Article Demise (SPEAD): An Analysis Tool for Spacecraft Safety Analysis and Ascent/Reentry Risk Assessment

    Science.gov (United States)

    Ling, Lisa

    2014-01-01

    For the purpose of performing safety analysis and risk assessment for a potential off-nominal atmospheric reentry resulting in vehicle breakup, a synthesis of trajectory propagation coupled with thermal analysis and the evaluation of node failure is required to predict the sequence of events, the timeline, and the progressive demise of spacecraft components. To provide this capability, the Simulation for Prediction of Entry Article Demise (SPEAD) analysis tool was developed. The software and methodology have been validated against actual flights, telemetry data, and validated software, and safety/risk analyses were performed for various programs using SPEAD. This report discusses the capabilities, modeling, validation, and application of the SPEAD analysis tool.

  13. The cardiovascular event reduction tool (CERT)--a simplified cardiac risk prediction model developed from the West of Scotland Coronary Prevention Study (WOSCOPS).

    Science.gov (United States)

    L'Italien, G; Ford, I; Norrie, J; LaPuerta, P; Ehreth, J; Jackson, J; Shepherd, J

    2000-03-15

    The clinical decision to treat hypercholesterolemia is premised on an awareness of patient risk, and cardiac risk prediction models offer a practical means of determining such risk. However, these models are based on observational cohorts where estimates of the treatment benefit are largely inferred. The West of Scotland Coronary Prevention Study (WOSCOPS) provides an opportunity to develop a risk-benefit prediction model from the actual observed primary event reduction seen in the trial. Five-year Cox model risk estimates were derived from all WOSCOPS subjects (n = 6,595 men, aged 45 to 64 years old at baseline) using factors previously shown to be predictive of definite fatal coronary heart disease or nonfatal myocardial infarction. Model risk factors included age, diastolic blood pressure, total cholesterol/ high-density lipoprotein ratio (TC/HDL), current smoking, diabetes, family history of fatal coronary heart disease, nitrate use or angina, and treatment (placebo/ 40-mg pravastatin). All risk factors were expressed as categorical variables to facilitate risk assessment. Risk estimates were incorporated into a simple, hand-held slide rule or risk tool. Risk estimates were identified for 5-year age bands (45 to 65 years), 4 categories of TC/HDL ratio ( or = 7.5), 2 levels of diastolic blood pressure ( or = 90 mm Hg), from 0 to 3 additional risk factors (current smoking, diabetes, family history of premature fatal coronary heart disease, nitrate use or angina), and pravastatin treatment. Five-year risk estimates ranged from 2% in very low-risk subjects to 61% in the very high-risk subjects. Risk reduction due to pravastatin treatment averaged 31%. Thus, the Cardiovascular Event Reduction Tool (CERT) is a risk prediction model derived from the WOSCOPS trial. Its use will help physicians identify patients who will benefit from cholesterol reduction.

  14. Using exposure prediction tools to link exposure and dosimetry for risk-based decisions: A case study with phthalates

    Science.gov (United States)

    A few different exposure prediction tools were evaluated for use in the new in vitro-based safety assessment paradigm using di-2-ethylhexyl phthalate (DEHP) and dibutyl phthalate (DnBP) as case compounds. Daily intake of each phthalate was estimated using both high-throughput (HT...

  15. New Tool to Predict Glaucoma

    Science.gov (United States)

    ... In This Section A New Tool to Predict Glaucoma email Send this article to a friend by ... Close Send Thanks for emailing that article! Tweet Glaucoma can be difficult to detect and diagnose. Measurement ...

  16. A predictive tool to estimate the risk of axillary metastases in breast cancer patients with negative axillary ultrasound

    DEFF Research Database (Denmark)

    Meretoja, T J; Heikkilä, P S; Mansfield, A S

    2014-01-01

    of this study was to evaluate the risk factors for axillary metastases in breast cancer patients with negative preoperative axillary ultrasound. METHODS: A total of 1,395 consecutive patients with invasive breast cancer and SNB formed the original patient series. A univariate analysis was conducted to assess...... risk factors for axillary metastases. Binary logistic regression analysis was conducted to form a predictive model based on the risk factors. The predictive model was first validated internally in a patient series of 566 further patients and then externally in a patient series of 2,463 patients from......BACKGROUND: Sentinel node biopsy (SNB) is the "gold standard" in axillary staging in clinically node-negative breast cancer patients. However, axillary treatment is undergoing a paradigm shift and studies are being conducted on whether SNB may be omitted in low-risk patients. The purpose...

  17. 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.

  18. The REDUCE metagram: a comprehensive prediction tool for determining the utility of dutasteride chemoprevention in men at risk for prostate cancer

    Directory of Open Access Journals (Sweden)

    Carvell eNguyen

    2012-10-01

    Full Text Available Introduction: 5-alpha reductase inhibitors can reduce the risk of prostate cancer but can be associated with significant side effects. A library of nomograms which predict the risk of clinical endpoints relevant to dutasteride treatment may help determine if chemoprevention is suited to the individual patient. Methods: Data from the REDUCE trial was used to identify predictive factors for nine endpoints relevant to dutasteride treatment. Using the treatment and placebo groups from the biopsy cohort, Cox proportional hazards and competing risks regression models were used to build 18 nomograms, whose predictive ability was measured by concordance index and calibration plots. Results: A total of 18 nomograms assessing the risks of cancer, high-grade cancer, high grade prostatic intraepithelial neoplasia (HGPIN, atypical small acinar proliferation (ASAP, erectile dysfunction (ED, acute urinary retention (AUR, gynecomastia, urinary tract infection (UTI and BPH-related surgery either on or off dutasteride were created. The nomograms for cancer, high grade cancer, ED, AUR, and BPH-related surgery demonstrated good discrimination and calibration while those for gynecomastia, UTI, HGPIN, and ASAP predicted no better than random chance. Conclusions: To aid patients in determining whether the benefits of dutasteride use outweigh the risks, we have developed a comprehensive metagram that can generate individualized risks of 9 outcomes relevant to men considering chemoprevention. Better models based on more predictive markers are needed for some of the endpoints but the current metagram demonstrates potential as a tool for patient counseling and decision making that is accessible, intuitive, and clinically relevant.

  19. Predictive validity of the identification of seniors at risk screening tool in a German emergency department setting.

    Science.gov (United States)

    Singler, Katrin; Heppner, Hans Jürgen; Skutetzky, Andreas; Sieber, Cornel; Christ, Michael; Thiem, Ulrich

    2014-01-01

    The identification of patients at high risk for adverse outcomes [death, unplanned readmission to emergency department (ED)/hospital, functional decline] plays an important role in emergency medicine. The Identification of Seniors at Risk (ISAR) instrument is one of the most commonly used and best-validated screening tools. As to the authors' knowledge so far there are no data on any screening tool for the identification of older patients at risk for a negative outcome in Germany. To evaluate the validity of the ISAR screening tool in a German ED. This was a prospective single-center observational cohort study in an ED of an urban university-affiliated hospital. Participants were 520 patients aged ≥75 years consecutively admitted to the ED. The German version of the ISAR screening tool was administered directly after triage of the patients. Follow-up telephone interviews to assess outcome variables were conducted 28 and 180 days after the index visit in the ED. The primary end point was death from any cause or hospitalization or recurrent ED visit or change of residency into a long-term care facility on day 28 after the index ED visit. The mean age ± SD was 82.8 ± 5.0 years. According to ISAR, 425 patients (81.7%) scored ≥2 points, and 315 patients (60.5%) scored ≥3 points. The combined primary end point was observed in 250 of 520 patients (48.1%) on day 28 and in 260 patients (50.0%) on day 180. Using a continuous ISAR score the area under the curve on day 28 was 0.621 (95% confidence interval, CI 0.573-0.669) and 0.661 (95% CI 0.615-0.708) on day 180, respectively. The German version of the ISAR screening tool acceptably identified elderly patients in the ED with an increased risk of a negative outcome. Using the cutoff ≥3 points instead of ≥2 points yielded better overall results.

  20. Habitat Modeling and Preferences of Marine Mammals as Function of Oceanographic Characteristics: Development of Predictive Tools for Assessing the Risks and the Impacts Due to Sound Emissions

    Science.gov (United States)

    2011-09-30

    evaluate WEC projects in the perspective of the environmental cost-benefit analysis. Proceedings of the ISOPE 2011, Maui, Hawaii, USA 19-24 June, 2011...Function of Oceanographic Characteristics: Development of Predictive Tools for Assessing the Risks and the Impacts Due to Sound Emissions Dr...detections) and the available environmental predictors; - Creating the knowledge-based background about potential mitigation measures appropriate for

  1. Is monocyte HLA-DR expression monitoring a useful tool to predict the risk of secondary infection?

    Science.gov (United States)

    Lukaszewicz, A-C; Faivre, V; Payen, D

    2010-09-01

    Downregulation of the immune response is common among Intensive Care Unit (ICU) patients after an acute inflammatory injury, whether it was septic or not. Such a modification could be seen as an adaptation to attenuate the effects of the inflammatory storm on tissues, but it exposes the subject to the risk of nosocomial infection and impairs recovery processes. The intensity of immunity downregulation is difficult to characterize, since clinical presentation is silent and non-specific, which urges the use of tools for immune monitoring. This review focuses on the use of monocyte HLA-DR expression to detect immune hyporesponsiveness and to select the appropriate immunomodulating therapy, as well as the efficiency of this technique in controlling secondary infections.

  2. The REDUCE metagram: a comprehensive prediction tool for determining the utility of dutasteride chemoprevention in men at risk for prostate cancer

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, Carvell T.; Isariyawongse, Brandon [Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH (United States); Yu, Changhong; Kattan, Michael W., E-mail: kattanm@ccf.org [Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH (United States)

    2012-10-11

    Introduction: 5-alpha reductase inhibitors can reduce the risk of prostate cancer (PCa) but can be associated with significant side effects. A library of nomograms which predict the risk of clinical endpoints relevant to dutasteride treatment may help determine if chemoprevention is suited to the individual patient. Methods: Data from the REDUCE trial was used to identify predictive factors for 9 endpoints relevant to dutasteride treatment. Using the treatment and placebo groups from the biopsy cohort, Cox proportional hazards (PH) and competing risks regression (CRR) models were used to build 18 nomograms, whose predictive ability was measured by concordance index (CI) and calibration plots. Results: A total of 18 nomograms assessing the risks of cancer, high grade cancer, high grade prostatic intraepithelial neoplasia (HGPIN), atypical small acinar proliferation (ASAP), erectile dysfunction (ED), acute urinary retention (AUR), gynecomastia, urinary tract infection (UTI) and BPH-related surgery either on or off dutasteride were created. The nomograms for cancer, high grade cancer, ED, AUR, and BPH-related surgery demonstrated good discrimination and calibration while those for gynecomastia, UTI, HGPIN, and ASAP predicted no better than random chance. Conclusions: To aid patients in determining whether the benefits of dutasteride use outweigh the risks, we have developed a comprehensive metagram that can generate individualized risks of 9 outcomes relevant to men considering chemoprevention. Better models based on more predictive markers are needed for some of the endpoints but the current metagram demonstrates potential as a tool for patient counseling and decision-making that is accessible, intuitive, and clinically relevant.

  3. The REDUCE metagram: a comprehensive prediction tool for determining the utility of dutasteride chemoprevention in men at risk for prostate cancer

    International Nuclear Information System (INIS)

    Nguyen, Carvell T.; Isariyawongse, Brandon; Yu, Changhong; Kattan, Michael W.

    2012-01-01

    Introduction: 5-alpha reductase inhibitors can reduce the risk of prostate cancer (PCa) but can be associated with significant side effects. A library of nomograms which predict the risk of clinical endpoints relevant to dutasteride treatment may help determine if chemoprevention is suited to the individual patient. Methods: Data from the REDUCE trial was used to identify predictive factors for 9 endpoints relevant to dutasteride treatment. Using the treatment and placebo groups from the biopsy cohort, Cox proportional hazards (PH) and competing risks regression (CRR) models were used to build 18 nomograms, whose predictive ability was measured by concordance index (CI) and calibration plots. Results: A total of 18 nomograms assessing the risks of cancer, high grade cancer, high grade prostatic intraepithelial neoplasia (HGPIN), atypical small acinar proliferation (ASAP), erectile dysfunction (ED), acute urinary retention (AUR), gynecomastia, urinary tract infection (UTI) and BPH-related surgery either on or off dutasteride were created. The nomograms for cancer, high grade cancer, ED, AUR, and BPH-related surgery demonstrated good discrimination and calibration while those for gynecomastia, UTI, HGPIN, and ASAP predicted no better than random chance. Conclusions: To aid patients in determining whether the benefits of dutasteride use outweigh the risks, we have developed a comprehensive metagram that can generate individualized risks of 9 outcomes relevant to men considering chemoprevention. Better models based on more predictive markers are needed for some of the endpoints but the current metagram demonstrates potential as a tool for patient counseling and decision-making that is accessible, intuitive, and clinically relevant.

  4. Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example

    Directory of Open Access Journals (Sweden)

    Benjamin A Goldstein

    2014-08-01

    Full Text Available Purpose: Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers. Materials & Methods: We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD in the Atherosclerosis Risk in Communities (ARIC cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.Results: The addition of a GRS to a clinical risk score (CRS improves both discrimination and calibration for CHD in ARIC. Results are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor SNPs are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider. Conclusion: The proposed method facilitates the standardized incorporation of a GRS in risk assessment.

  5. Predictive Validity of the STarT Back Tool for Risk of Persistent Disabling Back Pain in a U.S Primary Care Setting.

    Science.gov (United States)

    Suri, Pradeep; Delaney, Kristin; Rundell, Sean D; Cherkin, Daniel C

    2018-04-03

    To examine the predictive validity of the Subgrouping for Targeted Treatment (STarT Back) tool for classifying people with back pain into categories of low, medium, and high risk of persistent disabling back pain in U.S. primary care. Secondary analysis of data from participants receiving usual care in a randomized clinical trial. Primary care clinics. Adults (N = 1109) ≥18 years of age with back pain. Those with specific causes of back pain (pregnancy, disc herniation, vertebral fracture, spinal stenosis) and work-related injuries were not included. Not applicable. The original 9-item version of the STarT Back tool, administered at baseline, stratified patients by their risk (low, medium, high) of persistent disabling back pain (STarT Back risk group). Persistent disabling back pain was defined as Roland-Morris Disability Questionnaire scores of ≥7 at 6-month follow-up. The STarT Back risk group was a significant predictor of persistent disabling back pain (PSTarT Back risk groups successfully separated people with back pain into distinct categories of risk for persistent disabling back pain at 6-month follow-up in U.S. primary care. These results were very similar to those in the original STarT Back validation study. This validation study is a necessary first step toward identifying whether the entire STarT Back approach, including matched/targeted treatment, can be effectively used for primary care in the United States. Published by Elsevier Inc.

  6. Tools for Microbiological risk assessment

    DEFF Research Database (Denmark)

    Bassett, john; Nauta, Maarten; Lindqvist, Roland

    can increase the understanding of microbiological risks in foods. It is timely to inform food safety professionals about the availability and utility of MRA tools. Therefore, the focus of this report is to aid the food safety manager by providing a concise summary of the tools available for the MRA......Microbiological Risk Assessment (MRA) has emerged as a comprehensive and systematic approach for addressing the risk of pathogens in specific foods and/or processes. At government level, MRA is increasingly recognised as a structured and objective approach to understand the level of risk in a given...... food/pathogen scenario. Tools developed so far support qualitative and quantitative assessments of the risk that a food pathogen poses to a particular population. Risk can be expressed as absolute numbers or as relative (ranked) risks. The food industry is beginning to appreciate that the tools for MRA...

  7. Prospective validation of American Diabetes Association risk tool for predicting pre-diabetes and diabetes in Taiwan-Taichung community health study.

    Directory of Open Access Journals (Sweden)

    Chia-Ing Li

    Full Text Available BACKGROUND: A simple diabetes risk tool that does not require laboratory tests would be beneficial in screening individuals at higher risk. Few studies have evaluated the ability of these tools to identify new cases of pre-diabetes. This study aimed to assess the ability of the American Diabetes Association Risk Tool (ADART to predict the 3-year incidence of pre-diabetes and diabetes in Taiwanese. METHODS: This was a 3-year prospective study of 1021 residents with normoglycemia at baseline, gathered from a random sample of residents aged 40-88 years in a metropolitan city in Taiwan. The areas under the curve (AUCs of three models were compared: ADART only, ADART plus lifestyle behaviors at baseline, and ADART plus lifestyle behaviors and biomarkers at baseline. The performance of ADART was compared with that of 16 tools that had been reported in the literature. RESULTS: The AUCs and their 95% confidence intervals (CIs were 0.60 (0.54-0.66 for men and 0.72 (0.66-0.77 for women in model 1; 0.62 (0.56-0.68 for men and 0.74 (0.68-0.80 for women in model 2; and 0.64 (0.58-0.71 for men and 0.75 (0.69-0.80 for women in model 3. The AUCs of these three models were all above 0.7 in women, but not in men. No significant difference in either women or men (p = 0.268 and 0.156, respectively was observed in the AUC of these three models. Compared to 16 tools published in the literature, ADART had the second largest AUC in both men and women. CONCLUSIONS: ADART is a good screening tool for predicting the three-year incidence of pre-diabetes and diabetes in females of a Taiwanese population. The performance of ADART in men was similar to the results with other tools published in the literature. Its performance was one of the best among the tools reported in the literature.

  8. Thyroid Cancer Risk Assessment Tool

    Science.gov (United States)

    The R package thyroid implements a risk prediction model developed by NCI researchers to calculate the absolute risk of developing a second primary thyroid cancer (SPTC) in individuals who were diagnosed with a cancer during their childhood.

  9. Prediction of absolute risk of fragility fracture at 10 years in a Spanish population: validation of the WHO FRAX ™ tool in Spain

    Directory of Open Access Journals (Sweden)

    Solà Sílvia

    2011-01-01

    Full Text Available Abstract Background Age-related bone loss is asymptomatic, and the morbidity of osteoporosis is secondary to the fractures that occur. Common sites of fracture include the spine, hip, forearm and proximal humerus. Fractures at the hip incur the greatest morbidity and mortality and give rise to the highest direct costs for health services. Their incidence increases exponentially with age. Independently changes in population demography, the age - and sex- specific incidence of osteoporotic fractures appears to be increasing in developing and developed countries. This could mean more than double the expected burden of osteoporotic fractures in the next 50 years. Methods/Design To assess the predictive power of the WHO FRAX™ tool to identify the subjects with the highest absolute risk of fragility fracture at 10 years in a Spanish population, a predictive validation study of the tool will be carried out. For this purpose, the participants recruited by 1999 will be assessed. These were referred to scan-DXA Department from primary healthcare centres, non hospital and hospital consultations. Study population: Patients attended in the national health services integrated into a FRIDEX cohort with at least one Dual-energy X-ray absorptiometry (DXA measurement and one extensive questionnaire related to fracture risk factors. Measurements: At baseline bone mineral density measurement using DXA, clinical fracture risk factors questionnaire, dietary calcium intake assessment, history of previous fractures, and related drugs. Follow up by telephone interview to know fragility fractures in the 10 years with verification in electronic medical records and also to know the number of falls in the last year. The absolute risk of fracture will be estimated using the FRAX™ tool from the official web site. Discussion Since more than 10 years ago numerous publications have recognised the importance of other risk factors for new osteoporotic fractures in addition to

  10. Cardiovascular Disease Population Risk Tool (CVDPoRT): predictive algorithm for assessing CVD risk in the community setting. A study protocol.

    Science.gov (United States)

    Taljaard, Monica; Tuna, Meltem; Bennett, Carol; Perez, Richard; Rosella, Laura; Tu, Jack V; Sanmartin, Claudia; Hennessy, Deirdre; Tanuseputro, Peter; Lebenbaum, Michael; Manuel, Douglas G

    2014-10-23

    Recent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting. The Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77,251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619,886 person-years of follow-up until 31 December 2011). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50,000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers. This study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible

  11. Using exposure prediction tools to link exposure and dosimetry for risk based decisions: a case study with phthalates

    Science.gov (United States)

    The Population Life-course Exposure to Health Effects Modeling (PLETHEM) platform being developed provides a tool that links results from emerging toxicity testing tools to exposure estimates for humans as defined by the USEPA. A reverse dosimetry case study using phthalates was ...

  12. Predictive ecotoxicology as a tool to access risks of radionuclides on non human biota in a multi-contamination context

    International Nuclear Information System (INIS)

    Margerit, Adrien

    2015-01-01

    obtained on the basis of the descriptive and mechanistic approaches. The present study underlines the complexity of studying mixture toxicity and identifying chemical interactions. Despite some application problems, the mechanistic approach DEBtox is particularly promising to describe the toxicity of chemical mixtures over time and to test hypothetical interaction mechanisms. In the future, the improvement of tools to analyze the combined toxicity of contaminants would allow to better address the issue of mixtures in eco-toxicological risk assessment processes. (author)

  13. Validation of the MARS (Medical Admission Risk System): A combined physiological and laboratory risk prediction tool for 5- to 7-day in-hospital mortality.

    Science.gov (United States)

    Ohman, Malin Charlotta; Atkins, Tara E Holm; Cooksley, Tim; Brabrand, Mikkel

    2018-03-10

    The MARS (Medical Admission Risk System) uses 11 physiological and laboratory data and had promising results in its derivation study for predicting 5 and 7 day mortality. To perform an external independent validation of the MARS score. An unplanned secondary cohort study. Patients admitted to the medical admission unit (MAU) at The Hospital of South West Jutland were included from 2 October 2008 until 19 February 2009 and 23 February 2010 until 26 May 2010 were analysed. Validation of the MARS score using 5 and 7 day mortality was the primary endpoint. 5858 patients were included in the study. 2923 (49.9%) patients were women with a median age of 65 years (15-107). The MARS score had an AUROC of 0.858 (95% CI: 0.831-0.884) for 5-day mortality and 0.844 (0.818-0.870) for 7 day mortality with poor calibration for both outcomes. The MARS score had excellent discriminatory power but poor calibration in predicting both 5 and 7-day mortality. The development of accurate combination physiological/laboratory data risk scores has the potential to improve the recognition of at risk patients.

  14. A failure-type specific risk prediction tool for selection of head-and-neck cancer patients for experimental treatments

    DEFF Research Database (Denmark)

    Håkansson, Katrin; Rasmussen, Jacob H.; Rasmussen, Gregers B.

    2017-01-01

    : Retrospective data for 560HNSCC patients were used to generate a multi-endpoint model, combining three cause-specific Cox models (LRF, DM and death with no evidence of disease (death NED)). The model was used to generate risk profiles of patients eligible for/included in a de-intensification study (RTOG 1016...... variables (tumor subsite, T stage, N stage, smoking status, age and performance status) and one additional variable (tumor volume). The treatment failure discrimination ability of the developed model was superior of that of UICC staging, 8(th) edition (AUCLRF=72.7% vs 64.2%, p....8%, pde-intensification study had>20% risk of tumor relapse. Conversely, 9 of the 15 dose escalation trial participants had LRF risks

  15. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale

    Czech Academy of Sciences Publication Activity Database

    Thuiller, W.; Richardson, D. M.; Pyšek, Petr; Midgley, G. F.; Hughes, G. O.; Rouget, M.

    2005-01-01

    Roč. 11, - (2005), s. 2234-2250 ISSN 1354-1013 R&D Projects: GA ČR GA206/03/1216 Institutional research plan: CEZ:AV0Z60050516 Keywords : bioclimatic modelling * biological invasions * risk assessment Subject RIV: EF - Botanics Impact factor: 4.075, year: 2005

  16. Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces.

    Science.gov (United States)

    Samal, Lipika; D'Amore, John D; Bates, David W; Wright, Adam

    2017-11-01

    Clinical decision support tools for risk prediction are readily available, but typically require workflow interruptions and manual data entry so are rarely used. Due to new data interoperability standards for electronic health records (EHRs), other options are available. As a clinical case study, we sought to build a scalable, web-based system that would automate calculation of kidney failure risk and display clinical decision support to users in primary care practices. We developed a single-page application, web server, database, and application programming interface to calculate and display kidney failure risk. Data were extracted from the EHR using the Consolidated Clinical Document Architecture interoperability standard for Continuity of Care Documents (CCDs). EHR users were presented with a noninterruptive alert on the patient's summary screen and a hyperlink to details and recommendations provided through a web application. Clinic schedules and CCDs were retrieved using existing application programming interfaces to the EHR, and we provided a clinical decision support hyperlink to the EHR as a service. We debugged a series of terminology and technical issues. The application was validated with data from 255 patients and subsequently deployed to 10 primary care clinics where, over the course of 1 year, 569 533 CCD documents were processed. We validated the use of interoperable documents and open-source components to develop a low-cost tool for automated clinical decision support. Since Consolidated Clinical Document Architecture-based data extraction extends to any certified EHR, this demonstrates a successful modular approach to clinical decision support. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  17. Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT).

    Science.gov (United States)

    Hsu, Amy T; Manuel, Douglas G; Taljaard, Monica; Chalifoux, Mathieu; Bennett, Carol; Costa, Andrew P; Bronskill, Susan; Kobewka, Daniel; Tanuseputro, Peter

    2016-12-01

    Older adults living in the community often have multiple, chronic conditions and functional impairments. A challenge for healthcare providers working in the community is the lack of a predictive tool that can be applied to the broad spectrum of mortality risks observed and may be used to inform care planning. To predict survival time for older adults in the home care setting. The final mortality risk algorithm will be implemented as a web-based calculator that can be used by older adults needing care and by their caregivers. Open cohort study using the Resident Assessment Instrument for Home Care (RAI-HC) data in Ontario, Canada, from 1 January 2007 to 31 December 2013. The derivation cohort will consist of ∼437 000 older adults who had an RAI-HC assessment between 1 January 2007 and 31 December 2012. A split sample validation cohort will include ∼122 000 older adults with an RAI-HC assessment between 1 January and 31 December 2013. Predicted survival from the time of an RAI-HC assessment. All deaths (n≈245 000) will be ascertained through linkage to a population-based registry that is maintained by the Ministry of Health in Ontario. Proportional hazards regression will be estimated after assessment of assumptions. Predictors will include sociodemographic factors, social support, health conditions, functional status, cognition, symptoms of decline and prior healthcare use. Model performance will be evaluated for 6-month and 12-month predicted risks, including measures of calibration (eg, calibration plots) and discrimination (eg, c-statistics). The final algorithm will use combined development and validation data. Research ethics approval has been granted by the Sunnybrook Health Sciences Centre Review Board. Findings will be disseminated through presentations at conferences and in peer-reviewed journals. NCT02779309, Pre-results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to

  18. Behavior Prediction Tools Strengthen Nanoelectronics

    Science.gov (United States)

    2013-01-01

    Several years ago, NASA started making plans to send robots to explore the deep, dark craters on the Moon. As part of these plans, NASA needed modeling tools to help engineer unique electronics to withstand extremely cold temperatures. According to Jonathan Pellish, a flight systems test engineer at Goddard Space Flight Center, "An instrument sitting in a shadowed crater on one of the Moon s poles would hover around 43 K", that is, 43 kelvin, equivalent to -382 F. Such frigid temperatures are one of the main factors that make the extreme space environments encountered on the Moon and elsewhere so extreme. Radiation is another main concern. "Radiation is always present in the space environment," says Pellish. "Small to moderate solar energetic particle events happen regularly and extreme events happen less than a handful of times throughout the 7 active years of the 11-year solar cycle." Radiation can corrupt data, propagate to other systems, require component power cycling, and cause a host of other harmful effects. In order to explore places like the Moon, Jupiter, Saturn, Venus, and Mars, NASA must use electronic communication devices like transmitters and receivers and data collection devices like infrared cameras that can resist the effects of extreme temperature and radiation; otherwise, the electronics would not be reliable for the duration of the mission.

  19. Predictive Validity of the HKT-R Risk Assessment Tool: Two and 5-Year Violent Recidivism in a Nationwide Sample of Dutch Forensic Psychiatric Patients.

    Science.gov (United States)

    Bogaerts, Stefan; Spreen, Marinus; Ter Horst, Paul; Gerlsma, Coby

    2018-06-01

    This study has examined the predictive validity of the Historical Clinical Future [ Historisch Klinisch Toekomst] Revised risk assessment scheme in a cohort of 347 forensic psychiatric patients, which were discharged between 2004 and 2008 from any of 12 highly secure forensic centers in the Netherlands. Predictive validity was measured 2 and 5 years after release. Official reconviction data obtained from the Dutch Ministry of Security and Justice were used as outcome measures. Violent reoffending within 2 and 5 years after discharge was assessed. With regard to violent reoffending, results indicated that the predictive validity of the Historical domain was modest for 2 (area under the curve [AUC] = .75) and 5 (AUC = .74) years. The predictive validity of the Clinical domain was marginal for 2 (admission: AUC = .62; discharge: AUC = .63) and 5 (admission: AUC = .69; discharge: AUC = .62) years after release. The predictive validity of the Future domain was modest (AUC = .71) for 2 years and low for 5 (AUC = .58) years. The total score of the instrument was modest for 2 years (AUC = .78) and marginal for 5 (AUC = .68) years. Finally, the Final Risk Judgment was modest for 2 years (AUC = .78) and marginal for 5 (AUC = .63) years time at risk. It is concluded that this risk assessment instrument appears to be a satisfactory instrument for risk assessment.

  20. Predictive Data Tools Find Uses in Schools

    Science.gov (United States)

    Sparks, Sarah D.

    2011-01-01

    The use of analytic tools to predict student performance is exploding in higher education, and experts say the tools show even more promise for K-12 schools, in everything from teacher placement to dropout prevention. Use of such statistical techniques is hindered in precollegiate schools, however, by a lack of researchers trained to help…

  1. Identification of high-risk cutaneous melanoma tumors is improved when combining the online American Joint Committee on Cancer Individualized Melanoma Patient Outcome Prediction Tool with a 31-gene expression profile-based classification.

    Science.gov (United States)

    Ferris, Laura K; Farberg, Aaron S; Middlebrook, Brooke; Johnson, Clare E; Lassen, Natalie; Oelschlager, Kristen M; Maetzold, Derek J; Cook, Robert W; Rigel, Darrell S; Gerami, Pedram

    2017-05-01

    A significant proportion of patients with American Joint Committee on Cancer (AJCC)-defined early-stage cutaneous melanoma have disease recurrence and die. A 31-gene expression profile (GEP) that accurately assesses metastatic risk associated with primary cutaneous melanomas has been described. We sought to compare accuracy of the GEP in combination with risk determined using the web-based AJCC Individualized Melanoma Patient Outcome Prediction Tool. GEP results from 205 stage I/II cutaneous melanomas with sufficient clinical data for prognostication using the AJCC tool were classified as low (class 1) or high (class 2) risk. Two 5-year overall survival cutoffs (AJCC 79% and 68%), reflecting survival for patients with stage IIA or IIB disease, respectively, were assigned for binary AJCC risk. Cox univariate analysis revealed significant risk classification of distant metastasis-free and overall survival (hazard ratio range 3.2-9.4, P risk by GEP but low risk by AJCC. Specimens reflect tertiary care center referrals; more effective therapies have been approved for clinical use after accrual. The GEP provides valuable prognostic information and improves identification of high-risk melanomas when used together with the AJCC online prediction tool. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

  2. 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...

  3. Predictive validity of the HKT-R risk assessment tool: : Two and 5-year violent recidivism in a nationwide sample of Dutch forensic psychiatric patients

    NARCIS (Netherlands)

    Bogaerts, Stefan; Spreen, Marinus; Ter Horst, Paul; Gerlsma, Coby

    2017-01-01

    Abstract This study has examined the predictive validity of the Historical Clinical Future [Historisch Klinisch Toekomst] Revised risk assessment scheme in a cohort of 347 forensic psychiatric patients, which were discharged between 2004 and 2008 from any of 12 highly secure forensic centers in the

  4. 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.

  5. PISCES: A Tool for Predicting Software Testability

    Science.gov (United States)

    Voas, Jeffrey M.; Miller, Keith W.; Payne, Jeffery E.

    1991-01-01

    Before a program can fail, a software fault must be executed, that execution must alter the data state, and the incorrect data state must propagate to a state that results directly in an incorrect output. This paper describes a tool called PISCES (developed by Reliable Software Technologies Corporation) for predicting the probability that faults in a particular program location will accomplish all three of these steps causing program failure. PISCES is a tool that is used during software verification and validation to predict a program's testability.

  6. Daylight prediction techniques in energy design tools

    Energy Technology Data Exchange (ETDEWEB)

    Milne, M.; Zurick, J. [California Univ., Los Angeles, Dept. of Architecture, CA (United States)

    1998-09-01

    Four different whole-building energy design tool systems that calculate energy savings from daylighting and that display annual performance on an-hour-by-hour basis, have been tested. The nature of design tools, the sources of hourly outdoor illuminance data, the ways of predicting indoor illumination, the assumptions of each tool, and the resulting energy savings of the design tools tested are discussed. The tests were carried out with the essential criteria for evaluating whole-building daylighting and energy design tools in mind. These have been identified as user confidence, accuracy, response time, and the amount of detail. Results of the tests, all four of them run on a single elementary school classroom for the sake of comparability, were provided. 9 refs., 2 figs.

  7. 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.

  8. Metal Vapor Arcing Risk Assessment Tool

    Science.gov (United States)

    Hill, Monika C.; Leidecker, Henning W.

    2010-01-01

    The Tin Whisker Metal Vapor Arcing Risk Assessment Tool has been designed to evaluate the risk of metal vapor arcing and to help facilitate a decision toward a researched risk disposition. Users can evaluate a system without having to open up the hardware. This process allows for investigating components at risk rather than spending time and money analyzing every component. The tool points to a risk level and provides direction for appropriate action and documentation.

  9. Gaussian process regression for tool wear prediction

    Science.gov (United States)

    Kong, Dongdong; Chen, Yongjie; Li, Ning

    2018-05-01

    To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.

  10. GAPIT: genome association and prediction integrated tool.

    Science.gov (United States)

    Lipka, Alexander E; Tian, Feng; Wang, Qishan; Peiffer, Jason; Li, Meng; Bradbury, Peter J; Gore, Michael A; Buckler, Edward S; Zhang, Zhiwu

    2012-09-15

    Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results. http://www.maizegenetics.net/GAPIT. zhiwu.zhang@cornell.edu Supplementary data are available at Bioinformatics online.

  11. Web tools for predictive toxicology model building.

    Science.gov (United States)

    Jeliazkova, Nina

    2012-07-01

    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  12. 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

  13. Liquidity risk charges as a macroprudential tool

    NARCIS (Netherlands)

    Perotti, E.; Suarez, J.

    2009-01-01

    Liquidity risk charges were proposed in February 2009 as a new macro-prudential tool to discourage systemic risk creation by banks. A new CEPR Policy Insight refines this proposal in order to clarify challenging issues surrounding the implementation of liquidity risk charges.

  14. Development of computerized risk management tool

    International Nuclear Information System (INIS)

    Kil Yoo Kim; Mee Jung Hwang; Seung Cheol Jang; Sang Hoon Han; Tae Woon Kim

    1997-01-01

    The author describes the kinds of efforts for the development of computerized risk management tool; (1) development of a risk monitor, Risk Monster, (2) improvement of McFarm (Missing Cutsets Finding Algorithm for Risk Monitor) and finally (3) development of reliability database management system, KwDBMan. Risk Monster supports for plant operators and maintenance schedulers to monitor plant risk and to avoid high peak risk by rearranging maintenance work schedule. Improved McFarm significantly improved calculation speed of Risk Monster for the cases of supporting system OOS (Out Of Service). KwDBMan manages event data, generic data and CCF (Common Cause Failure) data to support Risk Monster as well as PSA tool, KIRAP (KAERI Integrated Reliability Analysis Package)

  15. Can we predict addiction to opioid analgesics? A possible tool to estimate the risk of opioid addiction in patients with pain.

    Science.gov (United States)

    Skala, Katrin; Reichl, Lukas; Ilias, Wilfried; Likar, Rudolf; Grogl-Aringer, Gabriele; Wallner, Christina; Schlaff, Golda; Herrmann, Peter; Lesch, Otto; Walter, Henriette

    2013-01-01

    origin of pain. We believe these factors have predictive value in estimating a patient with pain's risk of addiction.

  16. 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

  17. 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.

  18. Risk assessment: tools, techniques, and their applications

    National Research Council Canada - National Science Library

    Ostrom, Lee T; Wilhelmsen, Cheryl A

    2012-01-01

    .... The central task of the risk assessor is predicting the success of a project. This includes isolating the entire spectrum of adverse events that can derail a project or threaten the health and safety of individuals, organizations, and the environment...

  19. A new methodology for predictive tool wear

    Science.gov (United States)

    Kim, Won-Sik

    turned with various cutting conditions and the results were compared with the proposed analytical wear models. The crater surfaces after machining have been carefully studied to shed light on the physics behind the crater wear. In addition, the abrasive wear mechanism plays a major role in the development of crater wear. Laser shock processing (LSP) has been applied to locally relieve the deleterious tensile residual stresses on the crater surface of a coated tool, thus to improve the hardness of the coating. This thesis shows that LSP has indeed improve wear resistance of CVD coated alumina tool inserts, which has residual stress due to high processing temperature. LSP utilizes a very short laser pulse with high energy density, which induces high-pressure stress wave propagation. The residual stresses are relieved by incident shock waves on the coating surface. Residual stress levels of LSP CVD alumina-coated carbide insert were evaluated by the X-ray diffractometer. Based on these results, LSP parameters such as number of laser pulses and laser energy density can be controlled to reduce residual stress. Crater wear shows that the wear resistance increase with LSP treated tool inserts. Because the hardness data are used to predict the wear, the improvement in hardness and wear resistance shows that the mechanism of crater wear also involves abrasive wear.

  20. 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.

  1. Risk indicators as a tool for risk control

    International Nuclear Information System (INIS)

    Oien, K.

    2001-01-01

    This paper presents a general methodology for the establishment of risk indicators that can be used as a tool for risk control during operation of offshore petroleum installations. The risk indicators established are based on the platform specific quantitative risk analysis (QRA). The general methodology is evaluated against comparable approaches both in offshore and nuclear industry. There are two distinct features of this methodology. The first is that it is truly risk-based with the intention of covering the total risk picture. The second is that the identification of the risk factors contributing most to the total risk is based on realistic changes of each factor assessed by the platform personnel, not a theoretically assumed change. The set of risk indicators for one specific installation is presented along with test results

  2. About Using Predictive Models and Tools To Assess Chemicals under TSCA

    Science.gov (United States)

    As part of EPA's effort to promote chemical safety, OPPT provides public access to predictive models and tools which can help inform the public on the hazards and risks of substances and improve chemical management decisions.

  3. Development of configuration risk management tool

    International Nuclear Information System (INIS)

    Masuda, Takahiro; Doi, Eiji

    2003-01-01

    Tokyo Electric Power Company (referred to as TEPCO hereinafter), and other Japanese utilities as well, have been trying to improve the capacity factor of their Nuclear Power Plants (NPPs) through modernization of Operation and Maintenance strategy. TEPCO intends to apply risk information to O and M field with maintaining or even improving both safety and production efficiency. Under these situations, TEPCO with some BWR utilities started to develop a Configuration Risk Management (CRM) tool that can estimate risk in various plant conditions due to configuration changes during outage. Moreover, we also intend to apply CRM to on-line maintenance (OLM) in the near future. This tool can calculate the Core Damage Frequency (CDF) according to given plant condition, such as SSCs availability, decay heat level and the inventory of coolant in both outage state and full-power operation. From deterministic viewpoint, whether certain configuration meet the related requirements of Technical Specifications. User-friendly interface is one of the important features of this tool because this enables the site engineers with little experience in PSA to quantify and utilize the risk information by this tool. (author)

  4. Habitat Modeling of Marine Mammals as Function of Oceanographic Characteristics; Development of Predictive Tools for Assessing and Managing the Risks and the Impacts due to Sound Emissions

    Science.gov (United States)

    2010-09-30

    Mediterranean sea, using visual observations data obtained from the NURC/ Sirena databases (Figure 3). Cuvier’s beaked whale was chosen as target species. The...calibration site) was tested. LIGURIAN SEA ALBORAN SEA NURC Trials Sirena 01 Sirena 02 Sirena 03 Sirena 08 Time Period 17-Sep 5 - 23 July 25-Aug...These predictions were overlaid with the Cuvier’s beaked whale observations collected during the Sirena 08 cruise. The accuracy of the a priori

  5. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

  6. Using social media as a tool to predict syphilis.

    Science.gov (United States)

    Young, Sean D; Mercer, Neil; Weiss, Robert E; Torrone, Elizabeth A; Aral, Sevgi O

    2018-04-01

    Syphilis rates have been rapidly rising in the United States. New technologies, such as social media, might be used to anticipate and prevent the spread of disease. Because social media data collection is easy and inexpensive, integration of social media data into syphilis surveillance may be a cost-effective surveillance strategy, especially in low-resource regions. People are increasingly using social media to discuss health-related issues, such as sexual risk behaviors, allowing social media to be a potential tool for public health and medical research. This study mined Twitter data to assess whether social media could be used to predict syphilis cases in 2013 based on 2012 data. We collected 2012 and 2013 county-level primary and secondary (P&S) and early latent syphilis cases reported to the Center for Disease Control and Prevention, along with >8500 geolocated tweets in the United States that were filtered to include sexual risk-related keywords, including colloquial terms for intercourse. We assessed the relationship between syphilis-related tweets and actual case reports by county, controlling for socioeconomic indicators and prior year syphilis cases. We found a significant positive relationship between tweets and cases of P&S and early latent syphilis. This study shows that social media may be an additional tool to enhance syphilis prediction and surveillance. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Risk assessment tools to identify women with increased risk of osteoporotic fracture. Complexity or simplicity?

    DEFF Research Database (Denmark)

    Rubin, Katrine Hass; Friis-Holmberg, Teresa; Hermann, Anne Pernille

    2013-01-01

    A huge number of risk assessment tools have been developed. Far from all have been validated in external studies, more of them have absence of methodological and transparent evidence and few are integrated in national guidelines. Therefore, we performed a systematic review to provide an overview...... of existing valid and reliable risk assessment tools for prediction of osteoporotic fractures. Additionally, we aimed to determine if the performance each tool was sufficient for practical use and lastly to examine whether the complexity of the tools influenced their discriminative power. We searched Pub......Med, Embase and Cochrane databases for papers and evaluated these with respect to methodological quality using the QUADAS checklist. A total of 48 tools were identified, 20 had been externally validated, however only 6 tools had been tested more than once in a population-based setting with acceptable...

