Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction

DEFF Research Database (Denmark)

Paige, Ellie; Barrett, Jessica; Pennells, Lisa

2017-01-01

The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data...

Risk Matrix for Prediction of Disease Progression in a Referral Cohort of Patients with Crohn's Disease.

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Lakatos, Peter L; Sipeki, Nora; Kovacs, Gyorgy; Palyu, Eszter; Norman, Gary L; Shums, Zakera; Golovics, Petra A; Lovasz, Barbara D; Antal-Szalmas, Peter; Papp, Maria

2015-10-01

Early identification of patients with Crohn's disease (CD) at risk of subsequent complications is essential for adapting the treatment strategy. We aimed to develop a prediction model including clinical and serological markers for assessing the probability of developing advanced disease in a prospective referral CD cohort. Two hundred and seventy-one consecutive CD patients (42.4% males, median follow-up 108 months) were included and followed up prospectively. Anti-Saccharomyces cerevisiae antibodies (ASCA IgA/IgG) were determined by enzyme-linked immunosorbent assay. The final analysis was limited to patients with inflammatory disease behaviour at diagnosis. The final definition of advanced disease outcome was having intestinal resection or disease behaviour progression. Antibody (ASCA IgA and/or IgG) status, disease location and need for early azathioprine were included in a 3-, 5- and 7-year prediction matrix. The probability of advanced disease after 5 years varied from 6.2 to 55% depending on the combination of predictors. Similar findings were obtained in Kaplan-Meier analysis; the combination of ASCA, location and early use of azathioprine was associated with the probability of developing advanced disease (p < 0.001, log rank test). Our prediction models identified substantial differences in the probability of developing advanced disease in the early disease course of CD. Markers identified in this referral cohort were different from those previously published in a population-based cohort, suggesting that different prediction models should be used in the referral setting. Copyright © 2015 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Predictive risk factors for chronic low back pain in Parkinson's disease.

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Ozturk, Erhan Arif; Kocer, Bilge Gonenli

2018-01-01

Although previous studies have reported that the prevalence of low back pain in Parkinson's disease was over 50% and low back pain was often classified as chronic, risk factors of chronic low back pain have not been previously investigated. The aim of this study was to determine the predictive risk factors of chronic low back pain in Parkinson's disease. One hundred and sixty-eight patients with Parkinson's disease and 179 controls were consecutively included in the study. Demographic data of the two groups and disease characteristics of Parkinson's disease patient group were recorded. Low back pain lasting for ≥3 months was evaluated as chronic. Firstly, the bivariate correlations were calculated between chronic low back pain and all possible risk factors. Then, a multivariate regression was used to evaluate the impact of the predictors of chronic low back pain. The frequency of chronic low back pain in Parkinson's disease patients and controls were 48.2% and 26.7%, respectively (p chronic low back pain in Parkinson's disease were general factors including age (odds ratio = 1.053, p = 0.032) and Hospital Anxiety and Depression Scale - Depression subscore (odds ratio = 1.218, p = 0.001), and Parkinson's disease-related factors including rigidity (odds ratio = 5.109, p = 0.002) and posture item scores (odds ratio = 5.019, p = 0.0001). The chronic low back pain affects approximately half of the patients with Parkinson's disease. Prevention of depression or treatment recommendations for managing depression, close monitoring of anti- parkinsonian medication to keep motor symptoms under control, and attempts to prevent, correct or reduce abnormal posture may help reduce the frequency of chronic low back pain in Parkinson's disease. Copyright © 2017 Elsevier B.V. All rights reserved.

Plasma proteomics classifiers improve risk prediction for renal disease in patients with hypertension or type 2 diabetes

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Pena, Michelle J; Jankowski, Joachim; Heinze, Georg

2015-01-01

OBJECTIVE: Micro and macroalbuminuria are strong risk factors for progression of nephropathy in patients with hypertension or type 2 diabetes. Early detection of progression to micro and macroalbuminuria may facilitate prevention and treatment of renal diseases. We aimed to develop plasma...... proteomics classifiers to predict the development of micro or macroalbuminuria in hypertension or type 2 diabetes. METHODS: Patients with hypertension (n = 125) and type 2 diabetes (n = 82) were selected for this case-control study from the Prevention of REnal and Vascular ENd-stage Disease cohort....... RESULTS: In hypertensive patients, the classifier improved risk prediction for transition in albuminuria stage on top of the reference model (C-index from 0.69 to 0.78; P diabetes, the classifier improved risk prediction for transition from micro to macroalbuminuria (C-index from 0...

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

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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.

Cardiovascular disease risk score prediction models for women and its applicability to Asians

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Goh LGH

2014-03-01

Full Text Available Louise GH Goh,1 Satvinder S Dhaliwal,1 Timothy A Welborn,2 Peter L Thompson,2–4 Bruce R Maycock,1 Deborah A Kerr,1 Andy H Lee,1 Dean Bertolatti,1 Karin M Clark,1 Rakhshanda Naheed,1 Ranil Coorey,1 Phillip R Della5 1School of Public Health, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia; 2Sir Charles Gairdner Hospital, Nedlands, Perth, WA, Australia; 3School of Population Health, University of Western Australia, Perth, WA, Australia; 4Harry Perkins Institute for Medical Research, Perth, WA, Australia; 5School of Nursing and Midwifery, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia Purpose: Although elevated cardiovascular disease (CVD risk factors are associated with a higher risk of developing heart conditions across all ethnic groups, variations exist between groups in the distribution and association of risk factors, and also risk levels. This study assessed the 10-year predicted risk in a multiethnic cohort of women and compared the differences in risk between Asian and Caucasian women. Methods: Information on demographics, medical conditions and treatment, smoking behavior, dietary behavior, and exercise patterns were collected. Physical measurements were also taken. The 10-year risk was calculated using the Framingham model, SCORE (Systematic COronary Risk Evaluation risk chart for low risk and high risk regions, the general CVD, and simplified general CVD risk score models in 4,354 females aged 20–69 years with no heart disease, diabetes, or stroke at baseline from the third Australian Risk Factor Prevalence Study. Country of birth was used as a surrogate for ethnicity. Nonparametric statistics were used to compare risk levels between ethnic groups. Results: Asian women generally had lower risk of CVD when compared to Caucasian women. The 10-year predicted risk was, however, similar between Asian and Australian women, for some models. These findings were

Predicting coronary heart disease

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Sillesen, Henrik; Fuster, Valentin

2012-01-01

Atherosclerosis is the leading cause of death and disabling disease. Whereas risk factors are well known and constitute therapeutic targets, they are not useful for prediction of risk of future myocardial infarction, stroke, or death. Therefore, methods to identify atherosclerosis itself have bee...

Enteric disease episodes and the risk of acquiring a future sexually transmitted infection: a prediction model in Montreal residents.

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Caron, Melissa; Allard, Robert; Bédard, Lucie; Latreille, Jérôme; Buckeridge, David L

2016-11-01

The sexual transmission of enteric diseases poses an important public health challenge. We aimed to build a prediction model capable of identifying individuals with a reported enteric disease who could be at risk of acquiring future sexually transmitted infections (STIs). Passive surveillance data on Montreal residents with at least 1 enteric disease report was used to construct the prediction model. Cases were defined as all subjects with at least 1 STI report following their initial enteric disease episode. A final logistic regression prediction model was chosen using forward stepwise selection. The prediction model with the greatest validity included age, sex, residential location, number of STI episodes experienced prior to the first enteric disease episode, type of enteric disease acquired, and an interaction term between age and male sex. This model had an area under the curve of 0.77 and had acceptable calibration. A coordinated public health response to the sexual transmission of enteric diseases requires that a distinction be made between cases of enteric diseases transmitted through sexual activity from those transmitted through contaminated food or water. A prediction model can aid public health officials in identifying individuals who may have a higher risk of sexually acquiring a reportable disease. Once identified, these individuals could receive specialized intervention to prevent future infection. The information produced from a prediction model capable of identifying higher risk individuals can be used to guide efforts in investigating and controlling reported cases of enteric diseases and STIs. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

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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.

Body composition indices and predicted cardiovascular disease risk profile among urban dwellers in Malaysia.

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Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Dahlui, Maznah; Majid, Hazreen Abdul

2015-01-01

This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD) risk profile in an urban population in Kuala Lumpur, Malaysia. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions.

Body Composition Indices and Predicted Cardiovascular Disease Risk Profile among Urban Dwellers in Malaysia

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Tin Tin Su

2015-01-01

Full Text Available Objectives. This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD risk profile in an urban population in Kuala Lumpur, Malaysia. Methods. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Results. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. Conclusions. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions.

Cardiovascular disease (CVD) and chronic kidney disease (CKD) event rates in HIV-positive persons at high predicted CVD and CKD risk

DEFF Research Database (Denmark)

Boyd, Mark A; Mocroft, Amanda; Ryom, Lene

2017-01-01

BACKGROUND: The Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study has developed predictive risk scores for cardiovascular disease (CVD) and chronic kidney disease (CKD, defined as confirmed estimated glomerular filtration rate [eGFR] ≤ 60 ml/min/1.73 m2) events in HIV...

Cardiovascular risk prediction: the old has given way to the new but at what risk-benefit ratio?

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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

Framingham coronary heart disease risk score can be predicted from structural brain images in elderly subjects.

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Jane Maryam Rondina

2014-12-01

Full Text Available Recent literature has presented evidence that cardiovascular risk factors (CVRF play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer’s disease (AD and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies in a sample of healthy elderly individuals. We aim to answer the following questions: Is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: i we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease. ii When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. iii We found important gender differences, and the possible causes of that finding are discussed.

Poisson Mixture Regression Models for Heart Disease Prediction.

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Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

Science.gov (United States)

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus: The Steno Type 1 Risk Engine.

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Vistisen, Dorte; Andersen, Gregers Stig; Hansen, Christian Stevns; Hulman, Adam; Henriksen, Jan Erik; Bech-Nielsen, Henning; Jørgensen, Marit Eika

2016-03-15

Patients with type 1 diabetes mellitus are at increased risk of developing cardiovascular disease (CVD), but they are currently undertreated. There are no risk scores used on a regular basis in clinical practice for assessing the risk of CVD in type 1 diabetes mellitus. From 4306 clinically diagnosed adult patients with type 1 diabetes mellitus, we developed a prediction model for estimating the risk of first fatal or nonfatal CVD event (ischemic heart disease, ischemic stroke, heart failure, and peripheral artery disease). Detailed clinical data including lifestyle factors were linked to event data from validated national registers. The risk prediction model was developed by using a 2-stage approach. First, a nonparametric, data-driven approach was used to identify potentially informative risk factors and interactions (random forest and survival tree analysis). Second, based on results from the first step, Poisson regression analysis was used to derive the final model. The final CVD prediction model was externally validated in a different population of 2119 patients with type 1 diabetes mellitus. During a median follow-up of 6.8 years (interquartile range, 2.9-10.9) a total of 793 (18.4%) patients developed CVD. The final prediction model included age, sex, diabetes duration, systolic blood pressure, low-density lipoprotein cholesterol, hemoglobin A1c, albuminuria, glomerular filtration rate, smoking, and exercise. Discrimination was excellent for a 5-year CVD event with a C-statistic of 0.826 (95% confidence interval, 0.807-0.845) in the derivation data and a C-statistic of 0.803 (95% confidence interval, 0.767-0.839) in the validation data. The Hosmer-Lemeshow test showed good calibration (P>0.05) in both cohorts. This high-performing CVD risk model allows for the implementation of decision rules in a clinical setting. © 2016 American Heart Association, Inc.

Influence of Feature Encoding and Choice of Classifier on Disease Risk Prediction in Genome-Wide Association Studies.

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Florian Mittag

Full Text Available Various attempts have been made to predict the individual disease risk based on genotype data from genome-wide association studies (GWAS. However, most studies only investigated one or two classification algorithms and feature encoding schemes. In this study, we applied seven different classification algorithms on GWAS case-control data sets for seven different diseases to create models for disease risk prediction. Further, we used three different encoding schemes for the genotypes of single nucleotide polymorphisms (SNPs and investigated their influence on the predictive performance of these models. Our study suggests that an additive encoding of the SNP data should be the preferred encoding scheme, as it proved to yield the best predictive performances for all algorithms and data sets. Furthermore, our results showed that the differences between most state-of-the-art classification algorithms are not statistically significant. Consequently, we recommend to prefer algorithms with simple models like the linear support vector machine (SVM as they allow for better subsequent interpretation without significant loss of accuracy.

Predictive Accuracy of a Cardiovascular Disease Risk Prediction Model in Rural South India – A Community Based Retrospective Cohort Study

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Farah N Fathima

2015-03-01

Full Text Available Background: Identification of individuals at risk of developing cardiovascular diseases by risk stratification is the first step in primary prevention. Aims & Objectives: To assess the five year risk of developing a cardiovascular event from retrospective data and to assess the predictive accuracy of the non laboratory based National Health and Nutrition Examination Survey (NHANES risk prediction model among individuals in a rural South Indian population. Materials & Methods: A community based retrospective cohort study was conducted in three villages where risk stratification was done for all eligible adults aged between 35-74 years at the time of initial assessment using the NHANES risk prediction charts. Household visits were made after a period of five years by trained doctors to determine cardiovascular outcomes. Results: 521 people fulfilled the eligibility criteria of whom 486 (93.3% could be traced after five years. 56.8% were in low risk, 36.6% were in moderate risk and 6.6% were in high risk categories. 29 persons (5.97% had had cardiovascular events over the last five years of which 24 events (82.7% were nonfatal and five (17.25% were fatal. The mean age of the people who developed cardiovascular events was 57.24 ± 9.09 years. The odds ratios for the three levels of risk showed a linear trend with the odds ratios for the moderate risk and high risk category being 1.35 and 1.94 respectively with the low risk category as baseline. Conclusion: The non laboratory based NHANES charts did not accurately predict the occurrence of cardiovascular events in any of the risk categories.

Chronic obstructive pulmonary disease and coronary disease: COPDCoRi, a simple and effective algorithm for predicting the risk of coronary artery disease in COPD patients.

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Cazzola, Mario; Calzetta, Luigino; Matera, Maria Gabriella; Muscoli, Saverio; Rogliani, Paola; Romeo, Francesco

2015-08-01

Chronic obstructive pulmonary disease (COPD) is often associated with cardiovascular artery disease (CAD), representing a potential and independent risk factor for cardiovascular morbidity. Therefore, the aim of this study was to identify an algorithm for predicting the risk of CAD in COPD patients. We analyzed data of patients afferent to the Cardiology ward and the Respiratory Diseases outpatient clinic of Tor Vergata University (2010-2012, 1596 records). The study population was clustered as training population (COPD patients undergoing coronary arteriography), control population (non-COPD patients undergoing coronary arteriography), test population (COPD patients whose records reported information on the coronary status). The predicting model was built via causal relationship between variables, stepwise binary logistic regression and Hosmer-Lemeshow analysis. The algorithm was validated via split-sample validation method and receiver operating characteristics (ROC) curve analysis. The diagnostic accuracy was assessed. In training population the variables gender (men/women OR: 1.7, 95%CI: 1.237-2.5, P COPD patients, whereas in control population also age and diabetes were correlated. The stepwise binary logistic regressions permitted to build a well fitting predictive model for training population but not for control population. The predictive algorithm shown a diagnostic accuracy of 81.5% (95%CI: 77.78-84.71) and an AUC of 0.81 (95%CI: 0.78-0.85) for the validation set. The proposed algorithm is effective for predicting the risk of CAD in COPD patients via a rapid, inexpensive and non-invasive approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

A risk prediction score for invasive mold disease in patients with hematological malignancies.

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Marta Stanzani

Full Text Available BACKGROUND: A risk score for invasive mold disease (IMD in patients with hematological malignancies could facilitate patient screening and improve the targeted use of antifungal prophylaxis. METHODS: We retrospectively analyzed 1,709 hospital admissions of 840 patients with hematological malignancies (2005-2008 to collect data on 17 epidemiological and treatment-related risk factors for IMD. Multivariate regression was used to develop a weighted risk score based on independent risk factors associated with proven or probable IMD, which was prospectively validated during 1,746 hospital admissions of 855 patients from 2009-2012. RESULTS: Of the 17 candidate variables analyzed, 11 correlated with IMD by univariate analysis, but only 4 risk factors (neutropenia, lymphocytopenia or lymphocyte dysfunction in allogeneic hematopoietic stem cell transplant recipients, malignancy status, and prior IMD were retained in the final multivariate model, resulting in a weighted risk score 0-13. A risk score of 5% of IMD, with a negative predictive value (NPV of 0.99, (95% CI 0.98-0.99. During 2009-2012, patients with a calculated risk score at admission of 6 (0.9% vs. 10.6%, P <0.001. CONCLUSION: An objective, weighted risk score for IMD can accurately discriminate patients with hematological malignancies at low risk for developing mold disease, and could possibly facilitate "screening-out" of low risk patients less likely to benefit from intensive diagnostic monitoring or mold-directed antifungal prophylaxis.

Cardiovascular risk prediction in HIV-infected patients: comparing the Framingham, atherosclerotic cardiovascular disease risk score (ASCVD), Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) and Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) risk prediction models.

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Krikke, M; Hoogeveen, R C; Hoepelman, A I M; Visseren, F L J; Arends, J E

2016-04-01

The aim of the study was to compare the predictions of five popular cardiovascular disease (CVD) risk prediction models, namely the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) model, the Framingham Heart Study (FHS) coronary heart disease (FHS-CHD) and general CVD (FHS-CVD) models, the American Heart Association (AHA) atherosclerotic cardiovascular disease risk score (ASCVD) model and the Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) model. A cross-sectional design was used to compare the cumulative CVD risk predictions of the models. Furthermore, the predictions of the general CVD models were compared with those of the HIV-specific D:A:D model using three categories ( 20%) to categorize the risk and to determine the degree to which patients were categorized similarly or in a higher/lower category. A total of 997 HIV-infected patients were included in the study: 81% were male and they had a median age of 46 [interquartile range (IQR) 40-52] years, a known duration of HIV infection of 6.8 (IQR 3.7-10.9) years, and a median time on ART of 6.4 (IQR 3.0-11.5) years. The D:A:D, ASCVD and SCORE-NL models gave a lower cumulative CVD risk, compared with that of the FHS-CVD and FHS-CHD models. Comparing the general CVD models with the D:A:D model, the FHS-CVD and FHS-CHD models only classified 65% and 79% of patients, respectively, in the same category as did the D:A:D model. However, for the ASCVD and SCORE-NL models, this percentage was 89% and 87%, respectively. Furthermore, FHS-CVD and FHS-CHD attributed a higher CVD risk to 33% and 16% of patients, respectively, while this percentage was D:A:D, ASCVD and SCORE-NL models. This could have consequences regarding overtreatment, drug-related adverse events and drug-drug interactions. © 2015 British HIV Association.

Risk prediction and risk reduction in patients with manifest arterial disease

NARCIS (Netherlands)

Goessens, B.M.B.; Goessens, B.M.B.

2006-01-01

Risicovoorspelling en risicoverlaging bij patienten met manifest vaatlijden Engelstalig abstract The number of patients with clinical manifest arterial disease is increasing because of the aging of the population. Patients with manifest arterial disease have an increased risk of a new vascular event

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

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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.

Quantifying prognosis with risk predictions.

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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.

Changes in predicted protein disorder tendency may contribute to disease risk

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Hu Yang

2011-12-01

Full Text Available Abstract Background Recent studies suggest that many proteins or regions of proteins lack 3D structure. Defined as intrinsically disordered proteins, these proteins/peptides are functionally important. Recent advances in next generation sequencing technologies enable genome-wide identification of novel nucleotide variations in a specific population or cohort. Results Using the exonic single nucleotide variations (SNVs identified in the 1,000 Genomes Project and distributed by the Genetic Analysis Workshop 17, we systematically analysed the genetic and predicted disorder potential features of the non-synonymous variations. The result of experiments suggests that a significant change in the tendency of a protein region to be structured or disordered caused by SNVs may lead to malfunction of such a protein and contribute to disease risk. Conclusions After validation with functional SNVs on the traits distributed by GAW17, we conclude that it is valuable to consider structure/disorder tendencies while prioritizing and predicting mechanistic effects arising from novel genetic variations.

Value of multiple risk factors in predicting coronary artery disease

International Nuclear Information System (INIS)

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

2008-01-01

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

Development of a Coronary Heart Disease Risk Prediction Model for Type 1 Diabetes: The Pittsburgh CHD in Type 1 Diabetes Risk Mode

NARCIS (Netherlands)

Zgibor, J.C.; Ruppert, K.; Orchard, T.J.; Soedamah-Muthu, S.S.; Fuller, J.H.; Chaturvedi, N.; Roberts, M.S.

2010-01-01

Aim - To create a coronary heart disease (CHD) risk prediction model specific to type 1 diabetes. Methods - Development of the model used data from the Pittsburgh Epidemiology of Diabetes Complications Study (EDC). EDC subjects had type 1 diabetes diagnosed between 1950 and 1980, received their

Prediction of cardiovascular disease risk among low-income urban dwellers in metropolitan Kuala Lumpur, Malaysia.

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Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Bulgiba, Awang; Majid, Hazreen Abdul

2015-01-01

We aimed to predict the ten-year cardiovascular disease (CVD) risk among low-income urban dwellers of metropolitan Malaysia. Participants were selected from a cross-sectional survey conducted in Kuala Lumpur. To assess the 10-year CVD risk, we employed the Framingham risk scoring (FRS) models. Significant determinants of the ten-year CVD risk were identified using General Linear Model (GLM). Altogether 882 adults (≥30 years old with no CVD history) were randomly selected. The classic FRS model (figures in parentheses are from the modified model) revealed that 20.5% (21.8%) and 38.46% (38.9%) of respondents were at high and moderate risk of CVD. The GLM models identified the importance of education, occupation, and marital status in predicting the future CVD risk. Our study indicated that one out of five low-income urban dwellers has high chance of having CVD within ten years. Health care expenditure, other illness related costs and loss of productivity due to CVD would worsen the current situation of low-income urban population. As such, the public health professionals and policy makers should establish substantial effort to formulate the public health policy and community-based intervention to minimize the upcoming possible high mortality and morbidity due to CVD among the low-income urban dwellers.

Prediction of Cardiovascular Disease Risk among Low-Income Urban Dwellers in Metropolitan Kuala Lumpur, Malaysia

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Tin Tin Su

2015-01-01

Full Text Available We aimed to predict the ten-year cardiovascular disease (CVD risk among low-income urban dwellers of metropolitan Malaysia. Participants were selected from a cross-sectional survey conducted in Kuala Lumpur. To assess the 10-year CVD risk, we employed the Framingham risk scoring (FRS models. Significant determinants of the ten-year CVD risk were identified using General Linear Model (GLM. Altogether 882 adults (≥30 years old with no CVD history were randomly selected. The classic FRS model (figures in parentheses are from the modified model revealed that 20.5% (21.8% and 38.46% (38.9% of respondents were at high and moderate risk of CVD. The GLM models identified the importance of education, occupation, and marital status in predicting the future CVD risk. Our study indicated that one out of five low-income urban dwellers has high chance of having CVD within ten years. Health care expenditure, other illness related costs and loss of productivity due to CVD would worsen the current situation of low-income urban population. As such, the public health professionals and policy makers should establish substantial effort to formulate the public health policy and community-based intervention to minimize the upcoming possible high mortality and morbidity due to CVD among the low-income urban dwellers.

Incremental value of a genetic risk score for the prediction of new vascular events in patients with clinically manifest vascular disease.

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Weijmans, Maaike; de Bakker, Paul I W; van der Graaf, Yolanda; Asselbergs, Folkert W; Algra, Ale; Jan de Borst, Gert; Spiering, Wilko; Visseren, Frank L J

2015-04-01

Several genetic markers are related to incidence of cardiovascular events. We evaluated whether a genetic risk score (GRS) based on 30 single-nucleotide-polymorphisms associated with coronary artery disease (CAD) can improve prediction of 10-year risk of new cardiovascular events in patients with clinical manifest vascular disease. In 5742 patients with symptomatic vascular disease enrolled in the SMART study, we developed Cox regression models based on the SMART Risk Score (SRS) and based on the SRS plus the GRS in all patients, in patients with a history of acute arterial thrombotic events and in patients with a history of more stable atherosclerosis and without CAD. The discriminatory ability was expressed by the c-statistic. Model calibration was evaluated by calibration plots. The incremental value of adding the GRS was assessed by net reclassification index (NRI) and decision curve analysis. During a median follow-up of 6.5 years (IQR4.0-9.5), the composite outcome of myocardial infarction, stroke, or vascular death occurred in 933 patients. Hazard ratios of GRS ranging from 0.86 to 1.35 were observed. The discriminatory capacity of the SRS for prediction of 10-year risk of cardiovascular events was fairly good (c-statistic 0.70, 95%CI 0.68-0.72), similar to the model based on the SRS plus the GRS. Calibration of the models based on SRS and SRS plus GRS was adequate. No increase in c-statistics, categorical NRIs and decision curves was observed when adding the GRS. The continuous NRI improved only in patients with stable atherosclerosis (0.14, 95%CI 0.03-0.25), increasing further excluding patients with a history of CAD (0.21, 95%CI 0.06-0.36). In patients with symptomatic vascular disease, a GRS did not improve risk prediction of 10-year risk of cardiovascular events beyond clinical characteristics. The GRS might improve risk prediction of first vascular events in the subgroup of patients with a history of stable atherosclerosis. Copyright © 2015 Elsevier

PREDICT-PD: An online approach to prospectively identify risk indicators of Parkinson's disease.

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Noyce, Alastair J; R'Bibo, Lea; Peress, Luisa; Bestwick, Jonathan P; Adams-Carr, Kerala L; Mencacci, Niccolo E; Hawkes, Christopher H; Masters, Joseph M; Wood, Nicholas; Hardy, John; Giovannoni, Gavin; Lees, Andrew J; Schrag, Anette

2017-02-01

A number of early features can precede the diagnosis of Parkinson's disease (PD). To test an online, evidence-based algorithm to identify risk indicators of PD in the UK population. Participants aged 60 to 80 years without PD completed an online survey and keyboard-tapping task annually over 3 years, and underwent smell tests and genotyping for glucocerebrosidase (GBA) and leucine-rich repeat kinase 2 (LRRK2) mutations. Risk scores were calculated based on the results of a systematic review of risk factors and early features of PD, and individuals were grouped into higher (above 15th centile), medium, and lower risk groups (below 85th centile). Previously defined indicators of increased risk of PD ("intermediate markers"), including smell loss, rapid eye movement-sleep behavior disorder, and finger-tapping speed, and incident PD were used as outcomes. The correlation of risk scores with intermediate markers and movement of individuals between risk groups was assessed each year and prospectively. Exploratory Cox regression analyses with incident PD as the dependent variable were performed. A total of 1323 participants were recruited at baseline and >79% completed assessments each year. Annual risk scores were correlated with intermediate markers of PD each year and baseline scores were correlated with intermediate markers during follow-up (all P values < 0.001). Incident PD diagnoses during follow-up were significantly associated with baseline risk score (hazard ratio = 4.39, P = .045). GBA variants or G2019S LRRK2 mutations were found in 47 participants, and the predictive power for incident PD was improved by the addition of genetic variants to risk scores. The online PREDICT-PD algorithm is a unique and simple method to identify indicators of PD risk. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder

Periodontal profile classes predict periodontal disease progression and tooth loss.