  8. 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....

  9. Youth Offender Care Needs Assessment Tool (YO-CNAT): an actuarial risk assessment tool for predicting problematic child-rearing situations in juvenile offenders on the basis of police records

    NARCIS (Netherlands)

    van der Put, C.E.; Stams, G.J.J.M.

    2013-01-01

    In the juvenile justice system, much attention is paid to estimating the risk for recidivism among juvenile offenders. However, it is also important to estimate the risk for problematic child-rearing situations (care needs) in juvenile offenders, because these problems are not always related to

  10. Standardised risk analysis as a communication tool

    International Nuclear Information System (INIS)

    Pluess, Ch.; Montanarini, M.; Bernauer, M.

    1998-01-01

    Full text of publication follows: several European countries require a risk analysis for the production, storage or transport a dangerous goods. This requirement imposes considerable administrative effort for some sectors of the industry. In order to minimize the effort of such studies, a generic risk analysis for an industrial sector proved to help. Standardised procedures can consequently be derived for efficient performance of the risk investigations. This procedure was successfully established in Switzerland for natural gas transmission lines and fossil fuel storage plants. The development process of the generic risk analysis involved an intense discussion between industry and authorities about methodology of assessment and the criteria of acceptance. This process finally led to scientific consistent modelling tools for risk analysis and to an improved communication from the industry to the authorities and the public. As a recent example, the Holland-Italy natural gas transmission pipeline is demonstrated, where this method was successfully employed. Although this pipeline traverses densely populated areas in Switzerland, using this established communication method, the risk problems could be solved without delaying the planning process. (authors)

  11. A clinical tool to predict Plasmodium vivax recurrence in Malaysia.

    Science.gov (United States)

    Mat Ariffin, Norliza; Islahudin, Farida; Kumolosasi, Endang; Makmor-Bakry, Mohd

    2017-12-08

    Recurrence rates of Plasmodium vivax infections differ across various geographic regions. Interestingly, South-East Asia and the Asia-Pacific region are documented to exhibit the most frequent recurrence incidences. Identifying patients at a higher risk for recurrences gives valuable information in strengthening the efforts to control P. vivax infections. The aim of the study was to develop a tool to identify P. vivax- infected patients that are at a higher risk of recurrence in Malaysia. Patient data was obtained retrospectively through the Ministry of Health, Malaysia, from 2011 to 2016. Patients with incomplete data were excluded. A total of 2044 clinical P. vivax malaria cases treated with primaquine were included. Data collected were patient, disease, and treatment characteristics. Two-thirds of the cases (n = 1362) were used to develop a clinical risk score, while the remaining third (n = 682) was used for validation. Using multivariate analysis, age (p = 0.03), gametocyte sexual count (p = 0.04), indigenous transmission (p = 0.04), type of treatment (p = 0.12), and incomplete primaquine treatment (p = 0.14) were found to be predictors of recurrence after controlling for other confounding factors; these predictors were then used in developing the final model. The beta-coefficient values were used to develop a clinical scoring tool to predict possible recurrence. The total scores ranged between 0 and 8. A higher score indicated a higher risk for recurrence (odds ratio [OR]: 1.971; 95% confidence interval [CI]: 1.562-2.487; p ≤ 0.001). The area under the receiver operating characteristic (ROC) curve of the developed (n = 1362) and validated model (n = 682) was of good accuracy (ROC: 0.728, 95% CI: 0.670-0.785, p value useful tool in targeting patients at a higher risk for recurrence for closer monitoring during follow-up, after treatment with primaquine.

  12. A generic rabies risk assessment tool to support surveillance.

    Science.gov (United States)

    Ward, Michael P; Hernández-Jover, Marta

    2015-06-01

    The continued spread of rabies in Indonesia poses a risk to human and animal populations in the remaining free islands, as well as the neighbouring rabies-free countries of Timor Leste, Papua New Guinea and Australia. Here we describe the development of a generic risk assessment tool which can be used to rapidly determine the vulnerability of rabies-free islands, so that scarce resources can be targeted to surveillance activities and the sensitivity of surveillance systems increased. The tool was developed by integrating information on the historical spread of rabies, anthropological studies, and the opinions of local animal health experts. The resulting tool is based on eight critical parameters that can be estimated from the literature, expert opinion, observational studies and information generated from routine surveillance. In the case study presented, results generated by this tool were most sensitive to the probability that dogs are present on private and fishing boats and it was predicted that rabies-infection (one infected case) might occur in a rabies-free island (upper 95% prediction interval) with a volume of 1000 boats movements. With 25,000 boat movements, the median of the probability distribution would be equal to one infected case, with an upper 95% prediction interval of six infected cases. This tool could also be used at the national-level to guide control and eradication plans. An initial recommendation from this study is to develop a surveillance programme to determine the likelihood that boats transport dogs, for example by port surveillance or regularly conducted surveys of fisherman and passenger ferries. However, the illegal nature of dog transportation from rabies-infected to rabies-free islands is a challenge for developing such surveillance. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Risk analysis as a decision tool

    International Nuclear Information System (INIS)

    Yadigaroglu, G.; Chakraborty, S.

    1985-01-01

    From 1983 - 1985 a lecture series entitled ''Risk-benefit analysis'' was held at the Swiss Federal Institute of Technology (ETH), Zurich, in cooperation with the Central Department for the Safety of Nuclear Installations of the Swiss Federal Agency of Energy Economy. In that setting the value of risk-oriented evaluation models as a decision tool in safety questions was discussed on a broad basis. Experts of international reputation from the Federal Republic of Germany, France, Canada, the United States and Switzerland have contributed to report in this joint volume on the uses of such models. Following an introductory synopsis on risk analysis and risk assessment the book deals with practical examples in the fields of medicine, nuclear power, chemistry, transport and civil engineering. Particular attention is paid to the dialogue between analysts and decision makers taking into account the economic-technical aspects and social values. The recent chemical disaster in the Indian city of Bhopal again signals the necessity of such analyses. All the lectures were recorded individually. (orig./HP) [de

  14. Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention.

    Directory of Open Access Journals (Sweden)

    Sara L White

    Full Text Available All obese women are categorised as being of equally high risk of gestational diabetes (GDM whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0-18+6 weeks' gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337 developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria. A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios provided an area under the curve of 0.71 (95%CI 0.68-0.74. This increased to 0.77 (95%CI 0.73-0.80 with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c, fructosamine, adiponectin, sex hormone binding globulin, triglycerides, but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74-0.81. Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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…

  6. 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.

  7. 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.

  8. 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.

  9. An ensemble model of QSAR tools for regulatory risk assessment.

    Science.gov (United States)

    Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

    2016-01-01

    Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa ( κ ): 0

  10. 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

  11. Common features of microRNA target prediction tools

    Directory of Open Access Journals (Sweden)

    Sarah M. Peterson

    2014-02-01

    Full Text Available The human genome encodes for over 1800 microRNAs, which are short noncoding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one microRNA to target multiple gene transcripts, microRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of microRNA targets is a critical initial step in identifying microRNA:mRNA target interactions for experimental validation. The available tools for microRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to microRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all microRNA target prediction tools, four main aspects of the microRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MicroRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

  12. Water Impact Prediction Tool for Recoverable Rockets

    Science.gov (United States)

    Rooker, William; Glaese, John; Clayton, Joe

    2011-01-01

    Reusing components from a rocket launch can be cost saving. NASA's space shuttle system has reusable components that return to the Earth and impact the ocean. A primary example is the Space Shuttle Solid Rocket Booster (SRB) that descends on parachutes to the Earth after separation and impacts the ocean. Water impact generates significant structural loads that can damage the booster, so it is important to study this event in detail in the design of the recovery system. Some recent examples of damage due to water impact include the Ares I-X First Stage deformation as seen in Figure 1 and the loss of the SpaceX Falcon 9 First Stage.To ensure that a component can be recovered or that the design of the recovery system is adequate, an adequate set of structural loads is necessary for use in failure assessments. However, this task is difficult since there are many conditions that affect how a component impacts the water and the resulting structural loading that a component sees. These conditions include the angle of impact with respect to the water, the horizontal and vertical velocities, the rotation rate, the wave height and speed, and many others. There have been attempts to simulate water impact. One approach is to analyze water impact using explicit finite element techniques such as those employed by the LS-Dyna tool [1]. Though very detailed, this approach is time consuming and would not be suitable for running Monte Carlo or optimization analyses. The purpose of this paper is to describe a multi-body simulation tool that runs quickly and that captures the environments a component might see. The simulation incorporates the air and water interaction with the component, the component dynamics (i.e. modes and mode shapes), any applicable parachutes and lines, the interaction of winds and gusts, and the wave height and speed. It is capable of quickly conducting Monte Carlo studies to better capture the environments and genetic algorithm optimizations to reproduce a

  13. Prediction of Machine Tool Condition Using Support Vector Machine

    International Nuclear Information System (INIS)

    Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

    2011-01-01

    Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

  14. Virtual Beach: Decision Support Tools for Beach Pathogen Prediction

    Science.gov (United States)

    The Virtual Beach Managers Tool (VB) is decision-making software developed to help local beach managers make decisions as to when beaches should be closed due to predicted high levels of water borne pathogens. The tool is being developed under the umbrella of EPA's Advanced Monit...

  15. 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.

  16. RNA-SSPT: RNA Secondary Structure Prediction Tools.

    Science.gov (United States)

    Ahmad, Freed; Mahboob, Shahid; Gulzar, Tahsin; Din, Salah U; Hanif, Tanzeela; Ahmad, Hifza; Afzal, Muhammad

    2013-01-01

    The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.

  17. Can we predict Acute Medical readmissions using the BOOST tool? A retrospective case note review.

    Science.gov (United States)

    Lee, Geraldine A; Freedman, Daniel; Beddoes, Penelope; Lyness, Emily; Nixon, Imogen; Srivastava, Vivek

    2016-01-01

    Readmissions within 30-days of hospital discharge are a problem. The aim was to determine if the Better Outcomes for Older Adults through Safe Transitions (BOOST) risk assessment tool was applicable within the UK. Patients over 65 readmitted were identified retrospectively via a casenote review. BOOST assessment was applied with 1 point for each risk factor. 324 patients were readmitted (mean age 77 years) with a median of 7 days between discharge and readmission. The median BOOST score was 3 (IQR 2-4) with polypharmacy evident in 88% and prior hospitalisation in 70%. The tool correctly predicted 90% of readmissions using two or more risk factors and 99.1% if one risk factor was included. The BOOST assessment tool appears appropriate in predicting readmissions however further analysis is required to determine its precision.

  18. Systematic review of fall risk screening tools for older patients in acute hospitals.

    Science.gov (United States)

    Matarese, Maria; Ivziku, Dhurata; Bartolozzi, Francesco; Piredda, Michela; De Marinis, Maria Grazia

    2015-06-01

    To determine the most accurate fall risk screening tools for predicting falls among patients aged 65 years or older admitted to acute care hospitals. Falls represent a serious problem in older inpatients due to the potential physical, social, psychological and economic consequences. Older inpatients present with risk factors associated with age-related physiological and psychological changes as well as multiple morbidities. Thus, fall risk screening tools for older adults should include these specific risk factors. There are no published recommendations addressing what tools are appropriate for older hospitalized adults. Systematic review. MEDLINE, CINAHL and Cochrane electronic databases were searched between January 1981-April 2013. Only prospective validation studies reporting sensitivity and specificity values were included. Recommendations of the Cochrane Handbook of Diagnostic Test Accuracy Reviews have been followed. Three fall risk assessment tools were evaluated in seven articles. Due to the limited number of studies, meta-analysis was carried out only for the STRATIFY and Hendrich Fall Risk Model II. In the combined analysis, the Hendrich Fall Risk Model II demonstrated higher sensitivity than STRATIFY, while the STRATIFY showed higher specificity. In both tools, the Youden index showed low prognostic accuracy. The identified tools do not demonstrate predictive values as high as needed for identifying older inpatients at risk for falls. For this reason, no tool can be recommended for fall detection. More research is needed to evaluate fall risk screening tools for older inpatients. © 2014 John Wiley & Sons Ltd.

  19. Flight Experiment Verification of Shuttle Boundary Layer Transition Prediction Tool

    Science.gov (United States)

    Berry, Scott A.; Berger, Karen T.; Horvath, Thomas J.; Wood, William A.

    2016-01-01

    Boundary layer transition at hypersonic conditions is critical to the design of future high-speed aircraft and spacecraft. Accurate methods to predict transition would directly impact the aerothermodynamic environments used to size a hypersonic vehicle's thermal protection system. A transition prediction tool, based on wind tunnel derived discrete roughness correlations, was developed and implemented for the Space Shuttle return-to-flight program. This tool was also used to design a boundary layer transition flight experiment in order to assess correlation uncertainties, particularly with regard to high Mach-number transition and tunnel-to-flight scaling. A review is provided of the results obtained from the flight experiment in order to evaluate the transition prediction tool implemented for the Shuttle program.

  20. Predictions of titanium alloy properties using thermodynamic modeling tools

    Science.gov (United States)

    Zhang, F.; Xie, F.-Y.; Chen, S.-L.; Chang, Y. A.; Furrer, D.; Venkatesh, V.

    2005-12-01

    Thermodynamic modeling tools have become essential in understanding the effect of alloy chemistry on the final microstructure of a material. Implementation of such tools to improve titanium processing via parameter optimization has resulted in significant cost savings through the elimination of shop/laboratory trials and tests. In this study, a thermodynamic modeling tool developed at CompuTherm, LLC, is being used to predict β transus, phase proportions, phase chemistries, partitioning coefficients, and phase boundaries of multicomponent titanium alloys. This modeling tool includes Pandat, software for multicomponent phase equilibrium calculations, and PanTitanium, a thermodynamic database for titanium alloys. Model predictions are compared with experimental results for one α-β alloy (Ti-64) and two near-β alloys (Ti-17 and Ti-10-2-3). The alloying elements, especially the interstitial elements O, N, H, and C, have been shown to have a significant effect on the β transus temperature, and are discussed in more detail herein.

  1. RISK COMMUNICATION IN ACTION: THE TOOLS OF MESSAGE MAPPING

    Science.gov (United States)

    Risk Communication in Action: The Tools of Message Mapping, is a workbook designed to guide risk communicators in crisis situations. The first part of this workbook will review general guidelines for risk communication. The second part will focus on one of the most robust tools o...

  2. Dairy farmer use of price risk management tools.

    Science.gov (United States)

    Wolf, C A

    2012-07-01

    Volatility in milk and feed prices can adversely affect dairy farm profitability. Many risk management tools are available for use by US dairy farmers. This research uses surveys of Michigan dairy farmers to examine the extent to which price risk management tools have been used, the farm and operator characteristics that explain the use of these tools, and reasons farmers have not used these tools. A 1999 survey was used to benchmark the degree to which dairy producers had used milk and feed price risk management instruments to compare with 2011 use rates. The surveys collected information about the farm characteristics such as herd size, farmland operated, business organization, and solvency position. Farm operator characteristics collected include age, education, and experience. Dairy farmer use of both milk and feed price risk management tools increased between 1999 and 2011. In 2011, herd size was positively related to the use of milk price risk management tools, whereas farms organized as a sole proprietorship were less likely to use them. Also in 2011, herd size and land operated were positively related to feed price risk management tools, whereas operator age was negatively related. Reasons why farmers had not used price risk management tools included basis risk, cost, lack of management time, cooperative membership, and lack of understanding. Conclusions include the need for educational programming on price risk management tools and a broader exploration of dairy farm risk management programs. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  3. Empirical comparison of web-based antimicrobial peptide prediction tools.

    Science.gov (United States)

    Gabere, Musa Nur; Noble, William Stafford

    2017-07-01

    Antimicrobial peptides (AMPs) are innate immune molecules that exhibit activities against a range of microbes, including bacteria, fungi, viruses and protozoa. Recent increases in microbial resistance against current drugs has led to a concomitant increase in the need for novel antimicrobial agents. Over the last decade, a number of AMP prediction tools have been designed and made freely available online. These AMP prediction tools show potential to discriminate AMPs from non-AMPs, but the relative quality of the predictions produced by the various tools is difficult to quantify. We compiled two sets of AMP and non-AMP peptides, separated into three categories-antimicrobial, antibacterial and bacteriocins. Using these benchmark data sets, we carried out a systematic evaluation of ten publicly available AMP prediction methods. Among the six general AMP prediction tools-ADAM, CAMPR3(RF), CAMPR3(SVM), MLAMP, DBAASP and MLAMP-we find that CAMPR3(RF) provides a statistically significant improvement in performance, as measured by the area under the receiver operating characteristic (ROC) curve, relative to the other five methods. Surprisingly, for antibacterial prediction, the original AntiBP method significantly outperforms its successor, AntiBP2 based on one benchmark dataset. The two bacteriocin prediction tools, BAGEL3 and BACTIBASE, both provide very good performance and BAGEL3 outperforms its predecessor, BACTIBASE, on the larger of the two benchmarks. gaberemu@ngha.med.sa or william-noble@uw.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  4. 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 ...

  5. Development of funding project risk management tools.

    Science.gov (United States)

    2013-11-01

    Funding project risk management is a process for identifying, assessing, and prioritizing project funding risks. To plan to : minimize or eliminate the impact of negative events, one must identify what projects have higher risk to respond to potentia...

  6. Nutritional Risk Screening 2002, Short Nutritional Assessment Questionnaire, Malnutrition Screening Tool, and Malnutrition Universal Screening Tool Are Good Predictors of Nutrition Risk in an Emergency Service.

    Science.gov (United States)

    Rabito, Estela Iraci; Marcadenti, Aline; da Silva Fink, Jaqueline; Figueira, Luciane; Silva, Flávia Moraes

    2017-08-01

    There is an international consensus that nutrition screening be performed at the hospital; however, there is no "best tool" for screening of malnutrition risk in hospitalized patients. To evaluate (1) the accuracy of the MUST (Malnutrition Universal Screening Tool), MST (Malnutrition Screening Tool), and SNAQ (Short Nutritional Assessment Questionnaire) in comparison with the NRS-2002 (Nutritional Risk Screening 2002) to identify patients at risk of malnutrition and (2) the ability of these nutrition screening tools to predict morbidity and mortality. A specific questionnaire was administered to complete the 4 screening tools. Outcomes measures included length of hospital stay, transfer to the intensive care unit, presence of infection, and incidence of death. A total of 752 patients were included. The nutrition risk was 29.3%, 37.1%, 33.6%, and 31.3% according to the NRS-2002, MUST, MST, and SNAQ, respectively. All screening tools showed satisfactory performance to identify patients at nutrition risk (area under the receiver operating characteristic curve between 0.765-0.808). Patients at nutrition risk showed higher risk of very long length of hospital stay as compared with those not at nutrition risk, independent of the tool applied (relative risk, 1.35-1.78). Increased risk of mortality (2.34 times) was detected by the MUST. The MUST, MST, and SNAQ share similar accuracy to the NRS-2002 in identifying risk of malnutrition, and all instruments were positively associated with very long hospital stay. In clinical practice, the 4 tools could be applied, and the choice for one of them should be made per the particularities of the service.

  7. WPPT, a tool for on-line wind power prediction

    Energy Technology Data Exchange (ETDEWEB)

    Skov Nielsen, T. [Dept. of Mathematical Modelling (IMM-DTU), Kgs. Lyngby (Denmark); Madsen, H. [Dept. of Mathematical Modelling (IMM-DTU) Kgs. Lyngby (Denmark); Toefting, J. [Elsam, Fredericia (Denmark)

    2004-07-01

    This paper dsecribes VPPT (Wind Power Prediction Tool), an application for assessing the future available wind power up to 36 hours ahead in time. WPPT has been installed in the Eltra/Elsam central dispatch center since October 1997. The paper describes the prediction model used, the actual implementation of WPPT as well as the experience gained by the operators in the dispatch center (au)

  8. Prediction Of Abrasive And Diffusive Tool Wear Mechanisms In Machining

    Science.gov (United States)

    Rizzuti, S.; Umbrello, D.

    2011-01-01

    Tool wear prediction is regarded as very important task in order to maximize tool performance, minimize cutting costs and improve the quality of workpiece in cutting. In this research work, an experimental campaign was carried out at the varying of cutting conditions with the aim to measure both crater and flank tool wear, during machining of an AISI 1045 with an uncoated carbide tool P40. Parallel a FEM-based analysis was developed in order to study the tool wear mechanisms, taking also into account the influence of the cutting conditions and the temperature reached on the tool surfaces. The results show that, when the temperature of the tool rake surface is lower than the activation temperature of the diffusive phenomenon, the wear rate can be estimated applying an abrasive model. In contrast, in the tool area where the temperature is higher than the diffusive activation temperature, the wear rate can be evaluated applying a diffusive model. Finally, for a temperature ranges within the above cited values an adopted abrasive-diffusive wear model furnished the possibility to correctly evaluate the tool wear phenomena.

  9. Suicide Risk Screening Tools and the Youth Population.

    Science.gov (United States)

    Patterson, Sharon

    2016-08-01

    The use of suicide risk screening tools is a critical component of a comprehensive approach to suicide risk assessment. Since nurses frequently spend more time with patients than any other healthcare professional, they are in key positions to detect and prevent suicidal behavior in youth. To inform nurses about suicide risk screening tools for the youth population. Suicide risk screening tools are research-based standardized instruments that are used to identify people who may be at risk for suicide. A literature search was performed using the Athabasca University Library Resource, the databases of the Cumulative Index to Nursing and Allied Health Literature, ScienceDirect, and Google Scholar. Nurses are cautioned to utilize suicide risk screening tools as only part of the suicide risk assessment in youth populations and avoid the danger of relying on tools that may result in a blind application of evidence to the detriment of clinical experience and judgement. © 2016 Wiley Periodicals, Inc.

  10. MONRATE, a descriptive tool for calculation and prediction of re ...

    African Journals Online (AJOL)

    The objective of the study was to develop an interactive and systematic descriptive tool, MONRATE for calculating and predicting reinfection rates and time of Ascaris lumbricoides following mass chemotherapy using levamisole. Each pupil previously treated was retreated 6 or 7 months after the initial treatment in Ogun ...

  11. The predictive and external validity of the STarT Back Tool in Danish primary care.

    Science.gov (United States)

    Morsø, Lars; Kent, Peter; Albert, Hanne B; Hill, Jonathan C; Kongsted, Alice; Manniche, Claus

    2013-08-01

    The STarT Back Tool (SBT) was recently translated into Danish and its concurrent validity described. This study tested the predictive validity of the Danish SBT. Danish primary care patients (n = 344) were compared to a UK cohort. SBT subgroup validity for predicting high activity limitation at 3 months' follow-up was assessed using descriptive proportions, relative risks, AUC and odds ratios. The SBT had a statistically similar predictive ability in Danish primary care as in UK primary care. Unadjusted relative risks for poor clinical outcome on activity limitation in the Danish cohort were 2.4 (1.7-3.4) for the medium-risk subgroup and 2.8 (1.8-3.8) for the high-risk subgroup versus 3.1 (2.5-3.9) and 4.5 (3.6-5.6) for the UK cohort. Adjusting for confounders appeared to explain the lower predictive ability of the Danish high-risk group. The Danish SBT distinguished between low- and medium-risk subgroups with a similar predictive ability of the UK SBT. That distinction is useful information for informing patients about their expected prognosis and may help guiding clinicians' choice of treatment. However, cross-cultural differences in the SBT psychosocial subscale may reduce the predictive ability of the high-risk subgroup in Danish primary care.

  12. Risk Jyouhou Navi (risk information navigator). Web tool for fostering of risk literacy. Set of data

    International Nuclear Information System (INIS)

    Mitsui, Seiichiro

    2003-06-01

    In addition to the conventional public understanding activities, Risk communication study team of Japan Nuclear Cycle Development Institutes (JNC) Tokai Works has started practical studies to promote risk communication with its local communities. Since its establishment in 2001, Risk communication study team has conducted analyses of already available results of public attitude surveys, case studies of domestic and overseas risk communication activities, and development of risk communication tools. A web tool for fostering of risk literacy 'Risk Jyouhou Navi (risk information navigator in English)', was developed as a web content for the official home page of Techno Kouryuu Kan Ricotti (Techno Community Square Ricotti in English)'. The objectives of this content are to provide risk information for public and to provide an electronic platform for promoting risk communication with the local community. To develop 'Risk Jyouhou Navi', the following concepts were considered. 1) To create public interest in risks in daily lives and in global risks. 2) To provide risk knowledge and information. 3) To support risk communication activities in Techno community square ricotti. (author)

  13. RISK IDENTIFICATION TOOLS – POLISH MSMES COMPANIES PRACTICES

    Directory of Open Access Journals (Sweden)

    Iwona Gorzeń-Mitka

    2013-07-01

    Full Text Available The purpose of this study is to present risk identification tools in Polish micro, small and medium-sized enterprises (MSMEs. Risk identification is a key element of the risk management process in companies. Correctly fitting risk identification tools affect the accuracy of management decisions. The result of research is to identify the leading risk identification tools used by MSMEs. The study was conducted in 2010-2012 using a mixed survey-monographic method and questionnaires. The qualitative data were obtained during the study.

  14. 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.

  15. Tools for assessing fall risk in the elderly: a systematic review and meta-analysis.

    Science.gov (United States)

    Park, Seong-Hi

    2018-01-01

    The prevention of falls among the elderly is arguably one of the most important public health issues in today's aging society. The aim of this study was to assess which tools best predict the risk of falls in the elderly. Electronic searches were performed using Medline, EMBASE, the Cochrane Library, CINAHL, etc., using the following keywords: "fall risk assessment", "elderly fall screening", and "elderly mobility scale". The QUADAS-2 was applied to assess the internal validity of the diagnostic studies. Selected studies were meta-analyzed with MetaDisc 1.4. A total of 33 studies were eligible out of the 2,321 studies retrieved from selected databases. Twenty-six assessment tools for fall risk were used in the selected articles, and they tended to vary based on the setting. The fall risk assessment tools currently used for the elderly did not show sufficiently high predictive validity for differentiating high and low fall risks. The Berg Balance scale and Mobility Interaction Fall chart showed stable and high specificity, while the Downton Fall Risk Index, Hendrich II Fall Risk Model, St. Thomas's Risk Assessment Tool in Falling elderly inpatients, Timed Up and Go test, and Tinetti Balance scale showed the opposite results. We concluded that rather than a single measure, two assessment tools used together would better evaluate the characteristics of falls by the elderly that can occur due to a multitude of factors and maximize the advantages of each for predicting the occurrence of falls.

  16. New risk metrics and mathematical tools for risk analysis: Current and future challenges

    International Nuclear Information System (INIS)

    Skandamis, Panagiotis N.; Andritsos, Nikolaos; Psomas, Antonios; Paramythiotis, Spyridon

    2015-01-01

    The current status of the food safety supply world wide, has led Food and Agriculture Organization (FAO) and World Health Organization (WHO) to establishing Risk Analysis as the single framework for building food safety control programs. A series of guidelines and reports that detail out the various steps in Risk Analysis, namely Risk Management, Risk Assessment and Risk Communication is available. The Risk Analysis approach enables integration between operational food management systems, such as Hazard Analysis Critical Control Points, public health and governmental decisions. To do that, a series of new Risk Metrics has been established as follows: i) the Appropriate Level of Protection (ALOP), which indicates the maximum numbers of illnesses in a population per annum, defined by quantitative risk assessments, and used to establish; ii) Food Safety Objective (FSO), which sets the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the ALOP. Given that ALOP is rather a metric of the public health tolerable burden (it addresses the total ‘failure’ that may be handled at a national level), it is difficult to be interpreted into control measures applied at the manufacturing level. Thus, a series of specific objectives and criteria for performance of individual processes and products have been established, all of them assisting in the achievement of FSO and hence, ALOP. In order to achieve FSO, tools quantifying the effect of processes and intrinsic properties of foods on survival and growth of pathogens are essential. In this context, predictive microbiology and risk assessment have offered an important assistance to Food Safety Management. Predictive modelling is the basis of exposure assessment and the development of stochastic and kinetic models, which are also available in the form of Web-based applications, e.g., COMBASE and Microbial Responses Viewer), or introduced into user

  17. New risk metrics and mathematical tools for risk analysis: Current and future challenges

    Energy Technology Data Exchange (ETDEWEB)

    Skandamis, Panagiotis N., E-mail: pskan@aua.gr; Andritsos, Nikolaos, E-mail: pskan@aua.gr; Psomas, Antonios, E-mail: pskan@aua.gr; Paramythiotis, Spyridon, E-mail: pskan@aua.gr [Laboratory of Food Quality Control and Hygiene, Department of Food Science and Technology, Agricultural University of Athens, Iera Odos 75, 118 55, Athens (Greece)

    2015-01-22

    The current status of the food safety supply world wide, has led Food and Agriculture Organization (FAO) and World Health Organization (WHO) to establishing Risk Analysis as the single framework for building food safety control programs. A series of guidelines and reports that detail out the various steps in Risk Analysis, namely Risk Management, Risk Assessment and Risk Communication is available. The Risk Analysis approach enables integration between operational food management systems, such as Hazard Analysis Critical Control Points, public health and governmental decisions. To do that, a series of new Risk Metrics has been established as follows: i) the Appropriate Level of Protection (ALOP), which indicates the maximum numbers of illnesses in a population per annum, defined by quantitative risk assessments, and used to establish; ii) Food Safety Objective (FSO), which sets the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the ALOP. Given that ALOP is rather a metric of the public health tolerable burden (it addresses the total ‘failure’ that may be handled at a national level), it is difficult to be interpreted into control measures applied at the manufacturing level. Thus, a series of specific objectives and criteria for performance of individual processes and products have been established, all of them assisting in the achievement of FSO and hence, ALOP. In order to achieve FSO, tools quantifying the effect of processes and intrinsic properties of foods on survival and growth of pathogens are essential. In this context, predictive microbiology and risk assessment have offered an important assistance to Food Safety Management. Predictive modelling is the basis of exposure assessment and the development of stochastic and kinetic models, which are also available in the form of Web-based applications, e.g., COMBASE and Microbial Responses Viewer), or introduced into user

  18. New risk metrics and mathematical tools for risk analysis: Current and future challenges

    Science.gov (United States)

    Skandamis, Panagiotis N.; Andritsos, Nikolaos; Psomas, Antonios; Paramythiotis, Spyridon

    2015-01-01

    The current status of the food safety supply world wide, has led Food and Agriculture Organization (FAO) and World Health Organization (WHO) to establishing Risk Analysis as the single framework for building food safety control programs. A series of guidelines and reports that detail out the various steps in Risk Analysis, namely Risk Management, Risk Assessment and Risk Communication is available. The Risk Analysis approach enables integration between operational food management systems, such as Hazard Analysis Critical Control Points, public health and governmental decisions. To do that, a series of new Risk Metrics has been established as follows: i) the Appropriate Level of Protection (ALOP), which indicates the maximum numbers of illnesses in a population per annum, defined by quantitative risk assessments, and used to establish; ii) Food Safety Objective (FSO), which sets the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the ALOP. Given that ALOP is rather a metric of the public health tolerable burden (it addresses the total `failure' that may be handled at a national level), it is difficult to be interpreted into control measures applied at the manufacturing level. Thus, a series of specific objectives and criteria for performance of individual processes and products have been established, all of them assisting in the achievement of FSO and hence, ALOP. In order to achieve FSO, tools quantifying the effect of processes and intrinsic properties of foods on survival and growth of pathogens are essential. In this context, predictive microbiology and risk assessment have offered an important assistance to Food Safety Management. Predictive modelling is the basis of exposure assessment and the development of stochastic and kinetic models, which are also available in the form of Web-based applications, e.g., COMBASE and Microbial Responses Viewer), or introduced into user-friendly softwares

  19. 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

  20. 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

  1. 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

  2. Develop risk-based procurement management tools for SMEs

    NARCIS (Netherlands)

    Staal, Anne; Hagelaar, Geoffrey; Walhof, Gert; Holman, Richard

    2016-01-01

    This paper provides guidance for developing risk-based management tools to improve the procurement (purchasing) performance of SMEs. Extant academic literature only offers little support on developing such tools and does not consider the wide variety of SMEs. The paper defines a procurement tool for

  3. 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...

  4. Analysis of Alternatives for Risk Assessment Methodologies and Tools

    Energy Technology Data Exchange (ETDEWEB)

    Nachtigal, Noel M. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). System Analytics; Fruetel, Julia A. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Systems Research and Analysis; Gleason, Nathaniel J. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Systems Research and Analysis; Helms, Jovana [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Systems Research and Analysis; Imbro, Dennis Raymond [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Systems Research and Analysis; Sumner, Matthew C. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Systems Research and Analysis

    2013-10-01

    The purpose of this document is to provide a basic overview and understanding of risk assessment methodologies and tools from the literature and to assess the suitability of these methodologies and tools for cyber risk assessment. Sandia National Laboratories (SNL) performed this review in support of risk modeling activities performed for the Stakeholder Engagement and Cyber Infrastructure Resilience (SECIR) division of the Department of Homeland Security (DHS) Office of Cybersecurity and Communications (CS&C). The set of methodologies and tools covered in this document is not intended to be exhaustive; instead, it focuses on those that are commonly used in the risk assessment community. The classification of methodologies and tools was performed by a group of analysts with experience in risk analysis and cybersecurity, and the resulting analysis of alternatives has been tailored to address the needs of a cyber risk assessment.

  5. Predicting malicious behavior tools and techniques for ensuring global security

    CERN Document Server

    Jackson, Gary M

    2012-01-01

    A groundbreaking exploration of how to identify and fight security threats at every level This revolutionary book combines real-world security scenarios with actual tools to predict and prevent incidents of terrorism, network hacking, individual criminal behavior, and more. Written by an expert with intelligence officer experience who invented the technology, it explores the keys to understanding the dark side of human nature, various types of security threats (current and potential), and how to construct a methodology to predict and combat malicious behavior. The companion CD demonstrates ava

  6. Visualization tools for insurance risk processes

    OpenAIRE

    Krzysztof Burnecki; Rafal Weron

    2006-01-01

    This chapter develops on risk processes which, perhaps, are most suitable for computer visualization of all insurance objects. At the same time, risk processes are basic instruments for any non-life actuary – they are vital for calculating the amount of loss that an insurance company may incur.

  7. Emerging Tools to Estimate and to Predict Exposures to ...

    Science.gov (United States)

    The timely assessment of the human and ecological risk posed by thousands of existing and emerging commercial chemicals is a critical challenge facing EPA in its mission to protect public health and the environment The US EPA has been conducting research to enhance methods used to estimate and forecast exposures for tens of thousands of chemicals. This research is aimed at both assessing risks and supporting life cycle analysis, by developing new models and tools for high throughput exposure screening and prioritization, as well as databases that support these and other tools, especially regarding consumer products. The models and data address usage, and take advantage of quantitative structural activity relationships (QSARs) for both inherent chemical properties and function (why the chemical is a product ingredient). To make them more useful and widely available, the new tools, data and models are designed to be: • Flexible • Intraoperative • Modular (useful to more than one, stand-alone application) • Open (publicly available software) Presented at the Society for Risk Analysis Forum: Risk Governance for Key Enabling Technologies, Venice, Italy, March 1-3, 2017

  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. GOPET: A tool for automated predictions of Gene Ontology terms

    Directory of Open Access Journals (Sweden)

    Glatting Karl-Heinz

    2006-03-01

    Full Text Available Abstract Background Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions. Description We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology (GO. Now, this method has been made available to the public via our web-service GOPET (Gene Ontology term Prediction and Evaluation Tool. It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar Conclusion Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user.

  10. Popularity prediction tool for ATLAS distributed data management

    International Nuclear Information System (INIS)

    Beermann, T; Maettig, P; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

    2014-01-01

    This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distributions. This article describes the popularity prediction method and the simulator that is used to evaluate the redistribution.

  11. Popularity Prediction Tool for ATLAS Distributed Data Management

    Science.gov (United States)

    Beermann, T.; Maettig, P.; Stewart, G.; Lassnig, M.; Garonne, V.; Barisits, M.; Vigne, R.; Serfon, C.; Goossens, L.; Nairz, A.; Molfetas, A.; Atlas Collaboration

    2014-06-01

    This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distributions. This article describes the popularity prediction method and the simulator that is used to evaluate the redistribution.

  12. SitesIdentify: a protein functional site prediction tool

    Directory of Open Access Journals (Sweden)

    Doig Andrew J

    2009-11-01

    Full Text Available Abstract Background The rate of protein structures being deposited in the Protein Data Bank surpasses the capacity to experimentally characterise them and therefore computational methods to analyse these structures have become increasingly important. Identifying the region of the protein most likely to be involved in function is useful in order to gain information about its potential role. There are many available approaches to predict functional site, but many are not made available via a publicly-accessible application. Results Here we present a functional site prediction tool (SitesIdentify, based on combining sequence conservation information with geometry-based cleft identification, that is freely available via a web-server. We have shown that SitesIdentify compares favourably to other functional site prediction tools in a comparison of seven methods on a non-redundant set of 237 enzymes with annotated active sites. Conclusion SitesIdentify is able to produce comparable accuracy in predicting functional sites to its closest available counterpart, but in addition achieves improved accuracy for proteins with few characterised homologues. SitesIdentify is available via a webserver at http://www.manchester.ac.uk/bioinformatics/sitesidentify/

  13. Tools for Predicting Cleaning Efficiency in the LHC

    CERN Document Server

    Assmann, R W; Brugger, M; Hayes, M; Jeanneret, J B; Kain, V; Kaltchev, D I; Schmidt, F

    2003-01-01

    The computer codes SIXTRACK and DIMAD have been upgraded to include realistic models of proton scattering in collimator jaws, mechanical aperture restrictions, and time-dependent fields. These new tools complement long-existing simplified linear tracking programs used up to now for tracking with collimators. Scattering routines from STRUCT and K2 have been compared with one another and the results have been cross-checked to the FLUKA Monte Carlo package. A systematic error is assigned to the predictions of cleaning efficiency. Now, predictions of the cleaning efficiency are possible with a full LHC model, including chromatic effects, linear and nonlinear errors, beam-beam kicks and associated diffusion, and time-dependent fields. The beam loss can be predicted around the ring, both for regular and irregular beam losses. Examples are presented.

  14. HostPhinder: A Phage Host Prediction Tool

    Directory of Open Access Journals (Sweden)

    Julia Villarroel

    2016-05-01

    Full Text Available The current dramatic increase of antibiotic resistant bacteria has revitalised the interest in bacteriophages as alternative antibacterial treatment. Meanwhile, the development of bioinformatics methods for analysing genomic data places high-throughput approaches for phage characterization within reach. Here, we present HostPhinder, a tool aimed at predicting the bacterial host of phages by examining the phage genome sequence. Using a reference database of 2196 phages with known hosts, HostPhinder predicts the host species of a query phage as the host of the most genomically similar reference phages. As a measure of genomic similarity the number of co-occurring k-mers (DNA sequences of length k is used. Using an independent evaluation set, HostPhinder was able to correctly predict host genus and species for 81% and 74% of the phages respectively, giving predictions for more phages than BLAST and significantly outperforming BLAST on phages for which both had predictions. HostPhinder predictions on phage draft genomes from the INTESTI phage cocktail corresponded well with the advertised targets of the cocktail. Our study indicates that for most phages genomic similarity correlates well with related bacterial hosts. HostPhinder is available as an interactive web service [1] and as a stand alone download from the Docker registry [2].

  15. Optimising preoperative risk stratification tools for prostate cancer using mpMRI

    Energy Technology Data Exchange (ETDEWEB)

    Reisaeter, Lars A.R.; Losnegaard, Are; Biermann, Martin; Roervik, Jarle [Haukeland University Hospital, Department of Radiology, Bergen (Norway); University of Bergen, Department of Clinical Medicine, Bergen (Norway); Fuetterer, Jurgen J. [Radboud University Nijmegen Medical Centre, Department of Radiology, Nijmegen (Netherlands); Nygaard, Yngve [Haukeland University Hospital, Department of Urology, Bergen (Norway); Monssen, Jan [Haukeland University Hospital, Department of Radiology, Bergen (Norway); Gravdal, Karsten [Haukeland University Hospital, Department of Pathology, Bergen (Norway); Halvorsen, Ole J.; Akslen, Lars A. [Haukeland University Hospital, Department of Pathology, Bergen (Norway); Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen (Norway); Haukaas, Svein; Beisland, Christian [University of Bergen, Department of Clinical Medicine, Bergen (Norway); Haukeland University Hospital, Department of Urology, Bergen (Norway)

    2018-03-15

    To improve preoperative risk stratification for prostate cancer (PCa) by incorporating multiparametric MRI (mpMRI) features into risk stratification tools for PCa, CAPRA and D'Amico. 807 consecutive patients operated on by robot-assisted radical prostatectomy at our institution during the period 2010-2015 were followed to identify biochemical recurrence (BCR). 591 patients were eligible for final analysis. We employed stepwise backward likelihood methodology and penalised Cox cross-validation to identify the most significant predictors of BCR including mpMRI features. mpMRI features were then integrated into image-adjusted (IA) risk prediction models and the two risk prediction tools were then evaluated both with and without image adjustment using receiver operating characteristics, survival and decision curve analyses. 37 patients suffered BCR. Apparent diffusion coefficient (ADC) and radiological extraprostatic extension (rEPE) from mpMRI were both significant predictors of BCR. Both IA prediction models reallocated more than 20% of intermediate-risk patients to the low-risk group, reducing their estimated cumulative BCR risk from approximately 5% to 1.1%. Both IA models showed improved prognostic performance with a better separation of the survival curves. Integrating ADC and rEPE from mpMRI of the prostate into risk stratification tools improves preoperative risk estimation for BCR. (orig.)

  16. Evaluation of an inpatient fall risk screening tool to identify the most critical fall risk factors in inpatients.

    Science.gov (United States)

    Hou, Wen-Hsuan; Kang, Chun-Mei; Ho, Mu-Hsing; Kuo, Jessie Ming-Chuan; Chen, Hsiao-Lien; Chang, Wen-Yin

    2017-03-01

    To evaluate the accuracy of the inpatient fall risk screening tool and to identify the most critical fall risk factors in inpatients. Variations exist in several screening tools applied in acute care hospitals for examining risk factors for falls and identifying high-risk inpatients. Secondary data analysis. A subset of inpatient data for the period from June 2011-June 2014 was extracted from the nursing information system and adverse event reporting system of an 818-bed teaching medical centre in Taipei. Data were analysed using descriptive statistics, receiver operating characteristic curve analysis and logistic regression analysis. During the study period, 205 fallers and 37,232 nonfallers were identified. The results revealed that the inpatient fall risk screening tool (cut-off point of ≥3) had a low sensitivity level (60%), satisfactory specificity (87%), a positive predictive value of 2·0% and a negative predictive value of 99%. The receiver operating characteristic curve analysis revealed an area under the curve of 0·805 (sensitivity, 71·8%; specificity, 78%). To increase the sensitivity values, the Youden index suggests at least 1·5 points to be the most suitable cut-off point for the inpatient fall risk screening tool. Multivariate logistic regression analysis revealed a considerably increased fall risk in patients with impaired balance and impaired elimination. The fall risk factor was also significantly associated with days of hospital stay and with admission to surgical wards. The findings can raise awareness about the two most critical risk factors for falls among future clinical nurses and other healthcare professionals and thus facilitate the development of fall prevention interventions. This study highlights the needs for redefining the cut-off points of the inpatient fall risk screening tool to effectively identify inpatients at a high risk of falls. Furthermore, inpatients with impaired balance and impaired elimination should be closely

  17. 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.

  18. 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 ...

  19. RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL

    Energy Technology Data Exchange (ETDEWEB)

    William W. Weiss

    2000-06-30

    Incomplete or sparse information on geologic or formation characteristics introduces a high level of risk for oil exploration and development projects. Expert systems have been developed and used in several disciplines and industries, including medical diagnostics, with favorable results. A state-of-the-art exploration ''expert'' tool, relying on a computerized data base and computer maps generated by neural networks, is proposed through the use of ''fuzzy'' logic, a relatively new mathematical treatment of imprecise or non-explicit parameters and values. This project will develop an Artificial Intelligence system that will draw upon a wide variety of information to provide realistic estimates of risk. ''Fuzzy logic,'' a system of integrating large amounts of inexact, incomplete information with modern computational methods to derive usable conclusions, has been demonstrated as a cost-effective computational technology in many industrial applications. During project year 1, 90% of geologic, geophysical, production and price data were assimilated for installation into the database. Logs provided geologic data consisting of formation tops of the Brushy Canyon, Lower Brushy Canyon, and Bone Springs zones of 700 wells used to construct regional cross sections. Regional structure and isopach maps were constructed using kriging to interpolate between the measured points. One of the structure derivative maps (azimuth of curvature) visually correlates with Brushy Canyon fields on the maximum change contours. Derivatives of the regional geophysical data also visually correlate with the location of the fields. The azimuth of maximum dip approximately locates fields on the maximum change contours. In a similar manner the second derivative in the x-direction of the gravity map visually correlates with the alignment of the known fields. The visual correlations strongly suggest that neural network architectures will be

  20. Draught risk index tool for building energy simulations

    DEFF Research Database (Denmark)

    Vorre, Mette Havgaard; Jensen, Rasmus Lund; Nielsen, Peter V.

    2014-01-01

    Flow elements combined with a building energy simulation tool can be used to indicate areas and periods when there is a risk of draught in a room. The study tests this concept by making a tool for post-processing of data from building energy simulations. The objective is to show indications...... of draught risk during a whole year, giving building designers a tool for the design stage of a building. The tool uses simple one-at-a-time calculations of flow elements and assesses the uncertainty of the result by counting the number of overlapping flow elements. The calculation time is low, making...... it usable in the early design stage to optimise the building layout. The tool provides an overview of the general draught pattern over a period, e.g. a whole year, and of how often there is a draught risk....

  1. Analysis and Prediction of Micromilling Stability with Variable Tool Geometry

    Directory of Open Access Journals (Sweden)

    Ziyang Cao

    2014-11-01

    Full Text Available Micromilling can fabricate miniaturized components using micro-end mill at high rotational speeds. The analysis of machining stability in micromilling plays an important role in characterizing the cutting process, estimating the tool life, and optimizing the process. A numerical analysis and experimental method are presented to investigate the chatter stability in micro-end milling process with variable milling tool geometry. The schematic model of micromilling process is constructed and the calculation formula to predict cutting force and displacements is derived. This is followed by a detailed numerical analysis on micromilling forces between helical ball and square end mills through time domain and frequency domain method and the results are compared. Furthermore, a detailed time domain simulation for micro end milling with straight teeth and helical teeth end mill is conducted based on the machine-tool system frequency response function obtained through modal experiment. The forces and displacements are predicted and the simulation result between variable cutter geometry is deeply compared. The simulation results have important significance for the actual milling process.

  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. Risk Informed Design Using Integrated Vehicle Rapid Assessment Tools

    Data.gov (United States)

    National Aeronautics and Space Administration — A successful proof of concept was performed in FY 2012 integrating the Envision tool for parametric estimates of vehicle mass and the Rapid Response Risk Assessment...

  6. New Tools and Methods for Assessing Risk-Management Strategies

    National Research Council Canada - National Science Library

    Vendlinski, Terry P; Munro, Allen; Chung, Gregory K; De la Cruz, Girlie C; Pizzini, Quentin A; Bewley, William L; Stuart, Gale; Baker, Eva L

    2004-01-01

    .... The Decision Analysis Tool (DAT) allowed subjects to use Expected Value and Multi-attribute Utility Theories to evaluate the risks and benefits of various acquisition alternatives, and allowed us to monitor the process subjects used...

  7. Popularity Prediction Tool for ATLAS Distributed Data Management

    CERN Document Server

    Beermann, T; The ATLAS collaboration; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

    2013-01-01

    This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distri...

  8. Popularity Prediction Tool for ATLAS Distributed Data Management

    CERN Document Server

    Beermann, T; The ATLAS collaboration; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

    2014-01-01

    This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distri...

  9. PRmePRed: A protein arginine methylation prediction tool.

    Directory of Open Access Journals (Sweden)

    Pawan Kumar

    Full Text Available Protein methylation is an important Post-Translational Modification (PTMs of proteins. Arginine methylation carries out and regulates several important biological functions, including gene regulation and signal transduction. Experimental identification of arginine methylation site is a daunting task as it is costly as well as time and labour intensive. Hence reliable prediction tools play an important task in rapid screening and identification of possible methylation sites in proteomes. Our preliminary assessment using the available prediction methods on collected data yielded unimpressive results. This motivated us to perform a comprehensive data analysis and appraisal of features relevant in the context of biological significance, that led to the development of a prediction tool PRmePRed with better performance. The PRmePRed perform reasonably well with an accuracy of 84.10%, 82.38% sensitivity, 83.77% specificity, and Matthew's correlation coefficient of 66.20% in 10-fold cross-validation. PRmePRed is freely available at http://bioinfo.icgeb.res.in/PRmePRed/.

  10. Development of Next Generation Multiphase Pipe Flow Prediction Tools

    Energy Technology Data Exchange (ETDEWEB)

    Cem Sarica; Holden Zhang

    2006-05-31

    The developments of oil and gas fields in deep waters (5000 ft and more) will become more common in the future. It is inevitable that production systems will operate under multiphase flow conditions (simultaneous flow of gas, oil and water possibly along with sand, hydrates, and waxes). Multiphase flow prediction tools are essential for every phase of hydrocarbon recovery from design to operation. Recovery from deep-waters poses special challenges and requires accurate multiphase flow predictive tools for several applications, including the design and diagnostics of the production systems, separation of phases in horizontal wells, and multiphase separation (topside, seabed or bottom-hole). It is crucial for any multiphase separation technique, either at topside, seabed or bottom-hole, to know inlet conditions such as flow rates, flow patterns, and volume fractions of gas, oil and water coming into the separation devices. Therefore, the development of a new generation of multiphase flow predictive tools is needed. The overall objective of the proposed study is to develop a unified model for gas-oil-water three-phase flow in wells, flow lines, and pipelines to predict flow characteristics such as flow patterns, phase distributions, and pressure gradient encountered during petroleum production at different flow conditions (pipe diameter and inclination, fluid properties and flow rates). In the current multiphase modeling approach, flow pattern and flow behavior (pressure gradient and phase fractions) prediction modeling are separated. Thus, different models based on different physics are employed, causing inaccuracies and discontinuities. Moreover, oil and water are treated as a pseudo single phase, ignoring the distinct characteristics of both oil and water, and often resulting in inaccurate design that leads to operational problems. In this study, a new model is being developed through a theoretical and experimental study employing a revolutionary approach. The

  11. Chemical Risk Assessment Screening Tool of a Global Chemical Company

    OpenAIRE

    Evelyn Tjoe-Nij; Christophe Rochin; Nathalie Berne; Alessandro Sassi; Antoine Leplay

    2018-01-01

    Background: This paper describes a simple-to-use and reliable screening tool called Critical Task Exposure Screening (CTES), developed by a chemical company. The tool assesses if the exposure to a chemical for a task is likely to be within acceptable levels. Methods: CTES is a Microsoft Excel tool, where the inhalation risk score is calculated by relating the exposure estimate to the corresponding occupational exposure limit (OEL) or occupational exposure band (OEB). The inhalation exposure i...

  12. An Engineering Tool for the Prediction of Internal Dielectric Charging

    Science.gov (United States)

    Rodgers, D. J.; Ryden, K. A.; Wrenn, G. L.; Latham, P. M.; Sorensen, J.; Levy, L.

    1998-11-01

    A practical internal charging tool has been developed. It provides an easy-to-use means for satellite engineers to predict whether on-board dielectrics are vulnerable to electrostatic discharge in the outer radiation belt. The tool is designed to simulate irradiation of single-dielectric planar or cylindrical structures with or without shielding. Analytical equations are used to describe current deposition in the dielectric. This is fast and gives charging currents to sufficient accuracy given the uncertainties in other aspects of the problem - particularly material characteristics. Time-dependent internal electric fields are calculated, taking into account the effect on conductivity of electric field, dose rate and temperature. A worst-case model of electron fluxes in the outer belt has been created specifically for the internal charging problem and is built into the code. For output, the tool gives a YES or NO decision on the susceptibility of the structure to internal electrostatic breakdown and if necessary, calculates the required changes to bring the system below the breakdown threshold. A complementary programme of laboratory irradiations has been carried out to validate the tool. The results for Epoxy-fibreglass samples show that the code models electric field realistically for a wide variety of shields, dielectric thicknesses and electron spectra. Results for Teflon samples indicate that some further experimentation is required and the radiation-induced conductivity aspects of the code have not been validated.

  13. A tool model for predicting atmospheric kinetics with sensitivity analysis

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    A package( a tool model) for program of predicting atmospheric chemical kinetics with sensitivity analysis is presented. The new direct method of calculating the first order sensitivity coefficients using sparse matrix technology to chemical kinetics is included in the tool model, it is only necessary to triangularize the matrix related to the Jacobian matrix of the model equation. The Gear type procedure is used to integrate amodel equation and its coupled auxiliary sensitivity coefficient equations. The FORTRAN subroutines of the model equation, the sensitivity coefficient equations, and their Jacobian analytical expressions are generated automatically from a chemical mechanism. The kinetic representation for the model equation and its sensitivity coefficient equations, and their Jacobian matrix is presented. Various FORTRAN subroutines in packages, such as SLODE, modified MA28, Gear package, with which the program runs in conjunction are recommended.The photo-oxidation of dimethyl disulfide is used for illustration.

  14. 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.

  15. Using risk based tools in emergency response

    International Nuclear Information System (INIS)

    Dixon, B.W.; Ferns, K.G.

    1987-01-01

    Probabilistic Risk Assessment (PRA) techniques are used by the nuclear industry to model the potential response of a reactor subjected to unusual conditions. The knowledge contained in these models can aid in emergency response decision making. This paper presents requirements for a PRA based emergency response support system to date. A brief discussion of published work provides background for a detailed description of recent developments. A rapid deep assessment capability for specific portions of full plant models is presented. The program uses a screening rule base to control search space expansion in a combinational algorithm

  16. Predicting SPE Fluxes: Coupled Simulations and Analysis Tools

    Science.gov (United States)

    Gorby, M.; Schwadron, N.; Linker, J.; Caplan, R. M.; Wijaya, J.; Downs, C.; Lionello, R.

    2017-12-01

    Presented here is a nuts-and-bolts look at the coupled framework of Predictive Science Inc's Magnetohydrodynamics Around a Sphere (MAS) code and the Energetic Particle Radiation Environment Module (EPREM). MAS simulated coronal mass ejection output from a variety of events can be selected as the MHD input to EPREM and a variety of parameters can be set to run against: bakground seed particle spectra, mean free path, perpendicular diffusion efficiency, etc.. A standard set of visualizations are produced as well as a library of analysis tools for deeper inquiries. All steps will be covered end-to-end as well as the framework's user interface and availability.

  17. On-Line, Self-Learning, Predictive Tool for Determining Payload Thermal Response

    Science.gov (United States)

    Jen, Chian-Li; Tilwick, Leon

    2000-01-01

    This paper will present the results of a joint ManTech / Goddard R&D effort, currently under way, to develop and test a computer based, on-line, predictive simulation model for use by facility operators to predict the thermal response of a payload during thermal vacuum testing. Thermal response was identified as an area that could benefit from the algorithms developed by Dr. Jeri for complex computer simulations. Most thermal vacuum test setups are unique since no two payloads have the same thermal properties. This requires that the operators depend on their past experiences to conduct the test which requires time for them to learn how the payload responds while at the same time limiting any risk of exceeding hot or cold temperature limits. The predictive tool being developed is intended to be used with the new Thermal Vacuum Data System (TVDS) developed at Goddard for the Thermal Vacuum Test Operations group. This model can learn the thermal response of the payload by reading a few data points from the TVDS, accepting the payload's current temperature as the initial condition for prediction. The model can then be used as a predictive tool to estimate the future payload temperatures according to a predetermined shroud temperature profile. If the error of prediction is too big, the model can be asked to re-learn the new situation on-line in real-time and give a new prediction. Based on some preliminary tests, we feel this predictive model can forecast the payload temperature of the entire test cycle within 5 degrees Celsius after it has learned 3 times during the beginning of the test. The tool will allow the operator to play "what-if' experiments to decide what is his best shroud temperature set-point control strategy. This tool will save money by minimizing guess work and optimizing transitions as well as making the testing process safer and easier to conduct.

  18. Which screening tools can predict injury to the lower extremities in team sports?: a systematic review.

    Science.gov (United States)

    Dallinga, Joan M; Benjaminse, Anne; Lemmink, Koen A P M

    2012-09-01

    Injuries to lower extremities are common in team sports such as soccer, basketball, volleyball, football and field hockey. Considering personal grief, disabling consequences and high costs caused by injuries to lower extremities, the importance for the prevention of these injuries is evident. From this point of view it is important to know which screening tools can identify athletes who are at risk of injury to their lower extremities. The aim of this article is to determine the predictive values of anthropometric and/or physical screening tests for injuries to the leg, anterior cruciate ligament (ACL), knee, hamstring, groin and ankle in team sports. A systematic review was conducted in MEDLINE (1966 to September 2011), EMBASE (1989 to September 2011) and CINAHL (1982 to September 2011). Based on inclusion criteria defined a priori, titles, abstracts and full texts were analysed to find relevant studies. The analysis showed that different screening tools can be predictive for injuries to the knee, ACL, hamstring, groin and ankle. For injuries in general there is some support in the literature to suggest that general joint laxity is a predictive measure for leg injuries. The anterior right/left reach distance >4 cm and the composite reach distance injuries. Furthermore, an increasing age, a lower hamstring/quadriceps (H : Q) ratio and a decreased range of motion (ROM) of hip abduction may predict the occurrence of leg injuries. Hyperextension of the knee, side-to-side differences in anterior-posterior knee laxity and differences in knee abduction moment between both legs are suggested to be predictive tests for sustaining an ACL injury and height was a predictive screening tool for knee ligament injuries. There is some evidence that when age increases, the probability of sustaining a hamstring injury increases. Debate exists in the analysed literature regarding measurement of the flexibility of the hamstring as a predictive screening tool, as well as using the H

  19. Evaluation of the efficacy of six nutritional screening tools to predict malnutrition in the elderly.

    Science.gov (United States)

    Poulia, Kalliopi-Anna; Yannakoulia, Mary; Karageorgou, Dimitra; Gamaletsou, Maria; Panagiotakos, Demosthenes B; Sipsas, Nikolaos V; Zampelas, Antonis

    2012-06-01

    Malnutrition in the elderly is a multifactorial problem, more prevalent in hospitals and care homes. The absence of a gold standard in evaluating nutritional risk led us to evaluate the efficacy of six nutritional screening tools used in the elderly. Two hundred forty eight elderly patients (129 men, 119 female women, aged 75.2 ± 8.5 years) were examined. Nutritional screening was performed on admission using the following tools: Nutritional Risk Index (NRI), Geriatric Nutritional Risk Index (GNRI), Subjective Global Assessment (SGA), Mini Nutritional Assessment - Screening Form (MNA-SF), Malnutrition Universal Screening Tool (MUST) and Nutritional Risk Screening 2002 (NRS 2002). A combined index for malnutrition was also calculated. Nutritional risk and/or malnutrition varied greatly, ranging from 47.2 to 97.6%, depending on the nutritional screening tool used. MUST was the most valid screening tool (validity coefficient = 0.766, CI 95%: 0.690-0.841), while SGA was in better agreement with the combined index (κ = 0.707, p = 0.000). NRS 2002 although was the highest in sensitivity (99.4%), it was the lowest in specificity (6.1%) and positive predictive value (68.2%). MUST seem to be the most valid in the evaluation of the risk for malnutrition in the elderly upon admission to the hospital. NRS 2002 was found to overestimate nutritional risk in the elderly. Copyright © 2011 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

  20. Longitudinal Evaluation of Johns Hopkins Fall Risk Assessment Tool and Nurses' Experience.

    Science.gov (United States)

    Hur, Eun Young; Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

    The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) is relatively new in Korea, and it has not been fully evaluated. This study revealed that the JHFRAT had good predictive validity throughout the hospitalization period. However, 2 items (fall history and elimination patterns) on the tool were not determinants of falls in this population. Interestingly, the nurses indicated those 2 items were the most difficult items to assess and needed further training to develop the assessment skills.

  1. Risk based decision tool for space exploration missions

    Science.gov (United States)

    Meshkat, Leila; Cornford, Steve; Moran, Terrence

    2003-01-01

    This paper presents an approach and corresponding tool to assess and analyze the risks involved in a mission during the pre-phase A design process. This approach is based on creating a risk template for each subsystem expert involved in the mission design process and defining appropriate interactions between the templates.

  2. Promoting energy efficiency investments with risk management decision tools

    International Nuclear Information System (INIS)

    Jackson, Jerry

    2010-01-01

    This paper reviews current capital budgeting practices and their impact on energy efficiency investments. The prevalent use of short payback 'rule-of-thumb' requirements to screen efficiency projects for risk is shown to bias investment choices towards 'sure bet' investments bypassing many profitable efficiency investment options. A risk management investment strategy is presented as an alternative to risk avoidance practices applied with payback thresholds. The financial industry risk management tool Value-at-Risk is described and extended to provide an Energy-Budgets-at-Risk or EBaR risk management analysis to convey more accurate energy efficiency investment risk information. The paper concludes with recommendations to expand the use of Value-at-Risk-type energy efficiency analysis.

  3. Cognitive mapping tools: review and risk management needs.

    Science.gov (United States)

    Wood, Matthew D; Bostrom, Ann; Bridges, Todd; Linkov, Igor

    2012-08-01

    Risk managers are increasingly interested in incorporating stakeholder beliefs and other human factors into the planning process. Effective risk assessment and management requires understanding perceptions and beliefs of involved stakeholders, and how these beliefs give rise to actions that influence risk management decisions. Formal analyses of risk manager and stakeholder cognitions represent an important first step. Techniques for diagramming stakeholder mental models provide one tool for risk managers to better understand stakeholder beliefs and perceptions concerning risk, and to leverage this new understanding in developing risk management strategies. This article reviews three methodologies for assessing and diagramming stakeholder mental models--decision-analysis-based mental modeling, concept mapping, and semantic web analysis--and assesses them with regard to their ability to address risk manager needs. © 2012 Society for Risk Analysis.

  4. Predictive tools for designing new insulins and treatment regimens

    DEFF Research Database (Denmark)

    Klim, Søren

    The thesis deals with the development of "Predictive tools for designing new insulins and treatments regimens" and consists of two parts: A model based approach for bridging properties of new insulin analogues from glucose clamp experiments to meal tolerance tests (MTT) and a second part that des......The thesis deals with the development of "Predictive tools for designing new insulins and treatments regimens" and consists of two parts: A model based approach for bridging properties of new insulin analogues from glucose clamp experiments to meal tolerance tests (MTT) and a second part...... that describes an implemented software program able to handle stochastic differential equations (SDEs) with mixed effects. The thesis is supplemented with scientific papers published during the PhD. Developing an insulin analogue from candidate molecule to a clinical drug consists of a development programme...... and efficacy are investigated. Numerous methods are used to quantify dose and efficacy in Phase II - especially of interest is the 24-hour meal tolerance test as it tries to portray near normal living conditions. Part I describes an integrated model for insulin and glucose which is aimed at simulating 24-hour...

  5. PROJECT MANAGER SKILLS, RISK MANAGEMENT TOOLS

    Directory of Open Access Journals (Sweden)

    Vladut Iacob

    2013-12-01

    Full Text Available Although the projects are different from each other there are many common things that contribute to their success. Looked overall, the success of a project is the result of a multitude of factors. This person is considered the "engine" of the project. The man who makes the action set for the achievement of project objectives to be brought to an end. The project manager must have the technical knowledge and economic diverse. He should be able to choose a team and lead. You must be tenacious, combative, to know how to communicate both within the team and beyond. In a word, the project manager must have an impressive stock of knowledge, skills and abilities and appreciate as Peter Drucker, to "exist for the organization. To be its servant. Any management who forget this will only cause damage to the organization. "This study will focus on highlighting the skills of the project manager and their role in managing difficult situations or risk.

  6. Tools for predicting the PK/PD of therapeutic proteins.

    Science.gov (United States)

    Diao, Lei; Meibohm, Bernd

    2015-07-01

    Assessments of the pharmacokinetic/pharmacodynamic (PK/PD) characteristics are an integral part in the development of novel therapeutic agents. Compared with traditional small molecule drugs, therapeutic proteins possess many distinct PK/PD features that necessitate the application of modified or separate approaches for assessing their PK/PD relationships. In this review, the authors discuss tools that are utilized to describe and predict the PK/PD features of therapeutic proteins and that are valuable additions in the armamentarium of drug development approaches to facilitate and accelerate their successful preclinical and clinical development. A variety of state-of-the-art PK/PD tools is currently being applied and has been adjusted to support the development of proteins as therapeutics, including allometric scaling approaches, target-mediated disposition models, first-in-man dose calculations, physiologically based PK models and empirical and semi-mechanistic PK/PD modeling. With the advent of the next generation of biologics including bioengineered antibody constructs being developed, these tools will need to be further refined and adapted to ensure their applicability and successful facilitation of the drug development process for these novel scaffolds.

  7. The Development of a Plant Risk Evaluation (PRE) Tool for Assessing the Invasive Potential of Ornamental Plants

    OpenAIRE

    Conser, Christiana; Seebacher, Lizbeth; Fujino, David W.; Reichard, Sarah; DiTomaso, Joseph M.

    2015-01-01

    Weed Risk Assessment (WRA) methods for evaluating invasiveness in plants have evolved rapidly in the last two decades. Many WRA tools exist, but none were specifically designed to screen ornamental plants prior to being released into the environment. To be accepted as a tool to evaluate ornamental plants for the nursery industry, it is critical that a WRA tool accurately predicts non-invasiveness without falsely categorizing them as invasive. We developed a new Plant Risk Evaluation (PRE) too...

  8. An Integrative Review of Pediatric Fall Risk Assessment Tools.

    Science.gov (United States)

    DiGerolamo, Kimberly; Davis, Katherine Finn

    Patient fall prevention begins with accurate risk assessment. However, sustained improvements in prevention and quality of care include use of validated fall risk assessment tools (FRATs). The goal of FRATs is to identify patients at highest risk. Adult FRATs are often borrowed from to create tools for pediatric patients. Though factors associated with pediatric falls in the hospital setting are similar to those in adults, such as mobility, medication use, and cognitive impairment, adult FRATs and the factors associated with them do not adequately assess risk in children. Articles were limited to English language, ages 0-21years, and publish date 2006-2015. The search yielded 22 articles. Ten were excluded as the population was primarily adult or lacked discussion of a FRAT. Critical appraisal and findings were synthesized using the Johns Hopkins Nursing evidence appraisal system. Twelve articles relevant to fall prevention in the pediatric hospital setting that discussed fall risk assessment and use of a FRAT were reviewed. Comparison between and accuracy of FRATs is challenged when different classifications, definitions, risk stratification, and inclusion criteria are used. Though there are several pediatric FRATs published in the literature, none have been found to be reliable and valid across institutions and diverse populations. This integrative review highlights the importance of choosing a FRAT based on an institution's identified risk factors and validating the tool for one's own patient population as well as using the tool in conjunction with nursing clinical judgment to guide interventions. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Utility of Eating Assessment Tool-10 in Predicting Aspiration in Patients with Unilateral Vocal Fold Paralysis.

    Science.gov (United States)

    Zuniga, Steven A; Ebersole, Barbara; Jamal, Nausheen

    2018-03-01

    Objective Examine the incidence of penetration/aspiration in patients with unilateral vocal fold immobility and investigate the relationship with self-reported perception of dysphagia. Study Design Case series with chart review. Setting Academic cancer center. Subjects and Methods Adult patients with unilateral vocal fold immobility diagnosed between 2014 and 2016 were reviewed. Patients were stratified into an aspiration group and a nonaspiration group using objective findings on flexible endoscopic evaluation of swallowing, as scored using Rosenbek's Penetration Aspiration Scale. Objective findings were compared to patient perception of dysphagia. Bivariate linear correlation analysis was performed to evaluate correlation between Eating Assessment Tool-10 scores and presence of aspiration. Tests of diagnostic accuracy were calculated to investigate the predictive value of Eating Assessment Tool-10 scores >9 on aspiration risk. Results Of the 35 patients with new-onset unilateral vocal fold immobility were evaluated, 25.7% (9/35) demonstrated tracheal aspiration. Mean ± SD Eating Assessment Tool-10 scores were 19.2 ± 13.7 for aspirators and 7.0 ± 7.8 for nonaspirators ( P = .016). A statistically significant correlation was demonstrated between increasing Eating Assessment Tool-10 scores and Penetration Aspiration Scale scores ( r = 0.511, P = .002). Diagnostic accuracy analysis for aspiration risk in patients with an Eating Assessment Tool-10 score >9 revealed a sensitivity of 77.8% and a specificity of 73.1%. Conclusion Patient perception of swallowing difficulty may have utility in predicting aspiration risk. An EAT-10 of >9 in patients with unilateral vocal fold immobility may portend up to a 5 times greater risk of aspiration. Routine swallow testing to assess for penetration/aspiration may be indicated in patients with unilateral vocal fold immobility.

  10. Nutritional Risk in Emergency-2017: A New Simplified Proposal for a Nutrition Screening Tool.

    Science.gov (United States)

    Marcadenti, Aline; Mendes, Larissa Loures; Rabito, Estela Iraci; Fink, Jaqueline da Silva; Silva, Flávia Moraes

    2018-03-13

    There are many nutrition screening tools currently being applied in hospitals to identify risk of malnutrition. However, multivariate statistical models are not usually employed to take into account the importance of each variable included in the instrument's development. To develop and evaluate the concurrent and predictive validities of a new screening tool of nutrition risk. A prospective cohort study was developed, in which 4 nutrition screening tools were applied to all patients. Length of stay in hospital and mortality were considered to test the predictive validity, and the concurrent validity was tested by comparing the Nuritional Risk in Emergency (NRE)-2017 to the other tools. A total of 748 patients were included. The final NRE-2017 score was composed of 6 questions (advanced age, metabolic stress of the disease, decreased appetite, changing of food consistency, unintentional weight loss, and muscle mass loss) with answers yes or no. The prevalence of nutrition risk was 50.7% and 38.8% considering the cutoff points 1.0 and 1.5, respectively. The NRE-2017 showed a satisfactory power to indentify risk of malnutrition (area under the curve >0.790 for all analyses). According to the NRE-2017, patients at risk of malnutrition have twice as high relative risk of a very long hospital stay. The hazard ratio for mortality was 2.78 (1.03-7.49) when the cutoff adopted by the NRE-2017 was 1.5 points. NRE-2017 is a new, easy-to-apply nutrition screening tool which uses 6 bi-categoric features to detect the risk of malnutrition, and it presented a good concurrent and predictive validity. © 2018 American Society for Parenteral and Enteral Nutrition.

  11. 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.

  12. Using Search Engine Data as a Tool to Predict Syphilis.

    Science.gov (United States)

    Young, Sean D; Torrone, Elizabeth A; Urata, John; Aral, Sevgi O

    2018-07-01

    Researchers have suggested that social media and online search data might be used to monitor and predict syphilis and other sexually transmitted diseases. Because people at risk for syphilis might seek sexual health and risk-related information on the internet, we investigated associations between internet state-level search query data (e.g., Google Trends) and reported weekly syphilis cases. We obtained weekly counts of reported primary and secondary syphilis for 50 states from 2012 to 2014 from the US Centers for Disease Control and Prevention. We collected weekly internet search query data regarding 25 risk-related keywords from 2012 to 2014 for 50 states using Google Trends. We joined 155 weeks of Google Trends data with 1-week lag to weekly syphilis data for a total of 7750 data points. Using the least absolute shrinkage and selection operator, we trained three linear mixed models on the first 10 weeks of each year. We validated models for 2012 and 2014 for the following 52 weeks and the 2014 model for the following 42 weeks. The models, consisting of different sets of keyword predictors for each year, accurately predicted 144 weeks of primary and secondary syphilis counts for each state, with an overall average R of 0.9 and overall average root mean squared error of 4.9. We used Google Trends search data from the prior week to predict cases of syphilis in the following weeks for each state. Further research could explore how search data could be integrated into public health monitoring systems.

  13. THE FINANCIAL TOOLS FOR COVER POLITICAL RISKS IN PROJECT FINANCE

    Directory of Open Access Journals (Sweden)

    S. Naumenkova

    2016-10-01

    Full Text Available This article examines the risk-mitigation in public-private partnership. Today Ukraine is ranked as "CRT-5 country" and has high levels of economic and political risk. Political risk grows steadily because of financial and political instability in Ukraine. We conclude that investors continue to rank political risk as a key obstacle to long-term investing. The tools for cover many types of political risks such as war, terrorism, civil disturbance, breach of contract, export or operating license cancellation, currency inconvertibility and transfer restriction, change of laws and regulations etc. are described by authors. We focus on the advantages of World Bank Group Guarantee products. The guarantee instruments of the three WBG institutions for cover political risks under different circumstances are the most suitable for public-private partnership in Ukraine. In this article the political risk-mitigation with IBRD Partial Risk Guarantee put forward by authors for PPP projects in Ukraine.

  14. External validation of approaches to prediction of falls during hospital rehabilitation stays and development of a new simpler tool

    Directory of Open Access Journals (Sweden)

    Angela Vratsistas-Curto

    2017-12-01

    Full Text Available Objectives: To test the external validity of 4 approaches to fall prediction in a rehabilitation setting (Predict_FIRST, Ontario Modified STRATIFY (OMS, physiotherapists’ judgement of fall risk (PT_Risk, and falls in the past year (Past_Falls, and to develop and test the validity of a simpler tool for fall prediction in rehabilitation (Predict_CM2. Participants: A total of 300 consecutively-admitted rehabilitation inpatients. Methods: Prospective inception cohort study. Falls during the rehabilitation stay were monitored. Potential predictors were extracted from medical records. Results: Forty-one patients (14% fell during their rehabilitation stay. The external validity, area under the receiver operating characteristic curve (AUC, for predicting future fallers was: 0.71 (95% confidence interval (95% CI: 0.61–0.81 for OMS (Total_Score; 0.66 (95% CI: 0.57–0.74 for Predict_FIRST; 0.65 (95% CI 0.57–0.73 for PT_Risk; and 0.52 for Past_Falls (95% CI: 0.46–0.60. A simple 3-item tool (Predict_CM2 was developed from the most predictive individual items (impaired mobility/transfer ability, impaired cognition, and male sex. The accuracy of Predict_CM2 was 0.73 (95% CI: 0.66–0.81, comparable to OMS (Total_Score (p = 0.52, significantly better than Predict_FIRST (p = 0.04, and Past_Falls (p < 0.001, and approaching significantly better than PT_Risk (p = 0.09. Conclusion: Predict_CM2 is a simpler screening tool with similar accuracy for predicting fallers in rehabilitation to OMS (Total_Score and better accuracy than Predict_FIRST or Past_Falls. External validation of Predict_CM2 is required.

  15. 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

  16. Predicted impact and evaluation of North Carolina's phosphorus indexing tool.

    Science.gov (United States)

    Johnson, Amy M; Osmond, Deanna L; Hodges, Steven C

    2005-01-01

    Increased concern about potential losses of phosphorus (P) from agricultural fields receiving animal waste has resulted in the implementation of new state and federal regulations related to nutrient management. In response to strengthened nutrient management standards that require consideration of P, North Carolina has developed a site-specific P indexing system called the Phosphorus Loss Assessment Tool (PLAT) to predict relative amounts of potential P loss from agricultural fields. The purpose of this study was to apply the PLAT index on farms throughout North Carolina in an attempt to predict the percentage and types of farms that will be forced to change management practices due to implementation of new regulations. Sites from all 100 counties were sampled, with the number of samples taken from each county depending on the proportion of the state's agricultural land that occurs in that county. Results showed that approximately 8% of producers in the state will be required to apply animal waste or inorganic fertilizer on a P rather than nitrogen basis, with the percentage increasing for farmers who apply animal waste (approximately 27%). The PLAT index predicted the greatest amounts of P loss from sites in the Coastal Plain region of North Carolina and from sites receiving poultry waste. Loss of dissolved P through surface runoff tended to be greater than other loss pathways and presents an area of concern as no best management practices (BMPs) currently exist for the reduction of in-field dissolved P. The PLAT index predicted the areas in the state that are known to be disproportionately vulnerable to P loss due to histories of high P applications, high densities of animal units, or soil type and landscapes that are most susceptible to P loss.

  17. Quantitative risk assessment: an emerging tool for emerging foodborne pathogens.

    OpenAIRE

    Lammerding, A. M.; Paoli, G. M.

    1997-01-01

    New challenges to the safety of the food supply require new strategies for evaluating and managing food safety risks. Changes in pathogens, food preparation, distribution, and consumption, and population immunity have the potential to adversely affect human health. Risk assessment offers a framework for predicting the impact of changes and trends on the provision of safe food. Risk assessment models facilitate the evaluation of active or passive changes in how foods are produced, processed, d...

  18. Development of Next Generation Multiphase Pipe Flow Prediction Tools

    Energy Technology Data Exchange (ETDEWEB)

    Tulsa Fluid Flow

    2008-08-31

    The developments of fields in deep waters (5000 ft and more) is a common occurrence. It is inevitable that production systems will operate under multiphase flow conditions (simultaneous flow of gas-oil-and water possibly along with sand, hydrates, and waxes). Multiphase flow prediction tools are essential for every phase of the hydrocarbon recovery from design to operation. The recovery from deep-waters poses special challenges and requires accurate multiphase flow predictive tools for several applications including the design and diagnostics of the production systems, separation of phases in horizontal wells, and multiphase separation (topside, seabed or bottom-hole). It is very crucial to any multiphase separation technique that is employed either at topside, seabed or bottom-hole to know inlet conditions such as the flow rates, flow patterns, and volume fractions of gas, oil and water coming into the separation devices. The overall objective was to develop a unified model for gas-oil-water three-phase flow in wells, flow lines, and pipelines to predict the flow characteristics such as flow patterns, phase distributions, and pressure gradient encountered during petroleum production at different flow conditions (pipe diameter and inclination, fluid properties and flow rates). The project was conducted in two periods. In Period 1 (four years), gas-oil-water flow in pipes were investigated to understand the fundamental physical mechanisms describing the interaction between the gas-oil-water phases under flowing conditions, and a unified model was developed utilizing a novel modeling approach. A gas-oil-water pipe flow database including field and laboratory data was formed in Period 2 (one year). The database was utilized in model performance demonstration. Period 1 primarily consisted of the development of a unified model and software to predict the gas-oil-water flow, and experimental studies of the gas-oil-water project, including flow behavior description and

  19. 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...

  20. Risk management: A tool for improving nuclear power plant performance

    International Nuclear Information System (INIS)

    2001-04-01

    This technical document on risk management as a tool for improving nuclear power plant (NPP) operations is part of an ongoing project on management of NPP operations in a competitive environment. The overall objective of this project is to assist the management of operating organizations and NPPs in identifying and implementing appropriate measures to remain competitive in a rapidly changing business environment. Other reports developed through this project have identified overall strategies and techniques that NPP operating organization managers can use to succeed in more competitive energy markets. For example, in IAEA-TECDOC-1123, Strategies for Competitive Nuclear Power Plants, one of the most important strategies identified was integrated risk management. This publication provides a recommended structure for risk management along with examples of how NPP operating organizations are using this tool to help them integrate safety, operational and economic related risks in a changing business environment

  1. Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries.

    Science.gov (United States)

    Gautam, Aditi; Sharma, Asuda; Jaiswal, Sarika; Fatma, Samar; Arora, Vasu; Iquebal, M A; Nandi, S; Sundaray, J K; Jayasankar, P; Rai, Anil; Kumar, Dinesh

    2016-09-01

    Microbial diseases in fish, plant, animal and human are rising constantly; thus, discovery of their antidote is imperative. The use of antibiotic in aquaculture further compounds the problem by development of resistance and consequent consumer health risk by bio-magnification. Antimicrobial peptides (AMPs) have been highly promising as natural alternative to chemical antibiotics. Though AMPs are molecules of innate immune defense of all advance eukaryotic organisms, fish being heavily dependent on their innate immune defense has been a good source of AMPs with much wider applicability. Machine learning-based prediction method using wet laboratory-validated fish AMP can accelerate the AMP discovery using available fish genomic and proteomic data. Earlier AMP prediction servers are based on multi-phyla/species data, and we report here the world's first AMP prediction server in fishes. It is freely accessible at http://webapp.cabgrid.res.in/fishamp/ . A total of 151 AMPs related to fish collected from various databases and published literature were taken for this study. For model development and prediction, N-terminus residues, C-terminus residues and full sequences were considered. Best models were with kernels polynomial-2, linear and radial basis function with accuracy of 97, 99 and 97 %, respectively. We found that performance of support vector machine-based models is superior to artificial neural network. This in silico approach can drastically reduce the time and cost of AMP discovery. This accelerated discovery of lead AMP molecules having potential wider applications in diverse area like fish and human health as substitute of antibiotics, immunomodulator, antitumor, vaccine adjuvant and inactivator, and also for packaged food can be of much importance for industries.

  2. Chemical Risk Assessment Screening Tool of a Global Chemical Company

    Directory of Open Access Journals (Sweden)

    Evelyn Tjoe-Nij

    2018-03-01

    Full Text Available Background: This paper describes a simple-to-use and reliable screening tool called Critical Task Exposure Screening (CTES, developed by a chemical company. The tool assesses if the exposure to a chemical for a task is likely to be within acceptable levels. Methods: CTES is a Microsoft Excel tool, where the inhalation risk score is calculated by relating the exposure estimate to the corresponding occupational exposure limit (OEL or occupational exposure band (OEB. The inhalation exposure is estimated for tasks by preassigned ART1.5 activity classes and modifying factors. Results: CTES requires few inputs. The toxicological data, including OELs, OEBs, and vapor pressure are read from a database. Once the substance is selected, the user specifies its concentration and then chooses the task description and its duration. CTES has three outputs that may trigger follow-up: (1 inhalation risk score; (2 identification of the skin hazard with the skin warnings for local and systemic adverse effects; and (3 status for carcinogenic, mutagenic, or reprotoxic effects. Conclusion: The tool provides an effective way to rapidly screen low-concern tasks, and quickly identifies certain tasks involving substances that will need further review with, nevertheless, the appropriate conservatism. This tool shows that the higher-tier ART1.5 inhalation exposure assessment model can be included effectively in a screening tool. After 2 years of worldwide extensive use within the company, CTES is well perceived by the users, including the shop floor management, and it fulfills its target of screening tool. Keywords: occupational exposure, risk assessment, risk management

  3. 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 ...

  4. Risk D and D Rapid Prototype: Scenario Documentation and Analysis Tool

    International Nuclear Information System (INIS)

    Unwin, Stephen D.; Seiple, Timothy E.

    2009-01-01

    Report describes process and methodology associated with a rapid prototype tool for integrating project risk analysis and health and safety risk analysis for decontamination and decommissioning projects. The objective of the Decontamination and Decommissioning (D and D) Risk Management Evaluation and Work Sequencing Standardization Project under DOE EM-23 is to recommend or develop practical risk-management tools for decommissioning of nuclear facilities. PNNL has responsibility under this project for recommending or developing computer-based tools that facilitate the evaluation of risks in order to optimize the sequencing of D and D work. PNNL's approach is to adapt, augment, and integrate existing resources rather than to develop a new suite of tools. Methods for the evaluation of H and S risks associated with work in potentially hazardous environments are well-established. Several approaches exist which, collectively, are referred to as process hazard analysis (PHA). A PHA generally involves the systematic identification of accidents, exposures, and other adverse events associated with a given process or work flow. This identification process is usually achieved in a brainstorming environment or by other means of eliciting informed opinion. The likelihoods of adverse events (scenarios) and their associated consequence severities are estimated against pre-defined scales, based on which risk indices are then calculated. A similar process is encoded in various project risk software products that facilitate the quantification of schedule and cost risks associated with adverse scenarios. However, risk models do not generally capture both project risk and H and S risk. The intent of the project reported here is to produce a tool that facilitates the elicitation, characterization, and documentation of both project risk and H and S risk based on defined sequences of D and D activities. By considering alternative D and D sequences, comparison of the predicted risks can

  5. 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.

  6. The comparative risk assessment framework and tools (CRAFT)

    Science.gov (United States)

    Southern Research Station. USDA Forest Service

    2010-01-01

    To help address these challenges, the USDA Forest Service’s Eastern Forest Environmental Threat Assessment Center (EFETAC) and the University of North Carolina Asheville’s National Environmental Modeling and Analysis Center (NEMAC) designed a planning framework, called the Comparative Risk Assessment Framework and Tools (CRAFT). CRAFT is...