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Morelli, Thiago; Moss, Kevin L; Preisser, John S; Beck, James D; Divaris, Kimon; Wu, Di; Offenbacher, Steven

2018-02-01

Current periodontal disease taxonomies have limited utility for predicting disease progression and tooth loss; in fact, tooth loss itself can undermine precise person-level periodontal disease classifications. To overcome this limitation, the current group recently introduced a novel patient stratification system using latent class analyses of clinical parameters, including patterns of missing teeth. This investigation sought to determine the clinical utility of the Periodontal Profile Classes and Tooth Profile Classes (PPC/TPC) taxonomy for risk assessment, specifically for predicting periodontal disease progression and incident tooth loss. The analytic sample comprised 4,682 adult participants of two prospective cohort studies (Dental Atherosclerosis Risk in Communities Study and Piedmont Dental Study) with information on periodontal disease progression and incident tooth loss. The PPC/TPC taxonomy includes seven distinct PPCs (person-level disease pattern and severity) and seven TPCs (tooth-level disease). Logistic regression modeling was used to estimate relative risks (RR) and 95% confidence intervals (CI) for the association of these latent classes with disease progression and incident tooth loss, adjusting for examination center, race, sex, age, diabetes, and smoking. To obtain personalized outcome propensities, risk estimates associated with each participant's PPC and TPC were combined into person-level composite risk scores (Index of Periodontal Risk [IPR]). Individuals in two PPCs (PPC-G: Severe Disease and PPC-D: Tooth Loss) had the highest tooth loss risk (RR = 3.6; 95% CI = 2.6 to 5.0 and RR = 3.8; 95% CI = 2.9 to 5.1, respectively). PPC-G also had the highest risk for periodontitis progression (RR = 5.7; 95% CI = 2.2 to 14.7). Personalized IPR scores were positively associated with both periodontitis progression and tooth loss. These findings, upon additional validation, suggest that the periodontal/tooth profile classes and the derived

Molecular prediction of disease risk and severity in a large Dutch Crohn's disease cohort

NARCIS (Netherlands)

Weersma, R.K.; Stokkers, P.C.F.; van Bodegraven, A.A.; van Hogezand, R.A.; Verspaget, H.W.; de Jong, D.J.; van der Woude, C.J.; Oldenburg, B.; Linskens, R.K.; Festen, E.A.M.; van der Steege, G.; Hommes, D.W.; Crusius, J.B.A.; Wijmenga, C.; Nolte, I.M.; Dijkstra, G.

2009-01-01

Background: Crohn's disease and ulcerative colitis have a complex genetic background. We assessed the risk for both the development and severity of the disease by combining information from genetic variants associated with inflammatory bowel disease (IBD). Methods: We studied 2804 patients (1684

Prediction of tension-type headache risk in adolescents

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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

Entomologic index for human risk of Lyme disease.

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Mather, T N; Nicholson, M C; Donnelly, E F; Matyas, B T

1996-12-01

An entomologic index based on density estimates of Lyme disease spirochete-infected nymphal deer ticks (lxodes scapularis) was developed to assess human risk of Lyme disease. The authors used a standardized protocol to determine tick density and infection in numerous forested sites in six Rhode Island towns. An entomologic risk index calculated for each town was compared with the number of human Lyme disease cases reported to the Rhode Island State Health Department for the same year. A strong positive relation between entomologic risk index and the Lyme disease case rate for each town suggested that the entomologic index was predictive of Lyme disease risk.

Comparison of three lifecourse models of poverty in predicting cardiovascular disease risk in youth.

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Kakinami, Lisa; Séguin, Louise; Lambert, Marie; Gauvin, Lise; Nikiema, Béatrice; Paradis, Gilles

2013-08-01

Childhood poverty heightens the risk of adulthood cardiovascular disease (CVD), but the underlying pathways are poorly understood. Three lifecourse models have been proposed but have never been tested among youth. We assessed the longitudinal association of childhood poverty with CVD risk factors in 10-year-old youth according to the timing, accumulation, and mobility models. The Québec Longitudinal Study of Child Development birth cohort was established in 1998 (n = 2120). Poverty was defined as annual income below the low-income thresholds defined by Statistics Canada. Multiple imputation was used for missing data. Multivariable linear regression models adjusted for gender, pubertal stage, parental education, maternal age, whether the household was a single parent household, whether the child was overweight or obese, the child's physical activity in the past week, and family history. Approximately 40% experienced poverty at least once, 16% throughout childhood, and 25% intermittently. Poverty was associated with significantly elevated triglycerides and insulin according to the timing and accumulation models, although the timing model was superior for predicting insulin and the accumulation model was superior for predicting triglycerides. Early and prolonged exposure to poverty significantly increases CVD risk among 10-year-old youth. Copyright © 2013 Elsevier Inc. All rights reserved.

Machine learning derived risk prediction of anorexia nervosa.

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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.

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

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Perotte, Adler; Ranganath, Rajesh; Hirsch, Jamie S; Blei, David; Elhadad, Noémie

2015-07-01

As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

The potential of large studies for building genetic risk prediction models

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NCI scientists have developed a new paradigm to assess hereditary risk prediction in common diseases, such as prostate cancer. This genetic risk prediction concept is based on polygenic analysis—the study of a group of common DNA sequences, known as singl

Cardiovascular disease (CVD and chronic kidney disease (CKD event rates in HIV-positive persons at high predicted CVD and CKD risk: A prospective analysis of the D:A:D observational study.

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Mark A Boyd

2017-11-01

Full Text Available The Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D study has developed predictive risk scores for cardiovascular disease (CVD and chronic kidney disease (CKD, defined as confirmed estimated glomerular filtration rate [eGFR] ≤ 60 ml/min/1.73 m2 events in HIV-positive people. We hypothesized that participants in D:A:D at high (>5% predicted risk for both CVD and CKD would be at even greater risk for CVD and CKD events.We included all participants with complete risk factor (covariate data, baseline eGFR > 60 ml/min/1.73 m2, and a confirmed (>3 months apart eGFR 1%-5%, >5% and fitted Poisson models to assess whether CVD and CKD risk group effects were multiplicative. A total of 27,215 participants contributed 202,034 person-years of follow-up: 74% male, median (IQR age 42 (36, 49 years, median (IQR baseline year of follow-up 2005 (2004, 2008. D:A:D risk equations predicted 3,560 (13.1% participants at high CVD risk, 4,996 (18.4% participants at high CKD risk, and 1,585 (5.8% participants at both high CKD and high CVD risk. CVD and CKD event rates by predicted risk group were multiplicative. Participants at high CVD risk had a 5.63-fold (95% CI 4.47, 7.09, p < 0.001 increase in CKD events compared to those at low risk; participants at high CKD risk had a 1.31-fold (95% CI 1.09, 1.56, p = 0.005 increase in CVD events compared to those at low risk. Participants' CVD and CKD risk groups had multiplicative predictive effects, with no evidence of an interaction (p = 0.329 and p = 0.291 for CKD and CVD, respectively. The main study limitation is the difference in the ascertainment of the clinically defined CVD endpoints and the laboratory-defined CKD endpoints.We found that people at high predicted risk for both CVD and CKD have substantially greater risks for both CVD and CKD events compared with those at low predicted risk for both outcomes, and compared to those at high predicted risk for only CVD or CKD events. This suggests that CVD and

Low-density lipoprotein cholesterol and risk of gallstone disease

DEFF Research Database (Denmark)

Stender, Stefan; Frikke-Schmidt, Ruth; Benn, Marianne

2013-01-01

Drugs which reduce plasma low-density lipoprotein cholesterol (LDL-C) may protect against gallstone disease. Whether plasma levels of LDL-C per se predict risk of gallstone disease remains unclear. We tested the hypothesis that elevated LDL-C is a causal risk factor for symptomatic gallstone...

Resistance training and predicted risk of coronary heart disease in ...

African Journals Online (AJOL)

The purpose of this study was to determine the impact of resistance training, designed to prevent the development of coronary heart disease (CHD) based on the Framingham Risk Assessment (FRA) score. Twenty-five healthy sedentary men with low CHD risk were assigned to participate in a 16-week (three days per week) ...

Role of γ-glutamyl transferase levels in prediction of high cardiovascular risk among patients with non-alcoholic fatty liver disease

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Benan Kasapoglu

2016-01-01

among patients with fatty liver disease should be regarded as a sign of increased cardiovascular disease risk. Larger studies are warranted to elucidate the role of GGT in prediction of cardiovascular risk.

Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer's disease neuroimaging initiative.

Science.gov (United States)

Gomar, Jesus J; Bobes-Bascaran, Maria T; Conejero-Goldberg, Concepcion; Davies, Peter; Goldberg, Terry E

2011-09-01

Biomarkers have become increasingly important in understanding neurodegenerative processes associated with Alzheimer disease. Markers include regional brain volumes, cerebrospinal fluid measures of pathological Aβ1-42 and total tau, cognitive measures, and individual risk factors. To determine the discriminative utility of different classes of biomarkers and cognitive markers by examining their ability to predict a change in diagnostic status from mild cognitive impairment to Alzheimer disease. Longitudinal study. We analyzed the Alzheimer's Disease Neuroimaging Initiative database to study patients with mild cognitive impairment who converted to Alzheimer disease (n = 116) and those who did not convert (n = 204) within a 2-year period. We determined the predictive utility of 25 variables from all classes of markers, biomarkers, and risk factors in a series of logistic regression models and effect size analyses. The Alzheimer's Disease Neuroimaging Initiative public database. Primary outcome measures were odds ratios, pseudo- R(2)s, and effect sizes. In comprehensive stepwise logistic regression models that thus included variables from all classes of markers, the following baseline variables predicted conversion within a 2-year period: 2 measures of delayed verbal memory and middle temporal lobe cortical thickness. In an effect size analysis that examined rates of decline, change scores for biomarkers were modest for 2 years, but a change in an everyday functional activities measure (Functional Assessment Questionnaire) was considerably larger. Decline in scores on the Functional Assessment Questionnaire and Trail Making Test, part B, accounted for approximately 50% of the predictive variance in conversion from mild cognitive impairment to Alzheimer disease. Cognitive markers at baseline were more robust predictors of conversion than most biomarkers. Longitudinal analyses suggested that conversion appeared to be driven less by changes in the neurobiologic

Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

International Nuclear Information System (INIS)

Jairam, Pushpa M.; Jong, Pim A. de; Mali, Willem P.T.M.; Isgum, Ivana; Graaf, Yolanda van der

2015-01-01

Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

Energy Technology Data Exchange (ETDEWEB)

Jairam, Pushpa M. [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Jong, Pim A. de; Mali, Willem P.T.M. [University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Isgum, Ivana [University Medical Center Utrecht, Image Sciences Institute, Utrecht (Netherlands); Graaf, Yolanda van der [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); Collaboration: PROVIDI study-group

2015-06-01

Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking.

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Ian C Scott

Full Text Available The improved characterisation of risk factors for rheumatoid arthritis (RA suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA. Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls; UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls. HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG. Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001; ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively.

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.

Risk score for predicting long-term mortality after coronary artery bypass graft surgery.

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Wu, Chuntao; Camacho, Fabian T; Wechsler, Andrew S; Lahey, Stephen; Culliford, Alfred T; Jordan, Desmond; Gold, Jeffrey P; Higgins, Robert S D; Smith, Craig R; Hannan, Edward L

2012-05-22

No simplified bedside risk scores have been created to predict long-term mortality after coronary artery bypass graft surgery. The New York State Cardiac Surgery Reporting System was used to identify 8597 patients who underwent isolated coronary artery bypass graft surgery in July through December 2000. The National Death Index was used to ascertain patients' vital statuses through December 31, 2007. A Cox proportional hazards model was fit to predict death after CABG surgery using preprocedural risk factors. Then, points were assigned to significant predictors of death on the basis of the values of their regression coefficients. For each possible point total, the predicted risks of death at years 1, 3, 5, and 7 were calculated. It was found that the 7-year mortality rate was 24.2 in the study population. Significant predictors of death included age, body mass index, ejection fraction, unstable hemodynamic state or shock, left main coronary artery disease, cerebrovascular disease, peripheral arterial disease, congestive heart failure, malignant ventricular arrhythmia, chronic obstructive pulmonary disease, diabetes mellitus, renal failure, and history of open heart surgery. The points assigned to these risk factors ranged from 1 to 7; possible point totals for each patient ranged from 0 to 28. The observed and predicted risks of death at years 1, 3, 5, and 7 across patient groups stratified by point totals were highly correlated. The simplified risk score accurately predicted the risk of mortality after coronary artery bypass graft surgery and can be used for informed consent and as an aid in determining treatment choice.

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

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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.

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

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Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

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

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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.

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

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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

PREDICTION OF SURGICAL TREATMENT WITH POUR PERITONITIS QUANTIFYING RISK FACTORS

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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.

Chronic Kidney Disease – Where Next? Predicting Outcomes and Planning Care Pathways

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Angharad Marks

2014-07-01

Full Text Available With the introduction of the National Kidney Foundation Kidney Disease Outcomes Quality Initiative chronic kidney disease (CKD guidelines, CKD has been identified as common, particularly in the elderly. The outcomes for those with CKD can be poor: mortality, initiation of renal replacement therapy, and progressive deterioration in kidney function, with its associated complications. In young people with CKD, the risk of poor outcome is high and the social cost substantial, but the actual number of patients affected is relatively small. In the elderly, the risk of poor outcome is substantially lower, but due to the high prevalence of CKD the actual number of poor outcomes attributable to CKD is higher. Predicting which patients are at greatest risk, and being able to tailor care appropriately, has significant potential benefits. Risk prediction models in CKD are being developed and show promise but thus far have limitations. In this review we describe the pathway for developing and evaluating risk prediction tools, and consider what models we have for CKD prediction and where next.

An updated prediction model of the global risk of cardiovascular disease in HIV-positive persons

DEFF Research Database (Denmark)

Friis-Møller, Nina; Ryom, Lene; Smith, Colette

2016-01-01

,663 HIV-positive persons from 20 countries in Europe and Australia, who were free of CVD at entry into the Data-collection on Adverse Effects of Anti-HIV Drugs (D:A:D) study. Cox regression models (full and reduced) were developed that predict the risk of a global CVD endpoint. The predictive performance...... significantly predicted risk more accurately than the recalibrated Framingham model (Harrell's c-statistic of 0.791, 0.783 and 0.766 for the D:A:D full, D:A:D reduced, and Framingham models respectively; p models also more accurately predicted five-year CVD-risk for key prognostic subgroups...... to quantify risk and to guide preventive care....

Investigation on Cardiovascular Risk Prediction Using Physiological Parameters

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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.

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

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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.

Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

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Peng Lu

2018-01-01

Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

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

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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

Divorce and risk of hospital-diagnosed infectious diseases.

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Nielsen, Nete Munk; Davidsen, Rie B; Hviid, Anders; Wohlfahrt, Jan

2014-11-01

Although, divorce is considered to have a negative impact on morbidity, very little is known concerning exposure to divorce and risk of infectious diseases. We aimed to investigate the association between divorce and subsequent hospital contacts with infectious diseases. We performed a nation-wide cohort study, including all Danish men and women (n≈5.6 million) alive on the 1 January 1982 or later, and followed them for infectious disease diagnosed in hospital settings from 1982 to 2010. The association between divorce and risk of infectious diseases was evaluated through rate ratios (RRs) comparing incidence rates of infectious diseases between divorced and married pesons. Compared with married persons, divorced persons were overall at a 1.48 fold (RR=1.48 (95% CI: 1.47-1.50)) increased risk of hospital-diagnosed infectious diseases (RR adjusted for sex, age, period, income and education). The risk of infectious diseases was slightly more pronounced for divorced women (RR=1.54 (1.52-1.56)) than divorced men ((RR=1.42 (1.41-1.44)). The increased risk remained almost unchanged even more than 15 years after the divorce. Young age at divorce, short duration of marriage and number of divorces further increased the risk of infectious diseases, whereas number of children at time of divorce had no impact on risk of hospital-diagnosed infectious diseases following the divorce. Divorce appears to have a moderate but long lasting impact on the risk of infectious diseases the underlying mechanism is unknown but shared risk factors predicting divorce and infectious diseases could contribute to our findings. © 2014 the Nordic Societies of Public Health.

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

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

2014-05-21

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

Circulating biomarkers for predicting cardiovascular disease risk; a systematic review and comprehensive overview of meta-analyses.

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Thijs C van Holten

Full Text Available BACKGROUND: Cardiovascular disease is one of the major causes of death worldwide. Assessing the risk for cardiovascular disease is an important aspect in clinical decision making and setting a therapeutic strategy, and the use of serological biomarkers may improve this. Despite an overwhelming number of studies and meta-analyses on biomarkers and cardiovascular disease, there are no comprehensive studies comparing the relevance of each biomarker. We performed a systematic review of meta-analyses on levels of serological biomarkers for atherothrombosis to compare the relevance of the most commonly studied biomarkers. METHODS AND FINDINGS: Medline and Embase were screened on search terms that were related to "arterial ischemic events" and "meta-analyses". The meta-analyses were sorted by patient groups without pre-existing cardiovascular disease, with cardiovascular disease and heterogeneous groups concerning general populations, groups with and without cardiovascular disease, or miscellaneous. These were subsequently sorted by end-point for cardiovascular disease or stroke and summarized in tables. We have identified 85 relevant full text articles, with 214 meta-analyses. Markers for primary cardiovascular events include, from high to low result: C-reactive protein, fibrinogen, cholesterol, apolipoprotein B, the apolipoprotein A/apolipoprotein B ratio, high density lipoprotein, and vitamin D. Markers for secondary cardiovascular events include, from high to low result: cardiac troponins I and T, C-reactive protein, serum creatinine, and cystatin C. For primary stroke, fibrinogen and serum uric acid are strong risk markers. Limitations reside in that there is no acknowledged search strategy for prognostic studies or meta-analyses. CONCLUSIONS: For primary cardiovascular events, markers with strong predictive potential are mainly associated with lipids. For secondary cardiovascular events, markers are more associated with ischemia. Fibrinogen is a

Risk factors for cardiovascular disease and type 2 diabetes retained from childhood to adulthood predict adult outcomes: the Princeton LRC Follow-up Study

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Morrison John A

2012-04-01

Full Text Available Abstract Background Pediatric risk factors predict adult cardiovascular disease (CVD and type 2 diabetes (T2DM, but whether they predict events independently of adult risk factors is not fully known. Objective Assess whether risk factors for CVD and T2DM retained from childhood to adulthood predict CVD and T2DM in young adulthood. Study design 770 schoolchildren, ages 5–20 (mean age 12, 26-yr prospective follow-up. We categorized childhood and adult risk factors and 26-year changes (triglycerides [TG], LDL cholesterol, BMI, blood pressure [BP] and glucose ≥, and HDL cholesterol Results Children who had high TG and retained high TG as adults had increased CVD events as adults (p = .0005. Children who had normal BMI and retained normal BMI as adults had reduced CVD events as adults (p = .02. Children who had high BP or high TG and retained these as adults had increased T2DM as adults (p = .0006, p = .003. Conclusions Risk factors for CVD and T2DM retained from childhood to adulthood predict CVD and T2DM in young adulthood and support universal childhood screening.

Risk stratification of patients suspected of coronary artery disease

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Jensen, Jesper M; Voss, Mette; Hansen, Vibeke Bøgelund

2012-01-01

To compare the performance of five risk models (Diamond-Forrester, the updated Diamond-Forrester, Morise, Duke, and a new model designated COronary Risk SCORE (CORSCORE) in predicting significant coronary artery disease (CAD) in patients with chest pain suggestive of stable angina pectoris....

The metabolic syndrome: validity and utility of clinical definitions for cardiovascular disease and diabetes risk prediction.

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Cameron, Adrian

2010-02-01

The purpose of clinical definitions of the metabolic syndrome is frequently misunderstood. While the metabolic syndrome as a physiological process describes a clustering of numerous age-related metabolic abnormalities that together increase the risk for cardiovascular disease and type 2 diabetes, clinical definitions include obesity which is thought to be a cause rather than a consequence of metabolic disturbance, and several elements that are routinely measured in clinical practice, including high blood pressure, high blood glucose and dyslipidaemia. Obesity is frequently a central player in the development of the metabolic syndrome and should be considered a key component of clinical definitions. Previous clinical definitions have differed in the priority given to obesity. Perhaps more importantly than its role in a clinical definition, however, is obesity in isolation before the hallmarks of metabolic dysfunction that typify the syndrome have developed. This should be treated seriously as an opportunity to prevent the consequences of the global diabetes epidemic now apparent. Clinical definitions were designed to identify a population at high lifetime CVD and type 2 diabetes risk, but in the absence of several major risk factors for each condition, are not optimal risk prediction devices for either. Despite this, the metabolic syndrome has several properties that make it a useful construct, in conjunction with short-term risk prediction algorithms and sound clinical judgement, for the identification of those at high lifetime risk of CVD and diabetes. A recently published consensus definition provides some much needed clarity about what a clinical definition entails. Even this, however, remains a work in progress until more evidence becomes available, particularly in the area of ethnicity-specific waist cut-points. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

Predicting Long-term Ischemic Events Using Routine Clinical Parameters in Patients with Coronary Artery Disease: The OPT-CAD Risk Score.

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Han, Yaling; Chen, Jiyan; Qiu, Miaohan; Li, Yi; Li, Jing; Feng, Yingqing; Qiu, Jian; Meng, Liang; Sun, Yihong; Tao, Guizhou; Wu, Zhaohui; Yang, Chunyu; Guo, Jincheng; Pu, Kui; Chen, Shaoliang; Wang, Xiaozeng

2018-06-05

The prognosis of patients with coronary artery disease (CAD) at hospital discharge was constantly varying, and post-discharge risk of ischemic events remain a concern. However, risk prediction tools to identify risk of ischemia for these patients has not yet been reported. We sought to develop a scoring system for predicting long-term ischemic events in CAD patients receiving antiplatelet therapy that would be beneficial in appropriate personalized decision-making for these patients. In this prospective Optimal antiPlatelet Therapy for Chinese patients with Coronary Artery Disease (OPT-CAD, NCT01735305) registry, a total of 14,032 patients with CAD receiving at least one kind of antiplatelet agent were enrolled from 107 centers across China, from January 2012 to March 2014. The risk scoring system was developed in a derivation cohort (enrolled initially 10,000 patients in the database) using a logistic regression model and was subsequently tested in a validation cohort (the last 4,032 patients). Points in risk score was assigned based on the multivariable odds ratio of each factor. Ischemic events were defined as the composite of cardiac death, myocardial infarction or stroke. Ischemic events occurred in 342 (3.4%) patients in the derivation cohort and 160 (4.0%) patients in the validation cohort during 1-year follow-up. The OPT-CAD score, ranging from 0-257 points, consist of 10 independent risk factors, including age (0-71 points), heart rates (0-36 points), hypertension (0-20 points), prior myocardial infarction (16 points), prior stroke (16 points), renal insufficient (21 points), anemia (19 points), low ejection fraction (22 points), positive cardiac troponin (23 points) and ST-segment deviation (13 points). In predicting 1-year ischemic events, the area under receiver operating characteristics curve were 0.73 and 0.72 in derivation and validation cohort, respectively. The incidences of ischemic events in low- (0-90 points), medium- (91-150 points) and

The impact of global environmental change on vector-borne disease risk: a modelling study

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Rachel Lowe, PhD

2018-05-01

Full Text Available Background: Vector-borne diseases, such as dengue virus, Zika virus, and malaria, are highly sensitive to environmental changes, including variations in climate and land-surface characteristics. The emergence and spread of vector-borne diseases is also exacerbated by anthropogenic activities, such as deforestation, mining, urbanisation, and human mobility, which alter the natural habitats of vectors and increase vector–host interactions. Innovative epidemiological modelling tools can help to understand how environmental conditions interact with socioeconomic risk factors to predict the risk of disease transmission. In recent years, climate-health modelling has benefited from computational advances in fitting complex mathematical models; increasing availability of environmental, socioeconomic, and disease surveillance datasets; and improved ability to understand and model the climate system. Climate forecasts at seasonal time scales tend to improve in quality during El Niño-Southern Oscillation events in certain regions of the tropics. Thus, climate forecasts provide an opportunity to anticipate potential outbreaks of vector-borne diseases from several months to a year in advance. The aim of this study was to develop a framework to incorporate seasonal climate forecasts in predictive disease models to understand the future risk of vector-borne diseases, with a focus on dengue fever in Latin America. Methods: A Bayesian spatiotemporal model framework that quantifies the extent to which environmental and socioeconomic indicators can explain variations in disease risk was designed to disentangle the effects of climate from other risk factors using multi-source data and random effects, which account for unknown and unmeasured sources of spatial, seasonal, and inter-annual variation. The model was used to provide probabilistic predictions of monthly dengue incidence and the probability of exceeding outbreak thresholds, which were established in

Meta-Prediction of the Effect of Methylenetetrahydrofolate Reductase Polymorphisms and Air Pollution on Alzheimer’s Disease Risk

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Suh-Mian Wu

2017-01-01

Full Text Available Background: Alzheimer’s disease (AD is a significant public health issue. AD has been linked with methylenetetrahydrofolate reductase (MTHFR C677T polymorphism, but the findings have been inconsistent. The purpose of this meta-predictive analysis is to examine the associations between MTHFR polymorphisms and epigenetic factors, including air pollution, with AD risk using big data analytics approaches. Methods and Results: Forty-three studies (44 groups were identified by searching various databases. MTHFR C677T TT and CT genotypes had significant associations with AD risk in all racial populations (RR = 1.13, p = 0.0047; and RR = 1.12, p < 0.0001 respectively. Meta-predictive analysis showed significant increases of percentages of MTHFR C677T polymorphism with increased air pollution levels in both AD case group and control group (p = 0.0021–0.0457; with higher percentages of TT and CT genotypes in the AD case group than that in the control group with increased air pollution levels. Conclusions: The impact of MTHFR C677T polymorphism on susceptibility to AD was modified by level of air pollution. Future studies are needed to further examine the effects of gene-environment interactions including air pollution on AD risk for world populations.

Heart disease - risk factors

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Heart disease - prevention; CVD - risk factors; Cardiovascular disease - risk factors; Coronary artery disease - risk factors; CAD - risk ... a certain health condition. Some risk factors for heart disease you cannot change, but some you can. ...

Emerging infectious disease outbreaks: estimating disease risk in Australian blood donors travelling overseas.