  7. Can clinical prediction tools predict the need for computed tomography in blunt abdominal? A systematic review.

    Science.gov (United States)

    Sharples, Alistair; Brohi, Karim

    2016-08-01

    Blunt abdominal trauma is a common reason for admission to the Emergency Department. Early detection of injuries is an important goal but is often not straightforward as physical examination alone is not a good predictor of serious injury. Computed tomography (CT) has become the primary method for assessing the stable trauma patient. It has high sensitivity and specificity but there remains concern regarding the long term consequences of high doses of radiation. Therefore an accurate and reliable method of assessing which patients are at higher risk of injury and hence require a CT would be clinically useful. We perform a systematic review to investigate the use of clinical prediction tools (CPTs) for the identification of abdominal injuries in patients suffering blunt trauma. A literature search was performed using Medline, Embase, The Cochrane Library and NHS Evidence up to August 2014. English language, prospective and retrospective studies were included if they derived, validated or assessed a CPT, aimed at identifying intra-abdominal injuries or the need for intervention to treat an intra-abdominal after blunt trauma. Methodological quality was assessed using a 14 point scale. Performance was assessed predominantly by sensitivity. Seven relevant studies were identified. All studies were derivative studies and no CPT was validated in a separate study. There were large differences in the study design, composition of the CPTs, the outcomes analysed and the methodological quality of the included studies. Sensitivities ranged from 86 to 100%. The highest performing CPT had a lower limit of the 95% CI of 95.8% and was of high methodological quality (11 of 14). Had this rule been applied to the population then 25.1% of patients would have avoided a CT scan. Seven CPTs were identified of varying designs and methodological quality. All demonstrate relatively high sensitivity with some achieving very high sensitivity whilst still managing to reduce the number of CTs

  8. Application of Risk Assessment Tools in the Continuous Risk Management (CRM) Process

    Science.gov (United States)

    Ray, Paul S.

    2002-01-01

    Marshall Space Flight Center (MSFC) of the National Aeronautics and Space Administration (NASA) is currently implementing the Continuous Risk Management (CRM) Program developed by the Carnegie Mellon University and recommended by NASA as the Risk Management (RM) implementation approach. The four most frequently used risk assessment tools in the center are: (a) Failure Modes and Effects Analysis (FMEA), Hazard Analysis (HA), Fault Tree Analysis (FTA), and Probabilistic Risk Analysis (PRA). There are some guidelines for selecting the type of risk assessment tools during the project formulation phase of a project, but there is not enough guidance as to how to apply these tools in the Continuous Risk Management process (CRM). But the ways the safety and risk assessment tools are used make a significant difference in the effectiveness in the risk management function. Decisions regarding, what events are to be included in the analysis, to what level of details should the analysis be continued, make significant difference in the effectiveness of risk management program. Tools of risk analysis also depends on the phase of a project e.g. at the initial phase of a project, when not much data are available on hardware, standard FMEA cannot be applied; instead a functional FMEA may be appropriate. This study attempted to provide some directives to alleviate the difficulty in applying FTA, PRA, and FMEA in the CRM process. Hazard Analysis was not included in the scope of the study due to the short duration of the summer research project.

  9. Bitter or not? BitterPredict, a tool for predicting taste from chemical structure.

    Science.gov (United States)

    Dagan-Wiener, Ayana; Nissim, Ido; Ben Abu, Natalie; Borgonovo, Gigliola; Bassoli, Angela; Niv, Masha Y

    2017-09-21

    Bitter taste is an innately aversive taste modality that is considered to protect animals from consuming toxic compounds. Yet, bitterness is not always noxious and some bitter compounds have beneficial effects on health. Hundreds of bitter compounds were reported (and are accessible via the BitterDB http://bitterdb.agri.huji.ac.il/dbbitter.php ), but numerous additional bitter molecules are still unknown. The dramatic chemical diversity of bitterants makes bitterness prediction a difficult task. Here we present a machine learning classifier, BitterPredict, which predicts whether a compound is bitter or not, based on its chemical structure. BitterDB was used as the positive set, and non-bitter molecules were gathered from literature to create the negative set. Adaptive Boosting (AdaBoost), based on decision trees machine-learning algorithm was applied to molecules that were represented using physicochemical and ADME/Tox descriptors. BitterPredict correctly classifies over 80% of the compounds in the hold-out test set, and 70-90% of the compounds in three independent external sets and in sensory test validation, providing a quick and reliable tool for classifying large sets of compounds into bitter and non-bitter groups. BitterPredict suggests that about 40% of random molecules, and a large portion (66%) of clinical and experimental drugs, and of natural products (77%) are bitter.

  10. Comparison of the Nosocomial Pneumonia Mortality Prediction (NPMP) model with standard mortality prediction tools.

    Science.gov (United States)

    Srinivasan, M; Shetty, N; Gadekari, S; Thunga, G; Rao, K; Kunhikatta, V

    2017-07-01

    Severity or mortality prediction of nosocomial pneumonia could aid in the effective triage of patients and assisting physicians. To compare various severity assessment scoring systems for predicting intensive care unit (ICU) mortality in nosocomial pneumonia patients. A prospective cohort study was conducted in a tertiary care university-affiliated hospital in Manipal, India. One hundred patients with nosocomial pneumonia, admitted in the ICUs who developed pneumonia after >48h of admission, were included. The Nosocomial Pneumonia Mortality Prediction (NPMP) model, developed in our hospital, was compared with Acute Physiology and Chronic Health Evaluation II (APACHE II), Mortality Probability Model II (MPM 72  II), Simplified Acute Physiology Score II (SAPS II), Multiple Organ Dysfunction Score (MODS), Sequential Organ Failure Assessment (SOFA), Clinical Pulmonary Infection Score (CPIS), Ventilator-Associated Pneumonia Predisposition, Insult, Response, Organ dysfunction (VAP-PIRO). Data and clinical variables were collected on the day of pneumonia diagnosis. The outcome for the study was ICU mortality. The sensitivity and specificity of the various scoring systems was analysed by plotting receiver operating characteristic (ROC) curves and computing the area under the curve for each of the mortality predicting tools. NPMP, APACHE II, SAPS II, MPM 72  II, SOFA, and VAP-PIRO were found to have similar and acceptable discrimination power as assessed by the area under the ROC curve. The AUC values for the above scores ranged from 0.735 to 0.762. CPIS and MODS showed least discrimination. NPMP is a specific tool to predict mortality in nosocomial pneumonia and is comparable to other standard scores. Copyright © 2017 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

  11. Qualitative risk assessment as a remediation management tool

    International Nuclear Information System (INIS)

    Knutson, D.E.

    1991-01-01

    The technique used to complete this thesis utilizes existing NRC and EPA guidance on health-based risk to qualitatively prioritize preliminary assessments and provide a tool for the direction and management of remediation activities. This method is intended as a decision making tool to aid in prioritizing the remediation effort and manage the remedial investigation and feasibility study (RI/FS) process. It is not a replacement for the RI/FS. The methodology for qualitative risk assessment utilizes data gathered in preliminary assessments and calculates the health-based hazards and consequences from contaminants found at each individual location. The health-based qualitative risk indicated that number-sign 6 fuel oil, carbon tetrachloride, depleted uranium, and enriched uranium were the contaminants of major concern, in that order. Plutonium ranked approximately sixth in the contaminant of concern priority. 38 refs., 1 fig., 9 tabs

  12. Aqueduct: an interactive tool to empower global water risk assessment

    Science.gov (United States)

    Reig, Paul; Gassert, Francis

    2013-04-01

    The Aqueduct Water Risk Atlas (Aqueduct) is a publicly available, global database and interactive tool that maps indicators of water related risks for decision makers worldwide. Aqueduct makes use of the latest geo-statistical modeling techniques to compute a composite index and translate the most recently available hydrological data into practical information on water related risks for companies, investors, and governments alike. Twelve global indicators are grouped into a Water Risk Framework designed in response to the growing concerns from private sector actors around water scarcity, water quality, climate change, and increasing demand for freshwater. The Aqueduct framework includes indicators of water stress, variability in supply, storage, flood, drought, groundwater, water quality and social conflict, addressing both spatial and temporal variation in water hazards. It organizes indicators into three categories of risk that bring together multiple dimensions of water related risk into comprehensive aggregated scores, which allow for dynamic weighting to capture users' unique exposure to water hazards. All information is compiled into an online, open access platform, from which decision-makers can view indicators, scores, and maps, conduct global risk assessments, and export data and shape files for further analysis. Companies can use this tool to evaluate their exposure to water risks across operations and supply chains, investors to assess water-related risks in their portfolio, and public-sector actors to better understand water security. Additionally, the open nature of the data and maps allow other organizations to build off of this effort with new research, for example in the areas of water-energy or water-food relationships. This presentation will showcase the Aqueduct Water Risk Atlas online tool and the features and functionalities it offers, as well as explain how it can be used for both private and public sector applications. The session will

  13. Ability of different screening tools to predict positive effect on nutritional intervention among the elderly in primary health care

    DEFF Research Database (Denmark)

    Beck, Anne Marie; Beermann, Tina; Kjær, Stine

    2013-01-01

    Routine identification of nutritional risk screening is paramount as the first stage in nutritional treatment of the elderly. The major focus of former validation studies of screening tools has been on the ability to predict undernutrition. The aim of this study was to validate Mini Nutritional A...

  14. Fall Risk Assessment Tools for Elderly Living in the Community: Can We Do Better?

    Science.gov (United States)

    Palumbo, Pierpaolo; Palmerini, Luca; Bandinelli, Stefania; Chiari, Lorenzo

    2015-01-01

    Falls are a common, serious threat to the health and self-confidence of the elderly. Assessment of fall risk is an important aspect of effective fall prevention programs. In order to test whether it is possible to outperform current prognostic tools for falls, we analyzed 1010 variables pertaining to mobility collected from 976 elderly subjects (InCHIANTI study). We trained and validated a data-driven model that issues probabilistic predictions about future falls. We benchmarked the model against other fall risk indicators: history of falls, gait speed, Short Physical Performance Battery (Guralnik et al. 1994), and the literature-based fall risk assessment tool FRAT-up (Cattelani et al. 2015). Parsimony in the number of variables included in a tool is often considered a proxy for ease of administration. We studied how constraints on the number of variables affect predictive accuracy. The proposed model and FRAT-up both attained the same discriminative ability; the area under the Receiver Operating Characteristic (ROC) curve (AUC) for multiple falls was 0.71. They outperformed the other risk scores, which reported AUCs for multiple falls between 0.64 and 0.65. Thus, it appears that both data-driven and literature-based approaches are better at estimating fall risk than commonly used fall risk indicators. The accuracy-parsimony analysis revealed that tools with a small number of predictors (~1-5) were suboptimal. Increasing the number of variables improved the predictive accuracy, reaching a plateau at ~20-30, which we can consider as the best trade-off between accuracy and parsimony. Obtaining the values of these ~20-30 variables does not compromise usability, since they are usually available in comprehensive geriatric assessments.

  15. Fall Risk Assessment Tools for Elderly Living in the Community: Can We Do Better?

    Directory of Open Access Journals (Sweden)

    Pierpaolo Palumbo

    Full Text Available Falls are a common, serious threat to the health and self-confidence of the elderly. Assessment of fall risk is an important aspect of effective fall prevention programs.In order to test whether it is possible to outperform current prognostic tools for falls, we analyzed 1010 variables pertaining to mobility collected from 976 elderly subjects (InCHIANTI study. We trained and validated a data-driven model that issues probabilistic predictions about future falls. We benchmarked the model against other fall risk indicators: history of falls, gait speed, Short Physical Performance Battery (Guralnik et al. 1994, and the literature-based fall risk assessment tool FRAT-up (Cattelani et al. 2015. Parsimony in the number of variables included in a tool is often considered a proxy for ease of administration. We studied how constraints on the number of variables affect predictive accuracy.The proposed model and FRAT-up both attained the same discriminative ability; the area under the Receiver Operating Characteristic (ROC curve (AUC for multiple falls was 0.71. They outperformed the other risk scores, which reported AUCs for multiple falls between 0.64 and 0.65. Thus, it appears that both data-driven and literature-based approaches are better at estimating fall risk than commonly used fall risk indicators. The accuracy-parsimony analysis revealed that tools with a small number of predictors (~1-5 were suboptimal. Increasing the number of variables improved the predictive accuracy, reaching a plateau at ~20-30, which we can consider as the best trade-off between accuracy and parsimony. Obtaining the values of these ~20-30 variables does not compromise usability, since they are usually available in comprehensive geriatric assessments.

  16. Strategic Risk Assessment: A Decision Tool for Complex Decisions

    Energy Technology Data Exchange (ETDEWEB)

    Pollard, Simon; Duarte-Davidson, Raquel; Yearsley, Roger [Environment Agency, London (United Kingdom). National Centre for Risk Analysis and Options Appraisal; Kemp, Ray; Crawford, Mark [Galson Sciences Limited, Oakham (United Kingdom)

    2001-07-01

    Reporting on the state of the environment often requires policy makers and regulators to prioritise a range of diverse environmental issues for the purpose of directing future action on environmental protection and improvement. Information on environmental issues to inform this type of analysis can be disparate, it may be too voluminous or even absent. Data on a range of issues are rarely presented in a common format that allows easy comparison. Nevertheless, strategic judgements are required on the significance of impacts from various environmental pressures and on the inherent uncertainties. Prioritising issues forces a discussion among stakeholders of the relative significance of 'environmental harm' from pressures acting on various receptors in the environment. Discussions of this sort rapidly evolve into a discourse on risks and values. In an attempt to help systematise these discussions and provide practical tools for the analysis of environmental risks at a strategic level, the Environment Agency of England and Wales has initiated developmental research on strategic risk assessment. The tools developed under this research use the concept of 'environmental harm' as a common currency, viewed from technical, social and economic perspectives, to analyse impacts from a range of environmental pressures. Critical to an informed debate is an understanding and analysis both of the various characteristics of harm (spatial and temporal extent, reversibility, latency, etc.) and of the social response to the actual or potential environmental harm. Recent developments in this approach allow a presentation of the analysis in a structured fashion so as to better inform risk management decisions. Here, we present recent developments in the strategic risk assessment research tool, as tested by case studies from state of the environment reporting and the analysis of a regional environmental plan. We discuss its relative advantages and limitations and its

  17. Strategic Risk Assessment: A Decision Tool for Complex Decisions

    International Nuclear Information System (INIS)

    Pollard, Simon; Duarte-Davidson, Raquel; Yearsley, Roger

    2001-01-01

    Reporting on the state of the environment often requires policy makers and regulators to prioritise a range of diverse environmental issues for the purpose of directing future action on environmental protection and improvement. Information on environmental issues to inform this type of analysis can be disparate, it may be too voluminous or even absent. Data on a range of issues are rarely presented in a common format that allows easy comparison. Nevertheless, strategic judgements are required on the significance of impacts from various environmental pressures and on the inherent uncertainties. Prioritising issues forces a discussion among stakeholders of the relative significance of 'environmental harm' from pressures acting on various receptors in the environment. Discussions of this sort rapidly evolve into a discourse on risks and values. In an attempt to help systematise these discussions and provide practical tools for the analysis of environmental risks at a strategic level, the Environment Agency of England and Wales has initiated developmental research on strategic risk assessment. The tools developed under this research use the concept of 'environmental harm' as a common currency, viewed from technical, social and economic perspectives, to analyse impacts from a range of environmental pressures. Critical to an informed debate is an understanding and analysis both of the various characteristics of harm (spatial and temporal extent, reversibility, latency, etc.) and of the social response to the actual or potential environmental harm. Recent developments in this approach allow a presentation of the analysis in a structured fashion so as to better inform risk management decisions. Here, we present recent developments in the strategic risk assessment research tool, as tested by case studies from state of the environment reporting and the analysis of a regional environmental plan. We discuss its relative advantages and limitations and its wider potential role

  18. Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

    DEFF Research Database (Denmark)

    Maier, Robert; Moser, Gerhard; Chen, Guo-Bo

    2015-01-01

    Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk...... number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low...

  19. 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.

  20. Operation and evaluation of online risk communication assistant tool, 'ORCAT'

    International Nuclear Information System (INIS)

    Kimura, Hiroshi; Katsumura, Soichiro; Furuta, Kazuo; Matsumura, Kenichi; Tanaka, Hiroshi

    2005-01-01

    Risk communication about the high-level radioactive waste (HLW) disposal is necessary for public acceptance of HLW disposal program. Online Risk Communication Assistant Tool (ORCAT) system is developed in order to support risk communication for high-level radioactive disposal on World Wide Web. We have carried out two test operations of ORCAT system. First test operation is carried out from Jun. 26 to Feb. 13, 2003. After the first operation, we improved the ORCAT system, and carried out the second test operation from Dec. 4 to 22, 2004. In the second test operation, 20 participants replayed the questionnaire about usability of ORCAT system. In consequence, we found that the ORCAT system remains what need to refine, but is evaluated useful to the risk communication about the HLW disposal. (author)

  1. 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.

  2. 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

  3. Fracture risk assessment in postmenopausal women with diabetes: comparison between DeFRA and FRAX tools.

    Science.gov (United States)

    Bonaccorsi, Gloria; Messina, Carmelo; Cervellati, Carlo; Maietti, Elisa; Medini, Matilde; Rossini, Maurizio; Massari, Leo; Greco, Pantaleo

    2018-05-01

    This study aimed to compare the performance of Fracture Risk Assessment Tool (FRAX) with that of Derived FRAX (DeFRA) in estimating fracture risk in a cohort of type-2 diabetes mellitus (T2DM) postmenopausal women. One hundred nineteen T2DM postmenopausal women and 118 consecutive healthy postmenopausal women were enrolled. Fracture risk was assessed with FRAX (adjusted or non- for trabecular bone score, TBS) and DeFRA. Bone mineral density (BMD) and TBS were evaluated by dual-energy X-ray absorptiometry (DXA). The outcome was the presence of vertebral/non-vertebral fragility fractures (FFs). T2DM women showed higher spinal BMD T-score (p < .05), but lower TBS (p < .05), than controls. Diabetic patients had higher prevalence of FFs compared to controls (p < .05), but no significant difference were found in the scores of any of the predictor tools. Differently, in the T2DM group, the scores of DeFRA, FRAX and adjusted-FRAX were significantly (p < .01 for all) higher in fractured compared with non-fractured women. DeFRA showed the best discriminative power among all fracture risk predictor tools (area under curves: DeFra: 0.89; adjusted FRAX: 0.80; non-adjusted FRAX: 0.73). In summary, all fracture risk assessment tools appeared to be effective in predicting bone fractures in T2DM postmenopausal women, with DeFRA showing a slightly better diagnostic accuracy.

  4. Development of risk assessment tool for foundry workers.

    Science.gov (United States)

    Mohan, G Madhan; Prasad, P S S; Mokkapati, Anil Kumar; Venkataraman, G

    2008-01-01

    Occupational ill-health and work-related disorders are predominant in manufacturing industries due to the inevitable presence of manual work even after several waves of industrial automation and technological advancements. Ergonomic risk factors and musculoskeletal disorders like low-back symptoms have been noted amongst foundry workers. The purpose of this study was to formulate and develop a Physical Effort Index to assess risk factor. The questionnaire tool applicable to foundry environment has been designed and validated. The data recorded through survey across the foundries has been subjected to regression analysis to correlate between proposed physical effort index and the standard Borg's Ratings of Perceived Exertion (RPE) scale. The physical efforts of sixty seven workers in various foundry shop floors were assessed subjectively. The 'Job factors' and 'Work environment' were the two major parameters considered in assessing the worker discomfort level at workplace. A relation between Borg's RPE scale and the above two parameters were arrived at, through regression analysis. The study demonstrates the prevalence of risk factors amongst foundry workers and the effectiveness of the proposed index in estimating the risk factor levels. RELEVANCE TO THE INDUSTRY: The proposed tool will assist foundry supervisors and managers to assess the risk factors and helps in better understanding of the workplace to avoid work-related disorders, ensuring better output.

  5. RISK REDUCTION WITH A FUZZY EXPERT EXPLORATION TOOL

    Energy Technology Data Exchange (ETDEWEB)

    Robert S. Balch; Ron Broadhead

    2005-03-01

    Incomplete or sparse data such as geologic or formation characteristics introduce a high level of risk for oil exploration and development projects. ''Expert'' systems developed and used in several disciplines and industries have demonstrated beneficial results when working with sparse data. State-of-the-art expert exploration tools, relying on a database, and computer maps generated by neural networks and user inputs, have been developed through the use of ''fuzzy'' logic, a mathematical treatment of imprecise or non-explicit parameters and values. Oil prospecting risk has been reduced with the use of these properly verified and validated ''Fuzzy Expert Exploration (FEE) Tools.'' Through the course of this project, FEE Tools and supporting software were developed for two producing formations in southeast New Mexico. Tools of this type can be beneficial in many regions of the U.S. by enabling risk reduction in oil and gas prospecting as well as decreased prospecting and development costs. In today's oil industry environment, many smaller exploration companies lack the resources of a pool of expert exploration personnel. Downsizing, volatile oil prices, and scarcity of domestic exploration funds have also affected larger companies, and will, with time, affect the end users of oil industry products in the U.S. as reserves are depleted. The FEE Tools benefit a diverse group in the U.S., allowing a more efficient use of scarce funds, and potentially reducing dependence on foreign oil and providing lower product prices for consumers.

  6. Validation and inter-rater reliability of a three item falls risk screening tool

    Directory of Open Access Journals (Sweden)

    Catherine Maree Said

    2017-11-01

    Full Text Available Abstract Background Falls screening tools are routinely used in hospital settings and the psychometric properties of tools should be examined in the setting in which they are used. The aim of this study was to explore the concurrent and predictive validity of the Austin Health Falls Risk Screening Tool (AHFRST, compared with The Northern Hospital Modified St Thomas’s Risk Assessment Tool (TNH-STRATIFY, and the inter-rater reliability of the AHFRST. Methods A research physiotherapist used the AHFRST and TNH-STRATIFY to classify 130 participants admitted to Austin Health (five acute wards, n = 115 two subacute wards n = 15; median length of stay 6 days IQR 3–12 as ‘High’ or ‘Low’ falls risk. The AHFRST was also completed by nursing staff on patient admission. Falls data was collected from the hospital incident reporting system. Results Six falls occurred during the study period (fall rate of 4.6 falls per 1000 bed days. There was substantial agreement between the AHFRST and the TNH-STRATIFY (Kappa = 0.68, 95% CI 0.52–0.78. Both tools had poor predictive validity, with low specificity (AHFRST 46.0%, 95% CI 37.0–55.1; TNH-STRATIFY 34.7%, 95% CI 26.4–43.7 and positive predictive values (AHFRST 5.6%, 95% CI 1.6–13.8; TNH-STRATIFY 6.9%, 95% CI 2.6–14.4. The AHFRST showed moderate inter-rater reliability (Kappa = 0.54, 95% CI = 0.36–0.67, p < 0.001 although 18 patients did not have the AHFRST completed by nursing staff. Conclusions There was an acceptable level of agreement between the 3 item AHFRST classification of falls risk and the longer, 9 item TNH-STRATIFY classification. However, both tools demonstrated limited predictive validity in the Austin Health population. The results highlight the importance of evaluating the validity of falls screening tools, and the clinical utility of these tools should be reconsidered.

  7. Tools and techniques for ageing predictions in nuclear reactors through condition monitoring

    International Nuclear Information System (INIS)

    Verma, R.M.P.

    1994-01-01

    To operate the nuclear reactors beyond their design predicted life is gaining importance because of huge replacement and decommissioning costs. But experience shows that nuclear plant safety and reliability may decline in the later years of plant life due to ageing degradation. Ageing of nuclear plant components, structures and systems, if unmitigated reduces their safety margins provided in the design and thus increases risks to public health and safety. These safety margins must be monitored throughout plant service life including any extended life. Condition monitoring of nuclear reactor components/equipment and systems can be done to study the effect of ageing, status of safety margins and effect of corrective and mitigating actions taken. The tools and techniques of condition monitoring are also important in failure trending, predictive maintenance, evaluation of scheduled maintenance, in mitigation of ageing, life extension and reliability studies. (author). 1 fig., 1 annexure

  8. Screening Tool for Early Postnatal Prediction of Retinopathy of Prematurity in Preterm Newborns (STEP-ROP).

    Science.gov (United States)

    Ricard, Caroline A; Dammann, Christiane E L; Dammann, Olaf

    2017-01-01

    Retinopathy of prematurity (ROP) is a disorder of the preterm newborn characterized by neurovascular disruption in the immature retina that may cause visual impairment and blindness. To develop a clinical screening tool for early postnatal prediction of ROP in preterm newborns based on risk information available within the first 48 h of postnatal life. Using data submitted to the Vermont Oxford Network (VON) between 1995 and 2015, we created logistic regression models based on infants born <28 completed weeks gestational age. We developed a model with 60% of the data and identified birth weight, gestational age, respiratory distress syndrome, non-Hispanic ethnicity, and multiple gestation as predictors of ROP. We tested the model in the remaining 40%, performed tenfold cross-validation, and tested the score in ELGAN study data. Of the 1,052 newborns in the VON database, 627 recorded an ROP status. Forty percent had no ROP, 40% had mild ROP (stages 1 and 2), and 20% had severe ROP (stages 3-5). We created a weighted score to predict any ROP based on the multivariable regression model. A cutoff score of 5 had the best sensitivity (95%, 95% CI 93-97), while maintaining a strong positive predictive value (63%, 95% CI 57-68). When applied to the ELGAN data, sensitivity was lower (72%, 95% CI 69-75), but PPV was higher (80%, 95% CI 77-83). STEP-ROP is a promising screening tool. It is easy to calculate, does not rely on extensive postnatal data collection, and can be calculated early after birth. Early ROP screening may help physicians limit patient exposure to additional risk factors, and may be useful for risk stratification in clinical trials aimed at reducing ROP. © 2017 S. Karger AG, Basel.

  9. Performance of Multiple Risk Assessment Tools to Predict Mortality for Adult Respiratory Distress Syndrome with Extracorporeal Membrane Oxygenation Therapy: An External Validation Study Based on Chinese Single-center Data

    Directory of Open Access Journals (Sweden)

    Lei Huang

    2016-01-01

    Conclusions: The RESP, APCHAE II, and SOFA scorings systems show good predictive value for intra-hospital survival of ARDS patients treated with ECMO in our single-center evaluation. Future validation should include a larger study with either more patients' data at single-center or by integration of domestic multi-center data. Development of a scoring system with national characteristics might be warranted.

  10. 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...

  11. 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.

  12. Integrating Risk Analyses and Tools at the DOE Hanford Site

    International Nuclear Information System (INIS)

    LOBER, R.W.

    2002-01-01

    Risk assessment and environmental impact analysis at the U.S. Department of Energy (DOE) Hanford Site in Washington State has made significant progress in refining the strategy for using risk analysis to support closing of several hundred waste sites plus 149 single-shell tanks at the Hanford Site. A Single-Shell Tank System Closure Work Plan outlines the current basis for closing the single-shell tank systems. An analogous site approach has been developed to address closure of aggregated groups of similar waste sites. Because of the complexity, decision time frames, proximity of non-tank farm waste sites to tank farms, scale, and regulatory considerations, various projects are providing integrated assessments to support risk analyses and decision-making. Projects and the tools that are being developed and applied at Hanford to support retrieval and cleanup decisions include: (1) Life Cycle Model (LCM) and Risk Receptor Model (RRM)--A site-level set of tools to support strategic analyses through scoping level risk management to assess different alternatives and options for tank closure. (2) Systems Assessment Capability for Integrated Groundwater Nadose Zone (SAC) and the Site-Wide Groundwater Model (SWGM)--A site-wide groundwater modeling system coupled with a risk-based uncertainty analysis of inventory, vadose zone, groundwater, and river interactions for evaluating cumulative impacts from individual and aggregate waste sites. (3) Retrieval Performance Evaluation (RPE)--A site-specific, risk-based methodology developed to evaluate performance of waste retrieval, leak detection and closure on a tank-specific basis as a function of past tank Leaks, potential leakage during retrieval operations, and remaining residual waste inventories following completion of retrieval operations. (4) Field Investigation Report (FIR)--A corrective action program to investigate the nature and extent of past tank leaks through characterization activities and assess future impacts to

  13. 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.

  14. Evaluation of a visual risk communication tool: effects on knowledge and perception of blood transfusion risk.

    Science.gov (United States)

    Lee, D H; Mehta, M D

    2003-06-01

    Effective risk communication in transfusion medicine is important for health-care consumers, but understanding the numerical magnitude of risks can be difficult. The objective of this study was to determine the effect of a visual risk communication tool on the knowledge and perception of transfusion risk. Laypeople were randomly assigned to receive transfusion risk information with either a written or a visual presentation format for communicating and comparing the probabilities of transfusion risks relative to other hazards. Knowledge of transfusion risk was ascertained with a multiple-choice quiz and risk perception was ascertained by psychometric scaling and principal components analysis. Two-hundred subjects were recruited and randomly assigned. Risk communication with both written and visual presentation formats increased knowledge of transfusion risk and decreased the perceived dread and severity of transfusion risk. Neither format changed the perceived knowledge and control of transfusion risk, nor the perceived benefit of transfusion. No differences in knowledge or risk perception outcomes were detected between the groups randomly assigned to written or visual presentation formats. Risk communication that incorporates risk comparisons in either written or visual presentation formats can improve knowledge and reduce the perception of transfusion risk in laypeople.

  15. Predicting Great Lakes fish yields: tools and constraints

    Science.gov (United States)

    Lewis, C.A.; Schupp, D.H.; Taylor, W.W.; Collins, J.J.; Hatch, Richard W.

    1987-01-01

    Prediction of yield is a critical component of fisheries management. The development of sound yield prediction methodology and the application of the results of yield prediction are central to the evolution of strategies to achieve stated goals for Great Lakes fisheries and to the measurement of progress toward those goals. Despite general availability of species yield models, yield prediction for many Great Lakes fisheries has been poor due to the instability of the fish communities and the inadequacy of available data. A host of biological, institutional, and societal factors constrain both the development of sound predictions and their application to management. Improved predictive capability requires increased stability of Great Lakes fisheries through rehabilitation of well-integrated communities, improvement of data collection, data standardization and information-sharing mechanisms, and further development of the methodology for yield prediction. Most important is the creation of a better-informed public that will in turn establish the political will to do what is required.

  16. 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...

  17. 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

  18. Human System Risk Management - Tools of our Trade

    Science.gov (United States)

    Ott, C. Mark

    2009-01-01

    The risk of infectious disease to select individuals has historically been difficult to predict in either spaceflight or on Earth with health care efforts relying on broad-based prevention and post-infection treatment. Over the past 10 years, quantitative microbial risk assessment evaluations have evolved to formalize the assessment process and quantify the risk. This process of hazard identification, exposure assessment, dose-response assessment, and risk characterization has been applied by the water and food safety industries to address the public health impacts associated with the occurrence of and human exposure to pathogens in water and food for the development of preventive strategies for microbial disease. NASA is currently investigating the feasibility of using these techniques to better understand the risks to astronauts and refine their microbiological requirements. To assess these techniques, NASA began an evaluation of the potable water system on the International Space Station to determine how the microbial risk from water consumption during flight differed from terrestrial sources, such as municipal water systems. The ultimate goal of this work is to optimize microbial requirements which would minimize unnecessary cargo and use of crew time, while still protecting the health of the crew. Successful demonstration of this risk assessment framework with the water system holds the potential to maximize the use of available resources during spaceflight missions and facilitate investigations into the evaluation of other routes of infection, such as through the spaceflight foods system.

  19. The use of current risk analysis tools evaluated towards preventing external domino accidents

    NARCIS (Netherlands)

    Reniers, Genserik L L; Dullaert, W.; Ale, B. J.M.; Soudan, K.

    Risk analysis is an essential tool for company safety policy. Risk analysis consists of identifying and evaluating all possible risks. The efficiency of risk analysis tools depends on the rigueur of identifying and evaluating all possible risks. The diversity in risk analysis procedures is such that

  20. 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.

  1. Using Predictive Modelling to Identify Students at Risk of Poor University Outcomes

    Science.gov (United States)

    Jia, Pengfei; Maloney, Tim

    2015-01-01

    Predictive modelling is used to identify students at risk of failing their first-year courses and not returning to university in the second year. Our aim is twofold. Firstly, we want to understand the factors that lead to poor first-year experiences at university. Secondly, we want to develop simple, low-cost tools that would allow universities to…

  2. 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

  3. 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....

  4. 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 ...

  5. 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.

  6. The Stroke Assessment of Fall Risk (SAFR): predictive validity in inpatient stroke rehabilitation.

    Science.gov (United States)

    Breisinger, Terry P; Skidmore, Elizabeth R; Niyonkuru, Christian; Terhorst, Lauren; Campbell, Grace B

    2014-12-01

    To evaluate relative accuracy of a newly developed Stroke Assessment of Fall Risk (SAFR) for classifying fallers and non-fallers, compared with a health system fall risk screening tool, the Fall Harm Risk Screen. Prospective quality improvement study conducted at an inpatient stroke rehabilitation unit at a large urban university hospital. Patients admitted for inpatient stroke rehabilitation (N = 419) with imaging or clinical evidence of ischemic or hemorrhagic stroke, between 1 August 2009 and 31 July 2010. Not applicable. Sensitivity, specificity, and area under the curve for Receiver Operating Characteristic Curves of both scales' classifications, based on fall risk score completed upon admission to inpatient stroke rehabilitation. A total of 68 (16%) participants fell at least once. The SAFR was significantly more accurate than the Fall Harm Risk Screen (p Fall Harm Risk Screen, area under the curve was 0.56, positive predictive value was 0.19, and negative predictive value was 0.86. Sensitivity and specificity of the SAFR (0.78 and 0.63, respectively) was higher than the Fall Harm Risk Screen (0.57 and 0.48, respectively). An evidence-derived, population-specific fall risk assessment may more accurately predict fallers than a general fall risk screen for stroke rehabilitation patients. While the SAFR improves upon the accuracy of a general assessment tool, additional refinement may be warranted. © The Author(s) 2014.

  7. A prognostic tool to identify adolescents at high risk of becoming daily smokers

    Directory of Open Access Journals (Sweden)

    Paradis Gilles

    2011-08-01

    Full Text Available Abstract Background The American Academy of Pediatrics advocates that pediatricians should be involved in tobacco counseling and has developed guidelines for counseling. We present a prognostic tool for use by health care practitioners in both clinical and non-clinical settings, to identify adolescents at risk of becoming daily smokers. Methods Data were drawn from the Nicotine Dependence in Teens (NDIT Study, a prospective investigation of 1293 adolescents, initially aged 12-13 years, recruited in 10 secondary schools in Montreal, Canada in 1999. Questionnaires were administered every three months for five years. The prognostic tool was developed using estimated coefficients from multivariable logistic models. Model overfitting was corrected using bootstrap cross-validation. Goodness-of-fit and predictive ability of the models were assessed by R2, the c-statistic, and the Hosmer-Lemeshow test. Results The 1-year and 2-year probability of initiating daily smoking was a joint function of seven individual characteristics: age; ever smoked; ever felt like you needed a cigarette; parent(s smoke; sibling(s smoke; friend(s smoke; and ever drank alcohol. The models were characterized by reasonably good fit and predictive ability. They were transformed into user-friendly tables such that the risk of daily smoking can be easily computed by summing points for responses to each item. The prognostic tool is also available on-line at http://episerve.chumontreal.qc.ca/calculation_risk/daily-risk/daily_smokingadd.php. Conclusions The prognostic tool to identify youth at high risk of daily smoking may eventually be an important component of a comprehensive tobacco control system.

  8. IPMP 2013 - A comprehensive data analysis tool for predictive microbiology

    Science.gov (United States)

    Predictive microbiology is an area of applied research in food science that uses mathematical models to predict the changes in the population of pathogenic or spoilage microorganisms in foods undergoing complex environmental changes during processing, transportation, distribution, and storage. It f...

  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. The Predictive Accuracy of PREDICT : A Personalized Decision-Making Tool for Southeast Asian Women With Breast Cancer

    NARCIS (Netherlands)

    Wong, Hoong-Seam; Subramaniam, Shridevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M.; Hartman, Mikael; Bhoo Pathy, N

    Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480

  11. 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...

  12. 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

  13. 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...

  14. 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...

  15. Risk monitor - a tool for operational safety assessment risk monitor - user's manual

    International Nuclear Information System (INIS)

    Hari Prasad, M.; Vinod, Gopika; Saraf, R.K.; Ghosh, A.K.

    2006-06-01

    Probabilistic Safety Assessment has become a key tool as on today to identify and understand Nuclear Power Plant vulnerabilities. As a result of the availability of these PSA studies, there is a desire to use them to enhance plant safety and to operate the nuclear stations in the most efficient manner. Risk Monitor is a PC based tool, which computes the real time safety level and assists plant personnel to manage day-to-day activities. Risk Monitor is a PC based user friendly software tool used for modification and re-analysis of a nuclear Power plant. Operation of Risk Monitor is based on PSA methods for assisting in day to day applications. Risk Monitoring programs can assess the risk profile and are used to optimize the operation of Nuclear Power Plants with respect to a minimum risk level over the operating time. This report presents the background activities of Risk Monitor, its application areas and the step by step procedure for the user.to interact with the software. This software can be used with the PSA model of any Nuclear Power Plant. (author)

  16. Kenya develops tool to predict malaria | IDRC - International ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2010-10-13

    Oct 13, 2010 ... In collaboration with scientists from the Kenya Meteorological Department and the International Centre ... a scientific model that uses weather predictions, information about the reproductive mechanisms of ... Related articles ...

  17. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-05-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  18. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-04-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  19. Evaluation of the efficacy of nutritional screening tools to predict malnutrition in the elderly at a geriatric care hospital.

    Science.gov (United States)

    Baek, Myoung-Ha; Heo, Young-Ran

    2015-12-01

    Malnutrition in the elderly is a serious problem, prevalent in both hospitals and care homes. Due to the absence of a gold standard for malnutrition, herein we evaluate the efficacy of five nutritional screening tools developed or used for the elderly. Elected medical records of 141 elderly patients (86 men and 55 women, aged 73.5 ± 5.2 years) hospitalized at a geriatric care hospital were analyzed. Nutritional screening was performed using the following tools: Mini Nutrition Assessment (MNA), Mini Nutrition Assessment-Short Form (MNA-SF), Geriatric Nutritional Risk Index (GNRI), Malnutrition Universal Screening Tool (MUST) and Nutritional Risk Screening 2002 (NRS 2002). A combined index for malnutrition was also calculated as a reference tool. Each patient evaluated as malnourished to any degree or at risk of malnutrition according to at least four out of five of the aforementioned tools was categorized as malnourished in the combined index classification. According to the combined index, 44.0% of the patients were at risk of malnutrition to some degree. While the nutritional risk and/or malnutrition varied greatly depending on the tool applied, ranging from 36.2% (MUST) to 72.3% (MNA-SF). MUST showed good validity (sensitivity 80.6%, specificity 98.7%) and almost perfect agreement (k = 0.81) with the combined index. In contrast, MNA-SF showed poor validity (sensitivity 100%, specificity 49.4%) and only moderate agreement (k = 0.46) with the combined index. MNA-SF was found to overestimate the nutritional risk in the elderly. MUST appeared to be the most valid and useful screening tool to predict malnutrition in the elderly at a geriatric care hospital.

  20. 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.

  1. Prediction of Outcome After Emergency High-Risk Intra-abdominal Surgery Using the Surgical Apgar Score

    DEFF Research Database (Denmark)

    Cihoric, Mirjana; Toft Tengberg, Line; Bay-Nielsen, Morten

    2016-01-01

    BACKGROUND: With current literature quoting mortality rates up to 45%, emergency high-risk abdominal surgery has, compared with elective surgery, a significantly greater risk of death and major complications. The Surgical Apgar Score (SAS) is predictive of outcome in elective surgery, but has nev...... emergency high-risk abdominal surgery. Despite its predictive value, the SAS cannot in its current version be recommended as a standalone prognostic tool in an emergency setting....

  2. Evaluation of Malnutrition Risk after Liver Transplantation Using the Nutritional Screening Tools.

    Science.gov (United States)

    Lim, Hee-Sook; Kim, Hyung-Chul; Park, Yoon-Hyung; Kim, Soon-Kyung

    2015-10-01

    Malnutrition is a common problem in patients with end-stage liver disease requiring liver transplantation. The aim of this study was to evaluate nutritional status by using nutritional screening tools [Nutritional Risk Screening (NRS) 2002, Malnutrition Universal Screening Tool (MUST) and Subjective Global Assessment (SGA)] in patients before and after liver transplantation. We analyzed medical record, blood test, nutrient intake and malnutrition rate just before transplantation and at discharge, and at 3, 6, 12 months after transplantation respectively. Initially 33 patients enrolled as study subjects and finally 28 patients completed the study. Nutrients intake such as energy, fiber, calcium, potassium, vitamin C, and folate were insufficient at 12 months after transplantation. The rates of malnutrition before transplantation were very high, reported at 81.8% for the NRS 2002, 87.9% for the MUST, and 84.8% for the SGA. By 12 months after operation, malnutrition rates reported at NRS, MUST and SGA had decreased to 6.1%, 10.7%, and 10.7%, respectively. Sensitivity was 87.1% for the NRS 2002, 82.0% for the MUST, and 92.0% for the SGA. Of these screening tools the SGA was the highest sensitive tool that predict the risk of mortality in malnutrition patients who received transplantation. Further studies on nutritional status of patients and proper tools for nutrition intervention are needed to provide adequate nutritional care for patients.

  3. 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.

  4. The predictive validity of the HERO Scorecard in determining future health care cost and risk trends.

    Science.gov (United States)

    Goetzel, Ron Z; Henke, Rachel Mosher; Benevent, Richele; Tabrizi, Maryam J; Kent, Karen B; Smith, Kristyn J; Roemer, Enid Chung; Grossmeier, Jessica; Mason, Shawn T; Gold, Daniel B; Noeldner, Steven P; Anderson, David R

    2014-02-01

    To determine the ability of the Health Enhancement Research Organization (HERO) Scorecard to predict changes in health care expenditures. Individual employee health care insurance claims data for 33 organizations completing the HERO Scorecard from 2009 to 2011 were linked to employer responses to the Scorecard. Organizations were dichotomized into "high" versus "low" scoring groups and health care cost trends were compared. A secondary analysis examined the tool's ability to predict health risk trends. "High" scorers experienced significant reductions in inflation-adjusted health care costs (averaging an annual trend of -1.6% over 3 years) compared with "low" scorers whose cost trend remained stable. The risk analysis was inconclusive because of the small number of employers scoring "low." The HERO Scorecard predicts health care cost trends among employers. More research is needed to determine how well it predicts health risk trends for employees.

  5. PREDICTING THE RISK OF LATENT TUBERCULOUS INFECTION IN THOSE SERVING THEIR SENTENCES IN THE PENITENTIARY SYSTEM

    Directory of Open Access Journals (Sweden)

    V. S. Borovitsky

    2018-01-01

    Full Text Available The objective of the study: to assess the prevalence of latent tuberculous infection (LTI and risk of its development in the inmates servicing their sentence in a penal colony of the Federal Penitentiary System, using the tools of statistics analysisSubjects and Methods. 232 persons in the age from 18 to 67 (27; 225-35 years old were examined Results. Exposure to a tuberculosis case in the past and duration of imprisonment are statistically confident risk factors of developing LTI The use of statistic tools allows predicting the chances of LTI 

  6. 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

  7. 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.

  8. 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.

  9. 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...

  10. Predictive Monte Carlo tools for LHC physics (1/3)

    CERN Multimedia

    CERN. Geneva

    2012-01-01

    Simulations of events taking place at the LHC play key role in all experimental analyses. Starting from the basics concepts of QCD, we first review how accurate predictions can be obtained via fixed-order calculations at higher orders. Parton showers and event generation are then introduced as a means to achieve fully exclusive predictions. Finally the recent merging and matching techniques between fixed-order and fully exclusive simulations are presented, as well as their implementations via the MLM/CKKW and MC@NLO/POWHEG methods.

  11. Predictive Monte Carlo tools for LHC physics (3/3)

    CERN Multimedia

    CERN. Geneva

    2012-01-01

    Simulations of events taking place at the LHC play key role in all experimental analyses. Starting from the basics concepts of QCD, we first review how accurate predictions can be obtained via fixed-order calculations at higher orders. Parton showers and event generation are then introduced as a means to achieve fully exclusive predictions. Finally the recent merging and matching techniques between fixed-order and fully exclusive simulations are presented, as well as their implementations via the MLM/CKKW and MC@NLO/POWHEG methods.

  12. Predictive Monte Carlo tools for LHC physics (2/3)

    CERN Multimedia

    CERN. Geneva

    2012-01-01

    Simulations of events taking place at the LHC play key role in all experimental analyses. Starting from the basics concepts of QCD, we first review how accurate predictions can be obtained via fixed-order calculations at higher orders. Parton showers and event generation are then introduced as a means to achieve fully exclusive predictions. Finally the recent merging and matching techniques between fixed-order and fully exclusive simulations are presented, as well as their implementations via the MLM/CKKW and MC@NLO/POWHEG methods.

  13. Selecting a risk-based tool to aid in decision making

    Energy Technology Data Exchange (ETDEWEB)

    Bendure, A.O.

    1995-03-01

    Selecting a risk-based tool to aid in decision making is as much of a challenge as properly using the tool once it has been selected. Failure to consider customer and stakeholder requirements and the technical bases and differences in risk-based decision making tools will produce confounding and/or politically unacceptable results when the tool is used. Selecting a risk-based decisionmaking tool must therefore be undertaken with the same, if not greater, rigor than the use of the tool once it is selected. This paper presents a process for selecting a risk-based tool appropriate to a set of prioritization or resource allocation tasks, discusses the results of applying the process to four risk-based decision-making tools, and identifies the ``musts`` for successful selection and implementation of a risk-based tool to aid in decision making.

  14. Optical coherence tomography: a potential tool to predict premature rupture of fetal membranes.

    Science.gov (United States)

    Micili, Serap C; Valter, Markus; Oflaz, Hakan; Ozogul, Candan; Linder, Peter; Föckler, Nicole; Artmann, Gerhard M; Digel, Ilya; Artmann, Aysegul T

    2013-04-01

    A fundamental question addressed in this study was the feasibility of preterm birth prediction based on a noncontact investigation of fetal membranes in situ. Although the phenomena of preterm birth and the premature rupture of the fetal membrane are well known, currently, there are no diagnostic tools for their prediction. The aim of this study was to assess whether optical coherence tomography could be used for clinical investigations of high-risk pregnancies. The thickness of fetal membranes was measured in parallel by optical coherence tomography and histological techniques for the following types of birth: normal births, preterm births without premature ruptures and births at full term with premature rupture of membrane. Our study revealed that the membrane thickness correlates with the birth type. Normal births membranes were statistically significantly thicker than those belonging to the other two groups. Thus, in spite of almost equal duration of gestation of the normal births and the births at full term with premature rupture, the corresponding membrane thicknesses differed. This difference is possibly related to previously reported water accumulation in the membranes. The optical coherence tomography results were encouraging, suggesting that this technology could be used in future to predict and distinguish between different kinds of births.

  15. The fracture risk assessment tool (FRAX® score in subclinical hyperthyroidism

    Directory of Open Access Journals (Sweden)

    Polovina Snežana

    2015-01-01

    Full Text Available Background/Aim. The Fracture Risk Assessment Tool (FRAX® score is the 10-year estimated risk calculation tool for bone fracture that includes clinical data and hip bone mineral density measured by dual-energy x-ray absorptiometry (DXA. The aim of this cross-sectional study was to elucidate the ability of the FRAX® score in discriminating between bone fracture positive and negative pre- and post-menopausal women with subclinical hyperthyroidism. Methods. The bone mineral density (by DXA, thyroid stimulating hormone (TSH level, free thyroxine (fT4 level, thyroid peroxidase antibodies (TPOAb titre, osteocalcin and beta-cross-laps were measured in 27 pre- and post-menopausal women with newly discovered subclinical hyperthyroidism [age 58.85 ± 7.83 years, body mass index (BMI 27.89 ± 3.46 kg/m2, menopause onset in 46.88 ± 10.21 years] and 51 matched euthyroid controls (age 59.69 ± 5.72 years, BMI 27.68 ± 4.66 kg/m2, menopause onset in 48.53 ± 4.58 years. The etiology of subclinical hyperthyroisims was autoimmune thyroid disease or toxic goiter. FRAX® score calculation was performed in both groups. Results. In the group with subclinical hyperthyroidism the main FRAX® score was significantly higher than in the controls (6.50 ± 1.58 vs 4.35 ± 1.56 respectively; p = 0.015. The FRAX® score for hip was also higher in the evaluated group than in the controls (1.33 ± 3.92 vs 0.50 ± 0.46 respectively; p = 0.022. There was no correlations between low TSH and fracture risk (p > 0.05. The ability of the FRAX® score in discriminating between bone fracture positive and negative pre- and postmenopausal female subjects (p < 0.001 is presented by the area under the curve (AUC plotted via ROC analysis. The determined FRAX score cut-off value by this analysis was 6%, with estimated sensitivity and specificity of 95% and 75.9%, respectively. Conclusion. Pre- and postmenopausal women with subclinical hyperthyroidism have higher FRAX® scores and thus

  16. 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.

  17. CONSIDERATIONS IN RISK COMMUNICATION: A DIGEST OF RISK COMMUNICATION AS A RISK MANAGEMENT TOOL

    Science.gov (United States)

    Risk communication is the process of informing people about hazards. Like all communication, communicating risk is a two-way exchange in which you inform the target community about possible hazards, but also gather information about those affected by the risk. The purpose of risk...

  18. Predicting readmission risk of patients with diabetes hospitalized for cardiovascular disease: a retrospective cohort study.

    Science.gov (United States)

    Rubin, Daniel J; Golden, Sherita Hill; McDonnell, Marie E; Zhao, Huaqing

    2017-08-01

    To develop and validate a tool that predicts 30d readmission risk of patients with diabetes hospitalized for cardiovascular disease (CVD), the Diabetes Early Readmission Risk Indicator-CVD (DERRI-CVD™). A cohort of 8189 discharges was retrospectively selected from electronic records of adult patients with diabetes hospitalized for CVD. Discharges of 60% of the patients (n=4950) were randomly selected as a training sample and the remaining 40% (n=3219) were the validation sample. Statistically significant predictors of all-cause 30d readmission risk were identified by multivariable logistic regression modeling: education level, employment status, living within 5miles of the hospital, pre-admission diabetes therapy, macrovascular complications, admission serum creatinine and albumin levels, having a hospital discharge within 90days pre-admission, and a psychiatric diagnosis. Model discrimination and calibration were good (C-statistic 0.71). Performance in the validation sample was comparable. Predicted 30d readmission risk was similar in the training and validation samples (38.6% and 35.1% in the highest quintiles). The DERRI-CVD™ may be a valid tool to predict all-cause 30d readmission risk of patients with diabetes hospitalized for CVD. Identifying high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. How well does the Post-fire Erosion Risk Management Tool (ERMiT) really work?

    Science.gov (United States)

    Robichaud, Peter; Elliot, William; Lewis, Sarah; Miller, Mary Ellen

    2016-04-01

    The decision of where, when, and how to apply the most effective postfire erosion mitigation treatments requires land managers to assess the risk of damaging runoff and erosion events occurring after a fire. The Erosion Risk Management Tool (ERMiT) was developed to assist post fire assessment teams identify high erosion risk areas and effectiveness of various mitigation treatments to reduce that risk. ERMiT is a web-based application that uses the Water Erosion Prediction Project (WEPP) technology to estimate erosion, in probabilistic terms, on burned and recovering forest, range, and chaparral lands with and without the application of mitigation treatments. User inputs are processed by ERMiT to combine rain event variability with spatial and temporal variabilities of hillslope burn severity and soil properties which are then used as WEPP inputs. Since 2007, the model has been used in making hundreds of land management decisions in the US and elsewhere. We use eight published field study sites in the Western US to compare ERMiT predictions to observed hillslope erosion rates. Most sites experience only a few rainfall events that produced runoff and sediment except for a California site with a Mediterranean climate. When hillslope erosion occurred, significant correlations occurred between the observed hillslope erosion and those predicted by ERMiT. Significant correlation occurred for most mitigation treatments as well as the five recovery years. These model validation results suggest reasonable estimates of probabilistic post-fire hillslope sediment delivery when compared to observation.

  20. Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease.

    Science.gov (United States)

    Thyvalikakath, Thankam P; Padman, Rema; Vyawahare, Karnali; Darade, Pratiksha; Paranjape, Rhucha

    2015-01-01

    Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care.

  1. 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...

  2. PROFITABILITY RATIO AS A TOOL FOR BANKRUPTCY PREDICTION

    Directory of Open Access Journals (Sweden)

    Daniel BRÎNDESCU – OLARIU

    2016-07-01

    Full Text Available The current study evaluates the potential of the profitability ratio in predicting corporate bankruptcy. The research is focused on Romanian companies, with the targeted event being represented by the manifestation of bankruptcy 2 years after the date of the financial statements of reference. All tests were conducted over 2 paired samples of 1176 Romanian companies. The methodology employed in evaluating the potential of the profitability ratio was based on the Area Under the ROC Curve (0.663 and the general accuracy ensured by the ratio (62.6% out-of-sample accuracy. The results confirm the practical utility of the profitability ratio in the prediction of bankruptcy and thus validate the need for further research focused on developing a methodology of analysis.

  3. SOLVENCY RATIO AS A TOOL FOR BANKRUPTCY PREDICTION

    Directory of Open Access Journals (Sweden)

    Daniel BRÎNDESCU–OLARIU

    2016-08-01

    Full Text Available The current study evaluates the potential of the solvency ratio in predicting corporate bankruptcy. The research is focused on Romania and, in particular, on Timis County. The interest for the solvency ratio was based on the recommendations of the scientific literature, as well as on the availability of information concerning its values to all stakeholders. The event on which the research was focused was represented by the manifestation of bankruptcy 2 years after the date of the financial statements of reference. All tests were performed over 2 paired samples of 1176 companies in total. The methodology employed in evaluating the potential of the solvency ratio was based on the Area Under the ROC Curve (0.646 and the general accuracy ensured by the ratio (64.5% out-of-sample accuracy. The results confirm the practical utility of the solvency ratio in the prediction of bankruptcy.

  4. PROFITABILITY RATIO AS A TOOL FOR BANKRUPTCY PREDICTION

    OpenAIRE

    Daniel BRÎNDESCU – OLARIU

    2016-01-01

    The current study evaluates the potential of the profitability ratio in predicting corporate bankruptcy. The research is focused on Romanian companies, with the targeted event being represented by the manifestation of bankruptcy 2 years after the date of the financial statements of reference. All tests were conducted over 2 paired samples of 1176 Romanian companies. The methodology employed in evaluating the potential of the profitability ratio was based on the Area Under the ROC Curve (0.663...

  5. 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.

  6. Prediction of boiling points of organic compounds by QSPR tools.

    Science.gov (United States)

    Dai, Yi-min; Zhu, Zhi-ping; Cao, Zhong; Zhang, Yue-fei; Zeng, Ju-lan; Li, Xun

    2013-07-01

    The novel electro-negativity topological descriptors of YC, WC were derived from molecular structure by equilibrium electro-negativity of atom and relative bond length of molecule. The quantitative structure-property relationships (QSPR) between descriptors of YC, WC as well as path number parameter P3 and the normal boiling points of 80 alkanes, 65 unsaturated hydrocarbons and 70 alcohols were obtained separately. The high-quality prediction models were evidenced by coefficient of determination (R(2)), the standard error (S), average absolute errors (AAE) and predictive parameters (Qext(2),RCV(2),Rm(2)). According to the regression equations, the influences of the length of carbon backbone, the size, the degree of branching of a molecule and the role of functional groups on the normal boiling point were analyzed. Comparison results with reference models demonstrated that novel topological descriptors based on the equilibrium electro-negativity of atom and the relative bond length were useful molecular descriptors for predicting the normal boiling points of organic compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

  7. Predictive value of general movements' quality in low-risk infants for minor neurological dysfunction and behavioural problems at preschool age

    NARCIS (Netherlands)

    Bennema, Anne N; Schendelaar, Pamela; Seggers, Jorien; Haadsma, Maaike L; Heineman, Maas Jan; Hadders-Algra, Mijna

    Background: General movement (GM) assessment is a well-established tool to predict cerebral palsy in high-risk infants. Little is known on the predictive value of GM assessment in low-risk populations. Aims: To assess the predictive value of GM quality in early infancy for the development of the

  8. Pediatric Eating Assessment Tool-10 as an indicator to predict aspiration in children with esophageal atresia.

    Science.gov (United States)

    Soyer, Tutku; Yalcin, Sule; Arslan, Selen Serel; Demir, Numan; Tanyel, Feridun Cahit

    2017-10-01

    Airway aspiration is a common problem in children with esophageal atresia (EA). Pediatric Eating Assessment Tool-10 (pEAT-10) is a self-administered questionnaire to evaluate dysphagia symptoms in children. A prospective study was performed to evaluate the validity of pEAT-10 to predict aspiration in children with EA. Patients with EA were evaluated for age, sex, type of atresia, presence of associated anomalies, type of esophageal repair, time of definitive treatment, and the beginning of oral feeding. Penetration-aspiration score (PAS) was evaluated with videofluoroscopy (VFS) and parents were surveyed for pEAT-10, dysphagia score (DS) and functional oral intake scale (FOIS). PAS scores greater than 7 were considered as risk of aspiration. EAT-10 values greater than 3 were assessed as abnormal. Higher DS scores shows dysphagia whereas higher FOIS shows better feeding abilities. Forty patients were included. Children with PAS greater than 7 were assessed as PAS+ group, and scores less than 7 were constituted as PAS- group. Demographic features and results of surgical treatments showed no difference between groups (p>0.05). The median values of PAS, pEAT-10 and DS scores were significantly higher in PAS+ group when compared to PAS- group (p<0.05). The sensitivity and specificity of pEAT-10 to predict aspiration were 88% and 77%, and the positive and negative predictive values were 22% and 11%, respectively. Type-C cases had better pEAT-10 and FOIS scores with respect to type-A cases, and both scores were statistically more reliable in primary repair than delayed repair (p<0.05). Among the postoperative complications, only leakage had impact on DS, pEAT-10, PAS and FOIS scores (p<0.05). The pEAT-10 is a valid, simple and reliable tool to predict aspiration in children. Patients with higher pEAT-10 scores should undergo detailed evaluation of deglutitive functions and assessment of risks of aspiration to improve safer feeding strategies. Level II (Development of

  9. 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 ...

  10. Visual Impairment/lntracranial Pressure Risk Clinical Care Data Tools

    Science.gov (United States)

    Van Baalen, Mary; Mason, Sara S.; Taiym, Wafa; Wear, Mary L.; Moynihan, Shannan; Alexander, David; Hart, Steve; Tarver, William

    2014-01-01

    Prior to 2010, several ISS crewmembers returned from spaceflight with changes to their vision, ranging from a mild hyperopic shift to frank disc edema. As a result, NASA expanded clinical vision testing to include more comprehensive medical imaging, including Optical Coherence Tomography and 3 Tesla Brain and Orbit MRIs. The Space and Clinical Operations (SCO) Division developed a clinical practice guideline that classified individuals based on their symptoms and diagnoses to facilitate clinical care. For the purposes of clinical surveillance, this classification was applied retrospectively to all crewmembers who had sufficient testing for classification. This classification is also a tool that has been leveraged for researchers to identify potential risk factors. In March 2014, driven in part by a more comprehensive understanding of the imaging data and increased imaging capability on orbit, the SCO Division revised their clinical care guidance to outline in-flight care and increase post-flight follow up. The new clinical guidance does not include a classification scheme

  11. Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States

    Science.gov (United States)

    Magarey, Roger; Newton, Leslie; Hong, Seung C.; Takeuchi, Yu; Christie, Dave; Jarnevich, Catherine S.; Kohl, Lisa; Damus, Martin; Higgins, Steven I.; Miller, Leah; Castro, Karen; West, Amanda; Hastings, John; Cook, Gericke; Kartesz, John; Koop, Anthony

    2018-01-01

    This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions.

  12. 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...

  13. 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...

  14. 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.

  15. Augmenting Predictive Modeling Tools with Clinical Insights for Care Coordination Program Design and Implementation.

    Science.gov (United States)

    Johnson, Tracy L; Brewer, Daniel; Estacio, Raymond; Vlasimsky, Tara; Durfee, Michael J; Thompson, Kathy R; Everhart, Rachel M; Rinehart, Deborath J; Batal, Holly

    2015-01-01

    The Center for Medicare and Medicaid Innovation (CMMI) awarded Denver Health's (DH) integrated, safety net health care system $19.8 million to implement a "population health" approach into the delivery of primary care. This major practice transformation builds on the Patient Centered Medical Home (PCMH) and Wagner's Chronic Care Model (CCM) to achieve the "Triple Aim": improved health for populations, care to individuals, and lower per capita costs. This paper presents a case study of how DH integrated published predictive models and front-line clinical judgment to implement a clinically actionable, risk stratification of patients. This population segmentation approach was used to deploy enhanced care team staff resources and to tailor care-management services to patient need, especially for patients at high risk of avoidable hospitalization. Developing, implementing, and gaining clinical acceptance of the Health Information Technology (HIT) solution for patient risk stratification was a major grant objective. In addition to describing the Information Technology (IT) solution itself, we focus on the leadership and organizational processes that facilitated its multidisciplinary development and ongoing iterative refinement, including the following: team composition, target population definition, algorithm rule development, performance assessment, and clinical-workflow optimization. We provide examples of how dynamic business intelligence tools facilitated clinical accessibility for program design decisions by enabling real-time data views from a population perspective down to patient-specific variables. We conclude that population segmentation approaches that integrate clinical perspectives with predictive modeling results can better identify high opportunity patients amenable to medical home-based, enhanced care team interventions.

  16. Risk monitor-a tool for computer aided risk assessment for NPPs

    International Nuclear Information System (INIS)

    Vinod, Gopika; Saraf, R.K.; Babar, A.K.; Kushwaha, H.S.; Hadap, Nikhil

    2001-01-01

    Considerable changes occur in components status and system design and subsequent operation due to changes in plant configuration and their operating procedures. These changes are organised because some components are randomly down and other can be planned for test, maintenance and repair. This results in a fluctuation of risk level over operating time, which is termed as risk profile. Probabilistic Safety Assessment (PSA) is an analytical technique for assessing the risk by integrating diverse aspects of design and operation of a Nuclear Power Plant. Risk can be defined as the product of the probability of an accident and the consequences from that accident. Reactor Safety Division of BARC has developed PC based tool, which can assess the risk profile. This package can be used to optimise the operation in Nuclear Power Plants with respect to a minimum risk level over the operating time, and is termed as Risk Monitor. Risk Monitor is user friendly and can re-evaluate core damage frequency for changes in component status, test interval, initiating event frequency etc. Plant restoration advice, when the plant is in high risk configuration, current status of all plant equipment, and equipment prioritization are also provided by the package. (author)

  17. JV Task 5 - Predictive Coal Quality Effects Screening Tool (PCQUEST)

    Energy Technology Data Exchange (ETDEWEB)

    Jason Laumb; Joshua Stanislowski

    2007-07-01

    PCQUEST, a package of eight predictive indices, was developed with U.S. Department of Energy (DOE) support by the Energy & Environmental Research Center to predict fireside performance in coal-fired utility boilers more reliably than traditional indices. Since the development of PCQUEST, the need has arisen for additional improvement, validation, and enhancement of the model, as well as to incorporate additional fuel types into the program database. PCQUEST was developed using combustion inorganic transformation theory from previous projects and from empirical data derived from laboratory experiments and coal boiler field observations. The goal of this joint venture project between commercial industry clients and DOE is to further enhance PCQUEST and improve its utility for a variety of new fuels and systems. Specific objectives include initiating joint venture projects with utilities, boiler vendors, and coal companies that involve real-world situations and needs in order to strategically improve algorithms and input-output functions of PCQUEST, as well as to provide technology transfer to the industrial sector. The main body of this report provides a short summary of the projects that were closed from February 1999 through July 2007. All of the reports sent to the commercial clients can be found in the appendix.

  18. Cluster analysis as a prediction tool for pregnancy outcomes.

    Science.gov (United States)

    Banjari, Ines; Kenjerić, Daniela; Šolić, Krešimir; Mandić, Milena L

    2015-03-01

    Considering specific physiology changes during gestation and thinking of pregnancy as a "critical window", classification of pregnant women at early pregnancy can be considered as crucial. The paper demonstrates the use of a method based on an approach from intelligent data mining, cluster analysis. Cluster analysis method is a statistical method which makes possible to group individuals based on sets of identifying variables. The method was chosen in order to determine possibility for classification of pregnant women at early pregnancy to analyze unknown correlations between different variables so that the certain outcomes could be predicted. 222 pregnant women from two general obstetric offices' were recruited. The main orient was set on characteristics of these pregnant women: their age, pre-pregnancy body mass index (BMI) and haemoglobin value. Cluster analysis gained a 94.1% classification accuracy rate with three branch- es or groups of pregnant women showing statistically significant correlations with pregnancy outcomes. The results are showing that pregnant women both of older age and higher pre-pregnancy BMI have a significantly higher incidence of delivering baby of higher birth weight but they gain significantly less weight during pregnancy. Their babies are also longer, and these women have significantly higher probability for complications during pregnancy (gestosis) and higher probability of induced or caesarean delivery. We can conclude that the cluster analysis method can appropriately classify pregnant women at early pregnancy to predict certain outcomes.

  19. Social dataset analysis and mapping tools for Risk Perception: resilience, people preparation and communication tools

    Science.gov (United States)

    Peters-Guarin, Graciela; Garcia, Carolina; Frigerio, Simone

    2010-05-01

    Perception has been identified as resource and part of the resilience of a community to disasters. Risk perception, if present, may determine the potential damage a household or community experience. Different levels of risk perception and preparedness can influence directly people's susceptibility and the way they might react in case of an emergency caused by natural hazards. In spite of the profuse literature about risk perception, works to spatially portray this feature are really scarce. The spatial relationship to danger or hazard is being recognised as an important factor of the risk equation; it can be used as a powerful tool either for better knowledge or for operational reasons (e.g. management of preventive information). Risk perception and people's awareness when displayed in a spatial format can be useful for several actors in the risk management arena. Local authorities and civil protection can better address educational activities to increase the preparation of particularly vulnerable groups of clusters of households within a community. It can also be useful for the emergency personal in order to optimally direct the actions in case of an emergency. In the framework of the Marie Curie Research Project, a Community Based Early Warning System (CBEWS) it's been developed in the Mountain Community Valtellina of Tirano, northern Italy. This community has been continuously exposed to different mass movements and floods, in particular, a large event in 1987 which affected a large portion of the valley and left 58 dead. The actual emergency plan for the study area is composed by a real time, highly detailed, decision support system. This emergency plan contains detailed instructions for the rapid deployment of civil protection and other emergency personal in case of emergency, for risk scenarios previously defined. Especially in case of a large event, where timely reaction is crucial for reducing casualties, it is important for those in charge of emergency

  20. Predicting Sepsis Risk Using the "Sniffer" Algorithm in the Electronic Medical Record.

    Science.gov (United States)

    Olenick, Evelyn M; Zimbro, Kathie S; DʼLima, Gabrielle M; Ver Schneider, Patricia; Jones, Danielle

    The Sepsis "Sniffer" Algorithm (SSA) has merit as a digital sepsis alert but should be considered an adjunct to versus an alternative for the Nurse Screening Tool (NST), given lower specificity and positive predictive value. The SSA reduced the risk of incorrectly categorizing patients at low risk for sepsis, detected sepsis high risk in half the time, and reduced redundant NST screens by 70% and manual screening hours by 64% to 72%. Preserving nurse hours expended on manual sepsis alerts may translate into time directed toward other patient priorities.

  1. Risk predicting of macropore flow using pedotransfer functions, textural maps and modeling

    DEFF Research Database (Denmark)

    Iversen, Bo Vangsø; Børgesen, Christen Duus; Lægdsmand, Mette

    2011-01-01

    of this study were first to develop pedotransfer functions (PTFs) predicting near-saturated [k(−1)] and saturated (Ks) hydraulic conductivity using simple soil parameters as predictors and second to use this information and a newly developed rasterbased soil property map of Denmark to identify risk areas...... modeling were used to construct a new map dividing Denmark into risk categories for macropore flow. This map can be combined with other tools to identify areas where there is a high risk of contaminants leaching out of the root zone....

  2. Physics-based Modeling Tools for Life Prediction and Durability Assessment of Advanced Materials, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The technical objectives of this program are: (1) to develop a set of physics-based modeling tools to predict the initiation of hot corrosion and to address pit and...

  3. An Evaluation of Growth Models as Predictive Tools for Estimates at Completion (EAC)

    National Research Council Canada - National Science Library

    Trahan, Elizabeth N

    2009-01-01

    ...) as the Estimates at Completion (EAC). Our research evaluates the prospect of nonlinear growth modeling as an alternative to the current predictive tools used for calculating EAC, such as the Cost Performance Index (CPI...

  4. Predictive Maintenance--An Effective Money Saving Tool Being Applied in Industry Today.

    Science.gov (United States)

    Smyth, Tom

    2000-01-01

    Looks at preventive/predictive maintenance as it is used in industry. Discusses core preventive maintenance tools that must be understood to prepare students. Includes a list of websites related to the topic. (JOW)

  5. A two-question tool to assess the risk of repeated falls in the elderly.

    Directory of Open Access Journals (Sweden)

    Alejandro Rodríguez-Molinero

    Full Text Available Older adults' perception of their own risk of fall has never been included into screening tools. The goal of this study was to evaluate the predictive validity of questions on subjects' self-perception of their own risk of fall.This prospective study was conducted on a probabilistic sample of 772 Spanish community-dwelling older adults, who were followed-up for a one year period. At a baseline visit, subjects were asked about their recent history of falls (question 1: "Have you fallen in the last 6 months?", as well as on their perception of their own risk of fall by using two questions (question 2: "Do you think you may fall in the next few months?" possible answers: yes/no; question 3: "What is the probability that you fall in the next few months?" possible answers: low/intermediate/high. The follow-up consisted of quarterly telephone calls, where the number of falls occurred in that period was recorded.A short questionnaire built with questions 1 and 3 showed 70% sensitivity (95% CI: 56%-84%, 72% specificity (95% CI: 68%-76% and 0.74 area under the ROC curve (95% CI: 0.66-0.82 for prediction of repeated falls in the subsequent year.The estimation of one's own risk of fall has predictive validity for the occurrence of repeated falls in older adults. A short questionnaire including a question on perception of one's own risk of fall and a question on the recent history of falls had good predictive validity.

  6. A two-question tool to assess the risk of repeated falls in the elderly.

    Science.gov (United States)

    Rodríguez-Molinero, Alejandro; Gálvez-Barrón, César; Narvaiza, Leire; Miñarro, Antonio; Ruiz, Jorge; Valldosera, Esther; Gonzalo, Natalia; Ng, Thalia; Sanguino, María Jesús; Yuste, Antonio

    2017-01-01

    Older adults' perception of their own risk of fall has never been included into screening tools. The goal of this study was to evaluate the predictive validity of questions on subjects' self-perception of their own risk of fall. This prospective study was conducted on a probabilistic sample of 772 Spanish community-dwelling older adults, who were followed-up for a one year period. At a baseline visit, subjects were asked about their recent history of falls (question 1: "Have you fallen in the last 6 months?"), as well as on their perception of their own risk of fall by using two questions (question 2: "Do you think you may fall in the next few months?" possible answers: yes/no; question 3: "What is the probability that you fall in the next few months?" possible answers: low/intermediate/high). The follow-up consisted of quarterly telephone calls, where the number of falls occurred in that period was recorded. A short questionnaire built with questions 1 and 3 showed 70% sensitivity (95% CI: 56%-84%), 72% specificity (95% CI: 68%-76%) and 0.74 area under the ROC curve (95% CI: 0.66-0.82) for prediction of repeated falls in the subsequent year. The estimation of one's own risk of fall has predictive validity for the occurrence of repeated falls in older adults. A short questionnaire including a question on perception of one's own risk of fall and a question on the recent history of falls had good predictive validity.

  7. Transcultural adaptation of the Johns Hopkins Fall Risk Assessment Tool.

    Science.gov (United States)

    Martinez, Maria Carmen; Iwamoto, Viviane Ernesto; Latorre, Maria do Rosário Dias de Oliveira; Noronha, Adriana Moreira; Oliveira, Ana Paula de Sousa; Cardoso, Carlos Eduardo Alves; Marques, Ifigenia Augusta Braga; Vendramim, Patrícia; Lopes, Paula Cristina; Sant'Ana, Thais Helena Saes de

    2016-08-29

    to perform the transcultural adaptation and content validity analysis of the Johns Hopkins Fall Risk Assessment Tool to assess both fall risk and fall-related injury risk for hospitalized elderly in Brazil. the transcultural adaptation consisted of translating the scale to Portuguese (Brazil), back-translating it into its language of origin, establishing a consensus version, and having an expert committee verify its transcultural equivalence. Content assessment was conducted by a committee of judges, ending with the calculation of the items and scales' content validity index. Nurses tested the tool. the scale's translated version went through two evaluation rounds by the judges, based on which, the items with unsatisfactory performance were changed. The content validity index for the items was ≥80.0% and the global index 97.1%. The experimental application showed the scale is user-friendly. the scale presents valid content for the assessment of fall risk and risk of fall-related injuries and is easy to use, with the potential to contribute to the proper identification of risks and the establishment of care actions. realizar a adaptação transcultural para uso no Brasil e a avaliação da validade de conteúdo da Johns Hopkins Fall Risk Assessment Tool para avaliação de risco de quedas e de danos por quedas em pacientes adultos hospitalizados. adaptação transcultural consistiu na tradução da escala para a língua portuguesa (Brasil), retrotradução para a língua de origem, versão de consenso e análise da equivalência transcultural por um comitê de especialistas. A avaliação do conteúdo foi realizada por meio de um comitê de juízes, finalizando com o cálculo do índice de validade de conteúdo dos itens e da escala. Foi realizada a aplicação experimental do instrumento por enfermeiros. a versão traduzida da escala passou por duas rodadas de avaliação pelos juízes, a partir das quais os itens com desempenho insatisfatório foram modificados

  8. A clinical tool for predicting survival in ALS.

    Science.gov (United States)

    Knibb, Jonathan A; Keren, Noa; Kulka, Anna; Leigh, P Nigel; Martin, Sarah; Shaw, Christopher E; Tsuda, Miho; Al-Chalabi, Ammar

    2016-12-01

    Amyotrophic lateral sclerosis (ALS) is a progressive and usually fatal neurodegenerative disease. Survival from diagnosis varies considerably. Several prognostic factors are known, including site of onset (bulbar or limb), age at symptom onset, delay from onset to diagnosis and the use of riluzole and non-invasive ventilation (NIV). Clinicians and patients would benefit from a practical way of using these factors to provide an individualised prognosis. 575 consecutive patients with incident ALS from a population-based registry in South-East England register for ALS (SEALS) were studied. Their survival was modelled as a two-step process: the time from diagnosis to respiratory muscle involvement, followed by the time from respiratory involvement to death. The effects of predictor variables were assessed separately for each time interval. Younger age at symptom onset, longer delay from onset to diagnosis and riluzole use were associated with slower progression to respiratory involvement, and NIV use was associated with lower mortality after respiratory involvement, each with a clinically significant effect size. Riluzole may have a greater effect in younger patients and those with longer delay to diagnosis. A patient's survival time has a roughly 50% chance of falling between half and twice the predicted median. A simple and clinically applicable graphical method of predicting an individual patient's survival from diagnosis is presented. The model should be validated in an independent cohort, and extended to include other important prognostic factors. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  9. Degree of Agreement between Cardiovascular Risk Stratification Tools.

    Science.gov (United States)

    Garcia, Guilherme Thomé; Stamm, Ana Maria Nunes de Faria; Rosa, Ariel Córdova; Marasciulo, Antônio Carlos; Marasciulo, Rodrigo Conill; Battistella, Cristian; Remor, Alexandre Augusto de Costa

    2017-05-01

    Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in Brazil, and primary prevention care may be guided by risk stratification tools. The Framingham (FRS) and QRISK-2 (QRS) risk scores estimate 10-year overall cardiovascular risk in asymptomatic individuals, but the instrument of choice may lead to different therapeutic strategies. To evaluate the degree of agreement between FRS and QRS in 10-year overall cardiovascular risk stratification in disease-free individuals. Cross-sectional, observational, descriptive and analytical study in a convenience sample of 74 individuals attending the outpatient care service of a university hospital in Brazil between January 2014 and January 2015. After application of FRS and QRS, patients were classified in low/moderate risk (Brasil, e a prevenção primária pode ser direcionada com ferramentas que estratificam o risco. Os escores de Framingham (ERF) e QRISK-2 (ERQ) estimam o risco cardiovascular (RCV) global em 10 anos em indivíduos assintomáticos, mas a escolha do instrumento pode implicar em terapêuticas distintas. Observar o grau de concordância entre o ERF e o ERQ, na estratificação do risco cardiovascular global em 10 anos, nos indivíduos livres da doença. Estudo transversal, observacional, descritivo e analítico, com uma amostra de conveniência de 74 indivíduos, atendidos em um ambulatório de ensino de um hospital universitário brasileiro, no sul do país, de janeiro de 2014 a janeiro de 2015. O ERF e o ERQ foram aplicados nos pacientes, que foram classificados em baixo/moderado (superior no ERF que no ERQ (33,7% vs 21,6%), sendo identificado efeito sinérgico do gênero masculino com hipertensão arterial sistêmica nas duas ferramentas, e com faixa etária geriátrica no ERQ (p < 0,05) nesse estrato de risco. O índice de concordância Kappa entre os dois escores foi igual a 0,519 (IC95% = 0,386-0,652; p < 0,001). Houve concordância moderada entre o ERF e o ERQ, na estimativa de

  10. Predictive tool of energy performance of cold storage in agrifood industries: The Portuguese case study

    International Nuclear Information System (INIS)

    Nunes, José; Neves, Diogo; Gaspar, Pedro D.; Silva, Pedro D.; Andrade, Luís P.

    2014-01-01

    Highlights: • A predictive tool for assessment of the energy performance in agrifood industries that use cold storage is developed. • The correlations used by the predictive tool result from the greatest number of data sets collected to date in Portugal. • Strong relationships between raw material, energy consumption and volume of cold stores were established. • Case studies were analyzed that demonstrate the applicability of the tool. • The tool results are useful in the decision-making process of practice measures for the improvement of energy efficiency. - Abstract: Food processing and conservation represent decisive factors for the sustainability of the planet given the significant growth of the world population in the last decades. Therefore, the cooling process during the manufacture and/or storage of food products has been subject of study and improvement in order to ensure the food supply with good quality and safety. A predictive tool for assessment of the energy performance in agrifood industries that use cold storage is developed in order to contribute to the improvement of the energy efficiency of this industry. The predictive tool is based on a set of characteristic correlated parameters: amount of raw material annually processed, annual energy consumption and volume of cold rooms. Case studies of application of the predictive tool consider industries in the meat sector, specifically slaughterhouses. The results obtained help on the decision-making of practice measures for improvement of the energy efficiency in this industry

  11. Soil and Water Assessment Tool model predictions of annual maximum pesticide concentrations in high vulnerability watersheds.

    Science.gov (United States)

    Winchell, Michael F; Peranginangin, Natalia; Srinivasan, Raghavan; Chen, Wenlin

    2018-05-01

    Recent national regulatory assessments of potential pesticide exposure of threatened and endangered species in aquatic habitats have led to increased need for watershed-scale predictions of pesticide concentrations in flowing water bodies. This study was conducted to assess the ability of the uncalibrated Soil and Water Assessment Tool (SWAT) to predict annual maximum pesticide concentrations in the flowing water bodies of highly vulnerable small- to medium-sized watersheds. The SWAT was applied to 27 watersheds, largely within the midwest corn belt of the United States, ranging from 20 to 386 km 2 , and evaluated using consistent input data sets and an uncalibrated parameterization approach. The watersheds were selected from the Atrazine Ecological Exposure Monitoring Program and the Heidelberg Tributary Loading Program, both of which contain high temporal resolution atrazine sampling data from watersheds with exceptionally high vulnerability to atrazine exposure. The model performance was assessed based upon predictions of annual maximum atrazine concentrations in 1-d and 60-d durations, predictions critical in pesticide-threatened and endangered species risk assessments when evaluating potential acute and chronic exposure to aquatic organisms. The simulation results showed that for nearly half of the watersheds simulated, the uncalibrated SWAT model was able to predict annual maximum pesticide concentrations within a narrow range of uncertainty resulting from atrazine application timing patterns. An uncalibrated model's predictive performance is essential for the assessment of pesticide exposure in flowing water bodies, the majority of which have insufficient monitoring data for direct calibration, even in data-rich countries. In situations in which SWAT over- or underpredicted the annual maximum concentrations, the magnitude of the over- or underprediction was commonly less than a factor of 2, indicating that the model and uncalibrated parameterization

  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. Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

    Science.gov (United States)

    Zabor, Emily C; Coit, Daniel; Gershenwald, Jeffrey E; McMasters, Kelly M; Michaelson, James S; Stromberg, Arnold J; Panageas, Katherine S

    2018-02-22

    Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease. To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index. In this demonstration project, we found important differences across the three models that led to variability in individual patients' predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models. This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool's limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians.

  14. 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.

  15. DEEP--a tool for differential expression effector prediction.

    Science.gov (United States)

    Degenhardt, Jost; Haubrock, Martin; Dönitz, Jürgen; Wingender, Edgar; Crass, Torsten

    2007-07-01

    High-throughput methods for measuring transcript abundance, like SAGE or microarrays, are widely used for determining differences in gene expression between different tissue types, dignities (normal/malignant) or time points. Further analysis of such data frequently aims at the identification of gene interaction networks that form the causal basis for the observed properties of the systems under examination. To this end, it is usually not sufficient to rely on the measured gene expression levels alone; rather, additional biological knowledge has to be taken into account in order to generate useful hypotheses about the molecular mechanism leading to the realization of a certain phenotype. We present a method that combines gene expression data with biological expert knowledge on molecular interaction networks, as described by the TRANSPATH database on signal transduction, to predict additional--and not necessarily differentially expressed--genes or gene products which might participate in processes specific for either of the examined tissues or conditions. In a first step, significance values for over-expression in tissue/condition A or B are assigned to all genes in the expression data set. Genes with a significance value exceeding a certain threshold are used as starting points for the reconstruction of a graph with signaling components as nodes and signaling events as edges. In a subsequent graph traversal process, again starting from the previously identified differentially expressed genes, all encountered nodes 'inherit' all their starting nodes' significance values. In a final step, the graph is visualized, the nodes being colored according to a weighted average of their inherited significance values. Each node's, or sub-network's, predominant color, ranging from green (significant for tissue/condition A) over yellow (not significant for either tissue/condition) to red (significant for tissue/condition B), thus gives an immediate visual clue on which molecules

  16. Surging Seas Risk Finder: A Tool for Local-Scale Flood Risk Assessments in Coastal Cities

    Science.gov (United States)

    Kulp, S. A.; Strauss, B.

    2015-12-01

    Local decision makers in coastal cities require accurate, accessible, and thorough assessments of flood exposure risk within their individual municipality, in their efforts to mitigate against damage due to future sea level rise. To fill this need, we have developed Climate Central's Surging Seas Risk Finder, an interactive data toolkit which presents our sea level rise and storm surge analysis for every coastal town, city, county, and state within the USA. Using this tool, policy makers can easily zoom in on their local place of interest to receive a detailed flood risk assessment, which synthesizes a wide range of features including total population, socially vulnerable population, housing, property value, road miles, power plants, schools, hospitals, and many other critical facilities. Risk Finder can also be used to identify specific points of interest in danger of exposure at different flood levels. Additionally, this tool provides localized storm surge probabilities and sea level rise projections at tidal gauges along the coast, so that users can quickly understand the risk of flooding in their area over the coming decades.

  17. A systematic review on popularity, application and characteristics of protein secondary structure prediction tools.

    Science.gov (United States)

    Kashani-Amin, Elaheh; Tabatabaei-Malazy, Ozra; Sakhteman, Amirhossein; Larijani, Bagher; Ebrahim-Habibi, Azadeh

    2018-02-27

    Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple secondary structure prediction (SSP) options is challenging. The current study is an insight onto currently favored methods and tools, within various contexts. A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of 209 studies were finally found eligible to extract data. Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating a SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. This study provides a comprehensive insight about the recent usage of SSP tools which could be helpful for selecting a proper tool's choice. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  18. The development of a practical tool for risk assessment of manual work – the HAT-tool

    NARCIS (Netherlands)

    Kraker, H. de; Douwes, M.