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Coghlan, A; Hoad, V C; Seed, C R; Flower, R Lp; Harley, R J; Herbert, D; Faddy, H M

2018-01-01

International travel assists spread of infectious pathogens. Australians regularly travel to South-eastern Asia and the isles of the South Pacific, where they may become infected with infectious agents, such as dengue (DENV), chikungunya (CHIKV) and Zika (ZIKV) viruses that pose a potential risk to transfusion safety. In Australia, donors are temporarily restricted from donating for fresh component manufacture following travel to many countries, including those in this study. We aimed to estimate the unmitigated transfusion-transmission (TT) risk from donors travelling internationally to areas affected by emerging infectious diseases. We used the European Up-Front Risk Assessment Tool, with travel and notification data, to estimate the TT risk from donors travelling to areas affected by disease outbreaks: Fiji (DENV), Bali (DENV), Phuket (DENV), Indonesia (CHIKV) and French Polynesia (ZIKV). We predict minimal risk from travel, with the annual unmitigated risk of an infected component being released varying from 1 in 1·43 million to disease outbreak areas to source plasma collection provides a simple and effective risk management approach. © 2017 International Society of Blood Transfusion.

Common carotid artery intima-media thickness is as good as carotid intima-media thickness of all carotid artery segments in improving prediction of coronary heart disease risk in the Atherosclerosis Risk in Communities (ARIC) study.

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Nambi, Vijay; Chambless, Lloyd; He, Max; Folsom, Aaron R; Mosley, Tom; Boerwinkle, Eric; Ballantyne, Christie M

2012-01-01

Carotid intima-media thickness (CIMT) and plaque information can improve coronary heart disease (CHD) risk prediction when added to traditional risk factors (TRF). However, obtaining adequate images of all carotid artery segments (A-CIMT) may be difficult. Of A-CIMT, the common carotid artery intima-media thickness (CCA-IMT) is relatively more reliable and easier to measure. We evaluated whether CCA-IMT is comparable to A-CIMT when added to TRF and plaque information in improving CHD risk prediction in the Atherosclerosis Risk in Communities (ARIC) study. Ten-year CHD risk prediction models using TRF alone, TRF + A-CIMT + plaque, and TRF + CCA-IMT + plaque were developed for the overall cohort, men, and women. The area under the receiver operator characteristic curve (AUC), per cent individuals reclassified, net reclassification index (NRI), and model calibration by the Grønnesby-Borgan test were estimated. There were 1722 incident CHD events in 12 576 individuals over a mean follow-up of 15.2 years. The AUC for TRF only, TRF + A-CIMT + plaque, and TRF + CCA-IMT + plaque models were 0.741, 0.754, and 0.753, respectively. Although there was some discordance when the CCA-IMT + plaque- and A-CIMT + plaque-based risk estimation was compared, the NRI and clinical NRI (NRI in the intermediate-risk group) when comparing the CIMT models with TRF-only model, per cent reclassified, and test for model calibration were not significantly different. Coronary heart disease risk prediction can be improved by adding A-CIMT + plaque or CCA-IMT + plaque information to TRF. Therefore, evaluating the carotid artery for plaque presence and measuring CCA-IMT, which is easier and more reliable than measuring A-CIMT, provide a good alternative to measuring A-CIMT for CHD risk prediction.

Genetic risk prediction using a spatial autoregressive model with adaptive lasso.

Science.gov (United States)

Wen, Yalu; Shen, Xiaoxi; Lu, Qing

2018-05-31

With rapidly evolving high-throughput technologies, studies are being initiated to accelerate the process toward precision medicine. The collection of the vast amounts of sequencing data provides us with great opportunities to systematically study the role of a deep catalog of sequencing variants in risk prediction. Nevertheless, the massive amount of noise signals and low frequencies of rare variants in sequencing data pose great analytical challenges on risk prediction modeling. Motivated by the development in spatial statistics, we propose a spatial autoregressive model with adaptive lasso (SARAL) for risk prediction modeling using high-dimensional sequencing data. The SARAL is a set-based approach, and thus, it reduces the data dimension and accumulates genetic effects within a single-nucleotide variant (SNV) set. Moreover, it allows different SNV sets having various magnitudes and directions of effect sizes, which reflects the nature of complex diseases. With the adaptive lasso implemented, SARAL can shrink the effects of noise SNV sets to be zero and, thus, further improve prediction accuracy. Through simulation studies, we demonstrate that, overall, SARAL is comparable to, if not better than, the genomic best linear unbiased prediction method. The method is further illustrated by an application to the sequencing data from the Alzheimer's Disease Neuroimaging Initiative. Copyright © 2018 John Wiley & Sons, Ltd.

Insulin Resistance and Risk of Cardiovascular Disease in Postmenopausal Women

DEFF Research Database (Denmark)

Schmiegelow, Michelle D; Hedlin, Haley; Stefanick, Marcia L

2015-01-01

BACKGROUND: Insulin resistance is associated with diabetes mellitus, but it is uncertain whether it improves cardiovascular disease (CVD) risk prediction beyond traditional cardiovascular risk factors. METHODS AND RESULTS: We identified 15,288 women from the Women's Health Initiative Biomarkers....../HDL-C, or impaired fasting glucose (serum glucose ≥110 mg/dL) to traditional risk factors in separate Cox multivariable analyses and assessed risk discrimination and reclassification. The study end point was major CVD events (nonfatal and fatal coronary heart disease and ischemic stroke) within 10 years, which...

Recent development of risk-prediction models for incident hypertension: An updated systematic review.

Directory of Open Access Journals (Sweden)

Dongdong Sun

Full Text Available Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative.Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc.From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI, age, smoking, blood pressure (BP level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%.The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment.

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...

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...

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.

Quantifying and estimating the predictive accuracy for censored time-to-event data with competing risks.

Science.gov (United States)

Wu, Cai; Li, Liang

2018-05-15

This paper focuses on quantifying and estimating the predictive accuracy of prognostic models for time-to-event outcomes with competing events. We consider the time-dependent discrimination and calibration metrics, including the receiver operating characteristics curve and the Brier score, in the context of competing risks. To address censoring, we propose a unified nonparametric estimation framework for both discrimination and calibration measures, by weighting the censored subjects with the conditional probability of the event of interest given the observed data. The proposed method can be extended to time-dependent predictive accuracy metrics constructed from a general class of loss functions. We apply the methodology to a data set from the African American Study of Kidney Disease and Hypertension to evaluate the predictive accuracy of a prognostic risk score in predicting end-stage renal disease, accounting for the competing risk of pre-end-stage renal disease death, and evaluate its numerical performance in extensive simulation studies. Copyright © 2018 John Wiley & Sons, Ltd.

Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology

Directory of Open Access Journals (Sweden)

Nicola A. Wardrop

2014-11-01

Full Text Available The application of spatial modelling to epidemiology has increased significantly over the past decade, delivering enhanced understanding of the environmental and climatic factors affecting disease distributions and providing spatially continuous representations of disease risk (predictive maps. These outputs provide significant information for disease control programmes, allowing spatial targeting and tailored interventions. However, several factors (e.g. sampling protocols or temporal disease spread can influence predictive mapping outputs. This paper proposes a conceptual framework which defines several scenarios and their potential impact on resulting predictive outputs, using simulated data to provide an exemplar. It is vital that researchers recognise these scenarios and their influence on predictive models and their outputs, as a failure to do so may lead to inaccurate interpretation of predictive maps. As long as these considerations are kept in mind, predictive mapping will continue to contribute significantly to epidemiological research and disease control planning.

Predicting risk for childhood asthma by pre-pregnancy, perinatal, and postnatal factors.

Science.gov (United States)

Wen, Hui-Ju; Chiang, Tung-Liang; Lin, Shio-Jean; Guo, Yue Leon

2015-05-01

Symptoms of atopic disease start early in human life. Predicting risk for childhood asthma by early-life exposure would contribute to disease prevention. A birth cohort study was conducted to investigate early-life risk factors for childhood asthma and to develop a predictive model for the development of asthma. National representative samples of newborn babies were obtained by multistage stratified systematic sampling from the 2005 Taiwan Birth Registry. Information on potential risk factors and children's health was collected by home interview when babies were 6 months old and 5 yr old, respectively. Backward stepwise regression analysis was used to identify the risk factors of childhood asthma for predictive models that were used to calculate the probability of childhood asthma. A total of 19,192 children completed the study satisfactorily. Physician-diagnosed asthma was reported in 6.6% of 5-yr-old children. Pre-pregnancy factors (parental atopy and socioeconomic status), perinatal factors (place of residence, exposure to indoor mold and painting/renovations during pregnancy), and postnatal factors (maternal postpartum depression and the presence of atopic dermatitis before 6 months of age) were chosen for the predictive models, and the highest predicted probability of asthma in 5-yr-old children was 68.1% in boys and 78.1% in girls; the lowest probability in boys and girls was 4.1% and 3.2%, respectively. This investigation provides a technique for predicting risk of childhood asthma that can be used to developing a preventive strategy against asthma. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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...

Can dental pulp calcification predict the risk of ischemic cardiovascular disease?

Science.gov (United States)

Khojastepour, Leila; Bronoosh, Pegah; Khosropanah, Shahdad; Rahimi, Elham

2013-09-01

To report the association of pulp calcification with that of cardiovascular disease (CVD) using digital panoramic dental radiographs. Digital panoramic radiographs of patients referred from the angiography department were included if the patient was under 55 years old and had non-restored or minimally restored molars and canines. An oral and maxillofacial radiologist evaluated the images for pulpal calcifications in the selected teeth. The sensitivity, specificity, positive predictive value and negative predictive value of panoramic radiography in predicting CVD were calculated. Out of 122 patients who met the criteria, 68.2% of the patients with CVD had pulp chamber calcifications. Pulp calcification in panoramic radiography had a sensitivity of 68.9% to predict CVD. This study demonstrates that patients with CVD show an increased incidence of pulp calcification compared with healthy patients. The findings suggest that pulp calcification on panoramic radiography may have possibilities for use in CVD screening.

Chagas disease risk in Texas.

Science.gov (United States)

Sarkar, Sahotra; Strutz, Stavana E; Frank, David M; Rivaldi, Chissa-Louise; Sissel, Blake; Sánchez-Cordero, Victor

2010-10-05

Chagas disease, caused by Trypanosoma cruzi, remains a serious public health concern in many areas of Latin America, including México. It is also endemic in Texas with an autochthonous canine cycle, abundant vectors (Triatoma species) in many counties, and established domestic and peridomestic cycles which make competent reservoirs available throughout the state. Yet, Chagas disease is not reportable in Texas, blood donor screening is not mandatory, and the serological profiles of human and canine populations remain unknown. The purpose of this analysis was to provide a formal risk assessment, including risk maps, which recommends the removal of these lacunae. The spatial relative risk of the establishment of autochthonous Chagas disease cycles in Texas was assessed using a five-stage analysis. 1. Ecological risk for Chagas disease was established at a fine spatial resolution using a maximum entropy algorithm that takes as input occurrence points of vectors and environmental layers. The analysis was restricted to triatomine vector species for which new data were generated through field collection and through collation of post-1960 museum records in both México and the United States with sufficiently low georeferenced error to be admissible given the spatial resolution of the analysis (1 arc-minute). The new data extended the distribution of vector species to 10 new Texas counties. The models predicted that Triatoma gerstaeckeri has a large region of contiguous suitable habitat in the southern United States and México, T. lecticularia has a diffuse suitable habitat distribution along both coasts of the same region, and T. sanguisuga has a disjoint suitable habitat distribution along the coasts of the United States. The ecological risk is highest in south Texas. 2. Incidence-based relative risk was computed at the county level using the Bayesian Besag-York-Mollié model and post-1960 T. cruzi incidence data. This risk is concentrated in south Texas. 3. The

Chagas disease risk in Texas.

Directory of Open Access Journals (Sweden)

Sahotra Sarkar

Full Text Available BACKGROUND: Chagas disease, caused by Trypanosoma cruzi, remains a serious public health concern in many areas of Latin America, including México. It is also endemic in Texas with an autochthonous canine cycle, abundant vectors (Triatoma species in many counties, and established domestic and peridomestic cycles which make competent reservoirs available throughout the state. Yet, Chagas disease is not reportable in Texas, blood donor screening is not mandatory, and the serological profiles of human and canine populations remain unknown. The purpose of this analysis was to provide a formal risk assessment, including risk maps, which recommends the removal of these lacunae. METHODS AND FINDINGS: The spatial relative risk of the establishment of autochthonous Chagas disease cycles in Texas was assessed using a five-stage analysis. 1. Ecological risk for Chagas disease was established at a fine spatial resolution using a maximum entropy algorithm that takes as input occurrence points of vectors and environmental layers. The analysis was restricted to triatomine vector species for which new data were generated through field collection and through collation of post-1960 museum records in both México and the United States with sufficiently low georeferenced error to be admissible given the spatial resolution of the analysis (1 arc-minute. The new data extended the distribution of vector species to 10 new Texas counties. The models predicted that Triatoma gerstaeckeri has a large region of contiguous suitable habitat in the southern United States and México, T. lecticularia has a diffuse suitable habitat distribution along both coasts of the same region, and T. sanguisuga has a disjoint suitable habitat distribution along the coasts of the United States. The ecological risk is highest in south Texas. 2. Incidence-based relative risk was computed at the county level using the Bayesian Besag-York-Mollié model and post-1960 T. cruzi incidence data. This

Individualized Vascular Disease Prevention in High-Risk Patients

NARCIS (Netherlands)

Kaasenbrood, L

2016-01-01

In the pharmacologic prevention of vascular events, clinicians need to translate average effects from a clinical trial to the individual patient. Prediction models can contribute to individualized vascular disease prevention by selecting patients for treatment based on estimated risk or expected

Polygenic risk score is associated with increased disease risk in 52 Finnish breast cancer families

OpenAIRE

Muranen, Taru A.; Mavaddat, Nasim; Khan, Sofia; Fagerholm, Rainer; Pelttari, Liisa; Lee, Andrew; Aittom?ki, Kristiina; Blomqvist, Carl; Easton, Douglas F.; Nevanlinna, Heli

2016-01-01

The risk of developing breast cancer is increased in women with family history of breast cancer and particularly in families with multiple cases of breast or ovarian cancer. Nevertheless, many women with a positive family history never develop the disease. Polygenic risk scores (PRSs) based on the risk effects of multiple common genetic variants have been proposed for individual risk assessment on a population level. We investigate the applicability of the PRS for risk prediction within breas...

Prediction of complicated disease course for children newly diagnosed with Crohn's disease: a multicentre inception cohort study.

Science.gov (United States)

Kugathasan, Subra; Denson, Lee A; Walters, Thomas D; Kim, Mi-Ok; Marigorta, Urko M; Schirmer, Melanie; Mondal, Kajari; Liu, Chunyan; Griffiths, Anne; Noe, Joshua D; Crandall, Wallace V; Snapper, Scott; Rabizadeh, Shervin; Rosh, Joel R; Shapiro, Jason M; Guthery, Stephen; Mack, David R; Kellermayer, Richard; Kappelman, Michael D; Steiner, Steven; Moulton, Dedrick E; Keljo, David; Cohen, Stanley; Oliva-Hemker, Maria; Heyman, Melvin B; Otley, Anthony R; Baker, Susan S; Evans, Jonathan S; Kirschner, Barbara S; Patel, Ashish S; Ziring, David; Trapnell, Bruce C; Sylvester, Francisco A; Stephens, Michael C; Baldassano, Robert N; Markowitz, James F; Cho, Judy; Xavier, Ramnik J; Huttenhower, Curtis; Aronow, Bruce J; Gibson, Greg; Hyams, Jeffrey S; Dubinsky, Marla C

2017-04-29

Stricturing and penetrating complications account for substantial morbidity and health-care costs in paediatric and adult onset Crohn's disease. Validated models to predict risk for complications are not available, and the effect of treatment on risk is unknown. We did a prospective inception cohort study of paediatric patients with newly diagnosed Crohn's disease at 28 sites in the USA and Canada. Genotypes, antimicrobial serologies, ileal gene expression, and ileal, rectal, and faecal microbiota were assessed. A competing-risk model for disease complications was derived and validated in independent groups. Propensity-score matching tested the effect of anti-tumour necrosis factor α (TNFα) therapy exposure within 90 days of diagnosis on complication risk. Between Nov 1, 2008, and June 30, 2012, we enrolled 913 patients, 78 (9%) of whom experienced Crohn's disease complications. The validated competing-risk model included age, race, disease location, and antimicrobial serologies and provided a sensitivity of 66% (95% CI 51-82) and specificity of 63% (55-71), with a negative predictive value of 95% (94-97). Patients who received early anti-TNFα therapy were less likely to have penetrating complications (hazard ratio [HR] 0·30, 95% CI 0·10-0·89; p=0·0296) but not stricturing complication (1·13, 0·51-2·51; 0·76) than were those who did not receive early anti-TNFα therapy. Ruminococcus was implicated in stricturing complications and Veillonella in penetrating complications. Ileal genes controlling extracellular matrix production were upregulated at diagnosis, and this gene signature was associated with stricturing in the risk model (HR 1·70, 95% CI 1·12-2·57; p=0·0120). When this gene signature was included, the model's specificity improved to 71%. Our findings support the usefulness of risk stratification of paediatric patients with Crohn's disease at diagnosis, and selection of anti-TNFα therapy. Crohn's and Colitis Foundation of America, Cincinnati

Can Dental Pulp Calcification Predict the Risk of Ischemic Cardiovascular Disease?

Directory of Open Access Journals (Sweden)

Leila Khojastepour

2013-01-01

Full Text Available Objective: To report the association of pulp calcification with that of cardiovascular disease (CVD using digital panoramic dental radiographs.Materials and Methods: Digital panoramic radiographs of patients referred from the angiography department were included if the patient was under 55 years old and had non-restored or minimally restored molars and canines. An oral and maxillofacial radiologist evaluated the images for pulpal calcifications in the selected teeth. The sensitivity, specificity, positive predictive value and negative predictive value of panoramic radiography in predicting CVD were calculated.Results: Out of 122 patients who met the criteria, 68.2% of the patients with CVD had pulp chamber calcifications. Pulp calcification in panoramic radiography had a sensitivity of 68.9% to predict CVD.Conclusion: This study demonstrates that patients with CVD show an increased incidence of pulp calcification compared with healthy patients. The findings suggest that pulp calcification on panoramic radiography may have possibilities for use in CVD screening.

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

Women's Heart Disease: Heart Disease Risk Factors

Science.gov (United States)

... this page please turn JavaScript on. Feature: Women's Heart Disease Heart Disease Risk Factors Past Issues / Winter 2014 Table ... or habits may raise your risk for coronary heart disease (CHD). These conditions are known as risk ...

A prediction score for significant coronary artery disease in Chinese patients ≥50 years old referred for rheumatic valvular heart disease surgery.

Science.gov (United States)

Xu, Zhenjun; Pan, Jun; Chen, Tao; Zhou, Qing; Wang, Qiang; Cao, Hailong; Fan, Fudong; Luo, Xuan; Ge, Min; Wang, Dongjin

2018-04-01

Our goal was to establish a prediction score and protocol for the preoperative prediction of significant coronary artery disease (CAD) in patients with rheumatic valvular heart disease. Using multivariate logistic regression analysis, we validated the model based on 490 patients without a history of myocardial infarction and who underwent preoperative screening coronary angiography. Significant CAD was defined as ≥50% narrowing of the diameter of the lumen of the left main coronary artery or ≥70% narrowing of the diameter of the lumen of the left anterior descending coronary artery, left circumflex artery or right coronary artery. Significant CAD was present in 9.8% of patients. Age, smoking, diabetes mellitus, diastolic blood pressure, low-density lipoprotein cholesterol and ischaemia evident on an electrocardiogram were independently associated with significant CAD and were entered into the multivariate model. According to the logistic regression predictive risk score, preoperative coronary angiography is recommended in (i) postmenopausal women between 50 and 59 years of age with ≥9.1% logistic regression predictive risk score; (ii) postmenopausal women who are ≥60 years old with a logistic regression predictive risk score ≥6.6% and (iii) men ≥50 years old whose logistic regression predictive risk score was ≥2.8%. Based on this predictive model, 246 (50.2%) preoperative coronary angiograms could be safely avoided. The negative predictive value of the model was 98.8% (246 of 249). This model was accurate for the preoperative prediction of significant CAD in patients with rheumatic valvular heart disease. This model must be validated in larger cohorts and various populations.

A prospective blood RNA signature for tuberculosis disease risk

Science.gov (United States)

Zak, Daniel E.; Penn-Nicholson, Adam; Scriba, Thomas J.; Thompson, Ethan; Suliman, Sara; Amon, Lynn M.; Mahomed, Hassan; Erasmus, Mzwandile; Whatney, Wendy; Hussey, Gregory D.; Abrahams, Deborah; Kafaar, Fazlin; Hawkridge, Tony; Verver, Suzanne; Hughes, E. Jane; Ota, Martin; Sutherland, Jayne; Howe, Rawleigh; Dockrell, Hazel M.; Boom, W. Henry; Thiel, Bonnie; Ottenhoff, Tom H.M.; Mayanja-Kizza, Harriet; Crampin, Amelia C; Downing, Katrina; Hatherill, Mark; Valvo, Joe; Shankar, Smitha; Parida, Shreemanta K; Kaufmann, Stefan H.E.; Walzl, Gerhard; Aderem, Alan; Hanekom, Willem A.

2016-01-01

Background Identification of blood biomarkers that prospectively predict progression of Mycobacterium tuberculosis infection to tuberculosis disease may lead to interventions that impact the epidemic. Methods Healthy, M. tuberculosis infected South African adolescents were followed for 2 years; blood was collected every 6 months. A prospective signature of risk was derived from whole blood RNA-Sequencing data by comparing participants who ultimately developed active tuberculosis disease (progressors) with those who remained healthy (matched controls). After adaptation to multiplex qRT-PCR, the signature was used to predict tuberculosis disease in untouched adolescent samples and in samples from independent cohorts of South African and Gambian adult progressors and controls. The latter participants were household contacts of adults with active pulmonary tuberculosis disease. Findings Of 6,363 adolescents screened, 46 progressors and 107 matched controls were identified. A 16 gene signature of risk was identified. The signature predicted tuberculosis progression with a sensitivity of 66·1% (95% confidence interval, 63·2–68·9) and a specificity of 80·6% (79·2–82·0) in the 12 months preceding tuberculosis diagnosis. The risk signature was validated in an untouched group of adolescents (p=0·018 for RNA-Seq and p=0·0095 for qRT-PCR) and in the independent South African and Gambian cohorts (p values Bill and Melinda Gates Foundation, the National Institutes of Health, Aeras, the European Union and the South African Medical Research Council (detail at end of text). PMID:27017310

Peripheral Arterial Disease Study (PERART: Prevalence and predictive values of asymptomatic peripheral arterial occlusive disease related to cardiovascular morbidity and mortality

Directory of Open Access Journals (Sweden)

Bundó Magda

2007-12-01

Full Text Available Abstract Background The early diagnosis of atherosclerotic disease is essential for developing preventive strategies in populations at high risk and acting when the disease is still asymptomatic. A low ankle-arm index (AAI is a good marker of vascular events and may be diminished without presenting symptomatology (silent peripheral arterial disease. The aim of the PERART study (PERipheral ARTerial disease is to determine the prevalence of peripheral arterial disease (both silent and symptomatic in a general population of both sexes and determine its predictive value related to morbimortality (cohort study. Methods/Design This cross-over, cohort study consists of 2 phases: firstly a descriptive, transversal cross-over study to determine the prevalence of peripheral arterial disease, and secondly, a cohort study to evaluate the predictive value of AAI in relation to cardiovascular morbimortality. From September 2006 to June 2007, a total of 3,010 patients over the age of 50 years will be randomly selected from a population adscribed to 24 healthcare centres in the province of Barcelona (Spain. The diagnostic criteria of peripheral arterial disease will be considered as an AAI Discussion In this study we hope to determine the prevalence of peripheral arterial disease, especially the silent forms, in the general population and establish its relationship with cardiovascular morbimortality. A low AAI may be a better marker of arterial disease than the classical cardiovascular risk factors and may, therefore, contribute to improving the predictive value of the equations of cardiovascular risk and thereby allowing optimisation of multifactorial treatment of atherosclerotic disease.

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...

Turning 18 with congenital heart disease: prediction of infective endocarditis based on a large population

NARCIS (Netherlands)

Verheugt, Carianne L.; Uiterwaal, Cuno S. P. M.; van der Velde, Enno T.; Meijboom, Folkert J.; Pieper, Petronella G.; Veen, Gerrit; Stappers, Jan L. M.; Grobbee, Diederick E.; Mulder, Barbara J. M.

2011-01-01

The risk of infective endocarditis (IE) in adults with congenital heart disease is known to be increased, yet empirical risk estimates are lacking. We sought to predict the occurrence of IE in patients with congenital heart disease at the transition from childhood into adulthood. We identified

Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes

Directory of Open Access Journals (Sweden)

Sungkyoung Choi

2016-12-01

Full Text Available The success of genome-wide association studies (GWASs has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the “large p and small n” problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR, least absolute shrinkage and selection operator (LASSO, and Elastic-Net (EN. We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

Meta-Prediction of the Effect of Methylenetetrahydrofolate Reductase Polymorphisms and Air Pollution on Alzheimer's Disease Risk.

Science.gov (United States)

Wu, Suh-Mian; Chen, Zhao-Feng; Young, Lufei; Shiao, S Pamela K

2017-01-11

Background : Alzheimer's disease (AD) is a significant public health issue. AD has been linked with methylenetetrahydrofolate reductase ( MTHFR ) C677T polymorphism, but the findings have been inconsistent. The purpose of this meta-predictive analysis is to examine the associations between MTHFR polymorphisms and epigenetic factors, including air pollution, with AD risk using big data analytics approaches. Methods and Results : Forty-three studies (44 groups) were identified by searching various databases. MTHFR C677T TT and CT genotypes had significant associations with AD risk in all racial populations (RR = 1.13, p = 0.0047; and RR = 1.12, p analysis showed significant increases of percentages of MTHFR C677T polymorphism with increased air pollution levels in both AD case group and control group ( p = 0.0021-0.0457); with higher percentages of TT and CT genotypes in the AD case group than that in the control group with increased air pollution levels. Conclusions : The impact of MTHFR C677T polymorphism on susceptibility to AD was modified by level of air pollution. Future studies are needed to further examine the effects of gene-environment interactions including air pollution on AD risk for world populations.

Credit scores, cardiovascular disease risk, and human capital.

Science.gov (United States)

Israel, Salomon; Caspi, Avshalom; Belsky, Daniel W; Harrington, HonaLee; Hogan, Sean; Houts, Renate; Ramrakha, Sandhya; Sanders, Seth; Poulton, Richie; Moffitt, Terrie E

2014-12-02

Credit scores are the most widely used instruments to assess whether or not a person is a financial risk. Credit scoring has been so successful that it has expanded beyond lending and into our everyday lives, even to inform how insurers evaluate our health. The pervasive application of credit scoring has outpaced knowledge about why credit scores are such useful indicators of individual behavior. Here we test if the same factors that lead to poor credit scores also lead to poor health. Following the Dunedin (New Zealand) Longitudinal Study cohort of 1,037 study members, we examined the association between credit scores and cardiovascular disease risk and the underlying factors that account for this association. We find that credit scores are negatively correlated with cardiovascular disease risk. Variation in household income was not sufficient to account for this association. Rather, individual differences in human capital factors—educational attainment, cognitive ability, and self-control—predicted both credit scores and cardiovascular disease risk and accounted for ∼45% of the correlation between credit scores and cardiovascular disease risk. Tracing human capital factors back to their childhood antecedents revealed that the characteristic attitudes, behaviors, and competencies children develop in their first decade of life account for a significant portion (∼22%) of the link between credit scores and cardiovascular disease risk at midlife. We discuss the implications of these findings for policy debates about data privacy, financial literacy, and early childhood interventions.