    2008-01-01

    For the Dutch Ministry of Social Affairs and Employment we developed a tool to assess the risks of developing complaints of the arm, neck or shoulders during manual work. The tool was developed for every type of organization and is easy to use, does not require measurements other than time and can

  19. Predictive validity of the post-enrolment English language assessment tool for commencing undergraduate nursing students.

    Science.gov (United States)

    Glew, Paul J; Hillege, Sharon P; Salamonson, Yenna; Dixon, Kathleen; Good, Anthony; Lombardo, Lien

    2015-12-01

    Nursing students with English as an additional language (EAL) may underperform academically. The post-enrolment English language assessment (PELA) is used in literacy support, but its predictive validity in identifying those at risk of underperformance remains unknown. To validate a PELA, as a predictor of academic performance. Prospective survey design. The study was conducted at a university located in culturally and linguistically diverse areas of western Sydney, Australia. Commencing undergraduate nursing students who were Australian-born (n=1323, 49.6%) and born outside of Australia (n=1346, 50.4%) were recruited for this study. The 2669 (67% of 3957) participants provided consent and completed a first year nursing unit that focussed on developing literacy skills. Between 2010 and 2013, commencing students completed the PELA and English language acculturation scale (ELAS), a previously validated instrument. The grading levels of the PELA tool were: Level 1 (proficient), Level 2 (borderline), and Level 3 (poor, and requiring additional support). Participants with a PELA Level 2 or 3 were more likely to be: a) non-Australian-born (χ(2): 520.6, df: 2, pstudent (χ(2): 225.6, df: 2, pstudents who are at risk of academic underachievement. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

  20. Use of a functional movement screening tool to determine injury risk in female collegiate athletes.

    Science.gov (United States)

    Chorba, Rita S; Chorba, David J; Bouillon, Lucinda E; Overmyer, Corey A; Landis, James A

    2010-06-01

    Athletes often utilize compensatory movement strategies to achieve high performance. However, these inefficient movement strategies may reinforce poor biomechanical movement patterns during typical activities, resulting in injury. This study sought to determine if compensatory movement patterns predispose female collegiate athletes to injury, and if a functional movement screening (FMS™) tool can be used to predict injuries in this population. Scores on the FMS™, comprised of seven movement tests, were calculated for 38 NCAA Division II female collegiate athletes before the start of their respective fall and winter sport seasons (soccer, volleyball, and basketball). Seven athletes reported a previous history of anterior cruciate ligament reconstruction (ACLR). Injuries sustained while participating in sport activities were recorded throughout the seasons. The mean FMS™ score and standard deviation for all subjects was 14.3±1.77 (maximum score of 21). Eighteen injuries (17 lower extremity, 1 lower back) were recorded during this study. A score of 14/21 or less was significantly associated with injury (P=0.0496). Sixty-nine percent of athletes scoring 14 or less sustained an injury. Odds ratios were 3.85 with inclusion of all subjects, and 4.58 with exclusion of ACLR subjects. Sensitivity and specificity were 0.58 and 0.74 for all subjects, respectively. A significant correlation was found between low-scoring athletes and injury (P=0.0214, r=0.76). A score of 14 or less on the FMS™ tool resulted in a 4-fold increase in risk of lower extremity injury in female collegiate athletes participating in fall and winter sports. The screening tool was able to predict injury in female athletes without a history of major musculoskeletal injury such as ACLR. Compensatory fundamental movement patterns can increase the risk of injury in female collegiate athletes, and can be identified by using a functional movement screening tool.

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

    Directory of Open Access Journals (Sweden)

    Stephen Stonelake

    2015-09-01

    Conclusions: 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.

  2. Fracture risk assessed by Fracture Risk Assessment Tool (FRAX) compared with fracture risk derived from population fracture rates

    DEFF Research Database (Denmark)

    Rubin, Katrine Hass; Abrahamsen, Bo; Hermann, Anne Pernille

    2011-01-01

    Purpose: To evaluate the performance of the Swedish version of Fracture Risk Assessment Tool (FRAX)) without bone mass density (BMD) in a Danish population to examine the possibility of applying this version to Danish women. METHODS: From the Danish National Register of social security numbers, we...

  3. Mini-Nutritional Assessment, Malnutrition Universal Screening Tool, and Nutrition Risk Screening Tool for the Nutritional Evaluation of Older Nursing Home Residents.

    Science.gov (United States)

    Donini, Lorenzo M; Poggiogalle, Eleonora; Molfino, Alessio; Rosano, Aldo; Lenzi, Andrea; Rossi Fanelli, Filippo; Muscaritoli, Maurizio

    2016-10-01

    Malnutrition plays a major role in clinical and functional impairment in older adults. The use of validated, user-friendly and rapid screening tools for malnutrition in the elderly may improve the diagnosis and, possibly, the prognosis. The aim of this study was to assess the agreement between Mini-Nutritional Assessment (MNA), considered as a reference tool, MNA short form (MNA-SF), Malnutrition Universal Screening Tool (MUST), and Nutrition Risk Screening (NRS-2002) in elderly institutionalized participants. Participants were enrolled among nursing home residents and underwent a multidimensional evaluation. Predictive value and survival analysis were performed to compare the nutritional classifications obtained from the different tools. A total of 246 participants (164 women, age: 82.3 ± 9 years, and 82 men, age: 76.5 ± 11 years) were enrolled. Based on MNA, 22.6% of females and 17% of males were classified as malnourished; 56.7% of women and 61% of men were at risk of malnutrition. Agreement between MNA and MUST or NRS-2002 was classified as "fair" (k = 0.270 and 0.291, respectively; P < .001), whereas the agreement between MNA and MNA-SF was classified as "moderate" (k = 0.588; P < .001). Because of the high percentage of false negative participants, MUST and NRS-2002 presented a low overall predictive value compared with MNA and MNA-SF. Clinical parameters were significantly different in false negative participants with MUST or NRS-2002 from true negative and true positive individuals using the reference tool. For all screening tools, there was a significant association between malnutrition and mortality. MNA showed the best predictive value for survival among well-nourished participants. Functional, psychological, and cognitive parameters, not considered in MUST and NRS-2002 tools, are probably more important risk factors for malnutrition than acute illness in geriatric long-term care inpatient settings and may account for the low predictive

  4. A Successful ED Fall Risk Program Using the KINDER 1 Fall RiskAssessment Tool.

    Science.gov (United States)

    Townsend, Ann B; Valle-Ortiz, Marisol; Sansweet, Tracy

    2016-11-01

    Emergency nurses did not perform falls risk assessments routinely on our ED patients; the instrument used was aimed at inpatients. We identified a need to revise fall assessment practices specific to our emergency department. The purpose of the performance improvement project was to reduce ED falls and evaluate the use of an ED-specific fall risk tool, the KINDER 1 Fall Risk Assessment. The plan was to establish fall risk assessment practices at point of ED entry and to decrease total falls. We retrospectively reviewed ED fall data for each quarter of 2013, which included risk assessments scores, the total number of falls, and the circumstances of each fall. Using Kotter's framework to guide a successful change process, we implemented the KINDER 1 to assess fall risk. During the first 4 weeks of the project, 937 patients (27%) were identified as high risk for falls using the KINDER 1. During the subsequent 3 quarters, the total number of falls decreased; reported falls without injuries dropped from 0.21 to 0.07 per 1000 patients, and falls with injuries were reduced from 0.21 to 0.0 per 1000 patients. The results of this project represented a valuable step toward achieving our goal to keep ED patients safe from injuries as a result of falls. The findings add to the body of nursing knowledge on the application of clinical-based performance improvement projects to improve patient outcomes and to provide data on the use of the KINDER 1 tool, which has not been extensively tested. Copyright © 2016 Emergency Nurses Association. Published by Elsevier Inc. All rights reserved.

  5. Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population

    NARCIS (Netherlands)

    Maaren, M.C. van; Steenbeek, C.D. van; Pharoah, P.D.; Witteveen, A.; Sonke, G.S.; Strobbe, L.J.A.; Poortmans, P.; Siesling, S.

    2017-01-01

    BACKGROUND: PREDICT version 2.0 is increasingly used to estimate prognosis in breast cancer. This study aimed to validate this tool in specific prognostic subgroups in the Netherlands. METHODS: All operated women with non-metastatic primary invasive breast cancer, diagnosed in 2005, were selected

  6. Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population

    NARCIS (Netherlands)

    van Maaren, M. C.; van Steenbeek, C. D.; Pharoah, P. D.P.; Witteveen, A.; Sonke, Gabe S.; Strobbe, L.J.A.; Poortmans, P.M.P.; Siesling, S.

    2017-01-01

    Background PREDICT version 2.0 is increasingly used to estimate prognosis in breast cancer. This study aimed to validate this tool in specific prognostic subgroups in the Netherlands. Methods All operated women with non-metastatic primary invasive breast cancer, diagnosed in 2005, were selected from

  7. 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...

  8. 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.

  9. Validating SPICES as a Screening Tool for Frailty Risks among Hospitalized Older Adults

    Science.gov (United States)

    Aronow, Harriet Udin; Borenstein, Jeff; Haus, Flora; Braunstein, Glenn D.; Bolton, Linda Burnes

    2014-01-01

    Older patients are vulnerable to adverse hospital events related to frailty. SPICES, a common screening protocol to identify risk factors in older patients, alerts nurses to initiate care plans to reduce the probability of patient harm. However, there is little published validating the association between SPICES and measures of frailty and adverse outcomes. This paper used data from a prospective cohort study on frailty among 174 older adult inpatients to validate SPICES. Almost all patients met one or more SPICES criteria. The sum of SPICES was significantly correlated with age and other well-validated assessments for vulnerability, comorbid conditions, and depression. Individuals meeting two or more SPICES criteria had a risk of adverse hospital events three times greater than individuals with either no or one criterion. Results suggest that as a screening tool used within 24 hours of admission, SPICES is both valid and predictive of adverse events. PMID:24876954

  10. 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.

  11. Measurement of biofilm thickness. An effective Legionella risk assessment tool

    Energy Technology Data Exchange (ETDEWEB)

    Foret, Christophe [BKG France, Arnage (France); Martemianov, Serguei [Poitiers Univ. (FR). Lab. of Thermal Study (LET); Moscow Univ. (Russian Federation). Frumkin Inst. of Physical Chemistry and Electrochemistry; Hater, Wolfgang [BK Giulini GmbH, Duesseldorf (Germany); Merlet, Nicole; Chaussec, Guenole; Tribollet, Bernard

    2010-02-15

    The best way to prevent the risk of bacterial growth in water systems is to monitor and control the microorganisms (biofilm) attached to pipe walls. Three years of laboratory research led two Centre National de Recherche Scientifique (French National Center for Scientific Research) teams (UMR 6008 and UPR 15) to develop a tool designed to determine the average biofilm thickness. The average biofilm thickness measurements carried out on pilot plants fed with natural water were sufficiently accurate and sensitive to monitor the formation and development of biofilm in a water system and to determine the efficiency of the applied treatments. The implementation of appropriate treatments (type and dose of the treatment product) leads to a significant reduction in or even complete removal of the porous layer on the material surface. A reduction of the attached biomass, measured by the sensor, is connected to a decrease in the density of the bacterial attached to the material (viable flora in the plate count agar environment). (orig.)

  12. Using an Internet-Based Breast Cancer Risk Assessment Tool to Improve Social-Cognitive Precursors of Physical Activity.

    Science.gov (United States)

    Fowler, Stephanie L; Klein, William M P; Ball, Linda; McGuire, Jaclyn; Colditz, Graham A; Waters, Erika A

    2017-08-01

    Internet-based cancer risk assessment tools might serve as a strategy for translating epidemiological risk prediction research into public health practice. Understanding how such tools affect key social-cognitive precursors of behavior change is crucial for leveraging their potential into effective interventions. To test the effects of a publicly available, Internet-based, breast cancer risk assessment tool on social-cognitive precursors of physical activity. Women (N = 132) aged 40-78 with no personal cancer history indicated their perceived risk of breast cancer and were randomly assigned to receive personalized ( www.yourdiseaserisk.wustl.edu ) or nonpersonalized breast cancer risk information. Immediately thereafter, breast cancer risk perceptions and physical activity-related behavioral intentions, self-efficacy, and response efficacy were assessed. Personalized information elicited higher intentions, self-efficacy, and response efficacy than nonpersonalized information, P values Internet-based risk assessment tools can produce beneficial effects on important social-cognitive precursors of behavior change, but lingering skepticism, possibly due to defensive processing, needs to be addressed before the effects can be maximized.

  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. Influence of Genotype on Warfarin Maintenance Dose Predictions Produced Using a Bayesian Dose Individualization Tool.

    Science.gov (United States)

    Saffian, Shamin M; Duffull, Stephen B; Roberts, Rebecca L; Tait, Robert C; Black, Leanne; Lund, Kirstin A; Thomson, Alison H; Wright, Daniel F B

    2016-12-01

    A previously established Bayesian dosing tool for warfarin was found to produce biased maintenance dose predictions. In this study, we aimed (1) to determine whether the biased warfarin dose predictions previously observed could be replicated in a new cohort of patients from 2 different clinical settings, (2) to explore the influence of CYP2C9 and VKORC1 genotype on predictive performance of the Bayesian dosing tool, and (3) to determine whether the previous population used to develop the kinetic-pharmacodynamic model underpinning the Bayesian dosing tool was sufficiently different from the test (posterior) population to account for the biased dose predictions. The warfarin maintenance doses for 140 patients were predicted using the dosing tool and compared with the observed maintenance dose. The impact of genotype was assessed by predicting maintenance doses with prior parameter values known to be altered by genetic variability (eg, EC50 for VKORC1 genotype). The prior population was evaluated by fitting the published kinetic-pharmacodynamic model, which underpins the Bayesian tool, to the observed data using NONMEM and comparing the model parameter estimates with published values. The Bayesian tool produced positively biased dose predictions in the new cohort of patients (mean prediction error [95% confidence interval]; 0.32 mg/d [0.14-0.5]). The bias was only observed in patients requiring ≥7 mg/d. The direction and magnitude of the observed bias was not influenced by genotype. The prior model provided a good fit to our data, which suggests that the bias was not caused by different prior and posterior populations. Maintenance doses for patients requiring ≥7 mg/d were overpredicted. The bias was not due to the influence of genotype nor was it related to differences between the prior and posterior populations. There is a need for a more mechanistic model that captures warfarin dose-response relationship at higher warfarin doses.

  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. Recurrent epistaxis: predicting risk of 30-day readmission, derivation and validation of RHINO-ooze score.

    Science.gov (United States)

    Addison, A; Paul, C; Kuo, R; Lamyman, A; Martinez-Devesa, P; Hettige, R

    2017-06-01

    To derive and validate a predictive scoring tool (RHINO-ooze score) with good sensitivity and specificity in identifying patients with epistaxis at high risk of 30 day readmission and to enable risk stratification for possible definitive intervention. Using medical databases, we searched for factors influencing recurrent epistaxis. The information ascertained together with our analysis of retrospective data on patients admitted with epistaxis between October 2013 and September 2014, was used as the derivation cohort to develop the predictive scoring model (RHINO-ooze score). The tool was validated by performing statistical analysis on the validation cohort of patients admitted with epistaxis between October 2014 and October 2015. Multiple linear regressions with backwards elimination was used to derive the predictive model. The area under the curve (AUC), sensitivity and specificity were calculated. 834 admissions were encountered within the study period. Using the derivative cohort (n= 302) the RHINO-ooze score with a maximum score of 8 from five variables (Recent admission, Haemorrhage point unidentified, Increasing age over 70, posterior Nasal packing, Oral anticoagulant) was developed. The RHINO-ooze score had a chi-square value of 99.72 with a significance level of smaller than 0.0001 and hence an overall good model fit. Comparison between the derivative and validation groups revealed similar rates of 30-day readmission between the cohorts. The sensitivity and specificity of predicting 30-day readmission in high risk patients with recurrent epistaxis (RHINO-ooze score equal/larger than 6) was 81% and 84%, respectively. The RHINO-ooze scoring tool demonstrates good specificity and sensitivity in predicting the risk of 30 day readmission in patients with epistaxis and can be used as an adjunct to clinical decision making with regards to timing of operative intervention in order to reduce readmission rates.

  18. Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment

    DEFF Research Database (Denmark)

    Simonsen, Anja H; Mattila, Jussi; Hejl, Anne-Mette

    2012-01-01

    of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators. Results: A statistical...... significant trend (p classification accuracy (from 62.6 to 70.0%) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2%. Conclusion: Best...... classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented....

  19. 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

  20. Identification of seniors at risk (ISAR) screening tool in the emergency department: implementation using the plan-do-study-act model and validation results.

    Science.gov (United States)

    Asomaning, Nana; Loftus, Carla

    2014-07-01

    To better meet the needs of older adults in the emergency department, Senior Friendly care processes, such as high-risk screening are recommended. The identification of Seniors at Risk (ISAR) tool is a 6-item validated screening tool for identifying elderly patients at risk of the adverse outcomes post-ED visit. This paper describes the implementation of the tool in the Mount Sinai Hospital emergency department using a Plan-Do-Study-Act model; and demonstrates whether the tool predicts adverse outcomes. An observational study tracked tool implementation. A retrospective chart audit was completed to collect data about elderly ED patients during 2 time periods in 2010 and 2011. Data analysis compared the characteristics of patients with positive and negative screening tool results. The identification of Seniors at Risk tool was completed for 51.6% of eligible patients, with 61.2% of patients having a positive result. Patients with positive screening results were more likely to be over age 79 (P = .003); be admitted to hospital (P Risk tool was challenged by problematic compliance with tool completion. Strategies to address this included tool adaptation; and providing staff with knowledge of ED and inpatient geriatric resources and feedback on completion rates. Positive screening results predicted adverse outcomes in elderly Mount Sinai Hospital ED patients. © 2014. Published by Elsevier Inc. All rights reserved.

  1. Predicting tool life in turning operations using neural networks and image processing

    Science.gov (United States)

    Mikołajczyk, T.; Nowicki, K.; Bustillo, A.; Yu Pimenov, D.

    2018-05-01

    A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.

  2. Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning.

    Science.gov (United States)

    Yoo, Tae Keun; Kim, Sung Kean; Kim, Deok Won; Choi, Joon Yul; Lee, Wan Hyung; Oh, Ein; Park, Eun-Cheol

    2013-11-01

    A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools. We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS). SVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus. Considering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

  3. Development and validation of fall risk screening tools for use in residential aged care facilities.

    Science.gov (United States)

    Delbaere, Kim; Close, Jacqueline C T; Menz, Hylton B; Cumming, Robert G; Cameron, Ian D; Sambrook, Philip N; March, Lyn M; Lord, Stephen R

    2008-08-18

    To develop screening tools for predicting falls in nursing home and intermediate-care hostel residents who can and cannot stand unaided. Prospective cohort study in residential aged care facilities in northern Sydney, New South Wales, June 1999-June 2003. 2005 people aged 65-104 years (mean +/- SD, 85.7+/-7.1 years). Demographic, health, and physical function assessment measures; number of falls over a 6-month period; validity of the screening models. Ability to stand unaided was identified as a significant event modifier for falls. In people who could stand unaided, having either poor balance or two of three other risk factors (previous falls, nursing home residence, and urinary incontinence) increased the risk of falling in the next 6 months threefold (sensitivity, 73%; specificity, 55%). In people who could not stand unaided, having any one of three risk factors (previous falls, hostel residence, and using nine or more medications) increased the risk of falling twofold (sensitivity, 87%; specificity, 29%). These two screening models are useful for identifying older people living in residential aged care facilities who are at increased risk of falls. The screens are easy to administer and contain items that are routinely collected in residential aged care facilities in Australia.

  4. A summary risk score for the prediction of Alzheimer disease in elderly persons.

    Science.gov (United States)

    Reitz, Christiane; Tang, Ming-Xin; Schupf, Nicole; Manly, Jennifer J; Mayeux, Richard; Luchsinger, José A

    2010-07-01

    To develop a simple summary risk score for the prediction of Alzheimer disease in elderly persons based on their vascular risk profiles. A longitudinal, community-based study. New York, New York. Patients One thousand fifty-one Medicare recipients aged 65 years or older and residing in New York who were free of dementia or cognitive impairment at baseline. We separately explored the associations of several vascular risk factors with late-onset Alzheimer disease (LOAD) using Cox proportional hazards models to identify factors that would contribute to the risk score. Then we estimated the score values of each factor based on their beta coefficients and created the LOAD vascular risk score by summing these individual scores. Risk factors contributing to the risk score were age, sex, education, ethnicity, APOE epsilon4 genotype, history of diabetes, hypertension or smoking, high-density lipoprotein levels, and waist to hip ratio. The resulting risk score predicted dementia well. According to the vascular risk score quintiles, the risk to develop probable LOAD was 1.0 for persons with a score of 0 to 14 and increased 3.7-fold for persons with a score of 15 to 18, 3.6-fold for persons with a score of 19 to 22, 12.6-fold for persons with a score of 23 to 28, and 20.5-fold for persons with a score higher than 28. While additional studies in other populations are needed to validate and further develop the score, our study suggests that this vascular risk score could be a valuable tool to identify elderly individuals who might be at risk of LOAD. This risk score could be used to identify persons at risk of LOAD, but can also be used to adjust for confounders in epidemiologic studies.

  5. Bayesian network as a modelling tool for risk management in agriculture

    DEFF Research Database (Denmark)

    Rasmussen, Svend; Madsen, Anders Læsø; Lund, Mogens

    The importance of risk management increases as farmers become more exposed to risk. But risk management is a difficult topic because income risk is the result of the complex interaction of multiple risk factors combined with the effect of an increasing array of possible risk management tools. In ......, and that it has the ability to link uncertainty from different external sources to budget figures and to quantify risk at the farm level....

  6. Automatic generation of bioinformatics tools for predicting protein-ligand binding sites.

    Science.gov (United States)

    Komiyama, Yusuke; Banno, Masaki; Ueki, Kokoro; Saad, Gul; Shimizu, Kentaro

    2016-03-15

    Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful. We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands. The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux. shimizu@bi.a.u-tokyo.ac.jp Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

  7. Screening strategies and predictive diagnostic tools for the development of new-onset diabetes mellitus after transplantation: an overview

    Directory of Open Access Journals (Sweden)

    Pham PT

    2012-10-01

    Full Text Available Phuong-Thu T Pham,1 Kari L Edling,2 Harini A Chakkera,3 Phuong-Chi T Pham,4 Phuong-Mai T Pham51Department of Medicine, Nephrology Division, Kidney Transplant Program, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; 2Department of Medicine, Division of Endocrinology, Diabetes and Hypertension, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; 3Department of Medicine, Nephrology Division Kidney Transplant Program, Mayo Clinic Hospital, Phoenix, AZ, USA; 4Department of Medicine, Nephrology Division, UCLA-Olive View Medical Center, Sylmar, CA, USA; 5Department of Medicine, Greater Los Angeles, Veterans Administration Health Care System, CA, USAAbstract: New-onset diabetes mellitus after transplantation (NODAT is a serious and common complication following solid organ transplantation. NODAT has been reported in 2% to 53% of all solid organ transplants. Kidney transplant recipients who develop NODAT have variably been reported to be at increased risk of fatal and nonfatal cardiovascular events and other adverse outcomes including infection, reduced patient survival, graft rejection, and accelerated graft loss compared with those who do not develop diabetes. Limited clinical studies in liver, heart, and lung transplants similarly suggested that NODAT has an adverse impact on patient and graft outcomes. Early detection and management of NODAT must, therefore, be integrated into the treatment of transplant recipients. Studies investigating the best screening or predictive tool for identifying patients at risk for developing NODAT early after transplantation, however, are lacking. We review the clinical predictive values of fasting plasma glucose, oral glucose tolerance test, and A1C in assessing the risk for NODAT development and as a screening tool. Simple diabetes prediction models that incorporate clinical and/or metabolic risk factors (such as age, body mass index, hypertriglyceridemia, or metabolic syndrome are also

  8. 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...

  9. Acceptability of the Predicting Abusive Head Trauma (PredAHT) clinical prediction tool: A qualitative study with child protection professionals.

    Science.gov (United States)

    Cowley, Laura E; Maguire, Sabine; Farewell, Daniel M; Quinn-Scoggins, Harriet D; Flynn, Matthew O; Kemp, Alison M

    2018-05-09

    The validated Predicting Abusive Head Trauma (PredAHT) tool estimates the probability of abusive head trauma (AHT) based on combinations of six clinical features: head/neck bruising; apnea; seizures; rib/long-bone fractures; retinal hemorrhages. We aimed to determine the acceptability of PredAHT to child protection professionals. We conducted qualitative semi-structured interviews with 56 participants: clinicians (25), child protection social workers (10), legal practitioners (9, including 4 judges), police officers (8), and pathologists (4), purposively sampled across southwest United Kingdom. Interviews were recorded, transcribed and imported into NVivo for thematic analysis (38% double-coded). We explored participants' evaluations of PredAHT, their opinions about the optimal way to present the calculated probabilities, and their interpretation of probabilities in the context of suspected AHT. Clinicians, child protection social workers and police thought PredAHT would be beneficial as an objective adjunct to their professional judgment, to give them greater confidence in their decisions. Lawyers and pathologists appreciated its value for prompting multidisciplinary investigations, but were uncertain of its usefulness in court. Perceived disadvantages included: possible over-reliance and false reassurance from a low score. Interpretations regarding which percentages equate to 'low', 'medium' or 'high' likelihood of AHT varied; participants preferred a precise % probability over these general terms. Participants would use PredAHT with provisos: if they received multi-agency training to define accepted risk thresholds for consistent interpretation; with knowledge of its development; if it was accepted by colleagues. PredAHT may therefore increase professionals' confidence in their decision-making when investigating suspected AHT, but may be of less value in court. Copyright © 2018 Elsevier Ltd. All rights reserved.

  10. FRAT-up, a Web-based fall-risk assessment tool for elderly people living in the community.

    Science.gov (United States)

    Cattelani, Luca; Palumbo, Pierpaolo; Palmerini, Luca; Bandinelli, Stefania; Becker, Clemens; Chesani, Federico; Chiari, Lorenzo

    2015-02-18

    About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls. The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up. FRAT-up is based on the assumption that a subject's fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators. The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer

  11. Predictive Models and Tools for Assessing Chemicals under the Toxic Substances Control Act (TSCA)

    Science.gov (United States)

    EPA has developed databases and predictive models to help evaluate the hazard, exposure, and risk of chemicals released to the environment and how workers, the general public, and the environment may be exposed to and affected by them.

  12. Increased fracture risk assessed by fracture risk assessment tool in Greek patients with Crohn's disease.

    Science.gov (United States)

    Terzoudis, Sotirios; Zavos, Christos; Damilakis, John; Neratzoulakis, John; Dimitriadi, Daphne Anna; Roussomoustakaki, Maria; Kouroumalis, Elias A; Koutroubakis, Ioannis E

    2013-01-01

    The World Health Organization has recently developed the fracture risk assessment tool (FRAX) based on clinical risk factors and bone mineral density (BMD) for evaluation of the 10-year probability of a hip or a major osteoporotic fracture. The aim of this study was to evaluate the use of the FRAX tool in Greek patients with inflammatory bowel disease (IBD). FRAX scores were applied to 134 IBD patients [68 Crohn's disease (CD); 66 ulcerative colitis (UC)] who underwent dual-energy X-ray absorptiometry scans at the femoral neck and lumbar spine during the period 2007-2012. Calculation of the FRAX scores, with or without BMD, was made through a web-based probability model used to compute individual fracture probabilities according to specific clinical risk factors. The median 10-year probability of a major osteoporotic fracture for IBD patients based on clinical data was 7.1%, and including the BMD was 6.2%. A significant overestimation with the first method was found (P = 0.01). Both scores with and without BMD were significantly higher in CD patients compared with UC patients (P = 0.02 and P = 0.005, respectively). The median 10-year probability of hip fracture based on clinical data was 0.8%, and including the BMD was 0.9%. The score with use of BMD was significantly higher in CD compared with UC patients (P = 0.04). CD patients have significantly higher FRAX scores and possibly fracture risk compared with UC patients. The clinical FRAX score alone seems to overestimate the risk of osteoporotic fracture in Greek IBD patients.

  13. 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...

  14. 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...

  15. 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.

  16. 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.

  17. 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%.

  18. DEBRISK, a Tool for Re-Entry Risk Analysis

    Science.gov (United States)

    Omaly, P.; Spel, M.

    2012-01-01

    An act of French parliament, adopted in 2008, imposes satellite constructors to evaluate the end-of-life operations in order to assure the risk mitigation of their satellites. One important element in this evaluation is the estimation of the mass and impact energy of the satellite debris after atmospheric re-entry. For this purpose, CNES has developed the tool DEBRISK which allows the operator to simulate the re-entry phase and to study the demise altitudes or impact energy of the individual fragments of the original satellite. DEBRISK is based on the so called object based approach. Using this approach, a breakup altitude is assumed where the satellite disintegrates due to the pressure loads. This altitude is typically around 78 km. After breakup, the satellite structure is modelled by a parent-child approach, where each child has its birth criterion. In the simplest approach the child is born after demise of the parent object. This could be the case of an object A containing an object B which is in the interior of object A and thus not exposed to the atmosphere. Each object is defined by: - its shape, attitude and dimensions, - the material along with their physical properties - the state and velocity vectors. The shape, attitude and dimensions define the aerodynamic drag of the object which is input to the 3DOF trajectory modelling. The aerodynamic mass used in the equation of motion is defined as the sum of the object's own mass and the mass of the object's offspring. A new born object inherits the state vector of the parent object. The shape, attitude and dimensions also define the heating rates experienced by the object. The heating rate is integrated in time up to the point where the melting temperature is reached. The mass of melted material is computed from the excess heat and the material properties. After each step the amount of ablated material is determined using the lumped mass approach and is peeled off from the object, updating mass and shape of the

  19. ePORT, NASA's Computer Database Program for System Safety Risk Management Oversight (Electronic Project Online Risk Tool)

    Science.gov (United States)

    Johnson, Paul W.

    2008-01-01

    ePORT (electronic Project Online Risk Tool) provides a systematic approach to using an electronic database program to manage a program/project risk management processes. This presentation will briefly cover the standard risk management procedures, then thoroughly cover NASA's Risk Management tool called ePORT. This electronic Project Online Risk Tool (ePORT) is a web-based risk management program that provides a common framework to capture and manage risks, independent of a programs/projects size and budget. It is used to thoroughly cover the risk management paradigm providing standardized evaluation criterion for common management reporting, ePORT improves Product Line, Center and Corporate Management insight, simplifies program/project manager reporting, and maintains an archive of data for historical reference.

  20. 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.

  1. 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.

  2. Development of an Automated Security Risk Assessment Methodology Tool for Critical Infrastructures.

    Energy Technology Data Exchange (ETDEWEB)

    Jaeger, Calvin Dell; Roehrig, Nathaniel S.; Torres, Teresa M.

    2008-12-01

    This document presents the security automated Risk Assessment Methodology (RAM) prototype tool developed by Sandia National Laboratories (SNL). This work leverages SNL's capabilities and skills in security risk analysis and the development of vulnerability assessment/risk assessment methodologies to develop an automated prototype security RAM tool for critical infrastructures (RAM-CITM). The prototype automated RAM tool provides a user-friendly, systematic, and comprehensive risk-based tool to assist CI sector and security professionals in assessing and managing security risk from malevolent threats. The current tool is structured on the basic RAM framework developed by SNL. It is envisioned that this prototype tool will be adapted to meet the requirements of different CI sectors and thereby provide additional capabilities.

  3. Towards early software reliability prediction for computer forensic tools (case study).

    Science.gov (United States)

    Abu Talib, Manar

    2016-01-01

    Versatility, flexibility and robustness are essential requirements for software forensic tools. Researchers and practitioners need to put more effort into assessing this type of tool. A Markov model is a robust means for analyzing and anticipating the functioning of an advanced component based system. It is used, for instance, to analyze the reliability of the state machines of real time reactive systems. This research extends the architecture-based software reliability prediction model for computer forensic tools, which is based on Markov chains and COSMIC-FFP. Basically, every part of the computer forensic tool is linked to a discrete time Markov chain. If this can be done, then a probabilistic analysis by Markov chains can be performed to analyze the reliability of the components and of the whole tool. The purposes of the proposed reliability assessment method are to evaluate the tool's reliability in the early phases of its development, to improve the reliability assessment process for large computer forensic tools over time, and to compare alternative tool designs. The reliability analysis can assist designers in choosing the most reliable topology for the components, which can maximize the reliability of the tool and meet the expected reliability level specified by the end-user. The approach of assessing component-based tool reliability in the COSMIC-FFP context is illustrated with the Forensic Toolkit Imager case study.

  4. Musite, a tool for global prediction of general and kinase-specific phosphorylation sites.

    Science.gov (United States)

    Gao, Jianjiong; Thelen, Jay J; Dunker, A Keith; Xu, Dong

    2010-12-01

    Reversible protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. High throughput experimental studies using mass spectrometry have identified many phosphorylation sites, primarily from eukaryotes. However, the vast majority of phosphorylation sites remain undiscovered, even in well studied systems. Because mass spectrometry-based experimental approaches for identifying phosphorylation events are costly, time-consuming, and biased toward abundant proteins and proteotypic peptides, in silico prediction of phosphorylation sites is potentially a useful alternative strategy for whole proteome annotation. Because of various limitations, current phosphorylation site prediction tools were not well designed for comprehensive assessment of proteomes. Here, we present a novel software tool, Musite, specifically designed for large scale predictions of both general and kinase-specific phosphorylation sites. We collected phosphoproteomics data in multiple organisms from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates local sequence similarities to known phosphorylation sites, protein disorder scores, and amino acid frequencies. Application of Musite on several proteomes yielded tens of thousands of phosphorylation site predictions at a high stringency level. Cross-validation tests show that Musite achieves some improvement over existing tools in predicting general phosphorylation sites, and it is at least comparable with those for predicting kinase-specific phosphorylation sites. In Musite V1.0, we have trained general prediction models for six organisms and kinase-specific prediction models for 13 kinases or kinase families. Although the current pretrained models were not correlated with any particular cellular conditions, Musite provides a unique functionality for training customized prediction models

  5. Performance of the HEMORR(2)HAGES, ATRIA, and HAS-BLED Bleeding Risk-Prediction Scores in Patients With Atrial Fibrillation Undergoing Anticoagulation

    NARCIS (Netherlands)

    Apostolakis, Stavros; Lane, Deirdre A.; Guo, Yutao; Buller, Harry; Lip, Gregory Y. H.

    2012-01-01

    Objectives The objective of this study was to compare the predictive performance of bleeding risk-estimation tools in a cohort of patients with atrial fibrillation (AF) undergoing anticoagulation. Background Three bleeding risk-prediction schemes have been derived for and validated in patients with

  6. 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.

  7. The Aggregate Risk Index: An intuitive tool providing the health risks of air pollution to health care community and public

    Science.gov (United States)

    Sicard, Pierre; Talbot, Charles; Lesne, Olivia; Mangin, Antoine; Alexandre, Nicolas; Collomp, Rémy

    2012-01-01

    In the framework of the European project PASODOBLE (FP7), we set up downstream information services by combining environmental and health data with a view to support the health care community and to improve vulnerable people welfare. Indeed there is a profound relationship between human health, well-being and air pollution levels. The main objectives are to establish correlations between air quality, exposure of populations and their reactivity, to develop and validate air quality indexes and to construct a prediction model of this sanitary index. This index will be implemented on 3 European sites: Greece (Athens and Thessaloniki), the Netherlands and "Provence Alpes Côte d'Azur" (South East of France). The selected region and cities are among the most affected by the atmospheric pollution in Europe and leads to serious sanitary concerns. The service aims to provide up-to-date, detailed information on air quality discomfort. The Aggregate Risk Index is based on the Cairncross's concept, obtained from the Relative Risk associated with short-term exposure to common air pollutants and takes into account the possible effects of a mixture of pollutants. This communication tool, easy to use and intuitive, about the levels of air pollution and the associated health risks, will be used to communicate information to the general population, authorities and to the health care community and will provide advanced warning of potentially health-damaging air pollution events.

  8. Predicting Risk of Type 2 Diabetes Mellitus: A Population-Based Study

    Directory of Open Access Journals (Sweden)

    Mahmut Kilic

    2016-04-01

    Full Text Available Aim: One of the major risk factors that can cause death in the world is also type-2 diabetes mellitus (DM. Turkey does not have a vehicle in the society has been formulate predicting the risk of developing DM. The purpose of this study is to determine the level of DM risk in Turkish society using the Finnish Diabetes Risk Score (FINDRISC tool. Material and Method: This is a cross-sectional study. The data has been obtained from %u201Cbehavioral risk factors for chronic diseases study%u201D that was made in the province of Yozgat, in 2011. The study population included 825 subjects between 25 to 79 years old who had measured their blood sugar before, but who were not diagnosed DM. DM risk level was calculated using FINDRISC tool. The scale score is between 0-26, %u226515 points are considered high risk (risk ratio 1/3. In analyzing the data, t-test, ANOVA and chi-square test and binary logistic regression were used. Results: Of the subjects 10 years of DM risk score%u2019s mean was 8.8 ± 4.6. When FINDRISC score low / medium and high divided into 2 groups, the proportion of those in the high risk group is 11.5%. This rate is similar to the 10-year incidence of DM calculated (11-12.4% for Turkey. In this study, all of the factors taken into FINDRISC calculations were statistically significant (p 0.05. Discussion: FINDRISC used to be in the DM risk calculations of Turkish population. One out of every ten adults are at high risk of developing DM in 10 years. To avoid this problem urgently needs to be implemented by the various programs on an individual and societal level.

  9. Clinical utility of polymorphisms in one-carbon metabolism for breast cancer risk prediction

    Directory of Open Access Journals (Sweden)

    Shaik Mohammad Naushad

    2011-01-01

    Full Text Available This study addresses the issues in translating the laboratory derived data obtained during discovery phase of research to a clinical setting using a breast cancer model. Laboratory-based risk assessment indi-cated that a family history of breast cancer, reduced folate carrier 1 (RFC1 G80A, thymidylate synthase (TYMS 5’-UTR 28bp tandem repeat, methylene tetrahydrofolate reductase (MTHFR C677T and catecholamine-O-methyl transferase (COMT genetic polymorphisms in one-carbon metabolic pathway increase the risk for breast cancer. Glutamate carboxypeptidase II (GCPII C1561T and cytosolic serine hydroxymethyl transferase (cSHMT C1420T polymorphisms were found to decrease breast cancer risk. In order to test the clinical validity of this information in the risk prediction of breast cancer, data was stratified based on number of protective alleles into four categories and in each category sensitivity and 1-specificity values were obtained based on the distribution of number of risk alleles in cases and controls. Receiver operating characteristic (ROC curves were plotted and the area under ROC curve (C was used as a measure of discriminatory ability between cases and controls. In subjects without any protective allele, aberrations in one-carbon metabolism showed perfect prediction (C=0.93 while the predictability was lost in subjects with one protective allele (C=0.60. However, predictability increased steadily with increasing number of protective alleles (C=0.63 for 2 protective alleles and C=0.71 for 3 protective alleles. The cut-off point for discrimination was >4 alleles in all predictable combinations. Models of this kind can serve as valuable tools in translational re-search, especially in identifying high-risk individuals and reducing the disease risk either by life style modification or by medical intervention.

  10. The Climate-Agriculture-Modeling and Decision Tool (CAMDT) for Climate Risk Management in Agriculture

    Science.gov (United States)

    Ines, A. V. M.; Han, E.; Baethgen, W.

    2017-12-01

    Advances in seasonal climate forecasts (SCFs) during the past decades have brought great potential to improve agricultural climate risk managements associated with inter-annual climate variability. In spite of popular uses of crop simulation models in addressing climate risk problems, the models cannot readily take seasonal climate predictions issued in the format of tercile probabilities of most likely rainfall categories (i.e, below-, near- and above-normal). When a skillful SCF is linked with the crop simulation models, the informative climate information can be further translated into actionable agronomic terms and thus better support strategic and tactical decisions. In other words, crop modeling connected with a given SCF allows to simulate "what-if" scenarios with different crop choices or management practices and better inform the decision makers. In this paper, we present a decision support tool, called CAMDT (Climate Agriculture Modeling and Decision Tool), which seamlessly integrates probabilistic SCFs to DSSAT-CSM-Rice model to guide decision-makers in adopting appropriate crop and agricultural water management practices for given climatic conditions. The CAMDT has a functionality to disaggregate a probabilistic SCF into daily weather realizations (either a parametric or non-parametric disaggregation method) and to run DSSAT-CSM-Rice with the disaggregated weather realizations. The convenient graphical user-interface allows easy implementation of several "what-if" scenarios for non-technical users and visualize the results of the scenario runs. In addition, the CAMDT also translates crop model outputs to economic terms once the user provides expected crop price and cost. The CAMDT is a practical tool for real-world applications, specifically for agricultural climate risk management in the Bicol region, Philippines, having a great flexibility for being adapted to other crops or regions in the world. CAMDT GitHub: https://github.com/Agro-Climate/CAMDT

  11. 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.

  12. 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.

  13. 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.

  14. Fall Risk Score at the Time of Discharge Predicts Readmission Following Total Joint Arthroplasty.

    Science.gov (United States)

    Ravi, Bheeshma; Nan, Zhang; Schwartz, Adam J; Clarke, Henry D

    2017-07-01

    Readmission among Medicare recipients is a leading driver of healthcare expenditure. To date, most predictive tools are too coarse for direct clinical application. Our objective in this study is to determine if a pre-existing tool to identify patients at increased risk for inpatient falls, the Hendrich Fall Risk Score, could be used to accurately identify Medicare patients at increased risk for readmission following arthroplasty, regardless of whether the readmission was due to a fall. This study is a retrospective cohort study. We identified 2437 Medicare patients who underwent a primary elective total joint arthroplasty (TJA) of the hip or knee for osteoarthritis between 2011 and 2014. The Hendrich Fall Risk score was recorded for each patient preoperatively and postoperatively. Our main outcome measure was hospital readmission within 30 days of discharge. Of 2437 eligible TJA recipients, there were 226 (9.3%) patients who had a score ≥6. These patients were more likely to have an unplanned readmission (unadjusted odds ratio 2.84, 95% confidence interval 1.70-4.76, P 3 days (49.6% vs 36.6%, P = .0001), and were less likely to be sent home after discharge (20.8% vs 35.8%, P fall risk score after TJA is strongly associated with unplanned readmission. Application of this tool will allow hospitals to identify these patients and plan their discharge. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. Atomic Oxygen Erosion Yield Predictive Tool for Spacecraft Polymers in Low Earth Orbit

    Science.gov (United States)

    Bank, Bruce A.; de Groh, Kim K.; Backus, Jane A.

    2008-01-01

    A predictive tool was developed to estimate the low Earth orbit (LEO) atomic oxygen erosion yield of polymers based on the results of the Polymer Erosion and Contamination Experiment (PEACE) Polymers experiment flown as part of the Materials International Space Station Experiment 2 (MISSE 2). The MISSE 2 PEACE experiment accurately measured the erosion yield of a wide variety of polymers and pyrolytic graphite. The 40 different materials tested were selected specifically to represent a variety of polymers used in space as well as a wide variety of polymer chemical structures. The resulting erosion yield data was used to develop a predictive tool which utilizes chemical structure and physical properties of polymers that can be measured in ground laboratory testing to predict the in-space atomic oxygen erosion yield of a polymer. The properties include chemical structure, bonding information, density and ash content. The resulting predictive tool has a correlation coefficient of 0.914 when compared with actual MISSE 2 space data for 38 polymers and pyrolytic graphite. The intent of the predictive tool is to be able to make estimates of atomic oxygen erosion yields for new polymers without requiring expensive and time consumptive in-space testing.

  16. Development of METAL-ACTIVE SITE and ZINCCLUSTER tool to predict active site pockets.

    Science.gov (United States)

    Ajitha, M; Sundar, K; Arul Mugilan, S; Arumugam, S

    2018-03-01

    The advent of whole genome sequencing leads to increasing number of proteins with known amino acid sequences. Despite many efforts, the number of proteins with resolved three dimensional structures is still low. One of the challenging tasks the structural biologists face is the prediction of the interaction of metal ion with any protein for which the structure is unknown. Based on the information available in Protein Data Bank, a site (METALACTIVE INTERACTION) has been generated which displays information for significant high preferential and low-preferential combination of endogenous ligands for 49 metal ions. User can also gain information about the residues present in the first and second coordination sphere as it plays a major role in maintaining the structure and function of metalloproteins in biological system. In this paper, a novel computational tool (ZINCCLUSTER) is developed, which can predict the zinc metal binding sites of proteins even if only the primary sequence is known. The purpose of this tool is to predict the active site cluster of an uncharacterized protein based on its primary sequence or a 3D structure. The tool can predict amino acids interacting with a metal or vice versa. This tool is based on the occurrence of significant triplets and it is tested to have higher prediction accuracy when compared to that of other available techniques. © 2017 Wiley Periodicals, Inc.

  17. Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

    Science.gov (United States)

    Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

    2018-02-01

    Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

  18. Operational Risk Assesement Tools for Quality Management in Banking Services

    OpenAIRE

    Dima, Alina Mihaela

    2009-01-01

    Among all the different types of risks that can affect financial companies, the operational risk can be the most devastating and the most difficult to anticipate. The management of operational risk is a key component of financial and risk management discipline that drives net income results, 2capital management and customer satisfaction. The present paper contains a statistical analysis in order to determine the number of operational errors as quality based services determinants, depending on...

  19. Proarrhythmia risk prediction using human induced pluripotent stem cell-derived cardiomyocytes.

    Science.gov (United States)

    Yamazaki, Daiju; Kitaguchi, Takashi; Ishimura, Masakazu; Taniguchi, Tomohiko; Yamanishi, Atsuhiro; Saji, Daisuke; Takahashi, Etsushi; Oguchi, Masao; Moriyama, Yuta; Maeda, Sanae; Miyamoto, Kaori; Morimura, Kaoru; Ohnaka, Hiroki; Tashibu, Hiroyuki; Sekino, Yuko; Miyamoto, Norimasa; Kanda, Yasunari

    2018-04-01

    Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are expected to become a useful tool for proarrhythmia risk prediction in the non-clinical drug development phase. Several features including electrophysiological properties, ion channel expression profile and drug responses were investigated using commercially available hiPSC-CMs, such as iCell-CMs and Cor.4U-CMs. Although drug-induced arrhythmia has been extensively examined by microelectrode array (MEA) assays in iCell-CMs, it has not been fully understood an availability of Cor.4U-CMs for proarrhythmia risk. Here, we evaluated the predictivity of proarrhythmia risk using Cor.4U-CMs. MEA assay revealed linear regression between inter-spike interval and field potential duration (FPD). The hERG inhibitor E-4031 induced reverse-use dependent FPD prolongation. We next evaluated the proarrhythmia risk prediction by a two-dimensional map, which we have previously proposed. We determined the relative torsade de pointes risk score, based on the extent of FPD with Fridericia's correction (FPDcF) change and early afterdepolarization occurrence, and calculated the margins normalized to free effective therapeutic plasma concentrations. The drugs were classified into three risk groups using the two-dimensional map. This risk-categorization system showed high concordance with the torsadogenic information obtained by a public database CredibleMeds. Taken together, these results indicate that Cor.4U-CMs can be used for drug-induced proarrhythmia risk prediction. Copyright © 2018 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

  20. Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

    Science.gov (United States)

    Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

    2011-01-01

    A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

  1. 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.

  2. 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.

  3. Mortality Risk After Transcatheter Aortic Valve Implantation: Analysis of the Predictive Accuracy of the Transcatheter Valve Therapy Registry Risk Assessment Model.

    Science.gov (United States)

    Codner, Pablo; Malick, Waqas; Kouz, Remi; Patel, Amisha; Chen, Cheng-Han; Terre, Juan; Landes, Uri; Vahl, Torsten Peter; George, Isaac; Nazif, Tamim; Kirtane, Ajay J; Khalique, Omar K; Hahn, Rebecca T; Leon, Martin B; Kodali, Susheel

    2018-05-08

    Risk assessment tools currently used to predict mortality in transcatheter aortic valve implantation (TAVI) were designed for patients undergoing cardiac surgery. We aim to assess the accuracy of the TAVI dedicated American College of Cardiology / Transcatheter Valve Therapies (ACC/TVT) risk score in predicting mortality outcomes. Consecutive patients (n=1038) undergoing TAVI at a single institution from 2014 to 2016 were included. The ACC/TVT registry mortality risk score, the Society of Thoracic Surgeons - Patient Reported Outcomes (STS-PROM) score and the EuroSCORE II were calculated for all patients. In hospital and 30-day all-cause mortality rates were 1.3% and 2.9%, respectively. The ACC/TVT risk stratification tool scored higher for patients who died in-hospital than in those who survived the index hospitalization (6.4 ± 4.6 vs. 3.5 ± 1.6, p = 0.03; respectively). The ACC/TVT score showed a high level of discrimination, C-index for in-hospital mortality 0.74, 95% CI [0.59 - 0.88]. There were no significant differences between the performance of the ACC/TVT registry risk score, the EuroSCORE II and the STS-PROM for in hospital and 30-day mortality rates. The ACC/TVT registry risk model is a dedicated tool to aid in the prediction of in-hospital mortality risk after TAVI.

  4. Assessment of Interpersonal Risk (AIR) in Adults with Learning Disabilities and Challenging Behaviour--Piloting a New Risk Assessment Tool

    Science.gov (United States)

    Campbell, Martin; McCue, Michael

    2013-01-01

    A new risk assessment tool, "Assessment of Interpersonal Risk" (AIR), was piloted and evaluated to measure risk factors and compatibility between individuals living in an assessment and treatment unit in one NHS area. The adults with learning disabilities in this unit had severe and enduring mental health problems and/or behaviour that is severely…

  5. Prediction and Control of Cutting Tool Vibration in Cnc Lathe with Anova and Ann

    Directory of Open Access Journals (Sweden)

    S. S. Abuthakeer

    2011-06-01

    Full Text Available Machining is a complex process in which many variables can deleterious the desired results. Among them, cutting tool vibration is the most critical phenomenon which influences dimensional precision of the components machined, functional behavior of the machine tools and life of the cutting tool. In a machining operation, the cutting tool vibrations are mainly influenced by cutting parameters like cutting speed, depth of cut and tool feed rate. In this work, the cutting tool vibrations are controlled using a damping pad made of Neoprene. Experiments were conducted in a CNC lathe where the tool holder is supported with and without damping pad. The cutting tool vibration signals were collected through a data acquisition system supported by LabVIEW software. To increase the buoyancy and reliability of the experiments, a full factorial experimental design was used. Experimental data collected were tested with analysis of variance (ANOVA to understand the influences of the cutting parameters. Empirical models have been developed using analysis of variance (ANOVA. Experimental studies and data analysis have been performed to validate the proposed damping system. Multilayer perceptron neural network model has been constructed with feed forward back-propagation algorithm using the acquired data. On the completion of the experimental test ANN is used to validate the results obtained and also to predict the behavior of the system under any cutting condition within the operating range. The onsite tests show that the proposed system reduces the vibration of cutting tool to a greater extend.

  6. AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins.

    Science.gov (United States)

    Zhang, Zong Hong; Koh, Judice L Y; Zhang, Guang Lan; Choo, Khar Heng; Tammi, Martti T; Tong, Joo Chuan

    2007-02-15

    Assessment of potential allergenicity and patterns of cross-reactivity is necessary whenever novel proteins are introduced into human food chain. Current bioinformatic methods in allergology focus mainly on the prediction of allergenic proteins, with no information on cross-reactivity patterns among known allergens. In this study, we present AllerTool, a web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. The analysis tools include graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM). A 10-fold cross-validation results showed that the area under the receiver operating curve (A(ROC)) of SVM models is 0.90 with 86.00% sensitivity (SE) at specificity (SP) of 86.00%. AllerTool is freely available at http://research.i2r.a-star.edu.sg/AllerTool/.

  7. 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.

  8. 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

  9. A GIS-based tool for bioaccumulation risk analysis and its application to study polychlorinated biphenyls in the Great Lakes

    Directory of Open Access Journals (Sweden)

    Fernanda P. Maciel

    2018-01-01

    Full Text Available This paper presents a GIS-based tool named Arc-BEST (Bioaccumulation Evaluation Screening Tool to perform spatially distributed bioaccumulation risk analyses. Estimating bioaccumulation risk is important to help predict potentially adverse effects from contaminants on ecosystems and human health, which are key factors in the development of sound public policy. Arc-BEST is based on the BEST model in the U.S. Army Corps of Engineers BRAMS (Bioaccumulation Risk Assessment Modeling System software, released in 2012. It predicts concentration of concern contaminants in predators’ tissues from concentrations in organisms at the bottom of the food chain, and corresponding bioaccumulation factors. Additionally, it estimates carcinogenic and non-carcinogenic risks for humans that consume those species. The greatest contribution of Arc-BEST is that it enables the automated use of digital spatial data sets, which improves model creation speed, analysis and visualization of results, and comparison and cross-referencing with other geographic datasets. Furthermore, the model was improved to consider up to four trophic levels. The code is written in Python and is open-source. In this work Arc-BEST is used as part of a screening-level risk assessment process in order to identify hot spots where further studies and monitoring should be performed to ensure humans and ecosystems health. The tool is successfully applied to a case study in the Laurentian Great Lakes, where long-term effects of polychlorinated biphenyls (PCBs is performed, based on measured concentrations in zebra mussels (Dreissena polymorpha, and local bioaccumulation factors from previous studies. Zebra mussels have a great filtration capacity and high bioconcentration rates, increasing the bioavailability of contaminants for predator species. PCBs concentrations in different-level predators are predicted. Furthermore, health risks for humans that consume sport fish are estimated for various

  10. In silico tools to aid risk assessment of endocrine disrupting chemicals

    International Nuclear Information System (INIS)

    Jacobs, M.N.

    2004-01-01

    In silico or computational tools could be used more effectively in endocrine disruptor risk assessment for prescreening potential endocrine disruptors, improving experimental in vitro screening assay design and facilitating more thorough data analyses. The in silico tools reviewed here are three-fold and include the use of: (1) nuclear receptor (NR) crystal structures and homology models to examine potential modes of ligand binding by different representative compounds; (2) multivariate principal component analyses (PCA) techniques to select best predicted cell lines for endocrine disrupting chemicals (EDC) risk assessment purposes; (3) NR quantitative structure-activity relationships (QSARs) that can be constructed from varied biological data sources, using multivariate partial least squares (PLS) techniques and specific descriptors. The cytosolic and NR examples discussed here include the Ah receptor (AhR), the human oestrogen receptor α (hERα) and the human pregnane X receptor (PXR). The varied biological data sets can be compared to give a more integrated dimension to receptor cross talk mechanisms, with further support from molecular modelling studies

  11. Falls risk assessment begins with hello: lessons learned from the use of one home health agency's fall risk tool.

    Science.gov (United States)

    Flemming, Patricia J; Ramsay, Katherine

    2012-10-01

    Identifying older adults at risk for falls is a challenge all home healthcare agencies (HHAs) face. The process of assessing for falls risk begins with the initial home visit. One HHA affiliated with an academic medical center describes its experience in development and use of a Falls Risk Assessment (FRA) tool over a 10-year period. The FRA tool has been modified since initial development to clarify elements of the tool based on research and to reflect changes in the Outcome and Assessment Information Set (OASIS) document. The primary purpose of this article is to share a validated falls risk assessment tool to facilitate identification of fall-related risk factors in the homebound population. A secondary purpose is to share lessons learned by the HHA during the 10 years using the FRA.

  12. The Sydney Triage to Admission Risk Tool (START): A prospective validation study.

    Science.gov (United States)

    Ebker-White, Anja A; Bein, Kendall J; Dinh, Michael M

    2018-02-08

    The present study aims to prospectively validate the Sydney Triage to Admission Risk Tool (START) to predict ED disposition. This was a prospective validation study at two metropolitan EDs in Sydney, Australia. Consecutive triage encounters were observed by a trained researcher and START scores calculated. The primary outcome was patient disposition (discharge or inpatient admission) from the ED. Multivariable logistic regression was used to estimate area under curve of receiver operator characteristic (AUC ROC) for START scores as well as START score in combination with other variables such as frailty, general practitioner referral, overcrowding and major medical comorbidities. There were 894 patients analysed during the study period. The START score when applied to the data had AUC ROC of 0.80 (95% CI 0.77-0.83). The inclusion of other clinical variables identified at triage did not improve the overall performance of the model with an AUC ROC of 0.81 (95% CI 0.78-0.84) in the present study. The overall performance of the START tool with respect to model discrimination and accuracy has been prospectively validated. Further clinical trials are required to test the clinical effectiveness of the tool in improving patient flow and overall ED performance. © 2018 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

  13. Predictive technologies: Can smart tools augment the brain’s predictive abilities?

    Directory of Open Access Journals (Sweden)

    Giovanni ePezzulo

    2016-04-01

    Full Text Available The ability of looking into the future – namely, the capacity of anticipating future states of the environment or of the body – represents a fundamental function of human (and animal brains. A goalkeeper who tries to guess the ball’s direction; a chess player who attempts to anticipate the opponent’s next move; or a man-in-love who tries to calculate what are the chances of her saying yes – in all these cases, people are simulating possible future states of the world, in order to maximize the success of their decisions or actions. Research in neuroscience is showing that our ability to predict the behaviour of physical or social phenomena is largely dependent on the brain’s ability to integrate current and past information to generate (probabilistic simulations of the future. But could predictive processing be augmented using advanced technologies? In this contribution, we discuss how computational technologies may be used to support, facilitate or enhance the prediction of future events, by considering exemplificative scenarios across different domains, from simpler sensorimotor decisions to more complex cognitive tasks. We also examine the key scientific and technical challenges that must be faced to turn this vision into reality.

  14. 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.

  15. 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)

  16. Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups.

    Science.gov (United States)

    Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

    2012-03-14

    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). A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified

  17. Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

    Directory of Open Access Journals (Sweden)

    Marschollek Michael

    2012-03-01

    Full Text Available 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 data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493. A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. Results The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Conclusions Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack

  18. 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.

  19. PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites.

    Directory of Open Access Journals (Sweden)

    Jiangning Song

    Full Text Available The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s. Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate

  20. 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...

  1. Development and validation of the FRAGIRE tool for assessment an older person's risk for frailty.

    Science.gov (United States)

    Vernerey, Dewi; Anota, Amelie; Vandel, Pierre; Paget-Bailly, Sophie; Dion, Michele; Bailly, Vanessa; Bonin, Marie; Pozet, Astrid; Foubert, Audrey; Benetkiewicz, Magdalena; Manckoundia, Patrick; Bonnetain, Franck

    2016-11-17

    Frailty is highly prevalent in elderly people. While significant progress has been made to understand its pathogenesis process, few validated questionnaire exist to assess the multidimensional concept of frailty and to detect people frail or at risk to become frail. The objectives of this study were to construct and validate a new frailty-screening instrument named Frailty Groupe Iso-Ressource Evaluation (FRAGIRE) that accurately predicts the risk for frailty in older adults. A prospective multicenter recruitment of the elderly patients was undertaken in France. The subjects were classified into financially-helped group (FH, with financial assistance) and non-financially helped group (NFH, without any financial assistance), considering FH subjects are more frail than the NFH group and thus representing an acceptable surrogate population for frailty. Psychometric properties of the FRAGIRE grid were assessed including discrimination between the FH and NFH groups. Items reduction was made according to statistical analyses and experts' point of view. The association between items response and tests with "help requested status" was assessed in univariate and multivariate unconditional logistic regression analyses and a prognostic score to become frail was finally proposed for each subject. Between May 2013 and July 2013, 385 subjects were included: 338 (88%) in the FH group and 47 (12%) in the NFH group. The initial FRAGIRE grid included 65 items. After conducting the item selection, the final grid of the FRAGIRE was reduced to 19 items. The final grid showed fair discrimination ability to predict frailty (area under the curve (AUC) = 0.85) and good calibration (Hosmer-Lemeshow P-value = 0.580), reflecting a good agreement between the prediction by the final model and actual observation. The Cronbach's alpha for the developed tool scored as high as 0.69 (95% Confidence Interval: 0.64 to 0.74). The final prognostic score was excellent, with an AUC of 0

  2. Application of predictive modelling techniques in industry: from food design up to risk assessment.

    Science.gov (United States)

    Membré, Jeanne-Marie; Lambert, Ronald J W

    2008-11-30

    In this communication, examples of applications of predictive microbiology in industrial contexts (i.e. Nestlé and Unilever) are presented which cover a range of applications in food safety from formulation and process design to consumer safety risk assessment. A tailor-made, private expert system, developed to support safe product/process design assessment is introduced as an example of how predictive models can be deployed for use by non-experts. Its use in conjunction with other tools and software available in the public domain is discussed. Specific applications of predictive microbiology techniques are presented relating to investigations of either growth or limits to growth with respect to product formulation or process conditions. An example of a probabilistic exposure assessment model for chilled food application is provided and its potential added value as a food safety management tool in an industrial context is weighed against its disadvantages. The role of predictive microbiology in the suite of tools available to food industry and some of its advantages and constraints are discussed.

  3. An Online Tool for Nurse Triage to Evaluate Risk for Acute Coronary Syndrome at Emergency Department

    Directory of Open Access Journals (Sweden)

    Yuwares Sittichanbuncha

    2015-01-01

    Full Text Available Background. To differentiate acute coronary syndrome (ACS from other causes in patients presenting with chest pain at the emergency department (ED is crucial and can be performed by the nurse triage. We evaluated the effectiveness of the ED nurse triage for ACS of the tertiary care hospital. Methods. We retrospectively enrolled consecutive patients who were identified as ACS at risk patients by the ED nurse triage. Patients were categorized as ACS and non-ACS group by the final diagnosis. Multivariate logistic analysis was used to predict factors associated with ACS. An online model predictive of ACS for the ED nurse triage was constructed. Results. There were 175 patients who met the study criteria. Of those, 28 patients (16.0% were diagnosed with ACS. Patients with diabetes, patients with previous history of CAD, and those who had at least one character of ACS chest pain were independently associated with having ACS by multivariate logistic regression. The adjusted odds ratios (95% confidence interval were 4.220 (1.445, 12.327, 3.333 (1.040, 10.684, and 12.539 (3.876, 40.567, respectively. Conclusions. The effectiveness of the ED nurse triage for ACS was 16%. The online tool is available for the ED triage nurse to evaluate risk of ACS in individuals.

  4. 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

  5. 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.

  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. 2012 AAPS National Biotech Conference Open Forum: a perspective on the current state of immunogenicity prediction and risk management.

    Science.gov (United States)

    Rajadhyaksha, Manoj; Subramanyam, Meena; Rup, Bonnie

    2013-10-01

    The immunogenicity profile of a biotherapeutic is determined by multiple product-, process- or manufacturing-, patient- and treatment-related factors and the bioanalytical methodology used to monitor for immunogenicity. This creates a complex situation that limits direct correlation of individual factors to observed immunogenicity rates. Therefore, mechanistic understanding of how these factors individually or in concert could influence the overall incidence and clinical risk of immunogenicity is crucial to provide the best benefit/risk profile for a given biotherapeutic in a given indication and to inform risk mitigation strategies. Advances in the field of immunogenicity have included development of best practices for monitoring anti-drug antibody development, categorization of risk factors contributing to immunogenicity, development of predictive tools, and development of effective strategies for risk management and mitigation. Thus, the opportunity to ask "where we are now and where we would like to go from here?" was the main driver for organizing an Open Forum on Improving Immunogenicity Risk Prediction and Management, conducted at the 2012 American Association of Pharmaceutical Scientists' (AAPS) National Biotechnology Conference in San Diego. The main objectives of the Forum include the following: to understand the nature of immunogenicity risk factors, to identify analytical tools used and animal models and management strategies needed to improve their predictive value, and finally to identify collaboration opportunities to improve the reliability of risk prediction, mitigation, and management. This meeting report provides the Forum participant's and author's perspectives on the barriers to advancing this field and recommendations for overcoming these barriers through collaborative efforts.

  8. Predicting Knowledge Workers' Participation in Voluntary Learning with Employee Characteristics and Online Learning Tools

    Science.gov (United States)

    Hicks, Catherine

    2018-01-01

    Purpose: This paper aims to explore predicting employee learning activity via employee characteristics and usage for two online learning tools. Design/methodology/approach: Statistical analysis focused on observational data collected from user logs. Data are analyzed via regression models. Findings: Findings are presented for over 40,000…

  9. Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools

    DEFF Research Database (Denmark)

    Greenbaum, Jason A.; Andersen, Pernille; Blythe, Martin

    2007-01-01

    and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington...

  10. Users' experiences of an emergency department patient admission predictive tool: A qualitative evaluation.

    Science.gov (United States)

    Jessup, Melanie; Crilly, Julia; Boyle, Justin; Wallis, Marianne; Lind, James; Green, David; Fitzgerald, Gerard

    2016-09-01

    Emergency department overcrowding is an increasing issue impacting patients, staff and quality of care, resulting in poor patient and system outcomes. In order to facilitate better management of emergency department resources, a patient admission predictive tool was developed and implemented. Evaluation of the tool's accuracy and efficacy was complemented with a qualitative component that explicated the experiences of users and its impact upon their management strategies, and is the focus of this article. Semi-structured interviews were conducted with 15 pertinent users, including bed managers, after-hours managers, specialty department heads, nurse unit managers and hospital executives. Analysis realised dynamics of accuracy, facilitating communication and enabling group decision-making Users generally welcomed the enhanced potential to predict and plan following the incorporation of the patient admission predictive tool into their daily and weekly decision-making processes. They offered astute feedback with regard to their responses when faced with issues of capacity and communication. Participants reported an growing confidence in making informed decisions in a cultural context that is continually moving from reactive to proactive. This information will inform further patient admission predictive tool development specifically and implementation processes generally. © The Author(s) 2015.

  11. 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.)

  12. PAH plant uptake prediction: Evaluation of combined availability tools and modeling approach

    OpenAIRE

    Ouvrard, Stéphanie; DUPUY, Joan; Leglize, Pierre; Sterckeman, Thibault

    2015-01-01

    Transfer to plant is one of the main human exposure pathways of polycyclic aromatic hydrocarbons (PAH) from contaminated soils. However existing models implemented in risk assessment tools mostly rely on i) total contaminant concentration and ii) plant uptake models based on hydroponics experiments established with pesticides (Briggs et al., 1982, 1983). Total concentrations of soil contaminants are useful to indicate pollution, however they do not necessarily indicate risk. Me...

  13. TCF7L2 variant genotypes and type 2 diabetes risk in Brazil: significant association, but not a significant tool for risk stratification in the general population

    Directory of Open Access Journals (Sweden)

    Mill JG

    2008-12-01

    Full Text Available Abstract Background Genetic polymorphisms of the TCF7L2 gene are strongly associated with large increments in type 2 diabetes risk in different populations worldwide. In this study, we aimed to confirm the effect of the TCF7L2 polymorphism rs7903146 on diabetes risk in a Brazilian population and to assess the use of this genetic marker in improving diabetes risk prediction in the general population. Methods We genotyped the single nucleotide polymorphisms (SNP rs7903146 of the TCF7L2 gene in 560 patients with known coronary disease enrolled in the MASS II (Medicine, Angioplasty, or Surgery Study Trial and in 1,449 residents of Vitoria, in Southeast Brazil. The associations of this gene variant to diabetes risk and metabolic characteristics in these two different populations were analyzed. To access the potential benefit of using this marker for diabetes risk prediction in the general population we analyzed the impact of this genetic variant on a validated diabetes risk prediction tool based on clinical characteristics developed for the Brazilian general population. Results SNP rs7903146 of the TCF7L2 gene was significantly associated with type 2 diabetes in the MASS-II population (OR = 1.57 per T allele, p = 0.0032, confirming, in the Brazilian population, previous reports of the literature. Addition of this polymorphism to an established clinical risk prediction score did not increased model accuracy (both area under ROC curve equal to 0.776. Conclusion TCF7L2 rs7903146 T allele is associated with a 1.57 increased risk for type 2 diabetes in a Brazilian cohort of patients with known coronary heart disease. However, the inclusion of this polymorphism in a risk prediction tool developed for the general population resulted in no improvement of performance. This is the first study, to our knowledge, that has confirmed this recent association in a South American population and adds to the great consistency of this finding in studies around the world

  14. Evaluation of fetal anthropometric measures to predict the risk for shoulder dystocia.

    Science.gov (United States)

    Burkhardt, T; Schmidt, M; Kurmanavicius, J; Zimmermann, R; Schäffer, L

    2014-01-01

    To evaluate the quality of anthropometric measures to improve the prediction of shoulder dystocia by combining different sonographic biometric parameters. This was a retrospective cohort study of 12,794 vaginal deliveries with complete sonographic biometry data obtained within 7 days before delivery. Receiver-operating characteristics (ROC) curves of various combinations of the biometric parameters, namely, biparietal diameter (BPD), occipitofrontal diameter (OFD), head circumference, abdominal diameter (AD), abdominal circumference (AC) and femur length were analyzed. The influences of independent risk factors were calculated and their combination used in a predictive model. The incidence of shoulder dystocia was 1.14%. Different combinations of sonographic parameters showed comparable ROC curves without advantage for a particular combination. The difference between AD and BPD (AD - BPD) (area under the curve (AUC) = 0.704) revealed a significant increase in risk (odds ratio (OR) 7.6 (95% CI 4.2-13.9), sensitivity 8.2%, specificity 98.8%) at a suggested cut-off ≥ 2.6 cm. However, the positive predictive value (PPV) was low (7.5%). The AC as a single parameter (AUC = 0.732) with a cut-off ≥ 35 cm performed worse (OR 4.6 (95% CI 3.3-6.5), PPV 2.6%). BPD/OFD (a surrogate for fetal cranial shape) was not significantly different between those with and those without shoulder dystocia. The combination of estimated fetal weight, maternal diabetes, gender and AD - BPD provided a reasonable estimate of the individual risk. Sonographic fetal anthropometric measures appear not to be a useful tool to screen for the risk of shoulder dystocia due to a low PPV. However, AD - BPD appears to be a relevant risk factor. While risk stratification including different known risk factors may aid in counseling, shoulder dystocia cannot effectively be predicted. Copyright © 2013 ISUOG. Published by John Wiley & Sons Ltd.

  15. 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

  16. 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.

  17. RDNAnalyzer: A tool for DNA secondary structure prediction and sequence analysis.

    Science.gov (United States)

    Afzal, Muhammad; Shahid, Ahmad Ali; Shehzadi, Abida; Nadeem, Shahid; Husnain, Tayyab

    2012-01-01

    RDNAnalyzer is an innovative computer based tool designed for DNA secondary structure prediction and sequence analysis. It can randomly generate the DNA sequence or user can upload the sequences of their own interest in RAW format. It uses and extends the Nussinov dynamic programming algorithm and has various application for the sequence analysis. It predicts the DNA secondary structure and base pairings. It also provides the tools for routinely performed sequence analysis by the biological scientists such as DNA replication, reverse compliment generation, transcription, translation, sequence specific information as total number of nucleotide bases, ATGC base contents along with their respective percentages and sequence cleaner. RDNAnalyzer is a unique tool developed in Microsoft Visual Studio 2008 using Microsoft Visual C# and Windows Presentation Foundation and provides user friendly environment for sequence analysis. It is freely available. http://www.cemb.edu.pk/sw.html RDNAnalyzer - Random DNA Analyser, GUI - Graphical user interface, XAML - Extensible Application Markup Language.

  18. Long-Term Survival Prediction for Coronary Artery Bypass Grafting: Validation of the ASCERT Model Compared With The Society of Thoracic Surgeons Predicted Risk of Mortality.

    Science.gov (United States)

    Lancaster, Timothy S; Schill, Matthew R; Greenberg, Jason W; Ruaengsri, Chawannuch; Schuessler, Richard B; Lawton, Jennifer S; Maniar, Hersh S; Pasque, Michael K; Moon, Marc R; Damiano, Ralph J; Melby, Spencer J

    2018-05-01

    The recently developed American College of Cardiology Foundation-Society of Thoracic Surgeons (STS) Collaboration on the Comparative Effectiveness of Revascularization Strategy (ASCERT) Long-Term Survival Probability Calculator is a valuable addition to existing short-term risk-prediction tools for cardiac surgical procedures but has yet to be externally validated. Institutional data of 654 patients aged 65 years or older undergoing isolated coronary artery bypass grafting between 2005 and 2010 were reviewed. Predicted survival probabilities were calculated using the ASCERT model. Survival data were collected using the Social Security Death Index and institutional medical records. Model calibration and discrimination were assessed for the overall sample and for risk-stratified subgroups based on (1) ASCERT 7-year survival probability and (2) the predicted risk of mortality (PROM) from the STS Short-Term Risk Calculator. Logistic regression analysis was performed to evaluate additional perioperative variables contributing to death. Overall survival was 92.1% (569 of 597) at 1 year and 50.5% (164 of 325) at 7 years. Calibration assessment found no significant differences between predicted and actual survival curves for the overall sample or for the risk-stratified subgroups, whether stratified by predicted 7-year survival or by PROM. Discriminative performance was comparable between the ASCERT and PROM models for 7-year survival prediction (p validated for prediction of long-term survival after coronary artery bypass grafting in all risk groups. The widely used STS PROM performed comparably as a predictor of long-term survival. Both tools provide important information for preoperative decision making and patient counseling about potential outcomes after coronary artery bypass grafting. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

  19. Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm.

    Science.gov (United States)

    Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

    2017-04-01

    Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The 'predictAL-10' risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the 'predictAL-9'), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. © British Journal of General Practice 2017.

  20. 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

  1. 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...

  2. Predicted Interval Plots (PIPS): A Graphical Tool for Data Monitoring of Clinical Trials.

    Science.gov (United States)

    Li, Lingling; Evans, Scott R; Uno, Hajime; Wei, L J

    2009-11-01

    Group sequential designs are often used in clinical trials to evaluate efficacy and/or futility. Many methods have been developed for different types of endpoints and scenarios. However, few of these methods convey information regarding effect sizes (e.g., treatment differences) and none uses prediction to convey information regarding potential effect size estimates and associated precision, with trial continuation. To address these limitations, Evans et al. (2007) proposed to use prediction and predicted intervals as a flexible and practical tool for quantitative monitoring of clinical trials. In this article, we reaffirm the importance and usefulness of this innovative approach and introduce a graphical summary, predicted interval plots (PIPS), to display the information obtained in the prediction process in a straightforward yet comprehensive manner. We outline the construction of PIPS and apply this method in two examples. The results and the interpretations of the PIPS are discussed.

  3. 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.

  4. 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.

  5. Risk management plans as a tool for proactive pharmacovigilance

    DEFF Research Database (Denmark)

    Vermeer, N S; Duijnhoven, R G; Straus, S M J M

    2014-01-01

    Risk Management Plans (RMPs) have become a cornerstone in the pharmacovigilance of new drugs in Europe. The RMP was introduced in 2005 to support a proactive approach in gaining knowledge on safety concerns through early planning of pharmacovigilance activities. However, the rate at which...... of uncertainties, suggests that opportunities for optimization exist while ensuring feasible and risk-proportionate pharmacovigilance planning....

  6. NanoRisk - A Conceptual Decision Support Tool for Nanomaterials

    DEFF Research Database (Denmark)

    Hansen, Steffen Foss; Baun, Anders; Alstrup Jensen, K.

    2011-01-01

    Only a few risk assessment methodologies and approaches are useful for assessing the risk for professional end-users, consumers and the environment. We have developed a generic framework (NanoRiskCat) that can be used by companies and risk assessors to categorize nanomaterials considering existing...... environmental, health and safety information and known uncertainties. In NanoRiskCat’s simplest form, the final evaluation outcome for a specific nanomaterial in a given application will be communicated in the form of a short title (e.g. TiO2 in sunscreen) describing the use of the nanomaterial. This short...... to the exposure and hazard potential are green , yellow corresponding to none, possible, expected and unknown, respectively. The exposure potential was evaluated based on 1) the location of the nanomaterial and 2) a judgment of the potential of nanomaterial exposure based on the description and explanation...

  7. Quantitative Risk reduction estimation Tool For Control Systems, Suggested Approach and Research Needs

    Energy Technology Data Exchange (ETDEWEB)

    Miles McQueen; Wayne Boyer; Mark Flynn; Sam Alessi

    2006-03-01

    For the past year we have applied a variety of risk assessment technologies to evaluate the risk to critical infrastructure from cyber attacks on control systems. More recently, we identified the need for a stand alone control system risk reduction estimation tool to provide owners and operators of control systems with a more useable, reliable, and credible method for managing the risks from cyber attack. Risk is defined as the probability of a successful attack times the value of the resulting loss, typically measured in lives and dollars. Qualitative and ad hoc techniques for measuring risk do not provide sufficient support for cost benefit analyses associated with cyber security mitigation actions. To address the need for better quantitative risk reduction models we surveyed previous quantitative risk assessment research; evaluated currently available tools; developed new quantitative techniques [17] [18]; implemented a prototype analysis tool to demonstrate how such a tool might be used; used the prototype to test a variety of underlying risk calculational engines (e.g. attack tree, attack graph); and identified technical and research needs. We concluded that significant gaps still exist and difficult research problems remain for quantitatively assessing the risk to control system components and networks, but that a useable quantitative risk reduction estimation tool is not beyond reach.

  8. Malnutrition risk in hospitalized children: use of 3 screening tools in a large European population.

    Science.gov (United States)

    Chourdakis, Michael; Hecht, Christina; Gerasimidis, Konstantinos; Joosten, Koen Fm; Karagiozoglou-Lampoudi, Thomais; Koetse, Harma A; Ksiazyk, Janusz; Lazea, Cecilia; Shamir, Raanan; Szajewska, Hania; Koletzko, Berthold; Hulst, Jessie M

    2016-05-01

    Several malnutrition screening tools have been advocated for use in pediatric inpatients. We evaluated how 3 popular pediatric nutrition screening tools [i.e., the Pediatric Yorkhill Malnutrition Score (PYMS), the Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP), and the Screening Tool for Risk of Impaired Nutritional Status and Growth (STRONGKIDS)] compared with and were related to anthropometric measures, body composition, and clinical variables in patients who were admitted to tertiary hospitals across Europe. The 3 screening tools were applied in 2567 inpatients at 14 hospitals across 12 European countries. The classification of patients into different nutritional risk groups was compared between tools and related to anthropometric measures and clinical variables [e.g., length of hospital stay (LOS) and infection rates]. A similar rate of completion of the screening tools for each tool was achieved (PYMS: 86%; STAMP: 84%; and STRONGKIDS: 81%). Risk classification differed markedly by tool, with an overall agreement of 41% between tools. Children categorized as high risk (PYMS: 25%; STAMP: 23%; and STRONGKIDS: 10%) had a longer LOS than that of children at low risk (1.4, 1.4, and 1.8 d longer, respectively; P malnutrition risk varied across the pediatric tools used. A considerable portion of children with subnormal anthropometric measures were not identified with all of the tools. The data obtained do not allow recommending the use of any of these screening tools for clinical practice. This study was registered at clinicaltrials.gov as NCT01132742. © 2016 American Society for Nutrition.

  9. 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.

  10. The ACTA PORT-score for predicting perioperative risk of blood transfusion for adult cardiac surgery.

    Science.gov (United States)

    Klein, A A; Collier, T; Yeates, J; Miles, L F; Fletcher, S N; Evans, C; Richards, T

    2017-09-01

    A simple and accurate scoring system to predict risk of transfusion for patients undergoing cardiac surgery is lacking. We identified independent risk factors associated with transfusion by performing univariate analysis, followed by logistic regression. We then simplified the score to an integer-based system and tested it using the area under the receiver operator characteristic (AUC) statistic with a Hosmer-Lemeshow goodness-of-fit test. Finally, the scoring system was applied to the external validation dataset and the same statistical methods applied to test the accuracy of the ACTA-PORT score. Several factors were independently associated with risk of transfusion, including age, sex, body surface area, logistic EuroSCORE, preoperative haemoglobin and creatinine, and type of surgery. In our primary dataset, the score accurately predicted risk of perioperative transfusion in cardiac surgery patients with an AUC of 0.76. The external validation confirmed accuracy of the scoring method with an AUC of 0.84 and good agreement across all scores, with a minor tendency to under-estimate transfusion risk in very high-risk patients. The ACTA-PORT score is a reliable, validated tool for predicting risk of transfusion for patients undergoing cardiac surgery. This and other scores can be used in research studies for risk adjustment when assessing outcomes, and might also be incorporated into a Patient Blood Management programme. © The Author 2017. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  11. Application of Risk Monitor MAREas tool in operation and maintenance

    International Nuclear Information System (INIS)

    Carretero, J. A.; Fuentes, I.

    2004-01-01

    From the very beginning and ongoing application objective of the Probabilistic Safety Assessment (PSA) was to develop Monitors. Their development was contingent on the PSA model computing capacity of the computer tools. the availability of effective tools, as well as the requirements of the Consejo de Seguridad Nuclear to apply the Maintenance Rule, have driven their implementation in Spain. The MARE application of Empresarios Agrupados presented herein has been developed for that purpose and has been implemented in Almaraz NPP. This article describes the process followed and experience in using it. (Author)

  12. Deep learning architectures for multi-label classification of intelligent health risk prediction.

    Science.gov (United States)

    Maxwell, Andrew; Li, Runzhi; Yang, Bei; Weng, Heng; Ou, Aihua; Hong, Huixiao; Zhou, Zhaoxian; Gong, Ping; Zhang, Chaoyang

    2017-12-28

    Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging

  13. Application of structural reliability and risk assessment to life prediction and life extension decision making

    International Nuclear Information System (INIS)

    Meyer, T.A.; Balkey, K.R.; Bishop, B.A.

    1987-01-01

    There can be numerous uncertainties involved in performing component life assessments. In addition, sufficient data may be unavailable to make a useful life prediction. Structural Reliability and Risk Assessment (SRRA) is primarily an analytical methodology or tool that quantifies the impact of uncertainties on the structural life of plant components and can address the lack of data in component life prediction. As a prelude to discussing the technical aspects of SRRA, a brief review of general component life prediction methods is first made so as to better develop an understanding of the role of SRRA in such evaluations. SRRA is then presented as it is applied in component life evaluations with example applications being discussed for both nuclear and non-nuclear components

  14. The KnowRISK project: Tools and strategies for risk communication and learning

    Science.gov (United States)

    Musacchio, Gemma; Amaral Ferreira, Mónica; Falsaperla, Susanna; Piangiamore, Giovanna Lucia; Pino, Nicola Alessandro; Solarino, Stefano; Crescimbene, Massimo; Eva, Elena; Reitano, Danilo; Þorvaldsdottir, Solveig; Sousa Silva, Delta; Rupakhety, Rajesh; Sousa Oliveira, Carlos

    2016-04-01

    Damage of non-structural elements of buildings (i.e. partitions, ceilings, cladding, electrical and mechanical systems and furniture) is known to cause injuries and human losses. Also it has a significant impact on earthquake resilience and is yet being worldwide underestimated. The project KnowRISK (Know your city, Reduce seISmic risK through non-structural elements) is financed by the European Commission to develop prevention measures that may reduce non-structural damage in urban areas. Pilot areas of the project are within the three European participating countries, namely Portugal, Iceland and Italy. They were chosen because they are prone to damage level 2 and 3 (EMS-98, European Macroseismic Scale) that typically affects non-structural elements. We will develop and test a risk communication strategy taking into account the needs of households and schools, putting into practice a portfolio of best practice to reduce the most common non-structural vulnerabilities. We will target our actions to different societal groups, considering their cultural background and social vulnerabilities, and implement a participatory approach that will promote engagement and interaction between the scientific community, practitioners and citizens to foster knowledge on everyone's own neighborhoods, resilience and vulnerability. A Practical Guide for citizens will highlight that low-cost actions can be implemented to increase safety of households, meant as being the places where the most vulnerable societal groups, including children and elderly people, spend much of their time. Since our actions towards communication will include education, we will define tools that allow a clear and direct understanding of elements exposed to risk. Schools will be one of our target societal groups and their central role played at the community level will ensure spreading and strengthening of the communication process. Schools are often located in old or re-adapted buildings, formerly used for

  15. Predictive validity of the Hendrich fall risk model II in an acute geriatric unit.

    Science.gov (United States)

    Ivziku, Dhurata; Matarese, Maria; Pedone, Claudio

    2011-04-01

    Falls are the most common adverse events reported in acute care hospitals, and older patients are the most likely to fall. The risk of falling cannot be completely eliminated, but it can be reduced through the implementation of a fall prevention program. A major evidence-based intervention to prevent falls has been the use of fall-risk assessment tools. Many tools have been increasingly developed in recent years, but most instruments have not been investigated regarding reliability, validity and clinical usefulness. This study intends to evaluate the predictive validity and inter-rater reliability of Hendrich fall risk model II (HFRM II) in order to identify older patients at risk of falling in geriatric units and recommend its use in clinical practice. A prospective descriptive design was used. The study was carried out in a geriatric acute care unit of an Italian University hospital. All over 65 years old patients consecutively admitted to a geriatric acute care unit of an Italian University hospital over 8-month period were enrolled. The patients enrolled were screened for the falls risk by nurses with the HFRM II within 24h of admission. The falls occurring during the patient's hospital stay were registered. Inter-rater reliability, area under the ROC curve, sensitivity, specificity, positive and negative predictive values and time for the administration were evaluated. 179 elderly patients were included. The inter-rater reliability was 0.87 (95% CI 0.71-1.00). The administration time was about 1min. The most frequently reported risk factors were depression, incontinence, vertigo. Sensitivity and specificity were respectively 86% and 43%. The optimal cut-off score for screening at risk patients was 5 with an area under the ROC curve of 0.72. The risk factors more strongly associated with falls were confusion and depression. As falls of older patients are a common problem in acute care settings it is necessary that the nurses use specific validate and reliable

  16. Engineering a mobile health tool for resource-poor settings to assess and manage cardiovascular disease risk: SMARThealth study.