Insignificant disease among men with intermediate-risk prostate cancer.

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Hong, Sung Kyu; Vertosick, Emily; Sjoberg, Daniel D; Scardino, Peter T; Eastham, James A

2014-12-01

A paucity of data exists on the insignificant disease potentially suitable for active surveillance (AS) among men with intermediate-risk prostate cancer (PCa). We tried to identify pathologically insignificant disease and its preoperative predictors in men who underwent radical prostatectomy (RP) for intermediate-risk PCa. We analyzed data of 1,630 men who underwent RP for intermediate-risk disease. Total tumor volume (TTV) data were available in 332 men. We examined factors associated with classically defined pathologically insignificant cancer (organ-confined disease with TTV ≤0.5 ml with no Gleason pattern 4 or 5) and pathologically favorable cancer (organ-confined disease with no Gleason pattern 4 or 5) potentially suitable for AS. Decision curve analysis was used to assess clinical utility of a multivariable model including preoperative variables for predicting pathologically unfavorable cancer. In the entire cohort, 221 of 1,630 (13.6 %) total patients had pathologically favorable cancer. Among 332 patients with TTV data available, 26 (7.8 %) had classically defined pathologically insignificant cancer. Between threshold probabilities of 20 and 40 %, decision curve analysis demonstrated that using multivariable model to identify AS candidates would not provide any benefit over simply treating all men who have intermediate-risk disease with RP. Although a minority of patients with intermediate-risk disease may harbor pathologically favorable or insignificant cancer, currently available conventional tools are not sufficiently able to identify those patients.

A Risk Prediction Model for In-hospital Mortality in Patients with Suspected Myocarditis.

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Xu, Duo; Zhao, Ruo-Chi; Gao, Wen-Hui; Cui, Han-Bin

2017-04-05

Myocarditis is an inflammatory disease of the myocardium that may lead to cardiac death in some patients. However, little is known about the predictors of in-hospital mortality in patients with suspected myocarditis. Thus, the aim of this study was to identify the independent risk factors for in-hospital mortality in patients with suspected myocarditis by establishing a risk prediction model. A retrospective study was performed to analyze the clinical medical records of 403 consecutive patients with suspected myocarditis who were admitted to Ningbo First Hospital between January 2003 and December 2013. A total of 238 males (59%) and 165 females (41%) were enrolled in this study. We divided the above patients into two subgroups (survival and nonsurvival), according to their clinical in-hospital outcomes. To maximize the effectiveness of the prediction model, we first identified the potential risk factors for in-hospital mortality among patients with suspected myocarditis, based on data pertaining to previously established risk factors and basic patient characteristics. We subsequently established a regression model for predicting in-hospital mortality using univariate and multivariate logistic regression analyses. Finally, we identified the independent risk factors for in-hospital mortality using our risk prediction model. The following prediction model for in-hospital mortality in patients with suspected myocarditis, including creatinine clearance rate (Ccr), age, ventricular tachycardia (VT), New York Heart Association (NYHA) classification, gender and cardiac troponin T (cTnT), was established in the study: P = ea/(1 + ea) (where e is the exponential function, P is the probability of in-hospital death, and a = -7.34 + 2.99 × [Ccr model demonstrated that a Ccr prediction model for in-hospital mortality in patients with suspected myocarditis. In addition, sufficient life support during the early stage of the disease might improve the prognoses of patients with

Predicting the effect of prevention of ischaemic heart disease

DEFF Research Database (Denmark)

Brønnum-Hansen, Henrik

2002-01-01

Priority setting in public health policy must be based on information on the effectiveness of alternative preventive and therapeutic interventions. The purpose of this study is to predict the effect on mortality from ischaemic heart disease (IHD) in Denmark of reduced exposure to the risk factors...... hypertension, hypercholesterolaemia, cigarette smoking, and physical inactivity....

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....

Development of a flood-induced health risk prediction model for Africa

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Lee, D.; Block, P. J.

2017-12-01

Globally, many floods occur in developing or tropical regions where the impact on public health is substantial, including death and injury, drinking water, endemic disease, and so on. Although these flood impacts on public health have been investigated, integrated management of floods and flood-induced health risks is technically and institutionally limited. Specifically, while the use of climatic and hydrologic forecasts for disaster management has been highlighted, analogous predictions for forecasting the magnitude and impact of health risks are lacking, as is the infrastructure for health early warning systems, particularly in developing countries. In this study, we develop flood-induced health risk prediction model for African regions using season-ahead flood predictions with climate drivers and a variety of physical and socio-economic information, such as local hazard, exposure, resilience, and health vulnerability indicators. Skillful prediction of flood and flood-induced health risks can contribute to practical pre- and post-disaster responses in both local- and global-scales, and may eventually be integrated into multi-hazard early warning systems for informed advanced planning and management. This is especially attractive for areas with limited observations and/or little capacity to develop flood-induced health risk warning systems.

Predictive gene testing for Huntington disease and other neurodegenerative disorders.

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Wedderburn, S; Panegyres, P K; Andrew, S; Goldblatt, J; Liebeck, T; McGrath, F; Wiltshire, M; Pestell, C; Lee, J; Beilby, J

2013-12-01

Controversies exist around predictive testing (PT) programmes in neurodegenerative disorders. This study sets out to answer the following questions relating to Huntington disease (HD) and other neurodegenerative disorders: differences between these patients in their PT journeys, why and when individuals withdraw from PT, and decision-making processes regarding reproductive genetic testing. A case series analysis of patients having PT from the multidisciplinary Western Australian centre for PT over the past 20 years was performed using internationally recognised guidelines for predictive gene testing in neurodegenerative disorders. Of 740 at-risk patients, 518 applied for PT: 466 at risk of HD, 52 at risk of other neurodegenerative disorders - spinocerebellar ataxias, hereditary prion disease and familial Alzheimer disease. Thirteen percent withdrew from PT - 80.32% of withdrawals occurred during counselling stages. Major withdrawal reasons related to timing in the patients' lives or unknown as the patient did not disclose the reason. Thirty-eight HD individuals had reproductive genetic testing: 34 initiated prenatal testing (of which eight withdrew from the process) and four initiated pre-implantation genetic diagnosis. There was no recorded or other evidence of major psychological reactions or suicides during PT. People withdrew from PT in relation to life stages and reasons that are unknown. Our findings emphasise the importance of: (i) adherence to internationally recommended guidelines for PT; (ii) the role of the multidisciplinary team in risk minimisation; and (iii) patient selection. © 2013 The Authors; Internal Medicine Journal © 2013 Royal Australasian College of Physicians.

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.

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.

Molecular determination of RHD zygosity: predicting risk of hemolytic disease of the fetus and newborn related to anti-D.

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Pirelli, Kevin J; Pietz, Bradley C; Johnson, Susan T; Pinder, Holly L; Bellissimo, Daniel B

2010-12-01

Development of an accurate molecular method for paternal RHD zygosity to predict risk to a fetus for hemolytic disease of the fetus and newborn (HDFN) related to anti-D. Quantitative fluorescence polymerase chain reaction (QF-PCR) was used to detect RHD exons 5 and 7, using RHCE exon 7 as an internal control. The genotype and zygosity were determined from the peak area ratios of RHD exon 5 or 7 to RHCE exon 7. We tested 25 Caucasian and 25 African American (AA) samples whose zygosity was predicted from the Rh phenotype and an alternate molecular method. In addition, we tested 71 paternal samples from prenatal cases where fetal testing was performed. RHD/RHCE ratios clearly distinguished the RHD/D and RHD/d genotypes. RHD variants were recognized when RHD exon 5 copy number was discordant with exon 7. The molecular assay identified eight cases where the phenotype incorrectly assigned zygosity and we observed three false-negatives in the hybrid Rhesus box assay. The prenatal results were consistent with the zygosity determined for the paternal samples in our study. This QF-PCR method accurately determines RHD zygosity in Caucasians and AAs and will help predict the risk that a fetus will inherit RHD. Copyright © 2010 John Wiley & Sons, Ltd.

Predicting Climate-sensitive Infectious Diseases: Development of a Federal Science Plan and the Path Forward

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Trtanj, J.; Balbus, J. M.; Brown, C.; Shimamoto, M. M.

2017-12-01

The transmission and spread of infectious diseases, especially vector-borne diseases, water-borne diseases and zoonosis, are influenced by short and long-term climate factors, in conjunction with numerous other drivers. Public health interventions, including vaccination, vector control programs, and outreach campaigns could be made more effective if the geographic range and timing of increased disease risk could be more accurately targeted, and high risk areas and populations identified. While some progress has been made in predictive modeling for transmission of these diseases using climate and weather data as inputs, they often still start after the first case appears, the skill of those models remains limited, and their use by public health officials infrequent. And further, predictions with lead times of weeks, months or seasons are even rarer, yet the value of acting early holds the potential to save more lives, reduce cost and enhance both economic and national security. Information on high-risk populations and areas for infectious diseases is also potentially useful for the federal defense and intelligence communities as well. The US Global Change Research Program, through its Interagency Group on Climate Change and Human Health (CCHHG), has put together a science plan that pulls together federal scientists and programs working on predictive modeling of climate-sensitive diseases, and draws on academic and other partners. Through a series of webinars and an in-person workshop, the CCHHG has convened key federal and academic stakeholders to assess the current state of science and develop an integrated science plan to identify data and observation systems needs as well as a targeted research agenda for enhancing predictive modeling. This presentation will summarize the findings from this effort and engage AGU members on plans and next steps to improve predictive modeling for infectious diseases.

NT-proBNP is associated with coronary heart disease risk in healthy older women but fails to enhance prediction beyond established risk factors: results from the British Women's Heart and Health Study.

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Sattar, Naveed; Welsh, Paul; Sarwar, Nadeem; Danesh, John; Di Angelantonio, Emanuele; Gudnason, Vilmundur; Davey Smith, George; Ebrahim, Shah; Lawlor, Debbie A

2010-03-01

Limited evidence suggests NT-proBNP improves prediction of coronary heart disease (CHD) events but further data are needed, especially in people without pre-existing CHD and in women. We measured NT-proBNP in serum from 162 women with incident CHD events and 1226 controls (60-79 years) in a case-control study nested within the prospective British Women's Heart and Health Study. All cases and controls were free from CHD at baseline. We related NT-proBNP to CHD event risk, and determined to what extent NT-proBNP enhanced CHD risk prediction beyond established risk factors. The odds ratio for CHD per 1 standard deviation increase in log(e)NT-proBNP was 1.37 (95% CI: 1.13-1.68) in analyses adjusted for established CHD risk factors, social class, CRP and insulin. However, addition of log(e)NT-proBNP did not improve the discrimination of a prediction model including age, social class, smoking, physical activity, lipids, fasting glucose, waist:hip ratio, hypertension, statin and aspirin use, nor a standard Framingham risk score model; area under the receiver operator curve for the former model increased from 0.676 to 0.687 on inclusion of NT-proBNP (p=0.3). Furthermore, adding NT-proBNP did not improve calibration of a prediction model containing established risk factors, nor did inclusion more appropriately re-classify participants in relation to their final outcome. Findings were similar (independent associations, but no prediction improvement) for fasting insulin and CRP. These results caution against use of NT-proBNP for CHD risk prediction in healthy women and suggest a need for larger studies in both genders to resolve outstanding uncertainties.

Traditional Cardiovascular Risk Factors and Their Relation to Future Surgery for Valvular Heart Disease or Ascending Aortic Disease: A Case-Referent Study.

Science.gov (United States)

Ljungberg, Johan; Johansson, Bengt; Engström, Karl Gunnar; Albertsson, Elin; Holmer, Paul; Norberg, Margareta; Bergdahl, Ingvar A; Söderberg, Stefan

2017-05-05

Risk factors for developing heart valve and ascending aortic disease are based mainly on retrospective data. To elucidate these factors in a prospective manner, we have performed a nested case-referent study using data from large, population-based surveys. A total of 777 patients operated for heart valve disease or disease of the ascending aorta had previously participated in population-based health surveys in Northern Sweden. Median time (interquartile range) from survey to surgery was 10.5 (9.0) years. Primary indications for surgery were aortic stenosis (41%), aortic regurgitation (12%), mitral regurgitation (23%), and dilatation/dissection of the ascending aorta (17%). For each case, referents were allocated, matched for age, sex, and geographical area. In multivariable models, surgery for aortic stenosis was predicted by hypertension, high cholesterol levels, diabetes mellitus, and active smoking. Surgery for aortic regurgitation was associated with a low cholesterol level, whereas a high cholesterol level predicted surgery for mitral regurgitation. Hypertension, blood pressure, and previous smoking predicted surgery for disease of the ascending aorta whereas diabetes mellitus was associated with reduced risk. After exclusion of cases with coronary atherosclerosis, only the inverse associations between cholesterol and aortic regurgitation and between diabetes mellitus and disease of the ascending aorta remained. This is the first truly prospective study of traditional cardiovascular risk factors and their association with valvular heart disease and disease of the ascending aorta. We confirm the strong association between traditional risk factors and aortic stenosis, but only in patients with concomitant coronary artery disease. In isolated valvular heart disease, the impact of traditional risk factors is varying. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

[Analysis of 14 individuals who requested predictive genetic testing for hereditary neuromuscular diseases].

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Yoshida, Kunihiro; Tamai, Mariko; Kubota, Takeo; Kawame, Hiroshi; Amano, Naoji; Ikeda, Shu-ichi; Fukushima, Yoshimitsu

2002-02-01

Predictive genetic testing for hereditary neuromuscular diseases is a delicate issue for individuals at risk and their families, as well as for medical staff because these diseases are often late-onset and intractable. Therefore careful pre- and post-test genetic counseling and psychosocial support should be provided along with such genetic testing. The Division of Clinical and Molecular Genetics was established at our hospital in May 1996 to provide skilled professional genetic counseling. Since its establishment, 14 individuals have visited our clinic to request predictive genetic testing for hereditary neuromuscular diseases (4 for myotonic dystrophy, 6 for spinocerebellar ataxia, 3 for Huntington's disease, and 1 for Alzheimer's disease). The main reasons for considering testing were to remove uncertainty about the genetic status and to plan for the future. Nine of 14 individuals requested testing for making decisions about a forthcoming marriage or pregnancy (family planning). Other reasons raised by the individuals included career or financial planning, planning for their own health care, and knowing the risk for their children. At the first genetic counseling session, all of the individuals expressed hopes of not being a gene carrier and of escaping from fear of disease, and seemed not to be mentally well prepared for an increased-risk result. To date, 7 of the 14 individuals have received genetic testing and only one, who underwent predictive genetic testing for spinocerebellar ataxia, was given an increased-risk result. The seven individuals including the one with an increased-risk result, have coped well with their new knowledge about their genetic status after the testing results were disclosed. None of them has expressed regret. In pre-test genetic counseling sessions, we consider it quite important not only to determine the psychological status of the individual, but also to make the individual try to anticipate the changes in his/her life upon

PREDICTION OF SURGICAL TREATMENT WITH POUR PERITONITIS TAKING INTO ACCOUNT QUANTIFYING RISK FACTORS

Directory of Open Access Journals (Sweden)

І. К. Churpiy

2012-11-01

Full Text Available There was investigated the possibility of quantitative assessment of risk factors of complications in the treatment of diffuse peritonitis. There were ditermined 70 groups of features that are important in predicting the course of diffuse peritonitis. The proposed scheme is the definition of risk clinical course of diffuse peritonitis can quantify the severity of the original patients and in most cases is correctly to predict the results of treatment of disease.

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.

Knowledge of heart disease risk in a multicultural community sample of people with diabetes.

Science.gov (United States)

Wagner, Julie; Lacey, Kimberly; Abbott, Gina; de Groot, Mary; Chyun, Deborah

2006-06-01

Prevention of coronary heart disease (CHD) is a primary goal of diabetes management. Unfortunately, CHD risk knowledge is poor among people with diabetes. The objective is to determine predictors of CHD risk knowledge in a community sample of people with diabetes. A total of 678 people with diabetes completed the Heart Disease Facts Questionnaire (HDFQ), a valid and reliable measure of knowledge about the relationship between diabetes and heart disease. In regression analysis with demographics predicting HDFQ scores, sex, annual income, education, and health insurance status predicted HDFQ scores. In a separate regression analysis, having CHD risk factors did not predict HDFQ scores, however, taking medication for CHD risk factors did predict higher HDFQ scores. An analysis of variance showed significant differences between ethnic groups for HDFQ scores; Whites (M = 20.9) showed more CHD risk knowledge than African Americans (M = 19.6), who in turn showed more than Latinos (M = 18.2). Asians scored near Whites (M = 20.4) but did not differ significantly from any other group. Controlling for numerous demographic, socioeconomic, health care, diabetes, and cardiovascular health variables, the magnitude of ethnic differences was attenuated, but persisted. Education regarding modifiable risk factors must be delivered in a timely fashion so that lifestyle modification can be implemented and evaluated before pharmacotherapy is deemed necessary. African Americans and Latinos with diabetes are in the greatest need of education regarding CHD risk.

Peripheral Arterial Disease study (PERART): prevalence and predictive values of asymptomatic peripheral arterial occlusive disease related to cardiovascular morbidity and mortality.

Science.gov (United States)

Alzamora, María Teresa; Baena-Díez, José Miguel; Sorribes, Marta; Forés, Rosa; Toran, Pere; Vicheto, Marisa; Pera, Guillem; Reina, María Dolores; Albaladejo, Carlos; Llussà, Judith; Bundó, Magda; Sancho, Amparo; Heras, Antonio; Rubiés, Joan; Arenillas, Juan Francisco

2007-12-11

The early diagnosis of atherosclerotic disease is essential for developing preventive strategies in populations at high risk and acting when the disease is still asymptomatic. A low ankle-arm index (AAI) is a good marker of vascular events and may be diminished without presenting symptomatology (silent peripheral arterial disease). The aim of the PERART study (PERipheral ARTerial disease) is to determine the prevalence of peripheral arterial disease (both silent and symptomatic) in a general population of both sexes and determine its predictive value related to morbimortality (cohort study). This cross-over, cohort study consists of 2 phases: firstly a descriptive, transversal cross-over study to determine the prevalence of peripheral arterial disease, and secondly, a cohort study to evaluate the predictive value of AAI in relation to cardiovascular morbimortality. From September 2006 to June 2007, a total of 3,010 patients over the age of 50 years will be randomly selected from a population adscribed to 24 healthcare centres in the province of Barcelona (Spain). The diagnostic criteria of peripheral arterial disease will be considered as an AAI < 0.90, determined by portable Doppler (8 Mhz probe) measured twice by trained personnel. Cardiovascular risk will be calculated with the Framingham-Wilson tables, with Framingham calibrated by the REGICOR and SCORE groups. The subjects included will be evaluted every 6 months by telephone interview and the clnical history and death registries will be reviewed. The appearance of the following cardiovascular events will be considered as variables of response: transitory ischaemic accident, ictus, angina, myocardial infartction, symptomatic abdominal aneurysm and vascular mortality. In this study we hope to determine the prevalence of peripheral arterial disease, especially the silent forms, in the general population and establish its relationship with cardiovascular morbimortality. A low AAI may be a better marker of

Wildlife disease and risk perception.

Science.gov (United States)

Hanisch-Kirkbride, Shauna L; Riley, Shawn J; Gore, Meredith L

2013-10-01

Risk perception has an important influence on wildlife management and is particularly relevant to issues that present health risks, such as those associated with wildlife disease management. Knowledge of risk perceptions is useful to wildlife health professionals in developing communication messages that enhance public understanding of wildlife disease risks and that aim to increase public support for disease management. To promote knowledge of public understanding of disease risks in the context of wildlife disease management, we used a self-administered questionnaire mailed to a stratified random sample (n = 901) across the continental United States to accomplish three objectives: 1) assess zoonotic disease risk perceptions; 2) identify sociodemographic and social psychologic factors underlying these risk perceptions; and 3) examine the relationship between risk perception and agreement with wildlife disease management practices. Diseases we assessed in the surveys were rabies, plague, and West Nile virus. Risk perception, as measured by an index consisting of severity, susceptibility, and dread, was greatest for rabies and West Nile virus disease (x = 2.62 and 2.59, respectively, on a scale of 1 to 4 and least for plague (x = 2.39). The four most important variables associated with disease risk perception were gender, education, prior exposure to the disease, and concern for health effects. We found that stronger risk perception was associated with greater agreement with wildlife disease management. We found particular concern for the vulnerability of wildlife to zoonotic disease and for protection of wildlife health, indicating that stakeholders may be receptive to messages emphasizing the potential harm to wildlife from disease and to messages promoting One Health (i.e., those that emphasize the interdependence of human, domestic animal, wildlife, and ecosystem health).

Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

Science.gov (United States)

Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

2015-07-01

Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

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

Directory of Open Access Journals (Sweden)

Solarin Adewale RT

2008-05-01

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

Standard deviation of carotid young's modulus and presence or absence of plaque improves prediction of coronary heart disease risk.

Science.gov (United States)

Niu, Lili; Zhang, Yanling; Qian, Ming; Xiao, Yang; Meng, Long; Zheng, Rongqin; Zheng, Hairong

2017-11-01

The stiffness of large arteries and the presence or absence of plaque are associated with coronary heart disease (CHD). Because arterial walls are biologically heterogeneous, the standard deviation of Young's modulus (YM-std) of the large arteries may better predict coronary atherosclerosis. However, the role of YM-std in the occurrence of coronary events has not been addressed so far. Therefore, this study investigated whether the carotid YM-std and the presence or absence of plaque improved CHD risk prediction. One hundred and three patients with CHD (age 66 ± 11 years) and 107 patients at high risk of atherosclerosis (age 61 ± 7 years) were recruited. Carotid YM was measured by the vessel texture matching method, and YM-std was calculated. Carotid intima-media thickness was measured by the MyLab 90 ultrasound Platform employed dedicated software RF-tracking technology. In logistic regression analysis, YM-std (OR = 1·010; 95% CI = 1·003-1·016), carotid plaque (OR = 16·759; 95% CI = 3·719-75·533) and YM-std plus plaque (OR = 0·989; 95% CI = 0·981-0·997) were independent predictors of CHD. The traditional risk factors (TRF) plus YM-std plus plaque model showed a significant improvement in area under the receiver-operating characteristic curve (AUC), which increased from 0·717 (TRF only) to 0·777 (95% CI for the difference in adjusted AUC: 0·010-0·110). Carotid YM-std is a powerful independent predictor of CHD. Adding plaque and YM-std to TRF improves CHD risk prediction. © 2016 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

An artificial neural network prediction model of congenital heart disease based on risk factors: A hospital-based case-control study.

Science.gov (United States)

Li, Huixia; Luo, Miyang; Zheng, Jianfei; Luo, Jiayou; Zeng, Rong; Feng, Na; Du, Qiyun; Fang, Junqun

2017-02-01

An artificial neural network (ANN) model was developed to predict the risks of congenital heart disease (CHD) in pregnant women.This hospital-based case-control study involved 119 CHD cases and 239 controls all recruited from birth defect surveillance hospitals in Hunan Province between July 2013 and June 2014. All subjects were interviewed face-to-face to fill in a questionnaire that covered 36 CHD-related variables. The 358 subjects were randomly divided into a training set and a testing set at the ratio of 85:15. The training set was used to identify the significant predictors of CHD by univariate logistic regression analyses and develop a standard feed-forward back-propagation neural network (BPNN) model for the prediction of CHD. The testing set was used to test and evaluate the performance of the ANN model. Univariate logistic regression analyses were performed on SPSS 18.0. The ANN models were developed on Matlab 7.1.The univariate logistic regression identified 15 predictors that were significantly associated with CHD, including education level (odds ratio  = 0.55), gravidity (1.95), parity (2.01), history of abnormal reproduction (2.49), family history of CHD (5.23), maternal chronic disease (4.19), maternal upper respiratory tract infection (2.08), environmental pollution around maternal dwelling place (3.63), maternal exposure to occupational hazards (3.53), maternal mental stress (2.48), paternal chronic disease (4.87), paternal exposure to occupational hazards (2.51), intake of vegetable/fruit (0.45), intake of fish/shrimp/meat/egg (0.59), and intake of milk/soymilk (0.55). After many trials, we selected a 3-layer BPNN model with 15, 12, and 1 neuron in the input, hidden, and output layers, respectively, as the best prediction model. The prediction model has accuracies of 0.91 and 0.86 on the training and testing sets, respectively. The sensitivity, specificity, and Yuden Index on the testing set (training set) are 0.78 (0.83), 0.90 (0.95), and 0

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

Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.

Science.gov (United States)

Razavian, Narges; Blecker, Saul; Schmidt, Ann Marie; Smith-McLallen, Aaron; Nigam, Somesh; Sontag, David

2015-12-01

We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion, population-level risk

Identifying noncoding risk variants using disease-relevant gene regulatory networks.

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Gao, Long; Uzun, Yasin; Gao, Peng; He, Bing; Ma, Xiaoke; Wang, Jiahui; Han, Shizhong; Tan, Kai

2018-02-16

Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.

A biological approach to the interspecies prediction of radiation-induced mortality risk

International Nuclear Information System (INIS)

Carnes, B.A.; Grahn, D.; Olshansky, S.J.

1997-01-01

Evolutionary explanations for why sexually reproducing organisms grow old suggest that the forces of natural selection affect the ages when diseases occur that are subject to a genetic influence (referred to here as intrinsic diseases). When extended to the population level for a species, this logic leads to the general prediction that age-specific death rates from intrinsic causes should begin to rise as the force of selection wanes once the characteristic age of sexual maturity is attained. Results consistent with these predictions have been found for laboratory mice, beagles, and humans where, after adjusting for differences in life span, it was demonstrated that these species share a common age pattern of mortality for intrinsic causes of death. In quantitative models used to predict radiation-induced mortality, risks are often expressed as multiples of those observed in a control population. A control population, however, is an aging population. As such, mortality risks related to exposure must be interpreted relative to the age-specific risk of death associated with aging. Given the previous success in making interspecies predictions of age-related mortality, the purpose of this study was to determine whether radiation-induced mortality observed in one species could also be predicted quantitatively from a model used to describe the mortality consequences of exposure to radiation in a different species. Mortality data for B6CF 1 mice and beagles exposed to 60 Co γ-rays for the duration of life were used for analysis

Can we Predict Disease Course with Clinical Factors?