    Science.gov (United States)

    Raghu, Arvind; Praveen, Devarsetty; Peiris, David; Tarassenko, Lionel; Clifford, Gari

    2015-04-29

    The incidence of chronic diseases in low- and middle-income countries is rapidly increasing both in urban and rural regions. A major challenge for health systems globally is to develop innovative solutions for the prevention and control of these diseases. This paper discusses the development and pilot testing of SMARTHealth, a mobile-based, point-of-care Clinical Decision Support (CDS) tool to assess and manage cardiovascular disease (CVD) risk in resource-constrained settings. Through pilot testing, the preliminary acceptability, utility, and efficiency of the CDS tool was obtained. The CDS tool was part of an mHealth system comprising a mobile application that consisted of an evidence-based risk prediction and management algorithm, and a server-side electronic medical record system. Through an agile development process and user-centred design approach, key features of the mobile application that fitted the requirements of the end users and environment were obtained. A comprehensive analytics framework facilitated a data-driven approach to investigate four areas, namely, system efficiency, end-user variability, manual data entry errors, and usefulness of point-of-care management recommendations to the healthcare worker. A four-point Likert scale was used at the end of every risk assessment to gauge ease-of-use of the system. The system was field-tested with eleven village healthcare workers and three Primary Health Centre doctors, who screened a total of 292 adults aged 40 years and above. 34% of participants screened by health workers were identified by the CDS tool to be high CVD risk and referred to a doctor. In-depth analysis of user interactions found the CDS tool feasible for use and easily integrable into the workflow of healthcare workers. Following completion of the pilot, further technical enhancements were implemented to improve uptake of the mHealth platform. It will then be evaluated for effectiveness and cost-effectiveness in a cluster randomized

  17. Combining Results from Distinct MicroRNA Target Prediction Tools Enhances the Performance of Analyses

    Directory of Open Access Journals (Sweden)

    Arthur C. Oliveira

    2017-05-01

    Full Text Available Target prediction is generally the first step toward recognition of bona fide microRNA (miRNA-target interactions in living cells. Several target prediction tools are now available, which use distinct criteria and stringency to provide the best set of candidate targets for a single miRNA or a subset of miRNAs. However, there are many false-negative predictions, and consensus about the optimum strategy to select and use the output information provided by the target prediction tools is lacking. We compared the performance of four tools cited in literature—TargetScan (TS, miRanda-mirSVR (MR, Pita, and RNA22 (R22, and we determined the most effective approach for analyzing target prediction data (individual, union, or intersection. For this purpose, we calculated the sensitivity, specificity, precision, and correlation of these approaches using 10 miRNAs (miR-1-3p, miR-17-5p, miR-21-5p, miR-24-3p, miR-29a-3p, miR-34a-5p, miR-124-3p, miR-125b-5p, miR-145-5p, and miR-155-5p and 1,400 genes (700 validated and 700 non-validated as targets of these miRNAs. The four tools provided a subset of high-quality predictions and returned few false-positive predictions; however, they could not identify several known true targets. We demonstrate that union of TS/MR and TS/MR/R22 enhanced the quality of in silico prediction analysis of miRNA targets. We conclude that the union rather than the intersection of the aforementioned tools is the best strategy for maximizing performance while minimizing the loss of time and resources in subsequent in vivo and in vitro experiments for functional validation of miRNA-target interactions.

  18. Semantic Mediation Tool for Risk Reduction, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — This project focuses on providing an infrastructure to aid the building of ontologies from existing NASA applications, in a manner that leads to long-term risk...

  19. Development of a Breast Cancer Risk Prediction Model for Women in Nigeria.

    Science.gov (United States)

    Wang, Shengfeng; Ogundiran, Temidayo O; Ademola, Adeyinka; Oluwasola, Olayiwola A; Adeoye, Adewunmi O; Sofoluwe, Adenike; Morhason-Bello, Imran; Odedina, Stella O; Agwai, Imaria; Adebamowo, Clement; Obajimi, Millicent; Ojengbede, Oladosu; Olopade, Olufunmilayo I; Huo, Dezheng

    2018-04-20

    Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aim to develop a model for absolute breast cancer risk prediction for Nigerian women. A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998~2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model. The NBCS model included age, age at menarche, parity, duration of breast feeding, family history of breast cancer, height, body mass index, benign breast diseases and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model (area under ROC curve [AUC]=0.703, 95% confidence interval [CI]: 0.687-0.719) was better than the Black Women's Health Study (BWHS) model (AUC=0.605, 95% CI: 0.586-0.624), Gail model for White population (AUC=0.551, 95% CI: 0.531-0.571), and Gail model for Black population (AUC=0.545, 95% CI: 0.525-0.565). Compared to the BWHS, two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45% and 14.19%, respectively. We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in SSA populations. Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high-risk for breast cancer screening. Copyright ©2018, American Association for Cancer Research.

  20. Risk based service life prediction of underground cast iron pipes subjected to corrosion

    International Nuclear Information System (INIS)

    Li, C.Q.; Mahmoodian, M.

    2013-01-01

    Aging and deterioration of underground cast iron pipes is inevitable after their long time in service, with corrosion being the most predominant mechanism for pipe failures. Although considerable research has been undertaken in the past few decades, more is on the effects of corrosion on structural capacity of pipes than that on the prediction of their service life. This paper presents a methodology to quantitatively assess the risk of pipe collapse and predict its remaining service life using a time-dependent reliability theory. The concept of stress intensity in fracture mechanics is employed to establish the failure criterion of pipe collapse. An empirical model is derived for maximum pit growth of corrosion from the available data based on mathematical regressions. An example is provided to illustrate the application of the proposed method. It is found in the paper that the risk of pipe collapse increases with an increase in the diameter of the pipe for both external and internal corrosion. It is also found that the tougher the pipe is, the smaller the risk of its collapse. The paper concludes that a time-dependent reliability method is a very useful tool to predict the risk of pipe collapse and its remaining service life. The proposed method can help the water industry develop rehabilitation or replacement strategy for existing pipe networks with a view for better management of the pipe asset

  1. 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

  2. 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

  3. Risk management for engineering projects procedures, methods and tools

    CERN Document Server

    Munier, Nolberto

    2014-01-01

    Many people see risk in engineering projects as an imprecise and nebulous problem - something that exists, is feared and is impossible to deal with. Nothing could be further from the truth. While risk is certainly ubiquitous, sometimes difficult to detect, and cannot always be completely avoided, it can generally be mitigated, reduced or prevented through timely analysis and action.   This book covers the entire process of risk management by providing methodologies for determining the sources of project risk, and once threats have been identified, managing them through:   ·         identification and assessment (probability, relative importance, variables, risk breakdown structure, etc.) ·         implementation of measures for their prevention, reduction or mitigation ·         evaluation of impacts and quantification of risks ·         establishment of control measures   It also considers sensitivity analysis to determine the influence of uncertain parameters values ...

  4. Human factors questionnaire as a tool for risk assessment

    International Nuclear Information System (INIS)

    Santos, Isaac J.A.L.; Grecco, Claudio H.S.; Carvalho, Paulo V.R.; Mol, Antonio C.A.; Oliveira, Mauro V.; Augusto, Silas C.

    2009-01-01

    The human factors engineering (HFE) as a discipline, and as a process, seeks to discover and to apply knowledge about human capabilities and limitations to system and equipment design, ensuring that the system design, human tasks and work environment are compatible with the sensory, perceptual, cognitive and physical attributes of the personnel who operates systems and equipment. Risk significance considers the magnitude of the consequences (loss of life, material damage, environmental degradation) and the frequency of occurrence of a particular adverse event. The questionnaire design was based on the following definitions: the score and the classification of the nuclear safety risk. The principal benefit of applying an approach based on the risk significance in the development of the questionnaire is to ensure the identification and evaluation of the features of the projects, related to human factors, which affect the nuclear safety risk, the human actions and the safety of the nuclear plant systems. The human factors questionnaire developed in this study will provide valuable support for risk assessment, making possible the identification of design problems that can influence the evaluation of the nuclear safety risk. (author)

  5. A lower limb assessment tool for athletes at risk of developing patellar tendinopathy.

    Science.gov (United States)

    Mann, Kerry J; Edwards, Suzi; Drinkwater, Eric J; Bird, Stephen P

    2013-03-01

    Patellar tendon abnormality (PTA) on diagnostic imaging is part of the diagnostic criteria for patellar tendinopathy. PTA and altered landing strategies are primary risk factors that increase the likelihood of asymptomatic athletes developing patellar tendinopathy. Therefore, the aim of this study was to examine the risk factors that are predictors of the presence and severity of a PTA in junior pre-elite athletes. Ten junior pre-elite male basketball athletes with a PTA were matched with 10 athletes with normal patellar tendons. Participants had patellar tendon morphology, Victorian Institute of Sport Assessment (VISA) score, body composition, lower limb flexibility, and maximum vertical jump height measured before performing five successful stop-jump tasks. During each stop-jump task, both two-dimensional and three-dimensional kinematics and ground reaction forces were recorded. Multiple regression analyses were used to identify factors for estimating PTA presence and severity, and discriminate analysis was used to classify PTA presence. Sixty-eight percent of variance for presence of a PTA was accounted for by hip joint range of motion (ROM) and knee joint angle at initial foot-ground contact (IC) during stop-jump task and quadriceps flexibility, whereas hip joint ROM during stop-jump task and VISA score accounted for 62% of variance for PTA severity. Prediction of the presence of a PTA was achieved with 95% accuracy and 95% cross-validation. An easily implemented, reliable, and valid movement screening tool composed of three criteria enables coaches and/or clinicians to predict the presence and severity of a PTA in asymptomatic athletes. This enables identification of asymptomatic athletes at higher risk of developing patellar tendinopathy, which allows the development of effective preventative measures to aid in the reduction of patellar tendinopathy injury prevalence.

  6. The use of machine learning and nonlinear statistical tools for ADME prediction.

    Science.gov (United States)

    Sakiyama, Yojiro

    2009-02-01

    Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future.

  7. Dialysis Malnutrition and Malnutrition Inflammation Scores: screening tools for prediction of dialysis-related protein-energy wasting in Malaysia.

    Science.gov (United States)

    Harvinder, Gilcharan Singh; Swee, Winnie Chee Siew; Karupaiah, Tilakavati; Sahathevan, Sharmela; Chinna, Karuthan; Ahmad, Ghazali; Bavanandan, Sunita; Goh, Bak Leong

    2016-01-01

    Malnutrition is highly prevalent in Malaysian dialysis patients and there is a need for a valid screening tool for early identification and management. This cross-sectional study aims to examine the sensitivity of the Dialysis Malnutrition Score (DMS) and Malnutrition Inflammation Score (MIS) tools in predicting protein-energy wasting (PEW) among Malaysian dialysis patients. A total of 155 haemodialysis (HD) and 90 peritoneal dialysis (PD) patients were screened for risk of malnutrition using DMS and MIS and comparisons were made with established guidelines by International Society of Renal Nutrition and Metabolism (ISRNM) for PEW. MIS cut-off score of >=5 indicated presence of malnutrition in all patients. A total of 59% of HD and 83% of PD patients had PEW by ISRNM criteria. Based on DMS, 73% of HD and 71% of PD patients exhibited moderate malnutrition, whilst using MIS, 88% and 90%, respectively were malnourished. DMS and MIS correlated significantly in HD (r2=0.552, pmalnutrition classification were established (score >=5) for use amongst Malaysian dialysis patients. Both DMS and MIS are valid tools to be used for nutrition screening of dialysis patients especially those undergoing peritoneal dialysis. The DMS may be a more practical and simpler tool to be utilized in the Malaysian dialysis settings as it does not require laboratory markers.

  8. Flood Foresight: A near-real time flood monitoring and forecasting tool for rapid and predictive flood impact assessment

    Science.gov (United States)

    Revilla-Romero, Beatriz; Shelton, Kay; Wood, Elizabeth; Berry, Robert; Bevington, John; Hankin, Barry; Lewis, Gavin; Gubbin, Andrew; Griffiths, Samuel; Barnard, Paul; Pinnell, Marc; Huyck, Charles

    2017-04-01

    The hours and days immediately after a major flood event are often chaotic and confusing, with first responders rushing to mobilise emergency responders, provide alleviation assistance and assess loss to assets of interest (e.g., population, buildings or utilities). Preparations in advance of a forthcoming event are becoming increasingly important; early warning systems have been demonstrated to be useful tools for decision markers. The extent of damage, human casualties and economic loss estimates can vary greatly during an event, and the timely availability of an accurate flood extent allows emergency response and resources to be optimised, reduces impacts, and helps prioritise recovery. In the insurance sector, for example, insurers are under pressure to respond in a proactive manner to claims rather than waiting for policyholders to report losses. Even though there is a great demand for flood inundation extents and severity information in different sectors, generating flood footprints for large areas from hydraulic models in real time remains a challenge. While such footprints can be produced in real time using remote sensing, weather conditions and sensor availability limit their ability to capture every single flood event across the globe. In this session, we will present Flood Foresight (www.floodforesight.com), an operational tool developed to meet the universal requirement for rapid geographic information, before, during and after major riverine flood events. The tool provides spatial data with which users can measure their current or predicted impact from an event - at building, basin, national or continental scales. Within Flood Foresight, the Screening component uses global rainfall predictions to provide a regional- to continental-scale view of heavy rainfall events up to a week in advance, alerting the user to potentially hazardous situations relevant to them. The Forecasting component enhances the predictive suite of tools by providing a local

  9. 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

  10. Rosetta Structure Prediction as a Tool for Solving Difficult Molecular Replacement Problems.

    Science.gov (United States)

    DiMaio, Frank

    2017-01-01

    Molecular replacement (MR), a method for solving the crystallographic phase problem using phases derived from a model of the target structure, has proven extremely valuable, accounting for the vast majority of structures solved by X-ray crystallography. However, when the resolution of data is low, or the starting model is very dissimilar to the target protein, solving structures via molecular replacement may be very challenging. In recent years, protein structure prediction methodology has emerged as a powerful tool in model building and model refinement for difficult molecular replacement problems. This chapter describes some of the tools available in Rosetta for model building and model refinement specifically geared toward difficult molecular replacement cases.

  11. Numerical tools to predict the environmental loads for offshore structures under extreme weather conditions

    Science.gov (United States)

    Wu, Yanling

    2018-05-01

    In this paper, the extreme waves were generated using the open source computational fluid dynamic (CFD) tools — OpenFOAM and Waves2FOAM — using linear and nonlinear NewWave input. They were used to conduct the numerical simulation of the wave impact process. Numerical tools based on first-order (with and without stretching) and second-order NewWave are investigated. The simulation to predict force loading for the offshore platform under the extreme weather condition is implemented and compared.

  12. A risk assessment tool for contaminated sites in low-permeability fractured media

    DEFF Research Database (Denmark)

    Chambon, Julie Claire Claudia; Binning, Philip John; Jørgensen, Peter R.

    2011-01-01

    A risk assessment tool for contaminated sites in low-permeability fractured media is developed, based on simple transient and steady-state analytical solutions. The discrete fracture (DF) tool, which explicitly accounts for the transport along fractures, covers different source geometries...... and history (including secondary sources) and can be applied to a wide range of compounds. The tool successfully simulates published data from short duration column and field experiments. The use for risk assessment is illustrated by three typical risk assessment case studies, involving pesticides...

  13. The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer.

    Science.gov (United States)

    Wong, Hoong-Seam; Subramaniam, Shridevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M; Hartman, Mikael; Bhoo-Pathy, Nirmala

    2015-02-01

    Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients' actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: -1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74-0.81) and 0.73 (95% CI: 0.68-0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings.

  14. Risk score predicts high-grade prostate cancer in DNA-methylation positive, histopathologically negative biopsies.

    Science.gov (United States)

    Van Neste, Leander; Partin, Alan W; Stewart, Grant D; Epstein, Jonathan I; Harrison, David J; Van Criekinge, Wim

    2016-09-01

    Prostate cancer (PCa) diagnosis is challenging because efforts for effective, timely treatment of men with significant cancer typically result in over-diagnosis and repeat biopsies. The presence or absence of epigenetic aberrations, more specifically DNA-methylation of GSTP1, RASSF1, and APC in histopathologically negative prostate core biopsies has resulted in an increased negative predictive value (NPV) of ∼90% and thus could lead to a reduction of unnecessary repeat biopsies. Here, it is investigated whether, in methylation-positive men, DNA-methylation intensities could help to identify those men harboring high-grade (Gleason score ≥7) PCa, resulting in an improved positive predictive value. Two cohorts, consisting of men with histopathologically negative index biopsies, followed by a positive or negative repeat biopsy, were combined. EpiScore, a methylation intensity algorithm was developed in methylation-positive men, using area under the curve of the receiver operating characteristic as metric for performance. Next, a risk score was developed combining EpiScore with traditional clinical risk factors to further improve the identification of high-grade (Gleason Score ≥7) cancer. Compared to other risk factors, detection of DNA-methylation in histopathologically negative biopsies was the most significant and important predictor of high-grade cancer, resulting in a NPV of 96%. In methylation-positive men, EpiScore was significantly higher for those with high-grade cancer detected upon repeat biopsy, compared to those with either no or low-grade cancer. The risk score resulted in further improvement of patient risk stratification and was a significantly better predictor compared to currently used metrics as PSA and the prostate cancer prevention trial (PCPT) risk calculator (RC). A decision curve analysis indicated strong clinical utility for the risk score as decision-making tool for repeat biopsy. Low DNA-methylation levels in PCa-negative biopsies led

  15. 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?

  16. Dysphagia risk, low muscle strength and poor cognition predict malnutrition risk in older adults athospital admission.

    Science.gov (United States)

    Chatindiara, Idah; Allen, Jacqueline; Popman, Amy; Patel, Darshan; Richter, Marilize; Kruger, Marlena; Wham, Carol

    2018-03-21

    Malnutrition in patients admitted to hospital may have detrimental effects on recovery and healing. Malnutrition is preceded by a state of malnutrition risk, yet malnutrition risk is often not detected during admission. The aim of the current study was to investigate the magnitude and potential predictors of malnutrition risk in older adults, at hospital admission. A cross-sectional was study conducted in 234 older adults (age ≥ 65 or ≥ 55 for Māori or Pacific ethnicity) at admission to hospital in Auckland, New Zealand. Assessment of malnutrition risk status was performed using the Mini Nutritional Assessment Short-Form (MNA®-SF), dysphagia risk by the Eating Assessment Tool (EAT-10), muscle strength by hand grip strength and cognitive status by the Montreal Cognitive Assessment (MoCA) tool. Among 234 participants, mean age 83.6 ± 7.6 years, 46.6% were identified as at malnutrition risk and 26.9% malnourished. After adjusting for age, gender and ethnicity, the study identified [prevalence ratio (95% confidence interval)] high dysphagia risk [EAT-10 score: 0.98 (0.97-0.99)], low body mass index [kg/m 2 : 1.02 (1.02-1.03)], low muscle strength [hand grip strength, kg: 1.01 (1.00-1.02)] and decline in cognition [MoCA score: 1.01 (1.00-1.02)] as significant predictors of malnutrition risk in older adults at hospital admission. Among older adults recently admitted to the hospital, almost three-quarters were malnourished or at malnutrition risk. As the majority (88%) of participants were admitted from the community, this illustrates the need for routine nutrition screening both at hospital admission and in community-dwelling older adults. Factors such as dysphagia, unintentional weight loss, decline in muscle strength, and poor cognition may indicate increased risk of malnutrition.

  17. 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

  18. Prognostic factors in follicular lymphoma: new tools to personalize risk.

    Science.gov (United States)

    Casulo, Carla

    2016-12-02

    Follicular lymphoma (FL) is the most common indolent lymphoma, and it has a long median overall survival (OS). However, the recent discovery of clinical and biological prognostic biomarkers in FL is shedding light on FL heterogeneity and the need for a precise and risk-stratified individual approach at diagnosis and relapse. Many FL patients who are asymptomatic with indolent disease can be vulnerable to the toxicity, emotional distress, and financial burden of overtreatment. Yet a subset of FL patients develop chemoresistance to standard chemoimmunotherapy, experience transformation to aggressive lymphoma and rapid progression, and represent the population most in need of novel therapies and curative approaches. Novel biomarkers that incorporate both clinical and genetic determinants of poor risk are being developed with the hope of identifying high-risk patients at diagnosis in order to offer biologically rational targeted therapies. © 2016 by The American Society of Hematology. All rights reserved.

  19. Screening for substance abuse risk in cancer patients using the Opioid Risk Tool and urine drug screen.

    Science.gov (United States)

    Barclay, Joshua S; Owens, Justine E; Blackhall, Leslie J

    2014-07-01

    The use of opioids for management of cancer-related pain has increased significantly and has been associated with a substantial rise in rates of substance abuse and diversion. There is a paucity of data not only on the prevalence of substance abuse in cancer patients, but also for issues of drug use and diversion in family caregivers. This study aimed to evaluate the frequency of risk factors for substance abuse and diversion, and abnormal urine drug screens in cancer patients receiving palliative care. A retrospective chart review was performed for patients with cancer who were seen in the University of Virginia Palliative Care Clinic during the month of September 2012. We evaluated Opioid Risk Tool variables and total scores, insurance status, and urine drug screen results. Of the 114 cancer patients seen in September 2012, the mean Opioid Risk Tool score was 3.79, with 43% of patients defined as medium to high risk. Age (16-45 years old, 23%) and a personal history of alcohol (23%) or illicit drugs (21%) were the most common risk factors identified. We obtained a urine drug screen on 40% of patients, noting abnormal findings in 45.65%. Opioids are an effective treatment for cancer-related pain, yet substantial risk for substance abuse exits in the cancer population. Screening tools, such as the Opioid Risk Tool, should be used as part of a complete patient assessment to balance risk with appropriate relief of suffering.

  20. SU-D-BRB-01: A Predictive Planning Tool for Stereotactic Radiosurgery

    Energy Technology Data Exchange (ETDEWEB)

    Palefsky, S; Roper, J; Elder, E; Dhabaan, A [Winship Cancer Institute of Emory University, Atlanta, GA (United States)

    2015-06-15

    Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning guidance based on simple patient anatomical properties: PTV size, PTV shape and distance from critical structures. Methods: Ten framed SRS cases treated at Winship Cancer Institute of Emory University were analyzed to extract data on PTV size, sphericity (shape), and distance from critical structures such as the brainstem and optic chiasm. The cases consisted of five pairs. Each pair consisted of two cases with a similar diagnosis (such as pituitary adenoma or arteriovenous malformation) that were treated with different techniques: DCA, or IMRS. A Naive Bayes Classifier was trained on this data to establish the conditions under which each treatment modality was used. This model was validated by classifying ten other randomly-selected cases into DCA or IMRS classes, calculating the probability of each technique, and comparing results to the treated technique. Results: Of the ten cases used to validate the model, nine had their technique predicted correctly. The three cases treated with IMRS were all identified as such. Their probabilities of being treated with IMRS ranged between 59% and 100%. Six of the seven cases treated with DCA were correctly classified. These probabilities ranged between 51% and 95%. One case treated with DCA was incorrectly predicted to be an IMRS plan. The model’s confidence in this case was 91%. Conclusion: These findings indicate that a predictive planning tool based on simple patient anatomical properties can predict the SRS technique used for treatment. The algorithm operated with 90% accuracy. With further validation on larger patient populations, this tool may be used clinically to guide planners in choosing an appropriate treatment technique. The prediction algorithm could also be adapted to guide selection of treatment parameters such as treatment modality and number of fields for radiotherapy across anatomical sites.

  1. SU-D-BRB-01: A Predictive Planning Tool for Stereotactic Radiosurgery

    International Nuclear Information System (INIS)

    Palefsky, S; Roper, J; Elder, E; Dhabaan, A

    2015-01-01

    Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning guidance based on simple patient anatomical properties: PTV size, PTV shape and distance from critical structures. Methods: Ten framed SRS cases treated at Winship Cancer Institute of Emory University were analyzed to extract data on PTV size, sphericity (shape), and distance from critical structures such as the brainstem and optic chiasm. The cases consisted of five pairs. Each pair consisted of two cases with a similar diagnosis (such as pituitary adenoma or arteriovenous malformation) that were treated with different techniques: DCA, or IMRS. A Naive Bayes Classifier was trained on this data to establish the conditions under which each treatment modality was used. This model was validated by classifying ten other randomly-selected cases into DCA or IMRS classes, calculating the probability of each technique, and comparing results to the treated technique. Results: Of the ten cases used to validate the model, nine had their technique predicted correctly. The three cases treated with IMRS were all identified as such. Their probabilities of being treated with IMRS ranged between 59% and 100%. Six of the seven cases treated with DCA were correctly classified. These probabilities ranged between 51% and 95%. One case treated with DCA was incorrectly predicted to be an IMRS plan. The model’s confidence in this case was 91%. Conclusion: These findings indicate that a predictive planning tool based on simple patient anatomical properties can predict the SRS technique used for treatment. The algorithm operated with 90% accuracy. With further validation on larger patient populations, this tool may be used clinically to guide planners in choosing an appropriate treatment technique. The prediction algorithm could also be adapted to guide selection of treatment parameters such as treatment modality and number of fields for radiotherapy across anatomical sites

  2. 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.

  3. Estimation of the Cardiovascular Risk Using World Health Organization/International Society of Hypertension (WHO/ISH Risk Prediction Charts in a Rural Population of South India

    Directory of Open Access Journals (Sweden)

    Arun Gangadhar Ghorpade

    2015-08-01

    Full Text Available Background World Health Organization/International Society of Hypertension (WHO/ISH charts have been employed to predict the risk of cardiovascular outcome in heterogeneous settings. The aim of this research is to assess the prevalence of Cardiovascular Disease (CVD risk factors and to estimate the cardiovascular risk among adults aged >40 years, utilizing the risk charts alone, and by the addition of other parameters. Methods A cross-sectional study was performed in two of the villages availing health services of a medical college. Overall 570 subjects completed the assessment. The desired information was obtained using a pretested questionnaire and participants were also subjected to anthropometric measurements and laboratory investigations. The WHO/ISH risk prediction charts for the South-East Asian region was used to assess the cardiovascular risk among the study participants. Results The study covered 570 adults aged above 40 years. The mean age of the subjects was 54.2 (±11.1 years and 53.3% subjects were women. Seventeen percent of the participants had moderate to high risk for the occurrence of cardiovascular events by using WHO/ISH risk prediction charts. In addition, CVD risk factors like smoking, alcohol, low High-Density Lipoprotein (HDL cholesterol were found in 32%, 53%, 56.3%, and 61.5% study participants, respectively. Conclusion Categorizing people as low (20% risk is one of the crucial steps to mitigate the magnitude of cardiovascular fatal/non-fatal outcome. This cross-sectional study indicates that there is a high burden of CVD risk in the rural Pondicherry as assessed by WHO/ISH risk prediction charts. Use of WHO/ISH charts is easy and inexpensive screening tool in predicting the cardiovascular event.

  4. Climate risk screening tools and their application: A guide to the guidance

    Energy Technology Data Exchange (ETDEWEB)

    Traerup, S.; Olhoff, A.

    2011-07-01

    Climate risk screening is an integral part of efforts to ascertain current and future vulnerabilities and risks related to climate change. It is a prerequisite for identifying and designing adaptation measures, and an important element in the process of integrating, or mainstreaming, climate change adaptation into development project, planning and policy processes. There is an increasing demand and attention among national stakeholders in developing countries to take into account potential implications of climate variability and change for planning and prioritizing of development strategies and activities. Subsequently, there is a need for user friendly guidance on climate risk screening tools and their potentials for application that targets developing country stakeholders. This need is amplified by the sheer volume of climate change mainstreaming guidance documents and risk screening and assessment tools available and currently under development. Against this background, this paper sets out to provide potential users in developing countries, including project and programme developers and managers, with an informational entry point to climate risk screening tools. The emphasis in this report is on providing: 1) An overview of available climate risk screening and assessment tools along with indications of the tools available and relevant for specific purposes and contexts (Section 3). 2) Examples of application of climate risk screening and assessment tools along with links to further information (Section 4). Before turning to the respective sections on available climate risk screening tools and examples of their application, a delimitation of the tools included in this paper is included in Section 2. This section also provides a brief overview of how climate screening and related tools fit into decision making steps at various planning and decision making levels in conjunction with an outline of overall considerations to make when choosing a tool. The paper is

  5. Predictive analytics tools to adjust and monitor performance metrics for the ATLAS Production System

    CERN Document Server

    Barreiro Megino, Fernando Harald; The ATLAS collaboration

    2017-01-01

    Having information such as an estimation of the processing time or possibility of system outage (abnormal behaviour) helps to assist to monitor system performance and to predict its next state. The current cyber-infrastructure presents computing conditions in which contention for resources among high-priority data analysis happens routinely, that might lead to significant workload and data handling interruptions. The lack of the possibility to monitor and to predict the behaviour of the analysis process (its duration) and system’s state itself caused to focus on design of the built-in situational awareness analytic tools.

  6. 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.

  7. Stress Testing as a Tool of Bank Risk Management

    Directory of Open Access Journals (Sweden)

    Antonyuk Oksana I.

    2013-12-01

    Full Text Available The goal of the article is development of theoretical, methodological and practical recommendations on the use of stress testing by Ukrainian commercial banks. Stress testing is defined as a part of bank risk management on the basis of scientific studies of domestic and foreign scientists. The article marks the essence of the bank stress testing and identifies its role in the structure of banks’ risk management in Ukraine. It considers goals of conducting stress testing in banking institutions. It identifies main aspects and specific features of conducting stress testing of bank risks. It characterises main advantages and shortcomings of use of stress testing in the modern bank risk management. It generalises the world and European approaches to the methods of conducting stress testing in commercial banks in comparison with the Ukrainian methodical recommendations. It shows that results of stress testing have practical value, since they help to preliminary assess influence of potentially negative events upon the state of the loan portfolio of the bank and make relevant managerial decisions.

  8. CoCo design as a risk preventive tool

    NARCIS (Netherlands)

    Perotti, E.; Flannery, M.

    2011-01-01

    Contingent Convertible (CoCo) bonds have been suggested as a way to ensure that banks keep aside enough capital to help them through financial crises. This column proposes a market-triggered CoCo buffer to maintain risk incentives during periods of high leverage. It argues that this will also

  9. Risk Reduction with a Fuzzy Expert Exploration Tool

    Energy Technology Data Exchange (ETDEWEB)

    Weiss, William W.; Broadhead, Ron; Sung, Andrew

    2000-10-24

    This project developed an Artificial Intelligence system that drew up on a wide variety of information in providing realistic estimates of risk. ''Fuzzy logic,'' a system of integrating large amounts of inexact, incomplete information with modern computational methods derived usable conclusions, were demonstrated as a cost-effective computational technology in many industrial applications.

  10. Choice of corporate risk management tools under moral hazard

    Czech Academy of Sciences Publication Activity Database

    Bena, Jan

    -, č. 298 (2006), s. 1-43 ISSN 1211-3298 R&D Projects: GA MŠk LC542 Institutional research plan: CEZ:AV0Z70850503 Keywords : risk management * corporate insurance * moral hazard Subject RIV: AH - Economics http://www.cerge-ei.cz/pdf/wp/Wp298.pdf

  11. Choice of corporate risk management tools under moral hazard

    Czech Academy of Sciences Publication Activity Database

    Bena, Jan

    -, č. 566 (2006), s. 1-41 R&D Projects: GA MŠk LC542 Institutional research plan: CEZ:AV0Z70850503 Keywords : risk management * corporate insurance * moral hazard Subject RIV: AH - Economics http://fmg.lse.ac.uk/publications/searchdetail.php?pubid=1&wsid=1&wpdid=800

  12. Prognostic risk estimates of patients with multiple sclerosis and their physicians: comparison to an online analytical risk counseling tool.

    Directory of Open Access Journals (Sweden)

    Christoph Heesen

    Full Text Available BACKGROUND: Prognostic counseling in multiple sclerosis (MS is difficult because of the high variability of disease progression. Simultaneously, patients and physicians are increasingly confronted with making treatment decisions at an early stage, which requires taking individual prognoses into account to strike a good balance between benefits and harms of treatments. It is therefore important to understand how patients and physicians estimate prognostic risk, and whether and how these estimates can be improved. An online analytical processing (OLAP tool based on pooled data from placebo cohorts of clinical trials offers short-term prognostic estimates that can be used for individual risk counseling. OBJECTIVE: The aim of this study was to clarify if personalized prognostic information as presented by the OLAP tool is considered useful and meaningful by patients. Furthermore, we used the OLAP tool to evaluate patients' and physicians' risk estimates. Within this evaluation process we assessed short-time prognostic risk estimates of patients with MS (final n = 110 and their physicians (n = 6 and compared them with the estimates of OLAP. RESULTS: Patients rated the OLAP tool as understandable and acceptable, but to be only of moderate interest. It turned out that patients, physicians, and the OLAP tool ranked patients similarly regarding their risk of disease progression. Both patients' and physicians' estimates correlated most strongly with those disease covariates that the OLAP tool's estimates also correlated with most strongly. Exposure to the OLAP tool did not change patients' risk estimates. CONCLUSION: While the OLAP tool was rated understandable and acceptable, it was only of modest interest and did not change patients' prognostic estimates. The results suggest, however, that patients had some idea regarding their prognosis and which factors were most important in this regard. Future work with OLAP should assess long-term prognostic

  13. Prognostic risk estimates of patients with multiple sclerosis and their physicians: comparison to an online analytical risk counseling tool.

    Science.gov (United States)

    Heesen, Christoph; Gaissmaier, Wolfgang; Nguyen, Franziska; Stellmann, Jan-Patrick; Kasper, Jürgen; Köpke, Sascha; Lederer, Christian; Neuhaus, Anneke; Daumer, Martin

    2013-01-01

    Prognostic counseling in multiple sclerosis (MS) is difficult because of the high variability of disease progression. Simultaneously, patients and physicians are increasingly confronted with making treatment decisions at an early stage, which requires taking individual prognoses into account to strike a good balance between benefits and harms of treatments. It is therefore important to understand how patients and physicians estimate prognostic risk, and whether and how these estimates can be improved. An online analytical processing (OLAP) tool based on pooled data from placebo cohorts of clinical trials offers short-term prognostic estimates that can be used for individual risk counseling. The aim of this study was to clarify if personalized prognostic information as presented by the OLAP tool is considered useful and meaningful by patients. Furthermore, we used the OLAP tool to evaluate patients' and physicians' risk estimates. Within this evaluation process we assessed short-time prognostic risk estimates of patients with MS (final n = 110) and their physicians (n = 6) and compared them with the estimates of OLAP. Patients rated the OLAP tool as understandable and acceptable, but to be only of moderate interest. It turned out that patients, physicians, and the OLAP tool ranked patients similarly regarding their risk of disease progression. Both patients' and physicians' estimates correlated most strongly with those disease covariates that the OLAP tool's estimates also correlated with most strongly. Exposure to the OLAP tool did not change patients' risk estimates. While the OLAP tool was rated understandable and acceptable, it was only of modest interest and did not change patients' prognostic estimates. The results suggest, however, that patients had some idea regarding their prognosis and which factors were most important in this regard. Future work with OLAP should assess long-term prognostic estimates and clarify its usefulness for patients and physicians

  14. Screening for Malnutrition in Community Dwelling Older Japanese: Preliminary Development and Evaluation of the Japanese Nutritional Risk Screening Tool (NRST).

    Science.gov (United States)

    Htun, N C; Ishikawa-Takata, K; Kuroda, A; Tanaka, T; Kikutani, T; Obuchi, S P; Hirano, H; Iijima, K

    2016-02-01

    Early and effective screening for age-related malnutrition is an essential part of providing optimal nutritional care to older populations. This study was performed to evaluate the adaptation of the original SCREEN II questionnaire (Seniors in the Community: Risk Evaluation for Eating and Nutrition, version II) for use in Japan by examining its measurement properties and ability to predict nutritional risk and sarcopenia in community-dwelling older Japanese people. The ultimate objective of this preliminary validation study is to develop a license granted full Japanese version of the SCREEN II. The measurement properties and predictive validity of the NRST were examined in this cross-sectional study of 1921 community-dwelling older Japanese people. Assessments included medical history, and anthropometric and serum albumin measurements. Questions on dietary habits that corresponded to the original SCREEN II were applied to Nutritional Risk Screening Tool (NRST) scoring system. Nutritional risk was assessed by the Geriatric Nutrition Risk Index (GNRI) and the short form of the Mini-Nutritional Assessment (MNA-SF). Sarcopenia was diagnosed according to the criteria of the European Working Group on Sarcopenia in Older People. The nutritional risk prevalences determined by the GNRI and MNA-SF were 5.6% and 34.7%, respectively. The prevalence of sarcopenia was 13.3%. Mean NRST scores were significantly lower in the nutritionally at-risk than in the well-nourished groups. Concurrent validity analysis showed significant correlations between NRST scores and both nutritional risk parameters (GNRI or MNA-SF) and sarcopenia. The areas under the receiver operating characteristic curves (AUC) of NRST for the prediction of nutritional risk were 0.635 and 0.584 as assessed by GNRI and MNA-SF, respectively. AUCs for the prediction of sarcopenia were 0.602 (NRST), 0.655 (age-integrated NRST), and 0.676 (age and BMI-integrated NRST). These results indicate that the NRST is a

  15. Evaluation of the validity of osteoporosis and fracture risk assessment tools (IOF One Minute Test, SCORE, and FRAX) in postmenopausal Palestinian women.

    Science.gov (United States)

    Kharroubi, Akram; Saba, Elias; Ghannam, Ibrahim; Darwish, Hisham

    2017-12-01

    The need for simple self-assessment tools is necessary to predict women at high risk for developing osteoporosis. In this study, tools like the IOF One Minute Test, Fracture Risk Assessment Tool (FRAX), and Simple Calculated Osteoporosis Risk Estimation (SCORE) were found to be valid for Palestinian women. The threshold for predicting women at risk for each tool was estimated. The purpose of this study is to evaluate the validity of the updated IOF (International Osteoporosis Foundation) One Minute Osteoporosis Risk Assessment Test, FRAX, SCORE as well as age alone to detect the risk of developing osteoporosis in postmenopausal Palestinian women. Three hundred eighty-two women 45 years and older were recruited including 131 women with osteoporosis and 251 controls following bone mineral density (BMD) measurement, 287 completed questionnaires of the different risk assessment tools. Receiver operating characteristic (ROC) curves were evaluated for each tool using bone BMD as the gold standard for osteoporosis. The area under the ROC curve (AUC) was the highest for FRAX calculated with BMD for predicting hip fractures (0.897) followed by FRAX for major fractures (0.826) with cut-off values ˃1.5 and ˃7.8%, respectively. The IOF One Minute Test AUC (0.629) was the lowest compared to other tested tools but with sufficient accuracy for predicting the risk of developing osteoporosis with a cut-off value ˃4 total yes questions out of 18. SCORE test and age alone were also as good predictors of risk for developing osteoporosis. According to the ROC curve for age, women ≥64 years had a higher risk of developing osteoporosis. Higher percentage of women with low BMD (T-score ≤-1.5) or osteoporosis (T-score ≤-2.5) was found among women who were not exposed to the sun, who had menopause before the age of 45 years, or had lower body mass index (BMI) compared to controls. Women who often fall had lower BMI and approximately 27% of the recruited postmenopausal

  16. The predictive and external validity of the STarT Back Tool in Danish primary care

    DEFF Research Database (Denmark)

    Morsø, Lars; Kent, Peter; Albert, Hanne B

    2013-01-01

    distinguished between low- and medium-risk subgroups with a similar predictive ability of the UK SBT. That distinction is useful information for informing patients about their expected prognosis and may help guiding clinicians' choice of treatment. However, cross-cultural differences in the SBT psychosocial...

  17. Development of nonlinear acoustic propagation analysis tool toward realization of loud noise environment prediction in aeronautics

    Energy Technology Data Exchange (ETDEWEB)

    Kanamori, Masashi, E-mail: kanamori.masashi@jaxa.jp; Takahashi, Takashi, E-mail: takahashi.takashi@jaxa.jp; Aoyama, Takashi, E-mail: aoyama.takashi@jaxa.jp [Japan Aerospace Exploration Agency, 7-44-1, Jindaijihigashi-machi, Chofu, Tokyo (Japan)

    2015-10-28

    Shown in this paper is an introduction of a prediction tool for the propagation of loud noise with the application to the aeronautics in mind. The tool, named SPnoise, is based on HOWARD approach, which can express almost exact multidimensionality of the diffraction effect at the cost of back scattering. This paper argues, in particular, the prediction of the effect of atmospheric turbulence on sonic boom as one of the important issues in aeronautics. Thanks to the simple and efficient modeling of the atmospheric turbulence, SPnoise successfully re-creates the feature of the effect, which often emerges in the region just behind the front and rear shock waves in the sonic boom signature.

  18. Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

    Directory of Open Access Journals (Sweden)

    Swagata Payra

    2014-01-01

    Full Text Available The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a