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Vegh, Zsuzsanna; Kurti, Zsuzsanna; Golovics, Petra A; Lakatos, Peter L

2018-01-01

The disease phenotype at diagnosis and the disease course of Crohn's disease (CD) and ulcerative colitis (UC) show remarkable heterogeneity across patients. This review aims to summarize the currently available evidence on clinical and some environmental predictive factors, which clinicians should evaluate in the everyday practice together with other laboratory and imaging data to prevent disease progression, enable a more personalized therapy, and avoid negative disease outcomes. In recent population-based epidemiological and referral cohort studies, the evolution of disease phenotype of CD and UC varied significantly. Most CD and severe UC patients still require hospitalization or surgery/colectomy during follow-up. A change in the natural history of inflammatory bowel diseases (IBD) with improved outcomes in parallel with tailored positioning of aggressive immunomodulator and biological therapy has been suspected. According to the currently available literature, it is of major importance to refer IBD cases at risk for adverse disease outcomes as early during the disease course as possible. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Predicting the risk of cucurbit downy mildew in the eastern United States using an integrated aerobiological model

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Neufeld, K. N.; Keinath, A. P.; Gugino, B. K.; McGrath, M. T.; Sikora, E. J.; Miller, S. A.; Ivey, M. L.; Langston, D. B.; Dutta, B.; Keever, T.; Sims, A.; Ojiambo, P. S.

2017-11-01

Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good

Reproductive factors and Parkinson's disease risk in Danish women

DEFF Research Database (Denmark)

Greene, N; Lassen, C F; Rugbjerg, K

2014-01-01

and lifestyle factors. RESULTS: After adjusting for smoking, caffeine and alcohol use, education, age, and family Parkinson's disease history, inverse associations between Parkinson's disease and early menarche (first period at ≤11 years), oral contraceptives, high parity (≥4 children) and bilateral...... and fertile life length, age at menopause or post-menopausal hormone treatment was found. CONCLUSIONS: Reproductive factors related to women's early- to mid-reproductive lives appear to be predictive of subsequent Parkinson's disease risk whereas factors occurring later in life seem less important....

Prediction of complicated disease course for children newly diagnosed with Crohn’s disease: a multicentre inception cohort study

Science.gov (United States)

Kugathasan, Subra; Denson, Lee A; Walters, Thomas D; Kim, Mi-Ok; Marigorta, Urko M; Schirmer, Melanie; Mondal, Kajari; Liu, Chunyan; Griffiths, Anne; Noe, Joshua D; Crandall, Wallace V; Snapper, Scott; Rabizadeh, Shervin; Rosh, Joel R; Shapiro, Jason M; Guthery, Stephen; Mack, David R; Kellermayer, Richard; Kappelman, Michael D; Steiner, Steven; Moulton, Dedrick E; Keljo, David; Cohen, Stanley; Oliva-Hemker, Maria; Heyman, Melvin B; Otley, Anthony R; Baker, Susan S; Evans, Jonathan S; Kirschner, Barbara S; Patel, Ashish S; Ziring, David; Trapnell, Bruce C; Sylvester, Francisco A; Stephens, Michael C; Baldassano, Robert N; Markowitz, James F; Cho, Judy; Xavier, Ramnik J; Huttenhower, Curtis; Aronow, Bruce J; Gibson, Greg; Hyams, Jeffrey S; Dubinsky, Marla C

2017-01-01

Summary Background Stricturing and penetrating complications account for substantial morbidity and health-care costs in paediatric and adult onset Crohn’s disease. Validated models to predict risk for complications are not available, and the effect of treatment on risk is unknown. Methods We did a prospective inception cohort study of paediatric patients with newly diagnosed Crohn’s disease at 28 sites in the USA and Canada. Genotypes, antimicrobial serologies, ileal gene expression, and ileal, rectal, and faecal microbiota were assessed. A competing-risk model for disease complications was derived and validated in independent groups. Propensity-score matching tested the effect of anti-tumour necrosis factor α (TNFα) therapy exposure within 90 days of diagnosis on complication risk. Findings Between Nov 1, 2008, and June 30, 2012, we enrolled 913 patients, 78 (9%) of whom experienced Crohn’s disease complications. The validated competing-risk model included age, race, disease location, and antimicrobial serologies and provided a sensitivity of 66% (95% CI 51–82) and specificity of 63% (55–71), with a negative predictive value of 95% (94–97). Patients who received early anti-TNFα therapy were less likely to have penetrating complications (hazard ratio [HR] 0·30, 95% CI 0·10–0·89; p=0·0296) but not stricturing complication (1·13, 0·51–2·51; 0·76) than were those who did not receive early anti-TNFα therapy. Ruminococcus was implicated in stricturing complications and Veillonella in penetrating complications. Ileal genes controlling extracellular matrix production were upregulated at diagnosis, and this gene signature was associated with stricturing in the risk model (HR 1·70, 95% CI 1·12–2·57; p=0·0120). When this gene signature was included, the model’s specificity improved to 71%. Interpretation Our findings support the usefulness of risk stratification of paediatric patients with Crohn’s disease at diagnosis, and selection of

Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study.

Science.gov (United States)

Seyednasrollah, Fatemeh; Mäkelä, Johanna; Pitkänen, Niina; Juonala, Markus; Hutri-Kähönen, Nina; Lehtimäki, Terho; Viikari, Jorma; Kelly, Tanika; Li, Changwei; Bazzano, Lydia; Elo, Laura L; Raitakari, Olli T

2017-06-01

Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity. © 2017 American Heart Association, Inc.

Japanese scoring systems to predict resistance to intravenous immunoglobulin in Kawasaki disease were unreliable for Caucasian Israeli children.

Science.gov (United States)

Arane, Karen; Mendelsohn, Kerry; Mimouni, Michael; Mimouni, Francis; Koren, Yael; Simon, Dafna Brik; Bahat, Hilla; Helou, Mona Hanna; Mendelson, Amir; Hezkelo, Nofar; Glatstein, Miguel; Berkun, Yackov; Eisenstein, Eli; Aviel, Yonatan Butbul; Brik, Riva; Hashkes, Philip J; Uziel, Yosef; Harel, Liora; Amarilyo, Gil

2018-05-24

This study assessed the validity of using established Japanese risk scoring methods to predict intravenous immunoglobulin (IVIG) resistance to Kawasaki disease in Israeli children. We reviewed the medical records of 282 patients (70% male) with Kawasaki disease from six Israeli medical centres between 2004-2013. Their mean age was 2.5 years. The risk scores were calculated using the Kobayashi, Sano and Egami scoring methods and analysed to determine if a higher risk score predicted IVIG resistance in this population. Factors that predicted a lack of response to the initial IVIG dose were identified. We found that 18% did not respond to the first IVIG dose. The three scoring methods were unable to reliably predict IVIG resistance, with sensitivities of 23-32% and specificities of 67-87%. Calculating a predictive score that was specific for this population was also unsuccessful. The factors that predicted a lacked of response to the first IVIG dose included low albumin, elevated total bilirubin and ethnicity. The established risk scoring methods created for Japanese populations with Kawasaki disease were not suitable for predicting IVIG resistance in Caucasian Israeli children and we were unable to create a specific scoring method that was able to do this. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Predictive value of quantitative dipyridamole-thallium scintigraphy in assessing cardiovascular risk after vascular surgery in diabetes mellitus

International Nuclear Information System (INIS)

Lane, S.E.; Lewis, S.M.; Pippin, J.J.; Kosinski, E.J.; Campbell, D.; Nesto, R.W.; Hill, T.

1989-01-01

Cardiac complications represent a major risk to patients undergoing vascular surgery. Diabetic patients may be particularly prone to such complications due to the high incidence of concomitant coronary artery disease, the severity of which may be clinically unrecognized. Attempts to stratify groups by clinical criteria have been useful but lack the predictive value of currently used noninvasive techniques such as dipyridamole-thallium scintigraphy. One hundred one diabetic patients were evaluated with dipyridamole-thallium scintigraphy before undergoing vascular surgery. The incidence of thallium abnormalities was high (80%) and did not correlate with clinical markers of coronary disease. Even in a subgroup of patients with no overt clinical evidence of underlying heart disease, thallium abnormalities were present in 59%. Cardiovascular complications, however, occurred in only 11% of all patients. Statistically significant prediction of risk was not achieved with simple assessment of thallium results as normal or abnormal. Quantification of total number of reversible defects, as well as assessment of ischemia in the distribution of the left anterior descending coronary artery was required for optimum predictive accuracy. The prevalence of dipyridamole-thallium abnormalities in a diabetic population is much higher than that reported in nondiabetic patients and cannot be predicted by usual clinical indicators of heart disease. In addition, cardiovascular risk of vascular surgery can be optimally assessed by quantitative analysis of dipyridamole-thallium scintigraphy and identification of high- and low-risk subgroups

Late-Onset Alzheimer's Disease Polygenic Risk Profile Score Predicts Hippocampal Function.

Science.gov (United States)

Xiao, Ena; Chen, Qiang; Goldman, Aaron L; Tan, Hao Yang; Healy, Kaitlin; Zoltick, Brad; Das, Saumitra; Kolachana, Bhaskar; Callicott, Joseph H; Dickinson, Dwight; Berman, Karen F; Weinberger, Daniel R; Mattay, Venkata S

2017-11-01

We explored the cumulative effect of several late-onset Alzheimer's disease (LOAD) risk loci using a polygenic risk profile score (RPS) approach on measures of hippocampal function, cognition, and brain morphometry. In a sample of 231 healthy control subjects (19-55 years of age), we used an RPS to study the effect of several LOAD risk loci reported in a recent meta-analysis on hippocampal function (determined by its engagement with blood oxygen level-dependent functional magnetic resonance imaging during episodic memory) and several cognitive metrics. We also studied effects on brain morphometry in an overlapping sample of 280 subjects. There was almost no significant association of LOAD-RPS with cognitive or morphometric measures. However, there was a significant negative relationship between LOAD-RPS and hippocampal function (familywise error [small volume correction-hippocampal region of interest] p risk score based on APOE haplotype, and for a combined LOAD-RPS + APOE haplotype risk profile score (p risk genes on hippocampal function even in healthy volunteers. The effect of LOAD-RPS on hippocampal function in the relative absence of any effect on cognitive and morphometric measures is consistent with the reported temporal characteristics of LOAD biomarkers with the earlier manifestation of synaptic dysfunction before morphometric and cognitive changes. Copyright © 2017 Society of Biological Psychiatry. All rights reserved.

Anonymising the Sparse Dataset: A New Privacy Preservation Approach while Predicting Diseases

Directory of Open Access Journals (Sweden)

V. Shyamala Susan

2016-09-01

Full Text Available Data mining techniques analyze the medical dataset with the intention of enhancing patient’s health and privacy. Most of the existing techniques are properly suited for low dimensional medical dataset. The proposed methodology designs a model for the representation of sparse high dimensional medical dataset with the attitude of protecting the patient’s privacy from an adversary and additionally to predict the disease’s threat degree. In a sparse data set many non-zero values are randomly spread in the entire data space. Hence, the challenge is to cluster the correlated patient’s record to predict the risk degree of the disease earlier than they occur in patients and to keep privacy. The first phase converts the sparse dataset right into a band matrix through the Genetic algorithm along with Cuckoo Search (GCS.This groups the correlated patient’s record together and arranges them close to the diagonal. The next segment dissociates the patient’s disease, which is a sensitive value (SA with the parameters that determine the disease normally Quasi Identifier (QI.Finally, density based clustering technique is used over the underlying data to  create anonymized groups to maintain privacy and to predict the risk level of disease. Empirical assessments on actual health care data corresponding to V.A.Medical Centre heart disease dataset reveal the efficiency of this model pertaining to information loss, utility and privacy.

Natriuretic peptides and integrated risk assessment for cardiovascular disease

DEFF Research Database (Denmark)

Willeit, Peter; Kaptoge, S; Welsh, P.

2016-01-01

samples and collection of data from studies identified through a systematic search of the literature (PubMed, Scientific Citation Index Expanded, and Embase) for articles published up to Sept 4, 2014, using search terms related to natriuretic peptide family members and the primary outcomes......BACKGROUND: Guidelines for primary prevention of cardiovascular diseases focus on prediction of coronary heart disease and stroke. We assessed whether or not measurement of N-terminal-pro-B-type natriuretic peptide (NT-proBNP) concentration could enable a more integrated approach than at present...... by predicting heart failure and enhancing coronary heart disease and stroke risk assessment. METHODS: In this individual-participant-data meta-analysis, we generated and harmonised individual-participant data from relevant prospective studies via both de-novo NT-proBNP concentration measurement of stored...

Significant interarm blood pressure difference predicts cardiovascular risk in hypertensive patients

Science.gov (United States)

Kim, Su-A; Kim, Jang Young; Park, Jeong Bae

2016-01-01

Abstract There has been a rising interest in interarm blood pressure difference (IAD), due to its relationship with peripheral arterial disease and its possible relationship with cardiovascular disease. This study aimed to characterize hypertensive patients with a significant IAD in relation to cardiovascular risk. A total of 3699 patients (mean age, 61 ± 11 years) were prospectively enrolled in the study. Blood pressure (BP) was measured simultaneously in both arms 3 times using an automated cuff-oscillometric device. IAD was defined as the absolute difference in averaged BPs between the left and right arm, and an IAD ≥ 10 mm Hg was considered to be significant. The Framingham risk score was used to calculate the 10-year cardiovascular risk. The mean systolic IAD (sIAD) was 4.3 ± 4.1 mm Hg, and 285 (7.7%) patients showed significant sIAD. Patients with significant sIAD showed larger body mass index (P < 0.001), greater systolic BP (P = 0.050), more coronary artery disease (relative risk = 1.356, P = 0.034), and more cerebrovascular disease (relative risk = 1.521, P = 0.072). The mean 10-year cardiovascular risk was 9.3 ± 7.7%. By multiple regression, sIAD was significantly but weakly correlated with the 10-year cardiovascular risk (β = 0.135, P = 0.008). Patients with significant sIAD showed a higher prevalence of coronary artery disease, as well as an increase in 10-year cardiovascular risk. Therefore, accurate measurements of sIAD may serve as a simple and cost-effective tool for predicting cardiovascular risk in clinical settings. PMID:27310982

Association of Anthropometric Measurement Methods with Cardiovascular Disease Risk in Turkey

Directory of Open Access Journals (Sweden)

Kaan Sözmen

2016-03-01

Full Text Available Objective: The aim of this study is to compare the predic­tive power of anthropometric indices for risk of developing Coronary Heart Disease (CHD or CHD death. Methods: We used cross-sectional data from nationally representative Chronic Diseases and Risk Factors Sur­vey conducted by the Ministry of Health in 2011. Body mass index (BMI, waist circumference (WC, waist-to-hip ratio (WHR, waist to height ratio (WHtR, body adiposity index (BAI and A Body Shape Index (ABSI formed the anthropometric measures. For each participant risk of de­veloping CHD or dying from CVDs were calculated based on Framingham and SCORE risk equations. Predictive ability of anthropometric measurements was assessed by receiver operating characteristic (ROC curves. Results: Anthropometric measurements of central obe­sity recorded higher area under the ROC curve (AUC values than BMI in both men and women. While ABSI had the highest AUC values for Framingham 10-year pre­dicted risk (FRS for CHD death (AUC = 0.613, SCORE 10-year risk for CVD death (AUC = 0.633, in women AUC for ABSI was the highest for only SCORE risk threshold (AUC = 0.705. Among women, WHtR was found to be the best indicator for estimating CHD incidence (AUC = 0.706 and death from CVD (AUC = 0.696. Conclusion: Compared to traditional anthropometric measurements such as BMI, ABSI was a better indicator for given thresholds for estimating the risk of developing CHD and CVD death in men. Among women WHtR made better predictions for FRS thresholds, however, ABSI was better for predicting 10-year risk of CVD death calculated by SCORE risk equation.

The Clinical Features and Predictive Risk Factors for Reoperation in Patients With Perianal Crohn Diseases; A Multi-Center Study of a Korean Inflammatory Bowel Disease Study Group

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Lee, Jae Bum; Yoon, Seo-Gue; Park, Kyu Joo; Lee, Kang Young; Kim, Dae Dong; Yoon, Sang Nam

2015-01-01

Purpose Perianal lesions are common in Crohn disease, but their clinical course is unpredictable. Nevertheless, predicting the clinical course after surgery for perianal Crohn disease (PCD) is important because repeated operations may decrease patient's quality of life. The aim of this study was to predict the risk of reoperation in patients with PCD. Methods From September 1994 to February 2010, 377 patients with PCD were recruited in twelve major tertiary university-affiliated hospitals and two specialized colorectal hospitals in Korea. Data on the patient's demographics, clinical features, and surgical outcomes were analyzed. Results Among 377 patients, 227 patients were ultimately included in the study. Among the 227 patients, 64 patients underwent at least one reoperation. The median period of reoperation following the first perianal surgery was 94 months. Overall 3-year, 5-year, and 10-year cumulative rates of reoperation-free individuals were 68.8%, 61.2%, and 50.5%, respectively. In multivariate analysis (Cox-regression hazard model), reoperation was significantly correlated with an age of onset less than 20 years (hazard ratio [HR], 1.93; 95% confidence interval [CI], 1.07-3.48; P = 0.03), history of abdominal surgery (HR, 1.99; 95% CI, 1.08-3.64; P = 0.03), and the type of surgery. Among types of surgery, fistulotomy or fistulectomy was associated with a decreased incidence of reoperation in comparison with incision and drainage (HR, 0.19; 95% CI, 0.09-0.42; P < 0.001). Conclusion Young age of onset and a history of abdominal surgery were associated with a high risk of reoperation for PCD, and the risk of reoperation were relatively low in fistulotomy or fistulectomy procedures. PMID:26576395

Risks for Heart Disease & Stroke

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... Prevent Risks for Heart Disease & Stroke Risks for Heart Disease & Stroke About 1.5 million heart attacks and ... can’t change some of your risks for heart disease and stroke, but you can manage many of ...

Natriuretic peptides: prediction of cardiovascular disease in the general population and high risk populations

DEFF Research Database (Denmark)

Hildebrandt, Per

2009-01-01

(General Practitioner) setting as in the acute setting. Supporting this use is a very strong prognostic value of the natriuretic peptides. This has been shown in as well heart failure as acute coronary syndromes, but also in the general population and in high-risk groups as patients with diabetes......, hypertension and coronary artery disease. This has of course raised interest for the use of the natriuretic peptides as a risk marker and for screening for heart failure with reduced systolic function in these populations. In symptomatic persons and in high risk populations, the natriuretic peptides have...... demonstrated a high sensitivity for ruling out the disease, if the right decision limits are choosen. Thus the number of normal echocardiographies can be reduced. More recently, the use in screening asymptomatic persons for left ventricular systolic dysfunction has gained more interest. In the unselected...

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach.

Science.gov (United States)

Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

2009-11-22

Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach

Directory of Open Access Journals (Sweden)

Christensen Helen

2009-11-01

Full Text Available Abstract Background Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. Methods The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. Results The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. Conclusion The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

Sacrococcygeal pilonidal disease: analysis of previously proposed risk factors

Directory of Open Access Journals (Sweden)

Ali Harlak

2010-01-01

Full Text Available PURPOSE: Sacrococcygeal pilonidal disease is a source of one of the most common surgical problems among young adults. While male gender, obesity, occupations requiring sitting, deep natal clefts, excessive body hair, poor body hygiene and excessive sweating are described as the main risk factors for this disease, most of these need to be verified with a clinical trial. The present study aimed to evaluate the value and effect of these factors on pilonidal disease. METHOD: Previously proposed main risk factors were evaluated in a prospective case control study that included 587 patients with pilonidal disease and 2,780 healthy control patients. RESULTS: Stiffness of body hair, number of baths and time spent seated per day were the three most predictive risk factors. Adjusted odds ratios were 9.23, 6.33 and 4.03, respectively (p<0.001. With an adjusted odds ratio of 1.3 (p<.001, body mass index was another risk factor. Family history was not statistically different between the groups and there was no specific occupation associated with the disease. CONCLUSIONS: Hairy people who sit down for more than six hours a day and those who take a bath two or less times per week are at a 219-fold increased risk for sacrococcygeal pilonidal disease than those without these risk factors. For people with a great deal of hair, there is a greater need for them to clean their intergluteal sulcus. People who engage in work that requires sitting in a seat for long periods of time should choose more comfortable seats and should also try to stand whenever possible.

Performance of genetic risk factors in prediction of trichloroethylene induced hypersensitivity syndrome.

Science.gov (United States)

Dai, Yufei; Chen, Ying; Huang, Hanlin; Zhou, Wei; Niu, Yong; Zhang, Mingrong; Bin, Ping; Dong, Haiyan; Jia, Qiang; Huang, Jianxun; Yi, Juan; Liao, Qijun; Li, Haishan; Teng, Yanxia; Zang, Dan; Zhai, Qingfeng; Duan, Huawei; Shen, Juan; He, Jiaxi; Meng, Tao; Sha, Yan; Shen, Meili; Ye, Meng; Jia, Xiaowei; Xiang, Yingping; Huang, Huiping; Wu, Qifeng; Shi, Mingming; Huang, Xianqing; Yang, Huanming; Luo, Longhai; Li, Sai; Li, Lin; Zhao, Jinyang; Li, Laiyu; Wang, Jun; Zheng, Yuxin

2015-07-20

Trichloroethylene induced hypersensitivity syndrome is dose-independent and potentially life threatening disease, which has become one of the serious occupational health issues and requires intensive treatment. To discover the genetic risk factors and evaluate the performance of risk prediction model for the disease, we conducted genomewide association study and replication study with total of 174 cases and 1761 trichloroethylene-tolerant controls. Fifty seven SNPs that exceeded the threshold for genome-wide significance (P < 5 × 10(-8)) were screened to relate with the disease, among which two independent SNPs were identified, that is rs2857281 at MICA (odds ratio, 11.92; P meta = 1.33 × 10(-37)) and rs2523557 between HLA-B and MICA (odds ratio, 7.33; P meta = 8.79 × 10(-35)). The genetic risk score with these two SNPs explains at least 20.9% of the disease variance and up to 32.5-fold variation in inter-individual risk. Combining of two SNPs as predictors for the disease would have accuracy of 80.73%, the area under receiver operator characteristic curves (AUC) scores was 0.82 with sensitivity of 74% and specificity of 85%, which was considered to have excellent discrimination for the disease, and could be considered for translational application for screening employees before exposure.

Climate-Agriculture-Modeling and Decision Tool for Disease (CAMDT-Disease) for seasonal climate forecast-based crop disease risk management in agriculture

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Kim, K. H.; Lee, S.; Han, E.; Ines, A. V. M.

2017-12-01

Climate-Agriculture-Modeling and Decision Tool (CAMDT) is a decision support system (DSS) tool that aims to facilitate translations of probabilistic seasonal climate forecasts (SCF) to crop responses such as yield and water stress. Since CAMDT is a software framework connecting different models and algorithms with SCF information, it can be easily customized for different types of agriculture models. In this study, we replaced the DSSAT-CSM-Rice model originally incorporated in CAMDT with a generic epidemiological model, EPIRICE, to generate a seasonal pest outlook. The resulting CAMDT-Disease generates potential risks for selected fungal, viral, and bacterial diseases of rice over the next months by translating SCFs into agriculturally-relevant risk information. The integrated modeling procedure of CAMDT-Disease first disaggregates a given SCF using temporal downscaling methods (predictWTD or FResampler1), runs EPIRICE with the downscaled weather inputs, and finally visualizes the EPIRICE outputs as disease risk compared to that of the previous year and the 30-year-climatological average. In addition, the easy-to-use graphical user interface adopted from CAMDT allows users to simulate "what-if" scenarios of disease risks over different planting dates with given SCFs. Our future work includes the simulation of the effect of crop disease on yields through the disease simulation models with the DSSAT-CSM-Rice model, as disease remains one of the most critical yield-reducing factors in the field.

Risk prediction in stable cardiovascular disease using a high-sensitivity cardiac troponin T single biomarker strategy compared to the ESC-SCORE.

Science.gov (United States)

Biener, Moritz; Giannitsis, Evangelos; Kuhner, Manuel; Zelniker, Thomas; Mueller-Hennessen, Matthias; Vafaie, Mehrshad; Stoyanov, Kiril M; Neumann, Franz-Josef; Katus, Hugo A; Hochholzer, Willibald; Valina, Christian Marc

2018-01-01

To evaluate the prognostic performance of high-sensitivity cardiac troponin T (hs-cTnT) compared with the ESC-SCORE. We included low-risk outpatients with stable cardiovascular (CV) disease categorised into need for non-secondary and secondary prevention. The prognostication of hs-cTnT at index visit was compared with the European Society of Cardiology-Systematic COronary Risk Evaluation (ESC-SCORE) with respect to all-cause mortality (ACM) and two composite endpoints (ACM, acute myocardial infarction (AMI) and stroke and ACM, AMI, stroke and rehospitalisation for acute coronary syndrome (ACS) and decompensated heart failure (DHF)). Within a median follow-up of 796 days, a total of 16 deaths, 32 composite endpoints of ACM, AMI and stroke and 83 composite endpoints of ACM, AMI, stroke, rehospitalisation for ACS and DHF were observed among 693 stable low-risk outpatients. Using C-statistics, measurement of hs-cTnT alone outperformed the ESC-SCORE for the prediction of ACM in the entire study population (Δarea under the curve (AUC) 0.221, p=0.0039) and both prevention groups (non-secondary: ΔAUC 0.164, p=0.0208; secondary: ΔAUC 0.264, p=0.0134). For the prediction of all other secondary endpoints, hs-cTnT was at least as effective as the ESC-SCORE, both in secondary and non-secondary prevention. Using continuous and categorical net reclassification improvement and integrated discrimination improvement, hs-cTnT significantly improved reclassification regarding all endpoints in the entire population and in the secondary prevention cohort. In non-secondary prevention, hs-cTnT improved reclassification only for ACM. The results were confirmed in an independent external cohort on 2046 patients. Hs-cTnT is superior to the multivariable ESC-SCORE for the prediction of ACM and a composite endpoint in stable outpatients with and without relevant CV disease. NCT01954303; Pre-results.

Predicting the short-term risk of diabetes in HIV-positive patients

DEFF Research Database (Denmark)

Petoumenos, Kathy; Worm, Signe Westring; Fontas, Eric

2012-01-01

Introduction: HIV-positive patients receiving combination antiretroviral therapy (cART) frequently experience metabolic complications such as dyslipidemia and insulin resistance, as well as lipodystrophy, increasing the risk of cardiovascular disease (CVD) and diabetes mellitus (DM). Rates of DM ......). Factors predictive of DM included higher glucose, body mass index (BMI) and triglyceride levels, and older age. Among HIV-related factors, recent CD4 counts of...... and other glucose-associated disorders among HIV-positive patients have been reported to range between 2 and 14%, and in an ageing HIV-positive population, the prevalence of DM is expected to continue to increase. This study aims to develop a model to predict the short-term (six-month) risk of DM in HIV...

Circulating Total Bilirubin and Risk of Incident Cardiovascular Disease in the General Population

NARCIS (Netherlands)

Kunutsor, Setor K.; Bakker, Stephan J. L.; Gansevoort, Ronald T.; Chowdhury, Rajiv; Dullaart, Robin P. F.

OBJECTIVE: To assess the association of circulating total bilirubin and cardiovascular disease (CVD) risk in a new prospective study and to determine whether adding information on total bilirubin values to established cardiovascular risk factors is associated with improvement in prediction of CVD

A risk score for predicting coronary artery disease in women with angina pectoris and abnormal stress test finding.

Science.gov (United States)

Lo, Monica Y; Bonthala, Nirupama; Holper, Elizabeth M; Banks, Kamakki; Murphy, Sabina A; McGuire, Darren K; de Lemos, James A; Khera, Amit

2013-03-15

Women with angina pectoris and abnormal stress test findings commonly have no epicardial coronary artery disease (CAD) at catheterization. The aim of the present study was to develop a risk score to predict obstructive CAD in such patients. Data were analyzed from 337 consecutive women with angina pectoris and abnormal stress test findings who underwent cardiac catheterization at our center from 2003 to 2007. Forward selection multivariate logistic regression analysis was used to identify the independent predictors of CAD, defined by ≥50% diameter stenosis in ≥1 epicardial coronary artery. The independent predictors included age ≥55 years (odds ratio 2.3, 95% confidence interval 1.3 to 4.0), body mass index stress imaging (odds ratio 2.8, 95% confidence interval 1.5 to 5.5), and exercise capacity statistic of 0.745 (95% confidence interval 0.70 to 0.79), and an optimized cutpoint of a score of ≤2 included 62% of the subjects and had a negative predictive value of 80%. In conclusion, a simple clinical risk score of 7 characteristics can help differentiate those more or less likely to have CAD among women with angina pectoris and abnormal stress test findings. This tool, if validated, could help to guide testing strategies in women with angina pectoris. Copyright © 2013 Elsevier Inc. All rights reserved.

Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata

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Oliveira, Leonardo S. S.; Alfenas, Acelino C.; Neven, Lisa G.; Al-Sadi, Abdullah M.

2016-01-01

The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs. PMID:27415625

Association of chest pain and risk of cardiovascular disease with coronary atherosclerosis in patients with inflammatory joint diseases

Directory of Open Access Journals (Sweden)

Silvia eRollefstad

2015-11-01

Full Text Available Objectives: The relation between chest pain and coronary atherosclerosis (CA in patients with inflammatory joint diseases (IJD has not been explored previously. Our aim was to evaluate the associations of the presence of chest pain and the predicted 10-year risk of cardiovascular disease (CVD by use of several CVD risk algorithms, with multi-detector computer tomography (MDCT coronary angiography verified CA. Methods: Detailed information concerning chest pain and CVD risk factors was obtained in 335 patients with rheumatoid arthritis (RA and ankylosing spondylitis (AS. In addition, 119 of these patients underwent MDCT coronary angiography.Results: Thirty-one percent of the patients (104/335 reported chest pain. Only 6 patients (1.8% had atypical angina pectoris (pricking pain at rest. In 69 patients without chest pain, two thirds had CA, while in those who reported chest pain (n=50, CA was present in 48.0%. In a logistic regression analysis, chest pain was not associated with CA (dependent variable (p=0.43. About 30% (Nagelkerke R2 of CA was explained by any of the CVD risk calculators: SCORE, Framingham Risk Score or Reynolds Risk Score.Conclusion: The presence of chest pain was surprisingly infrequently reported in patients with IJD who were referred for a CVD risk evaluation. However, when present, chest pain was weakly associated with CA, in contrast to the predicted CVD risk by several risk calculators which was highly associated with the presence of CA. These findings suggest that clinicians treating patients with IJD should be alert of coronary atherosclerotic disease also in absence of chest pain symptoms.

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...

Towards a resource-based habitat approach for spatial modelling of vector-borne disease risks

NARCIS (Netherlands)

Hartemink, N.; Vanwambeke, S.O.; Purse, B.V.; Gilbert, M.; Van Dyck, H.

2015-01-01

Given the veterinary and public health impact of vector-borne diseases, there is a clear need to assess the suitability of landscapes for the emergence and spread of these diseases. Current approaches for predicting disease risks neglect key features of the landscape as components of the functional

A classification tree for the prediction of benign versus malignant disease in patients with small renal masses.

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Rendon, Ricardo A; Mason, Ross J; Kirkland, Susan; Lawen, Joseph G; Abdolell, Mohamed

2014-08-01

To develop a classification tree for the preoperative prediction of benign versus malignant disease in patients with small renal masses. This is a retrospective study including 395 consecutive patients who underwent surgical treatment for a renal mass classification tree to predict the risk of having a benign renal mass preoperatively was developed using recursive partitioning analysis for repeated measures outcomes. Age, sex, volume on preoperative imaging, tumor location (central/peripheral), degree of endophytic component (1%-100%), and tumor axis position were used as potential predictors to develop the model. Forty-five patients (11.4%) were found to have a benign mass postoperatively. A classification tree has been developed which can predict the risk of benign disease with an accuracy of 88.9% (95% CI: 85.3 to 91.8). The significant prognostic factors in the classification tree are tumor volume, degree of endophytic component and symptoms at diagnosis. As an example of its utilization, a renal mass with a volume of classification tree to predict the risk of benign disease in small renal masses has been developed to aid the clinician when deciding on treatment strategies for small renal masses.

Assessing the risk of Legionnaires' disease: the inhalation exposure model and the estimated risk in residential bathrooms.

Science.gov (United States)

Azuma, Kenichi; Uchiyama, Iwao; Okumura, Jiro

2013-02-01

Legionella are widely found in the built environment. Patients with Legionnaires' disease have been increasing in Japan; however, health risks from Legionella bacteria in the environment are not appropriately assessed. We performed a quantitative health risk assessment modeled on residential bathrooms in the Adachi outbreak area and estimated risk levels. The estimated risks in the Adachi outbreak approximately corresponded to the risk levels exponentially extrapolated into lower levels on the basis of infection and mortality rates calculated from actual outbreaks, suggesting that the model of Legionnaires' disease in residential bathrooms was adequate to predict disease risk for the evaluated outbreaks. Based on this model, the infection and mortality risk levels per year in 10 CFU/100 ml (100 CFU/L) of the Japanese water quality guideline value were approximately 10(-2) and 10(-5), respectively. However, acceptable risk levels of infection and mortality from Legionnaires' disease should be adjusted to approximately 10(-4) and 10(-7), respectively, per year. Therefore, a reference value of 0.1 CFU/100 ml (1 CFU/L) as a water quality guideline for Legionella bacteria is recommended. This value is occasionally less than the actual detection limit. Legionella levels in water system should be maintained as low as reasonably achievable (<1 CFU/L). Copyright © 2012 Elsevier Inc. All rights reserved.

Apolipoprotein B levels, APOB alleles, and risk of ischemic cardiovascular disease in the general population, a review

DEFF Research Database (Denmark)

Benn, Marianne

2009-01-01

capturing the entire variation in APOB cannot be identified, and thus most polymorphisms must be evaluated separately in association studies; (3) APOB mutations and polymorphisms are associated with a range of apolipoprotein B and LDL cholesterol levels, although the magnitude of effect sizes of common...... for the E4154K polymorphism that possibly predicts a reduction in risk of ischemic cerebrovascular disease and ischemic stroke, common APOB polymorphisms with modest effect sizes on lipid levels do not predict risk of ischemic heart disease, myocardial infarction, ischemic cerebrovascular disease...

Incremental Value of Repeated Risk Factor Measurements for Cardiovascular Disease Prediction in Middle-Aged Korean Adults: Results From the NHIS-HEALS (National Health Insurance System-National Health Screening Cohort).

Science.gov (United States)

Cho, In-Jeong; Sung, Ji Min; Chang, Hyuk-Jae; Chung, Namsik; Kim, Hyeon Chang

2017-11-01

Increasing evidence suggests that repeatedly measured cardiovascular disease (CVD) risk factors may have an additive predictive value compared with single measured levels. Thus, we evaluated the incremental predictive value of incorporating periodic health screening data for CVD prediction in a large nationwide cohort with periodic health screening tests. A total of 467 708 persons aged 40 to 79 years and free from CVD were randomly divided into development (70%) and validation subcohorts (30%). We developed 3 different CVD prediction models: a single measure model using single time point screening data; a longitudinal average model using average risk factor values from periodic screening data; and a longitudinal summary model using average values and the variability of risk factors. The development subcohort included 327 396 persons who had 3.2 health screenings on average and 25 765 cases of CVD over 12 years. The C statistics (95% confidence interval [CI]) for the single measure, longitudinal average, and longitudinal summary models were 0.690 (95% CI, 0.682-0.698), 0.695 (95% CI, 0.687-0.703), and 0.752 (95% CI, 0.744-0.760) in men and 0.732 (95% CI, 0.722-0.742), 0.735 (95% CI, 0.725-0.745), and 0.790 (95% CI, 0.780-0.800) in women, respectively. The net reclassification index from the single measure model to the longitudinal average model was 1.78% in men and 1.33% in women, and the index from the longitudinal average model to the longitudinal summary model was 32.71% in men and 34.98% in women. Using averages of repeatedly measured risk factor values modestly improves CVD predictability compared with single measurement values. Incorporating the average and variability information of repeated measurements can lead to great improvements in disease prediction. URL: https://www.clinicaltrials.gov. Unique identifier: NCT02931500. © 2017 American Heart Association, Inc.

Fatty Liver Index and Lipid Accumulation Product Can Predict Metabolic Syndrome in Subjects without Fatty Liver Disease

Directory of Open Access Journals (Sweden)

Yuan-Lung Cheng

2017-01-01

Full Text Available Background. Fatty liver index (FLI and lipid accumulation product (LAP are indexes originally designed to assess the risk of fatty liver and cardiovascular disease, respectively. Both indexes have been proven to be reliable markers of subsequent metabolic syndrome; however, their ability to predict metabolic syndrome in subjects without fatty liver disease has not been clarified. Methods. We enrolled consecutive subjects who received health check-up services at Taipei Veterans General Hospital from 2002 to 2009. Fatty liver disease was diagnosed by abdominal ultrasonography. The ability of the FLI and LAP to predict metabolic syndrome was assessed by analyzing the area under the receiver operating characteristic (AUROC curve. Results. Male sex was strongly associated with metabolic syndrome, and the LAP and FLI were better than other variables to predict metabolic syndrome among the 29,797 subjects. Both indexes were also better than other variables to detect metabolic syndrome in subjects without fatty liver disease (AUROC: 0.871 and 0.879, resp., and the predictive power was greater among women. Conclusion. Metabolic syndrome increases the cardiovascular disease risk. The FLI and LAP could be used to recognize the syndrome in both subjects with and without fatty liver disease who require lifestyle modifications and counseling.

Validation of a risk prediction model for Barrett's esophagus in an Australian population.

Science.gov (United States)

Ireland, Colin J; Gordon, Andrea L; Thompson, Sarah K; Watson, David I; Whiteman, David C; Reed, Richard L; Esterman, Adrian

2018-01-01

Esophageal adenocarcinoma is a disease that has a high mortality rate, the only known precursor being Barrett's esophagus (BE). While screening for BE is not cost-effective at the population level, targeted screening might be beneficial. We have developed a risk prediction model to identify people with BE, and here we present the external validation of this model. A cohort study was undertaken to validate a risk prediction model for BE. Individuals with endoscopy and histopathology proven BE completed a questionnaire containing variables previously identified as risk factors for this condition. Their responses were combined with data from a population sample for analysis. Risk scores were derived for each participant. Overall performance of the risk prediction model in terms of calibration and discrimination was assessed. Scores from 95 individuals with BE and 636 individuals from the general population were analyzed. The Brier score was 0.118, suggesting reasonable overall performance. The area under the receiver operating characteristic was 0.83 (95% CI 0.78-0.87). The Hosmer-Lemeshow statistic was p =0.14. Minimizing false positives and false negatives, the model achieved a sensitivity of 74% and a specificity of 73%. This study has validated a risk prediction model for BE that has a higher sensitivity than previous models.

Framingham risk score for estimation of 10-years of cardiovascular diseases risk in patients with metabolic syndrome.

Science.gov (United States)

Jahangiry, Leila; Farhangi, Mahdieh Abbasalizad; Rezaei, Fatemeh

2017-11-13

There are a few studies evaluating the predictive value of Framingham risk score (FRS) for cardiovascular disease (CVD) risk assessment in patients with metabolic syndrome in Iran. Because of the emerging high prevalence of CVD among Iranian population, it is important to predict its risk among populations with potential predictive tools. Therefore, the aim of the current study is to evaluate the FRS and its determinants in patients with metabolic syndrome. In the current cross-sectional study, 160 patients with metabolic syndrome diagnosed according to the National Cholesterol Education Adult Treatment Panel (ATP) III criteria were enrolled. The FRS was calculated using a computer program by a previously suggested algorithm. Totally, 77.5, 16.3, and 6.3% of patients with metabolic syndrome were at low, intermediate, and high risk of CVD according to FRS categorization. The highest prevalence of all of metabolic syndrome components were in low CVD risk according to the FRS grouping (P metabolic syndrome and different FRS categorization among patients with metabolic syndrome were identified. High SBP and FSG were associated with meaningfully increased risk of CVD compared with other parameters. The study is not a trial; the registration number is not applicable.

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...

Efficacy of adjuvant therapy with 3.7 GBq radioactive iodine in intermediate-risk patients with 'higher risk features' and predictive value of postoperative nonstimulated thyroglobulin.

Science.gov (United States)

Rosario, Pedro W; Mourão, Gabriela F; Calsolari, Maria Regina

2016-11-01

This study evaluated the efficacy of adjuvant therapy with 3.7 GBq radioactive iodine (RAI) in patients with papillary thyroid carcinoma (PTC) of intermediate risk with higher risk features and determined the predictive value of postoperative nonstimulated thyroglobulin (Tg). This was a prospective study including 85 patients with PTC of intermediate risk and higher risk features: tumor greater than 1 cm and aggressive histological subtype or vascular invasion; and/or more than three positive lymph node (LN) or LN greater than 1.5 cm or showing macroscopic extracapsular extension; and/or a combination of tumor greater than 4 cm, microscopic extrathyroidal extension, aggressive histology, and LN metastases (cN1). After thyroidectomy, all patients had nonstimulated Tg of at least 0.3 ng/ml and ultrasonography showed no anomalies. When evaluated 12 months after RAI therapy, an excellent response to initial therapy was achieved in 61 patients (71.7%). Structural disease was detected in five patients (5.9%). During follow-up, 6/80 patients (7.5%) without structural disease 1 year after RAI developed relapse. In the last assessment, 80 patients (94.1%) had nonstimulated Tg less than 1 ng/ml and no evidence of structural disease. There was no case of death because of the tumor. Postoperative nonstimulated Tg was a predictive factor of the main outcome (structural disease 1 year after RAI or recurrence) and the best cut-off was 1.8 ng/ml (sensitivity: 72.7%, specificity: 83.4%, negative predictive value: 95.4%). In patients with PTC of intermediate risk with higher risk features treated with 3.7 GBq RAI, postoperative nonstimulated Tg up to 1.8 ng/ml was a predictor of low risk of structural disease 1 year after therapy or recurrence.

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

DEFF Research Database (Denmark)

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

2014-01-01

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

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach

OpenAIRE

Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

2009-01-01

Abstract Background Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. Methods The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled fr...

Standard cardiovascular disease risk algorithms underestimate the risk of cardiovascular disease in schizophrenia: evidence from a national primary care database.

Science.gov (United States)

McLean, Gary; Martin, Julie Langan; Martin, Daniel J; Guthrie, Bruce; Mercer, Stewart W; Smith, Daniel J

2014-10-01

Schizophrenia is associated with increased cardiovascular mortality. Although cardiovascular disease (CVD) risk prediction algorithms are widely in the general population, their utility for patients with schizophrenia is unknown. A primary care dataset was used to compare CVD risk scores (Joint British Societies (JBS) score), cardiovascular risk factors, rates of pre-existing CVD and age of first diagnosis of CVD for schizophrenia (n=1997) relative to population controls (n=215,165). Pre-existing rates of CVD and the recording of risk factors for those without CVD were higher in the schizophrenia cohort in the younger age groups, for both genders. Those with schizophrenia were more likely to have a first diagnosis of CVD at a younger age, with nearly half of men with schizophrenia plus CVD diagnosed under the age of 55 (schizophrenia men 46.1% vs. control men 34.8%, pschizophrenia women 28.9% vs. control women 23.8%, prisk factors within the schizophrenia group, only a very small percentage (3.2% of men and 7.5% of women) of those with schizophrenia under age 55 were correctly identified as high risk for CVD according to the JBS risk algorithm. The JBS2 risk score identified only a small proportion of individuals with schizophrenia under the age of 55 as being at high risk of CVD, despite high rates of risk factors and high rates of first diagnosis of CVD within this age group. The validity of CVD risk prediction algorithms for schizophrenia needs further research. Copyright © 2014 Elsevier B.V. All rights reserved.

New technologies in predicting, preventing and controlling emerging infectious diseases.

Science.gov (United States)

Christaki, Eirini

2015-01-01

Surveillance of emerging infectious diseases is vital for the early identification of public health threats. Emergence of novel infections is linked to human factors such as population density, travel and trade and ecological factors like climate change and agricultural practices. A wealth of new technologies is becoming increasingly available for the rapid molecular identification of pathogens but also for the more accurate monitoring of infectious disease activity. Web-based surveillance tools and epidemic intelligence methods, used by all major public health institutions, are intended to facilitate risk assessment and timely outbreak detection. In this review, we present new methods for regional and global infectious disease surveillance and advances in epidemic modeling aimed to predict and prevent future infectious diseases threats.

Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

Science.gov (United States)

Luo, Wei; Tran, Truyen; Berk, Michael; Venkatesh, Svetha

2016-01-01

Background Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk. Objective The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data. Methods We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC). Results The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians. Conclusions This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk. PMID:27400764

Is the disease course predictable in inflammatory bowel diseases?

Science.gov (United States)

Lakatos, Peter Laszlo; Kiss, Lajos S

2010-01-01

During the course of the disease, most patients with Crohn’s disease (CD) may eventually develop a stricturing or a perforating complication, and a significant number of patients with both CD and ulcerative colitis will undergo surgery. In recent years, research has focused on the determination of factors important in the prediction of disease course in inflammatory bowel diseases to improve stratification of patients, identify individual patient profiles, including clinical, laboratory and molecular markers, which hopefully will allow physicians to choose the most appropriate management in terms of therapy and intensity of follow-up. This review summarizes the available evidence on clinical, endoscopic variables and biomarkers in the prediction of short and long-term outcome in patients with inflammatory bowel diseases. PMID:20518079

Huntington's disease : Psychological aspects of predictive testing

NARCIS (Netherlands)

Timman, Reinier

2005-01-01

Predictive testing for Huntington's disease appears to have long lasting psychological effects. The predictive test for Huntington's disease (HD), a hereditary disease of the nervous system, was introduced in the Netherlands in the late eighties. As adverse consequences of the test were

The prevalence and risk factors for gallstone disease in taiwanese vegetarians.

Directory of Open Access Journals (Sweden)

Yen-Chun Chen

Full Text Available Gallstone disease (GSD and its complications are major public health issues globally. Although many community-based studies had addressed the risk factors for GSD, little is known about GSD prevalence and risk factors among Taiwanese vegetarians.This study included 1721 vegetarians who completed a questionnaire detailing their demographics, medical history, and life-styles. GSD was ascertained by ultrasonography or surgical history of cholecystectomy for GSD. The predictive probability of GSD for male and female vegetarians was estimated from the fitted model.The prevalence of GSD was 8.2% for both male and female vegetarians. The risk of GSD is similar in men and women across all age groups, and increases steadily with increasing age. For male vegetarians, age (OR: 1.04; 95% CI: 1.00-1.08 and serum total bilirubin level (OR: 2.35; 95% CI: 1.31-4.22 predict risk for GSD. For female vegetarians, age (OR: 1.03; 95% CI: 1.01-1.05, BMI (OR: 1.07; 95% CI: 1.01-1.13, and alcohol consumption (OR: 7.85; 95% CI: 1.83-33.73 are associated with GSD. GSD is not associated with type of vegetarian diet, duration of vegetarianism, low education level, physical inactivity, diabetes, coronary artery disease, cerebral vascular accident, chronic renal failure, hepatitis C virus infection, and lipid abnormalities. GSD is also not associated with age at menarche, postmenopausal status, and multiparity in female vegetarians.Risk factors useful for predicting GSD in vegetarians are (1 age and total bilirubin level in men, and (2 age, BMI, and alcohol consumption in women. Many previously identified risk factors for general population does not seem to apply to Taiwanese vegetarians.

The prevalence and risk factors for gallstone disease in taiwanese vegetarians.

Science.gov (United States)

Chen, Yen-Chun; Chiou, Chia; Lin, Ming-Nan; Lin, Chin-Lon

2014-01-01

Gallstone disease (GSD) and its complications are major public health issues globally. Although many community-based studies had addressed the risk factors for GSD, little is known about GSD prevalence and risk factors among Taiwanese vegetarians. This study included 1721 vegetarians who completed a questionnaire detailing their demographics, medical history, and life-styles. GSD was ascertained by ultrasonography or surgical history of cholecystectomy for GSD. The predictive probability of GSD for male and female vegetarians was estimated from the fitted model. The prevalence of GSD was 8.2% for both male and female vegetarians. The risk of GSD is similar in men and women across all age groups, and increases steadily with increasing age. For male vegetarians, age (OR: 1.04; 95% CI: 1.00-1.08) and serum total bilirubin level (OR: 2.35; 95% CI: 1.31-4.22) predict risk for GSD. For female vegetarians, age (OR: 1.03; 95% CI: 1.01-1.05), BMI (OR: 1.07; 95% CI: 1.01-1.13), and alcohol consumption (OR: 7.85; 95% CI: 1.83-33.73) are associated with GSD. GSD is not associated with type of vegetarian diet, duration of vegetarianism, low education level, physical inactivity, diabetes, coronary artery disease, cerebral vascular accident, chronic renal failure, hepatitis C virus infection, and lipid abnormalities. GSD is also not associated with age at menarche, postmenopausal status, and multiparity in female vegetarians. Risk factors useful for predicting GSD in vegetarians are (1) age and total bilirubin level in men, and (2) age, BMI, and alcohol consumption in women. Many previously identified risk factors for general population does not seem to apply to Taiwanese vegetarians.

Alzheimer's Disease: Genes, pathogenesis and risk prediction

NARCIS (Netherlands)

K. Sleegers (Kristel); C.M. van Duijn (Cornelia)

2001-01-01

textabstractWith the aging of western society the contribution to morbidity of diseases of the elderly, such as dementia, will increase exponentially. Thorough preventative and curative strategies are needed to constrain the increasing prevalence of these disabling diseases. Better understanding of

Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.

Science.gov (United States)

Taslimitehrani, Vahid; Dong, Guozhu; Pereira, Naveen L; Panahiazar, Maryam; Pathak, Jyotishman

2016-04-01

Computerized survival prediction in healthcare identifying the risk of disease mortality, helps healthcare providers to effectively manage their patients by providing appropriate treatment options. In this study, we propose to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5year survival in heart failure (HF) using data from electronic health records (EHRs) at Mayo Clinic. The CPXR(Log) constructs a pattern aided logistic regression model defined by several patterns and corresponding local logistic regression models. One of the models generated by CPXR(Log) achieved an AUC and accuracy of 0.94 and 0.91, respectively, and significantly outperformed prognostic models reported in prior studies. Data extracted from EHRs allowed incorporation of patient co-morbidities into our models which helped improve the performance of the CPXR(Log) models (15.9% AUC improvement), although did not improve the accuracy of the models built by other classifiers. We also propose a probabilistic loss function to determine the large error and small error instances. The new loss function used in the algorithm outperforms other functions used in the previous studies by 1% improvement in the AUC. This study revealed that using EHR data to build prediction models can be very challenging using existing classification methods due to the high dimensionality and complexity of EHR data. The risk models developed by CPXR(Log) also reveal that HF is a highly heterogeneous disease, i.e., different subgroups of HF patients require different types of considerations with their diagnosis and treatment. Our risk models provided two valuable insights for application of predictive modeling techniques in biomedicine: Logistic risk models often make systematic prediction errors, and it is prudent to use subgroup based prediction models such as those given by CPXR

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.

Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria

Directory of Open Access Journals (Sweden)

Kimmel Marek

2011-05-01

Full Text Available Abstract Background Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome. Methods Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data. Results Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET. Conclusions The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.

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.

The Veterans Affairs Cardiac Risk Score: Recalibrating the Atherosclerotic Cardiovascular Disease Score for Applied Use.

Science.gov (United States)

Sussman, Jeremy B; Wiitala, Wyndy L; Zawistowski, Matthew; Hofer, Timothy P; Bentley, Douglas; Hayward, Rodney A

2017-09-01

Accurately estimating cardiovascular risk is fundamental to good decision-making in cardiovascular disease (CVD) prevention, but risk scores developed in one population often perform poorly in dissimilar populations. We sought to examine whether a large integrated health system can use their electronic health data to better predict individual patients' risk of developing CVD. We created a cohort using all patients ages 45-80 who used Department of Veterans Affairs (VA) ambulatory care services in 2006 with no history of CVD, heart failure, or loop diuretics. Our outcome variable was new-onset CVD in 2007-2011. We then developed a series of recalibrated scores, including a fully refit "VA Risk Score-CVD (VARS-CVD)." We tested the different scores using standard measures of prediction quality. For the 1,512,092 patients in the study, the Atherosclerotic cardiovascular disease risk score had similar discrimination as the VARS-CVD (c-statistic of 0.66 in men and 0.73 in women), but the Atherosclerotic cardiovascular disease model had poor calibration, predicting 63% more events than observed. Calibration was excellent in the fully recalibrated VARS-CVD tool, but simpler techniques tested proved less reliable. We found that local electronic health record data can be used to estimate CVD better than an established risk score based on research populations. Recalibration improved estimates dramatically, and the type of recalibration was important. Such tools can also easily be integrated into health system's electronic health record and can be more readily updated.

Notification of occupational disease and the risk of work disability

DEFF Research Database (Denmark)

Kolstad, Henrik A; Christensen, Michael V; Jensen, Lone Donbæk

2012-01-01

for patients who were not working. CONCLUSIONS: Notification of an occupational disease may, as an unintended side effect, increase the risk of work disability. A cautious interpretation is warranted because data analyses may not fully have accounted for the poorer vocational prognosis already present......OBJECTIVES: The aim of this study was to analyze if notification of an occupational disease increases the risk of work disability. METHODS: We included 2304 patients examined at the Department of Occupational Medicine, Aarhus University Hospital, 1998-2005 and followed them for two years. A total......, occupational, and social characteristics that predict poorer vocational prognosis. Analyses that adjusted for these differences showed an increased risk of work disability following notification for patients who were working when notified at baseline (HR (adj)1.46, 95% CI 1.17-1.82). No effect was seen...

Predicting the short-term risk of diabetes in HIV-positive patients

DEFF Research Database (Denmark)

Petoumenos, Kathy; Worm, Signe W; Fontas, Eric

2012-01-01

HIV-positive patients receiving combination antiretroviral therapy (cART) frequently experience metabolic complications such as dyslipidemia and insulin resistance, as well as lipodystrophy, increasing the risk of cardiovascular disease (CVD) and diabetes mellitus (DM). Rates of DM and other...... glucose-associated disorders among HIV-positive patients have been reported to range between 2 and 14%, and in an ageing HIV-positive population, the prevalence of DM is expected to continue to increase. This study aims to develop a model to predict the short-term (six-month) risk of DM in HIV...

Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

Science.gov (United States)

Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

2017-07-01

To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

Predicting Coronary Artery Aneurysms in Kawasaki Disease at a North American Center: An Assessment of Baseline z Scores.

Science.gov (United States)

Son, Mary Beth F; Gauvreau, Kimberlee; Kim, Susan; Tang, Alexander; Dedeoglu, Fatma; Fulton, David R; Lo, Mindy S; Baker, Annette L; Sundel, Robert P; Newburger, Jane W

2017-05-31

Accurate risk prediction of coronary artery aneurysms (CAAs) in North American children with Kawasaki disease remains a clinical challenge. We sought to determine the predictive utility of baseline coronary dimensions adjusted for body surface area ( z scores) for future CAAs in Kawasaki disease and explored the extent to which addition of established Japanese risk scores to baseline coronary artery z scores improved discrimination for CAA development. We explored the relationships of CAA with baseline z scores; with Kobayashi, Sano, Egami, and Harada risk scores; and with the combination of baseline z scores and risk scores. We defined CAA as a maximum z score (zMax) ≥2.5 of the left anterior descending or right coronary artery at 4 to 8 weeks of illness. Of 261 patients, 77 patients (29%) had a baseline zMax ≥2.0. CAAs occurred in 15 patients (6%). CAAs were strongly associated with baseline zMax ≥2.0 versus Baseline zMax ≥2.0 had a C statistic of 0.77, good sensitivity (80%), and excellent negative predictive value (98%). None of the risk scores alone had adequate discrimination. When high-risk status per the Japanese risk scores was added to models containing baseline zMax ≥2.0, none were significantly better than baseline zMax ≥2.0 alone. In a North American center, baseline zMax ≥2.0 in children with Kawasaki disease demonstrated high predictive utility for later development of CAA. Future studies should validate the utility of our findings. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

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.

Predicting Acute Exacerbations in Chronic Obstructive Pulmonary Disease.

Science.gov (United States)

Samp, Jennifer C; Joo, Min J; Schumock, Glen T; Calip, Gregory S; Pickard, A Simon; Lee, Todd A

2018-03-01

With increasing health care costs that have outpaced those of other industries, payers of health care are moving from a fee-for-service payment model to one in which reimbursement is tied to outcomes. Chronic obstructive pulmonary disease (COPD) is a disease where this payment model has been implemented by some payers, and COPD exacerbations are a quality metric that is used. Under an outcomes-based payment model, it is important for health systems to be able to identify patients at risk for poor outcomes so that they can target interventions to improve outcomes. To develop and evaluate predictive models that could be used to identify patients at high risk for COPD exacerbations. This study was retrospective and observational and included COPD patients treated with a bronchodilator-based combination therapy. We used health insurance claims data to obtain demographics, enrollment information, comorbidities, medication use, and health care resource utilization for each patient over a 6-month baseline period. Exacerbations were examined over a 6-month outcome period and included inpatient (primary discharge diagnosis for COPD), outpatient, and emergency department (outpatient/emergency department visits with a COPD diagnosis plus an acute prescription for an antibiotic or corticosteroid within 5 days) exacerbations. The cohort was split into training (75%) and validation (25%) sets. Within the training cohort, stepwise logistic regression models were created to evaluate risk of exacerbations based on factors measured during the baseline period. Models were evaluated using sensitivity, specificity, and positive and negative predictive values. The base model included all confounding or effect modifier covariates. Several other models were explored using different sets of observations and variables to determine the best predictive model. There were 478,772 patients included in the analytic sample, of which 40.5% had exacerbations during the outcome period. Patients with

The study on risk factor of metabolic diseases in pancreatic steatosis

Energy Technology Data Exchange (ETDEWEB)

Cho, Jin Young; Ye, Soo Young; Kim, Dong Hyun [Dept. of Radiological Science, College of Health Sciences, Catholic University of Pusan, Busan (Korea, Republic of)

2016-03-15

The body of the fat tissue increased in obese represented by risk factors such as cardiovascular diseases, diabetes, metabolic disease and dyslipidemia. Such metabolic diseases and the like of the cardiovascular and cerebrovascular disease, hypertension, dyslipidemia, increase in the adipose tissue of the pancreas is known to be a risk factor of these diseases. Study on the diagnosis and treatment of pancreatic cancer was conducted actively, case studies on pancreatic steatosis is not much. In this study, divided into a control group diagnosed with pancreatic steatosis as a result of ultrasonography to evaluation the physical characteristics and serologic tests and blood pressure and arterial stiffness. The control group and the test pancreas steatosis age and waist circumference, body mass index, total cholesterol, HDL cholesterol, LDL cholesterol, and systolic and diastolic blood pressure, fasting blood glucose, arterial elasticity is higher in pancreatic steatosis. And the lower ankle brachial stenosis and HDLcholesterol were lower than the normal control group, so the pancreatic steatosis harmful to blood vessels.(P <0.05). The difference between the control group and it was confirmed that the pancreatic jibanggun statistically significant. In conclusion, pancreatic steatosis at abdominal ultrasound can predict the risk of metabolic diseases, and there was a correlation with cardiovascular disease.

The study on risk factor of metabolic diseases in pancreatic steatosis

International Nuclear Information System (INIS)

Cho, Jin Young; Ye, Soo Young; Kim, Dong Hyun

2016-01-01

The body of the fat tissue increased in obese represented by risk factors such as cardiovascular diseases, diabetes, metabolic disease and dyslipidemia. Such metabolic diseases and the like of the cardiovascular and cerebrovascular disease, hypertension, dyslipidemia, increase in the adipose tissue of the pancreas is known to be a risk factor of these diseases. Study on the diagnosis and treatment of pancreatic cancer was conducted actively, case studies on pancreatic steatosis is not much. In this study, divided into a control group diagnosed with pancreatic steatosis as a result of ultrasonography to evaluation the physical characteristics and serologic tests and blood pressure and arterial stiffness. The control group and the test pancreas steatosis age and waist circumference, body mass index, total cholesterol, HDL cholesterol, LDL cholesterol, and systolic and diastolic blood pressure, fasting blood glucose, arterial elasticity is higher in pancreatic steatosis. And the lower ankle brachial stenosis and HDLcholesterol were lower than the normal control group, so the pancreatic steatosis harmful to blood vessels.(P <0.05). The difference between the control group and it was confirmed that the pancreatic jibanggun statistically significant. In conclusion, pancreatic steatosis at abdominal ultrasound can predict the risk of metabolic diseases, and there was a correlation with cardiovascular disease

Circulating biomarkers for predicting cardiovascular disease risk : a systematic review and comprehensive overview of meta-analyses

NARCIS (Netherlands)

Holten, van T.C.; Waanders, L.F.; Groot, de P.G.; Vissers, J.; Hoefer, I.E.; Pasterkamp, G.; Prins, M.W.J.; Roest, M.

2013-01-01

Background : Cardiovascular disease is one of the major causes of death worldwide. Assessing the risk for cardiovascular disease is an important aspect in clinical decision making and setting a therapeutic strategy, and the use of serological biomarkers may improve this. Despite an overwhelming

Risk of cardiovascular disease

DEFF Research Database (Denmark)

Gejl, Michael; Starup-Linde, Jakob; Scheel-Thomsen, Jan

2014-01-01

AIMS: Type 2 diabetes (DM) increases the risk of cardiovascular disease. We investigated the effects of antidiabetic drugs on the composite endpoint (CE) of ischemic heart disease, heart failure or stroke in DM patients. METHODS: We conducted a nested case-control study. Cases were DM patients who......% CI: 16.88-24.12), neuropathy (OR=1.39, 95% CI: 1.05-1.85) and peripheral artery disease (OR=1.31, 95% CI: 1.02-1.69) increased the risk of CE. Biguanides (OR=0.62 95% CI; 0.54-0.71) and liraglutide (OR=0.48 95% CI; 0.38-0.62) significantly decreased the risk of CE as did statin treatment (OR=0.63, 95...

A score to predict short-term risk of COPD exacerbations (SCOPEX

Directory of Open Access Journals (Sweden)

Make BJ

2015-01-01

Full Text Available Barry J Make,1 Göran Eriksson,2 Peter M Calverley,3 Christine R Jenkins,4 Dirkje S Postma,5 Stefan Peterson,6 Ollie Östlund,7 Antonio Anzueto8 1Division of Pulmonary Sciences and Critical Care Medicine, National Jewish Health, University of Colorado Denver School of Medicine, Denver, CO, USA; 2Department of Respiratory Medicine and Allergology, University Hospital, Lund, Sweden; 3Pulmonary and Rehabilitation Research Group, University Hospital Aintree, Liverpool, UK; 4George Institute for Global Health, The University of Sydney and Concord Clinical School, Woolcock Institute of Medical Research, Sydney, NSW, Australia; 5Department of Pulmonology, University of Groningen and GRIAC Research Institute, University Medical Center Groningen, Groningen, The Netherlands; 6StatMind AB, Lund, Sweden; 7Department of Medical Sciences and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden; 8Department of Pulmonary/Critical Care, University of Texas Health Sciences Center and South Texas Veterans Healthcare System, San Antonio, TX, USA Background: There is no clinically useful score to predict chronic obstructive pulmonary disease (COPD exacerbations. We aimed to derive this by analyzing data from three existing COPD clinical trials of budesonide/formoterol, formoterol, or placebo in patients with moderate-to-very-severe COPD and a history of exacerbations in the previous year. Methods: Predictive variables were selected using Cox regression for time to first severe COPD exacerbation. We determined absolute risk estimates for an exacerbation by identifying variables in a binomial model, adjusting for observation time, study, and treatment. The model was further reduced to clinically useful variables and the final regression coefficients scaled to obtain risk scores of 0–100 to predict an exacerbation within 6 months. Receiver operating characteristic (ROC curves and the corresponding C-index were used to investigate the discriminatory

Serum YKL-40 predicts long-term mortality in patients with stable coronary disease

DEFF Research Database (Denmark)

Harutyunyan, Marina; Gøtze, Jens P; Winkel, Per

2013-01-01

We investigated whether the inflammatory biomarker YKL-40 could improve the long-term prediction of death made by common risk factors plus high-sensitivity C-reactive protein (hs-CRP) and N-terminal-pro-B natriuretic peptide (NT-proBNP) in patients with stable coronary artery disease (CAD)....

Prediction of Adult Dyslipidemia Using Genetic and Childhood Clinical Risk Factors: The Cardiovascular Risk in Young Finns Study.

Science.gov (United States)

Nuotio, Joel; Pitkänen, Niina; Magnussen, Costan G; Buscot, Marie-Jeanne; Venäläinen, Mikko S; Elo, Laura L; Jokinen, Eero; Laitinen, Tomi; Taittonen, Leena; Hutri-Kähönen, Nina; Lyytikäinen, Leo-Pekka; Lehtimäki, Terho; Viikari, Jorma S; Juonala, Markus; Raitakari, Olli T

2017-06-01

Dyslipidemia is a major modifiable risk factor for cardiovascular disease. We examined whether the addition of novel single-nucleotide polymorphisms for blood lipid levels enhances the prediction of adult dyslipidemia in comparison to childhood lipid measures. Two thousand four hundred and twenty-two participants of the Cardiovascular Risk in Young Finns Study who had participated in 2 surveys held during childhood (in 1980 when aged 3-18 years and in 1986) and at least once in a follow-up study in adulthood (2001, 2007, and 2011) were included. We examined whether inclusion of a lipid-specific weighted genetic risk score based on 58 single-nucleotide polymorphisms for low-density lipoprotein cholesterol, 71 single-nucleotide polymorphisms for high-density lipoprotein cholesterol, and 40 single-nucleotide polymorphisms for triglycerides improved the prediction of adult dyslipidemia compared with clinical childhood risk factors. Adjusting for age, sex, body mass index, physical activity, and smoking in childhood, childhood lipid levels, and weighted genetic risk scores were associated with an increased risk of adult dyslipidemia for all lipids. Risk assessment based on 2 childhood lipid measures and the lipid-specific weighted genetic risk scores improved the accuracy of predicting adult dyslipidemia compared with the approach using only childhood lipid measures for low-density lipoprotein cholesterol (area under the receiver-operating characteristic curve 0.806 versus 0.811; P =0.01) and triglycerides (area under the receiver-operating characteristic curve 0.740 versus area under the receiver-operating characteristic curve 0.758; P dyslipidemia in adulthood. © 2017 American Heart Association, Inc.

Remote health monitoring: predicting outcome success based on contextual features for cardiovascular disease.

Science.gov (United States)

Alshurafa, Nabil; Eastwood, Jo-Ann; Pourhomayoun, Mohammad; Liu, Jason J; Sarrafzadeh, Majid

2014-01-01

Current studies have produced a plethora of remote health monitoring (RHM) systems designed to enhance the care of patients with chronic diseases. Many RHM systems are designed to improve patient risk factors for cardiovascular disease, including physiological parameters such as body mass index (BMI) and waist circumference, and lipid profiles such as low density lipoprotein (LDL) and high density lipoprotein (HDL). There are several patient characteristics that could be determining factors for a patient's RHM outcome success, but these characteristics have been largely unidentified. In this paper, we analyze results from an RHM system deployed in a six month Women's Heart Health study of 90 patients, and apply advanced feature selection and machine learning algorithms to identify patients' key baseline contextual features and build effective prediction models that help determine RHM outcome success. We introduce Wanda-CVD, a smartphone-based RHM system designed to help participants with cardiovascular disease risk factors by motivating participants through wireless coaching using feedback and prompts as social support. We analyze key contextual features that secure positive patient outcomes in both physiological parameters and lipid profiles. Results from the Women's Heart Health study show that health threat of heart disease, quality of life, family history, stress factors, social support, and anxiety at baseline all help predict patient RHM outcome success.

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

Exploring gene expression signatures for predicting disease free survival after resection of colorectal cancer liver metastases.

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Nikol Snoeren

Full Text Available BACKGROUND AND OBJECTIVES: This study was designed to identify and validate gene signatures that can predict disease free survival (DFS in patients undergoing a radical resection for their colorectal liver metastases (CRLM. METHODS: Tumor gene expression profiles were collected from 119 patients undergoing surgery for their CRLM in the Paul Brousse Hospital (France and the University Medical Center Utrecht (The Netherlands. Patients were divided into high and low risk groups. A randomly selected training set was used to find predictive gene signatures. The ability of these gene signatures to predict DFS was tested in an independent validation set comprising the remaining patients. Furthermore, 5 known clinical risk scores were tested in our complete patient cohort. RESULT: No gene signature was found that significantly predicted DFS in the validation set. In contrast, three out of five clinical risk scores were able to predict DFS in our patient cohort. CONCLUSIONS: No gene signature was found that could predict DFS in patients undergoing CRLM resection. Three out of five clinical risk scores were able to predict DFS in our patient cohort. These results emphasize the need for validating risk scores in independent patient groups and suggest improved designs for future studies.

Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients

DEFF Research Database (Denmark)

Brorsson, Caroline A; Nielsen, Lotte B; Andersen, Marie-Louise

2016-01-01

Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci on β-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type...... 1 diabetes (T1D). As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease...... constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO) analyses revealed that several...

Coronary artery disease risk assessment from unstructured electronic health records using text mining.

Science.gov (United States)

Jonnagaddala, Jitendra; Liaw, Siaw-Teng; Ray, Pradeep; Kumar, Manish; Chang, Nai-Wen; Dai, Hong-Jie

2015-12-01

Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstructured clinical narratives if the data is not collected specifically for risk assessment purposes. Clinical text mining can be used to extract data related to risk factors from unstructured clinical notes. This study presents methods to extract Framingham risk factors from unstructured electronic health records using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients. We developed a rule-based system to extract risk factors: age, gender, total cholesterol, HDL-C, blood pressure, diabetes history and smoking history. The results showed that the output from the text mining system was reliable, but there was a significant amount of missing data to calculate the Framingham risk score. A systematic approach for understanding missing data was followed by implementation of imputation strategies. An analysis of the 10-year Framingham risk scores for coronary artery disease in this cohort has shown that the majority of the diabetic patients are at moderate risk of CAD. Copyright © 2015 Elsevier Inc. All rights reserved.

Developmental dyslexia: predicting individual risk.

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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

Relationship satisfaction reduces the risk of maternal infectious diseases in pregnancy: the Norwegian Mother and Child Cohort Study.

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Roger Ekeberg Henriksen

Full Text Available The aims of this study were to explore the degree to which relationship satisfaction predicts the risk of infectious diseases during pregnancy and to examine whether relationship satisfaction moderates the association between stressful life events and the risk of infections.This was a prospective study based on data from the Norwegian Mother and Child Cohort Study (MoBa conducted by the Norwegian Institute of Public Health. Pregnant women (n = 67,244 completed questionnaires concerning relationship satisfaction and nine different categories of infectious diseases as well as socioeconomic characteristics and stressful life events. Associations between the predictor variables and the infectious diseases were assessed by logistic regression analyses. A multiple regression analysis was performed to assess a possible interaction of relationship satisfaction with stressful life events on the risk for infectious diseases.After controlling for marital status, age, education, income, and stressful life events, high levels of relationship satisfaction at week 15 of gestation were found to predict a significantly lower risk for eight categories of infectious diseases at gestational weeks 17-30. No significant interaction effect was found between relationship satisfaction and stressful life events on the risk for infections.

Relationship satisfaction reduces the risk of maternal infectious diseases in pregnancy: the Norwegian Mother and Child Cohort Study.

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Henriksen, Roger Ekeberg; Torsheim, Torbjørn; Thuen, Frode

2015-01-01

The aims of this study were to explore the degree to which relationship satisfaction predicts the risk of infectious diseases during pregnancy and to examine whether relationship satisfaction moderates the association between stressful life events and the risk of infections. This was a prospective study based on data from the Norwegian Mother and Child Cohort Study (MoBa) conducted by the Norwegian Institute of Public Health. Pregnant women (n = 67,244) completed questionnaires concerning relationship satisfaction and nine different categories of infectious diseases as well as socioeconomic characteristics and stressful life events. Associations between the predictor variables and the infectious diseases were assessed by logistic regression analyses. A multiple regression analysis was performed to assess a possible interaction of relationship satisfaction with stressful life events on the risk for infectious diseases. After controlling for marital status, age, education, income, and stressful life events, high levels of relationship satisfaction at week 15 of gestation were found to predict a significantly lower risk for eight categories of infectious diseases at gestational weeks 17-30. No significant interaction effect was found between relationship satisfaction and stressful life events on the risk for infections.

Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records

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Jitendra Jonnagaddala

2015-01-01

Full Text Available Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302.

Increased NT-proANP predicts risk of congestive heart failure in Cavalier King Charles spaniels with mitral regurgitation caused by myxomatous valve disease.

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Eriksson, Anders S; Häggström, Jens; Pedersen, Henrik Duelund; Hansson, Kerstin; Järvinen, Anna-Kaisa; Haukka, Jari; Kvart, Clarence

2014-09-01

To evaluate the predictive value of plasma N-terminal pro-atrial natriuretic peptide (NT-proANP) and nitric oxide end-products (NOx) as markers for progression of mitral regurgitation caused by myxomatous mitral valve disease. Seventy-eight privately owned Cavalier King Charles spaniels with naturally occurring myxomatous mitral valve disease. Prospective longitudinal study comprising 312 measurements over a 4.5 year period. Clinical values were recorded, NT-proANP concentrations were measured by radioimmunoassay, and NOx were analyzed colorimetrically. To predict congestive heart failure (CHF), Cox proportional hazards models with time-varying covariates were constructed. The hazard ratio for NT-proANP (per 1000 pmol/l increase) to predict future CHF was 6.7 (95% confidence interval, 3.6-12.5; p 1000 pmol/l was 11 months (95% confidence interval, 5.6-12.6 months), compared to 54 months (46 - infinity) for dogs with concentrations ≤ 1000 pmol/l (p 130 beats per minute) and grade of murmur (≥ 3/6). The risk of CHF due to mitral regurgitation is increased in dogs with blood NT-proANP concentrations above 1000 pmol/l. Measurement of NT-proANP can be a valuable tool to identify dogs that may develop CHF within months. Copyright © 2014 Elsevier B.V. All rights reserved.

Long-term predictive value of postsurgical cortisol concentrations for cure and risk of recurrence in Cushing's disease

NARCIS (Netherlands)

Pereira, Alberto M.; van Aken, Maarten O.; van Dulken, Hans; Schutte, Pieter J.; Biermasz, Nienke R.; Smit, Jan W. A.; Roelfsema, Ferdinand; Romijn, Johannes A.

2003-01-01

We assessed the value of postoperative plasma cortisol concentrations to predict cure and recurrence of Cushing's disease after transsphenoidal surgery (TS). Seventy-eight of 80 consecutive patients treated by TS for Cushing's disease were evaluated. TS cured 72% (n = 56) of the patients. Two weeks

Improving Cardiovascular Risk Prediction--Biomarkers and Beyond; Implications for Astronaut Selection and Monitoring During Prolonged Spaceflight

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National Aeronautics and Space Administration — Our primary objective is to identify and validate novel strategies to enhance global cardiovascular disease (CVD) risk prediction over two time windows: 1) 10-20...

Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models

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Lucky eMehra

2016-03-01

Full Text Available Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB, caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum. The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early

Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models.

Science.gov (United States)

Mehra, Lucky K; Cowger, Christina; Gross, Kevin; Ojiambo, Peter S

2016-01-01

Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR) and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF), in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors) collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early assessment of

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...

Nucleus basalis of Meynert degeneration precedes and predicts cognitive impairment in Parkinson's disease.

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Schulz, Jonathan; Pagano, Gennaro; Fernández Bonfante, Juan Alberto; Wilson, Heather; Politis, Marios

2018-05-01

Currently, no reliable predictors of cognitive impairment in Parkinson's disease exist. We hypothesized that microstructural changes at grey matter T1-weighted MRI and diffusion tensor imaging in the cholinergic system nuclei and associated limbic pathways underlie cognitive impairment in Parkinson's disease. We performed a cross-sectional comparison between patients with Parkinson's disease with and without cognitive impairment. We also performed a longitudinal 36-month follow-up study of cognitively intact Parkinson's disease patients, comparing patients who remained cognitively intact to those who developed cognitive impairment. Patients with Parkinson's disease with cognitive impairment showed lower grey matter volume and increased mean diffusivity in the nucleus basalis of Meynert, compared to patients with Parkinson's disease without cognitive impairment. These results were confirmed both with region of interest and voxel-based analyses, and after partial volume correction. Lower grey matter volume and increased mean diffusivity in the nucleus basalis of Meynert was predictive for developing cognitive impairment in cognitively intact patients with Parkinson's disease, independent of other clinical and non-clinical markers of the disease. Structural and microstructural alterations in entorhinal cortex, amygdala, hippocampus, insula, and thalamus were not predictive for developing cognitive impairment in Parkinson's disease. Our findings provide evidence that degeneration of the nucleus basalis of Meynert precedes and predicts the onset of cognitive impairment, and might be used in a clinical setting as a reliable biomarker to stratify patients at higher risk of cognitive decline.

Conditional predictive inference for online surveillance of spatial disease incidence

Science.gov (United States)

Corberán-Vallet, Ana; Lawson, Andrew B.

2012-01-01

This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina. PMID:21898522

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

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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.

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.

Worldwide risks of animal diseases: introduction.

Science.gov (United States)

Pearson, J E

2006-01-01

Animal diseases impact food supplies, trade and commerce, and human health and well-being in every part of the world. Outbreaks draw the attention of those in agriculture, regulatory agencies, and government, as well as the general public. This was demonstrated by the 2000-2001 foot and mouth disease (FMD) outbreaks that occurred in Europe, South America, Asia and Africa and by the recent increased occurrence of emerging diseases transmitted from animals to humans. Examples of these emerging zoonotic diseases are highly pathogenic avian influenza, bovine spongiform encephalopathy, West Nile virus and severe acute respiratory syndrome. There is also the risk of well-known and preventable zoonotic diseases, such as rabies, brucellosis, leishmaniasis, and echinococcosis/hydatidosis, in certain countries; these diseases have a high morbidity with the potential for a very high mortality. Animal agriculturalists should have a global disease awareness of disease risks and develop plans of action to deal with them; in order to better respond to these diseases, they should develop the skills and competencies in politics, media interactions, and community engagement. This issue of Veterinaria Italiana presents information on the risk of animal diseases; their impact on animals and humans at the international, national, industry, and societal levels; and the responses to them. In addition, specific information is provided on national and international disease monitoring, surveillance and reporting, the risk of spread of disease by bioterrorism and on import risk analysis.

Predicting erectile dysfunction following surgical correction of Peyronie's disease without inflatable penile prosthesis placement: vascular assessment and preoperative risk factors.

Science.gov (United States)

Taylor, Frederick L; Abern, Michael R; Levine, Laurence A

2012-01-01

Surgical therapy remains the gold standard treatment for Peyronie's Disease (PD). Surgical options include plication, grafting, and placement of inflatable penile prosthesis (IPP). Postoperative erectile dysfunction (ED) is a potential complication for PD surgery without IPP. We present our large series follow-up to evaluate preoperative risk factors for postoperative ED. The aim of this study is to evaluate preoperative risk factors for the development of ED following surgical correction of PD taking into account the degree of curvature, graft size, surgical approach, hypertension, hyperlipidemia, diabetes, smoking history, preoperative use of phosphodiesterase 5 inhibitors (PDE5), and preoperative duplex ultrasound findings including peak systolic and end diastolic velocities and resistive index. We identified 218 men undergoing either tunica albuginea plication (TAP) or partial plaque excision with pericardial grafting for PD following a previously published algorithm between November 1992 and April 2007. Preoperative and postoperative erectile function, curvature characteristics, presence of vascular risk factors, and duplex ultrasound findings were available on 109 patients. Our primary outcome measure is the development of ED after surgery for PD. Ten percent of TAP and 21% of plaque excision with grafting patients developed postoperative ED. Neither curve direction (P = 0.76), graft area (P = 0.78), surgical approach (P = 0.12), chronic hypertension (P = 0.51), hyperlipidemia (P = 0.87), diabetes (P = 0.69), nor smoking history (P = 0.99) were significant predictors of postoperative ED. No combination of risk factors was found to be predictive of postoperative ED. Preoperative use of PDE5 was not a significant predictor of postoperative ED (P = 0.33). Neither peak systolic, end diastolic, nor resistive index were significant predictors of ED (P = 0.28, 0.28, and 0.25, respectively). This long-term follow-up of a large published series suggests that neither

Prevalence of 10-Year Risk of Cardiovascular Diseases and Associated Risks in Canadian Adults: The Contribution of Cardiometabolic Risk Assessment Introduction

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Solmaz Setayeshgar

2013-01-01

Full Text Available Background. Cardiovascular disease (CVD is the leading cause of death in adult Canadians. Cardiometabolic risk (CMR derived from 10-year risk of cardiovascular diseases and metabolic syndrome (MetS needs to be evaluated in Canadian adults. Objective. To determine CMR among Canadian adults by sociodemographic and lifestyle characteristics. Subjects and Methods. Data from the Canadian Health Measures Survey (CHMS, Cycle 1, 2007–2009, was used. Framingham Risk Score (FRS was implemented to predict 10-year risk of CVD, and metabolic syndrome was identified using the most recent criteria. The 10-year risk of CVD was multiplied by 1.5 in individuals with MetS to obtain CMR. Data were weighted and bootstrapped to be able to generalize the results nationally. Results and Conclusion. CMR gave more accurate estimation of 10-year risk of CVD in Canadian adults from 30 to 74 years than using only FRS. The 10-year risk of CVD in Canadian adults significantly increased when CMR was taken into account from 8.10% to 9.86%. The CVD risk increased by increase in age, decrease in education, and decrease in physical activity and in smokers. Canadians with medium risk of CVD consumed significantly less fruit and vegetable juice compared to Canadians with low risk. No other dietary differences were found.

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.

Rift Valley Fever Prediction and Risk Mapping: 2014-2015 Season

Science.gov (United States)

Anyamba, Assaf

2015-01-01

Extremes in either direction (+-) of precipitation temperature have significant implications for disease vectors and pathogen emergence and spread Magnitude of ENSO influence on precipitation temperature cannot be currently predicted rely on average history and patterns. Timing of event and emergence disease can be exploited (GAP) in to undertake vector control and preparedness measures. Currently - no risk for ecologically-coupled RVFV activity however we need to be vigilant during the coming fall season due the ongoing buildup of energy in the central Pacific Ocean. Potential for the dual-use of the RVF Monitor system for other VBDs Need to invest in early ground surveillance and the use of rapid field diagnostic capabilities for vector identification and virus isolation.

Clinical Utility of a Coronary Heart Disease Risk Prediction Gene Score in UK Healthy Middle Aged Men and in the Pakistani Population.

Directory of Open Access Journals (Sweden)

Katherine E Beaney

Full Text Available Numerous risk prediction algorithms based on conventional risk factors for Coronary Heart Disease (CHD are available but provide only modest discrimination. The inclusion of genetic information may improve clinical utility.We tested the use of two gene scores (GS in the prospective second Northwick Park Heart Study (NPHSII of 2775 healthy UK men (284 cases, and Pakistani case-control studies from Islamabad/Rawalpindi (321 cases/228 controls and Lahore (414 cases/219 controls. The 19-SNP GS included SNPs in loci identified by GWAS and candidate gene studies, while the 13-SNP GS only included SNPs in loci identified by the CARDIoGRAMplusC4D consortium.In NPHSII, the mean of both gene scores was higher in those who went on to develop CHD over 13.5 years of follow-up (19-SNP p=0.01, 13-SNP p=7x10-3. In combination with the Framingham algorithm the GSs appeared to show improvement in discrimination (increase in area under the ROC curve, 19-SNP p=0.48, 13-SNP p=0.82 and risk classification (net reclassification improvement (NRI, 19-SNP p=0.28, 13-SNP p=0.42 compared to the Framingham algorithm alone, but these were not statistically significant. When considering only individuals who moved up a risk category with inclusion of the GS, the improvement in risk classification was statistically significant (19-SNP p=0.01, 13-SNP p=0.04. In the Pakistani samples, risk allele frequencies were significantly lower compared to NPHSII for 13/19 SNPs. In the Islamabad study, the mean gene score was higher in cases than controls only for the 13-SNP GS (2.24 v 2.34, p=0.04. There was no association with CHD and either score in the Lahore study.The performance of both GSs showed potential clinical utility in European men but much less utility in subjects from Pakistan, suggesting that a different set of risk loci or SNPs may be required for risk prediction in the South Asian population.

Prospective study of coffee consumption and risk of Parkinson's disease.

Science.gov (United States)

Sääksjärvi, K; Knekt, P; Rissanen, H; Laaksonen, M A; Reunanen, A; Männistö, S

2008-07-01

To examine the prediction of coffee consumption on the incidence of Parkinson's disease. The study population comprised 6710 men and women, aged 50-79 years and free from Parkinson's disease at the baseline. At baseline, enquiries were made about coffee consumption in a self-administered questionnaire as the average number of cups per day. During a 22-year follow-up, 101 incident cases of Parkinson's disease occurred. Parkinson's disease cases were identified through a nationwide registry of patients receiving medication reimbursement, which is based on certificates from neurologist. After adjustments for age, sex, marital status, education, community density, alcohol consumption, leisure-time physical activity, smoking, body mass index, hypertension and serum cholesterol, the relative risk for subjects drinking 10 or more cups of coffee per day compared with non-drinkers was 0.26 (95% confidence interval 0.07-0.99, P-value for trend=0.18). The association was stronger among overweight persons and among persons with lower serum cholesterol level (P-value for interaction=0.04 and 0.03, respectively). The results support the hypothesis that coffee consumption reduces the risk of Parkinson's disease, but protective effect of coffee may vary by exposure to other factors.

Volume of Lytic Vertebral Body Metastatic Disease Quantified Using Computed Tomography–Based Image Segmentation Predicts Fracture Risk After Spine Stereotactic Body Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Thibault, Isabelle [Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario (Canada); Department of Radiation Oncology, Centre Hospitalier de L' Universite de Québec–Université Laval, Quebec, Quebec (Canada); Whyne, Cari M. [Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Department of Surgery, University of Toronto, Toronto, Ontario (Canada); Zhou, Stephanie; Campbell, Mikki [Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario (Canada); Atenafu, Eshetu G. [Department of Biostatistics, University Health Network, University of Toronto, Toronto, Ontario (Canada); Myrehaug, Sten; Soliman, Hany; Lee, Young K. [Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario (Canada); Ebrahimi, Hamid [Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Department of Surgery, University of Toronto, Toronto, Ontario (Canada); Yee, Albert J.M. [Division of Orthopaedic Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario (Canada); Sahgal, Arjun, E-mail: arjun.sahgal@sunnybrook.ca [Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario (Canada)

2017-01-01

Purpose: To determine a threshold of vertebral body (VB) osteolytic or osteoblastic tumor involvement that would predict vertebral compression fracture (VCF) risk after stereotactic body radiation therapy (SBRT), using volumetric image-segmentation software. Methods and Materials: A computational semiautomated skeletal metastasis segmentation process refined in our laboratory was applied to the pretreatment planning CT scan of 100 vertebral segments in 55 patients treated with spine SBRT. Each VB was segmented and the percentage of lytic and/or blastic disease by volume determined. Results: The cumulative incidence of VCF at 3 and 12 months was 14.1% and 17.3%, respectively. The median follow-up was 7.3 months (range, 0.6-67.6 months). In all, 56% of segments were determined lytic, 23% blastic, and 21% mixed, according to clinical radiologic determination. Within these 3 clinical cohorts, the segmentation-determined mean percentages of lytic and blastic tumor were 8.9% and 6.0%, 0.2% and 26.9%, and 3.4% and 15.8% by volume, respectively. On the basis of the entire cohort (n=100), a significant association was observed for the osteolytic percentage measures and the occurrence of VCF (P<.001) but not for the osteoblastic measures. The most significant lytic disease threshold was observed at ≥11.6% (odds ratio 37.4, 95% confidence interval 9.4-148.9). On multivariable analysis, ≥11.6% lytic disease (P<.001), baseline VCF (P<.001), and SBRT with ≥20 Gy per fraction (P=.014) were predictive. Conclusions: Pretreatment lytic VB disease volumetric measures, independent of the blastic component, predict for SBRT-induced VCF. Larger-scale trials evaluating our software are planned to validate the results.

Predicting cognitive decline in Alzheimer's disease: an integrated analysis

DEFF Research Database (Denmark)

Lopez, Oscar L; Schwam, Elias; Cummings, Jeffrey

2010-01-01

Numerous patient- and disease-related factors increase the risk of rapid cognitive decline in patients with Alzheimer's disease (AD). The ability of pharmacological treatment to attenuate this risk remains undefined.......Numerous patient- and disease-related factors increase the risk of rapid cognitive decline in patients with Alzheimer's disease (AD). The ability of pharmacological treatment to attenuate this risk remains undefined....

Asymptomatic Extracranial Artery Stenosis and the Risk of Cardiovascular and Cerebrovascular Diseases

OpenAIRE

Wang, Dandan; Wang, Jing; Jin, Cheng; Ji, Ruijun; Wang, Anxin; Li, Xin; Gao, Xiang; Wu, Shouling; Zhou, Yong; Zhao, Xingquan

2016-01-01

Asymptomatic extracranial artery stenosis (ECAS) is a well-known risk factor for stroke events, but it remains unclear whether it has the same role in predicting cardiovascular and cerebrovascular diseases, especially in China. We investigated the potential associations between ECAS, carotid plaque and carotid intima-media thickness and the new occurrence of cardiovascular and cerebrovascular diseases in the study. Out of 5440 study participants, 364 showed an asymptomatic ECAS at baseline, a...

Poor caregiver mental health predicts mortality of patients with neurodegenerative disease.

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Lwi, Sandy J; Ford, Brett Q; Casey, James J; Miller, Bruce L; Levenson, Robert W

2017-07-11

Dementia and other neurodegenerative diseases cause profound declines in functioning; thus, many patients require caregivers for assistance with daily living. Patients differ greatly in how long they live after disease onset, with the nature and severity of the disease playing an important role. Caregiving can also be extremely stressful, and many caregivers experience declines in mental health. In this study, we investigated the role that caregiver mental health plays in patient mortality. In 176 patient-caregiver dyads, we found that worse caregiver mental health predicted greater patient mortality even when accounting for key risk factors in patients (i.e., diagnosis, age, sex, dementia severity, and patient mental health). These findings highlight the importance of caring for caregivers as well as patients when attempting to improve patients' lives.

Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models.

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Campbell, William; Ganna, Andrea; Ingelsson, Erik; Janssens, A Cecile J W

2016-01-01

We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance. Copyright © 2016 Elsevier Inc. All rights reserved.

Differential incremental value of ultrasound carotid intima-media thickness, carotid plaque, and cardiac calcium to predict angiographic coronary artery disease across Framingham risk score strata in the APRES multicentre study.

Science.gov (United States)

Gaibazzi, Nicola; Rigo, Fausto; Facchetti, Rita; Carerj, Scipione; Giannattasio, Cristina; Moreo, Antonella; Mureddu, Gian Francesco; Salvetti, Massimo; Grolla, Elisabetta; Faden, Giacomo; Cesana, Francesca; Faggiano, Pompilio

2016-09-01

According to recent data, more accurate selection of patients undergoing coronary angiography for suspected coronary artery disease (CAD) is needed. From the Active PREvention Study multicentre prospective study, we further analyse whether carotid intima-media thickness (cIMT), carotid plaques (cPL), and echocardiographic cardiac calcium score (eCS) have incremental discriminatory and reclassification predictive value for CAD over clinical risk score in subjects undergoing coronary angiography, specifically depending on their low, intermediate, or high class of clinical risk. In eight centres, 445 subjects without history of prior CAD but with chest pain of recent onset and/or a positive/inconclusive stress test for ischaemia prospectively underwent clinically indicated elective coronary angiography after cardiac and carotid ultrasound assessments with measurements of cIMT, cPL, and eCS. The study population was divided into subjects at low (10%), intermediate (10-20%), and high (>20%) Framingham risk score (FRS). Ultrasound parameters were tested for their incremental value to predict CAD over FRS, in each pre-test risk category. No significant difference could be appreciated between the discrimination value of FRS and Diagnostic Imaging for Coronary Artery Disease score for the presence of CAD. eCS or cPL demonstrated significant incremental prediction over FRS, consistently in the three FRS categories (P risk subjects, in whom cPL was apparently not incremental over FRS, and eCS was only of borderline significance for better discrimination. Ultrasound eCS and cPL assessments were significant predictors of angiographic CAD in patients without prior CAD but with signs or symptoms suspect for CAD, independently and incrementally to FRS, across all pre-test risk probability strata, although in high-risk subjects, only eCS maintained an incremental value. The use of cIMT was not significantly incrementally useful in any FRS risk category. Published on behalf of the

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.

Using structured decision making to manage disease risk for Montana wildlife

Science.gov (United States)

Mitchell, Michael S.; Gude, Justin A.; Anderson, Neil J.; Ramsey, Jennifer M.; Thompson, Michael J.; Sullivan, Mark G.; Edwards, Victoria L.; Gower, Claire N.; Cochrane, Jean Fitts; Irwin, Elise R.; Walshe, Terry

2013-01-01

We used structured decision-making to develop a 2-part framework to assist managers in the proactive management of disease outbreaks in Montana, USA. The first part of the framework is a model to estimate the probability of disease outbreak given field observations available to managers. The second part of the framework is decision analysis that evaluates likely outcomes of management alternatives based on the estimated probability of disease outbreak, and applies managers' values for different objectives to indicate a preferred management strategy. We used pneumonia in bighorn sheep (Ovis canadensis) as a case study for our approach, applying it to 2 populations in Montana that differed in their likelihood of a pneumonia outbreak. The framework provided credible predictions of both probability of disease outbreaks, as well as biological and monetary consequences of management actions. The structured decision-making approach to this problem was valuable for defining the challenges of disease management in a decentralized agency where decisions are generally made at the local level in cooperation with stakeholders. Our approach provides local managers with the ability to tailor management planning for disease outbreaks to local conditions. Further work is needed to refine our disease risk models and decision analysis, including robust prediction of disease outbreaks and improved assessment of management alternatives.

A model to predict multivessel coronary artery disease from the exercise thallium-201 stress test

International Nuclear Information System (INIS)

Pollock, S.G.; Abbott, R.D.; Boucher, C.A.; Watson, D.D.; Kaul, S.

1991-01-01

The aim of this study was to (1) determine whether nonimaging variables add to the diagnostic information available from exercise thallium-201 images for the detection of multivessel coronary artery disease; and (2) to develop a model based on the exercise thallium-201 stress test to predict the presence of multivessel disease. The study populations included 383 patients referred to the University of Virginia and 325 patients referred to the Massachusetts General Hospital for evaluation of chest pain. All patients underwent both cardiac catheterization and exercise thallium-201 stress testing between 1978 and 1981. In the University of Virginia cohort, at each level of thallium-201 abnormality (no defects, one defect, more than one defect), ST depression and patient age added significantly in the detection of multivessel disease. Logistic regression analysis using data from these patients identified three independent predictors of multivessel disease: initial thallium-201 defects, ST depression, and age. A model was developed to predict multivessel disease based on these variables. As might be expected, the risk of multivessel disease predicted by the model was similar to that actually observed in the University of Virginia population. More importantly, however, the model was accurate in predicting the occurrence of multivessel disease in the unrelated population studied at the Massachusetts General Hospital. It is, therefore, concluded that (1) nonimaging variables (age and exercise-induced ST depression) add independent information to thallium-201 imaging data in the detection of multivessel disease; and (2) a model has been developed based on the exercise thallium-201 stress test that can accurately predict the probability of multivessel disease in other populations

Diagnostic performance of an acoustic-based system for coronary artery disease risk stratification

DEFF Research Database (Denmark)

Winther, Simon; Nissen, Louise; Schmidt, Samuel Emil

2017-01-01

CAD-score value ≤20. At this cut-off, sensitivity was 81% (95% CI 73% to 87%), specificity 53% (95% CI 50% to 56%), positive predictive value 16% (95% CI 13% to 18%) and negative predictive value 96% (95% CI 95% to 98%) for diagnosing haemodynamically significant CAD. CONCLUSION: Sound-based detection......OBJECTIVE: Diagnosing coronary artery disease (CAD) continues to require substantial healthcare resources. Acoustic analysis of transcutaneous heart sounds of cardiac movement and intracoronary turbulence due to obstructive coronary disease could potentially change this. The aim of this study...... features and clinical risk factors. Low risk is indicated by a CAD-score value ≤20. RESULTS: Haemodynamically significant CAD assessed from FFR was present in 145 (10.0%) patients. In the entire cohort, the predefined CAD-score had a sensitivity of 63% and a specificity of 44%. In total, 50% had an updated...

Predicted risks of radiogenic cardiac toxicity in two pediatric patients undergoing photon or proton radiotherapy

International Nuclear Information System (INIS)

Zhang, Rui; Howell, Rebecca M; Homann, Kenneth; Giebeler, Annelise; Taddei, Phillip J; Mahajan, Anita; Newhauser, Wayne D

2013-01-01

Hodgkin disease (HD) and medulloblastoma (MB) are common malignancies found in children and young adults, and radiotherapy is part of the standard treatment. It was reported that these patients who received radiation therapy have an increased risk of cardiovascular late effects. We compared the predicted risk of developing radiogenic cardiac toxicity after photon versus proton radiotherapies for a pediatric patient with HD and a pediatric patient with MB. In the treatment plans, each patient’s heart was contoured in fine detail, including substructures of the pericardium and myocardium. Risk calculations took into account both therapeutic and stray radiation doses. We calculated the relative risk (RR) of cardiac toxicity using a linear risk model and the normal tissue complication probability (NTCP) values using relative seriality and Lyman models. Uncertainty analyses were also performed. The RR values of cardiac toxicity for the HD patient were 7.27 (proton) and 8.37 (photon), respectively; the RR values for the MB patient were 1.28 (proton) and 8.39 (photon), respectively. The predicted NTCP values for the HD patient were 2.17% (proton) and 2.67% (photon) for the myocardium, and were 2.11% (proton) and 1.92% (photon) for the whole heart. The predicted ratios of NTCP values (proton/photon) for the MB patient were much less than unity. Uncertainty analyses revealed that the predicted ratio of risk between proton and photon therapies was sensitive to uncertainties in the NTCP model parameters and the mean radiation weighting factor for neutrons, but was not sensitive to heart structure contours. The qualitative findings of the study were not sensitive to uncertainties in these factors. We conclude that proton and photon radiotherapies confer similar predicted risks of cardiac toxicity for the HD patient in this study, and that proton therapy reduced the predicted risk for the MB patient in this study

Forecasting the future risk of Barmah Forest virus disease under climate change scenarios in Queensland, Australia.

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Naish, Suchithra; Mengersen, Kerrie; Hu, Wenbiao; Tong, Shilu

2013-01-01

Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia. We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000-2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland.

BRAIN NATRIURETIC PEPTIDE (BNP: BIOMARKER FOR RISK STRATIFICATION AND FUNCTIONAL RECOVERY PREDICTION IN ISCHEMIC STROKE

Directory of Open Access Journals (Sweden)

STANESCU Ioana

2015-02-01

Full Text Available Functional outcome after cardiovascular and cerebrovascular events is traditionally predicted using demographic and clinical variables like age, gender, blood pressure, cholesterol levels, diabetes status, smoking habits or pre-existing morbidity. Identification of new variables will improve the risk stratification of specific categories of patients. Numerous blood-based biomarkers associated with increased cardiovascular risk have been identified; some of them even predict cardiovascular events. Investigators have tried to produce prediction models by incorporating traditional risk factors and biomarkers. (1. Widely-available, rapidly processed and less expensive biomarkers could be used in the future to guide management of complex cerebrovascular patients in order to maximize their recovery (2 Recently, studies have demonstrated that biomarkers can predict not only the risk for a specific clinical event, but also the risk of death of vascular cause and the functional outcome after cardiovascular or cerebrovascular events. Early prediction of fatal outcome after stroke may improve therapeutic strategies (such as the use of more aggressive treatments or inclusion of patients in clinical trials and guide decision-making processes in order to maximize patient’s chances for survival and recovery. (3 Long term functional outcome after stroke is one of the most difficult variables to predict. Elevated serum levels of brain natriuretic peptide (BNP are powerful predictor of outcomes in patients with cardiovascular disease (heart failure, atrial fibrillation. Potential role of BNP in predicting atrial fibrillation occurrence, cardio-embolic stroke and post-stroke mortality have been proved in many studies. However, data concerning the potential role of BNP in predicting short term and long term functional outcomes after stroke remain controversial.

Risk and course of motor complications in a population-based incident Parkinson's disease cohort.

Science.gov (United States)

Bjornestad, Anders; Forsaa, Elin B; Pedersen, Kenn Freddy; Tysnes, Ole-Bjorn; Larsen, Jan Petter; Alves, Guido

2016-01-01

Motor complications may become major challenges in the management of patients with Parkinson's disease. In this study, we sought to determine the incidence, risk factors, evolution, and treatment of motor fluctuations and dyskinesias in a population-representative, incident Parkinson's disease cohort. In this prospective population-based 5-year longitudinal study, we followed 189 incident and initially drug-naïve Parkinson's disease patients biannually for detailed examination of dyskinesias and motor fluctuations as defined by the Unified Parkinson's disease Rating Scale. We performed Kaplan-Meier survival and Cox regression analyses to assess cumulative incidence and risk factors of these motor complications. The 5-year cumulative incidence of motor complications was 52.4%. Motor fluctuations occurred in 42.9% and dyskinesias in 24.3%. Besides higher motor severity predicting both motor fluctuations (p = 0.016) and dyskinesias (p motor fluctuations (p = 0.001), whereas female gender predicted dyskinesias (p = 0.001). Actual levodopa dose at onset of motor fluctuations (p = 0.037) or dyskinesias (p 0.1) independently predicted development of motor complications. Motor fluctuations reversed in 37% and dyskinesias in 49% of patients on oral treatment and remained generally mild in those with persistent complications. No patients received device-aided therapies during the study. More than 50% in the general Parkinson's disease population develop motor complications within 5 years of diagnosis. However, they remain mild in the vast majority and are reversible in a substantial proportion of patients. Copyright © 2015 Elsevier Ltd. All rights reserved.

Food Security and Cardiovascular Disease Risk Among Adults in the United States: Findings From the National Health and Nutrition Examination Survey, 2003?2008

OpenAIRE

Ford, Earl S.

2013-01-01

Introduction Little is known about the relationship between food security status and predicted 10-year cardiovascular disease risk. The objective of this study was to examine the associations between food security status and cardiovascular disease risk factors and predicted 10-year risk in a national sample of US adults. Methods A cross-sectional analysis using data from 10,455 adults aged 20 years or older from the National Health and Nutrition Examination Survey 2003?2008 was conducted. Fou...

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.

Simulating infectious disease risk based on climatic drivers: from numerical weather prediction to long term climate change scenario

Science.gov (United States)

Caminade, C.; Ndione, J. A.; Diallo, M.; MacLeod, D.; Faye, O.; Ba, Y.; Dia, I.; Medlock, J. M.; Leach, S.; McIntyre, K. M.; Baylis, M.; Morse, A. P.

2012-04-01

Climate variability is an important component in determining the incidence of a number of diseases with significant health and socioeconomic impacts. In particular, vector born diseases are the most likely to be affected by climate; directly via the development rates and survival of both the pathogen and the vector, and indirectly through changes in the surrounding environmental conditions. Disease risk models of various complexities using different streams of climate forecasts as inputs have been developed within the QWeCI EU and ENHanCE ERA-NET project frameworks. This work will present two application examples, one for Africa and one for Europe. First, we focus on Rift Valley fever over sub-Saharan Africa, a zoonosis that affects domestic animals and humans by causing an acute fever. We show that the Rift Valley fever outbreak that occurred in late 2010 in the northern Sahelian region of Mauritania might have been anticipated ten days in advance using the GFS numerical weather prediction system. Then, an ensemble of regional climate projections is employed to model the climatic suitability of the Asian tiger mosquito for the future over Europe. The Asian tiger mosquito is an invasive species originally from Asia which is able to transmit West Nile and Chikungunya Fever among others. This species has spread worldwide during the last decades, mainly through the shipments of goods from Asia. Different disease models are employed and inter-compared to achieve such a task. Results show that the climatic conditions over southern England, central Western Europe and the Balkans might become more suitable for the mosquito (including the proviso that the mosquito has already been introduced) to establish itself in the future.

Gender and age effects on risk factor-based prediction of coronary artery calcium in symptomatic patients

DEFF Research Database (Denmark)

Nicoll, R; Wiklund, U; Zhao, Y

2016-01-01

BACKGROUND AND AIMS: The influence of gender and age on risk factor prediction of coronary artery calcification (CAC) in symptomatic patients is unclear. METHODS: From the European Calcific Coronary Artery Disease (EURO-CCAD) cohort, we retrospectively investigated 6309 symptomatic patients, 62......, diabetes and smoking were independently predictive of CAC presence in both genders. In addition to a progressive increase in CAC with age, the most important predictors of CAC presence were dyslipidaemia and diabetes (β = 0.64 and 0.63, respectively) in males and diabetes (β = 1.08) followed by smoking (β...... = 0.68) in females; these same risk factors were also important in predicting increasing CAC scores. There was no difference in the predictive ability of diabetes, hypertension and dyslipidaemia in either gender for CAC presence in patients aged 70, only...

Inflammatory bowel disease and risk of Parkinson's disease in Medicare beneficiaries.

Science.gov (United States)

Camacho-Soto, Alejandra; Gross, Anat; Searles Nielsen, Susan; Dey, Neelendu; Racette, Brad A

2018-05-01

Gastrointestinal (GI) dysfunction precedes the motor symptoms of Parkinson's disease (PD) by several years. PD patients have abnormal aggregation of intestinal α-synuclein, the accumulation of which may be promoted by inflammation. The relationship between intestinal α-synuclein aggregates and central nervous system neuropathology is unknown. Recently, we observed a possible inverse association between inflammatory bowel disease (IBD) and PD as part of a predictive model of PD. Therefore, the objective of this study was to examine the relationship between PD risk and IBD and IBD-associated conditions and treatment. Using a case-control design, we identified 89,790 newly diagnosed PD cases and 118,095 population-based controls >65 years of age using comprehensive Medicare data from 2004-2009 including detailed claims data. We classified IBD using International Classification of Diseases version 9 (ICD-9) diagnosis codes. We used logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to evaluate the association between PD and IBD. Covariates included age, sex, race/ethnicity, smoking, Elixhauser comorbidities, and health care use. PD was inversely associated with IBD overall (OR = 0.85, 95% CI 0.80-0.91) and with both Crohn's disease (OR = 0.83, 95% CI 0.74-0.93) and ulcerative colitis (OR = 0.88, 95% CI 0.82-0.96). Among beneficiaries with ≥2 ICD-9 codes for IBD, there was an inverse dose-response association between number of IBD ICD-9 codes, as a potential proxy for IBD severity, and PD (p-for-trend = 0.006). IBD is associated with a lower risk of developing PD. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records.

Science.gov (United States)

Huang, Zhengxing; Dong, Wei; Duan, Huilong; Liu, Jiquan

2018-05-01

Acute coronary syndrome (ACS), as a common and severe cardiovascular disease, is a leading cause of death and the principal cause of serious long-term disability globally. Clinical risk prediction of ACS is important for early intervention and treatment. Existing ACS risk scoring models are based mainly on a small set of hand-picked risk factors and often dichotomize predictive variables to simplify the score calculation. This study develops a regularized stacked denoising autoencoder (SDAE) model to stratify clinical risks of ACS patients from a large volume of electronic health records (EHR). To capture characteristics of patients at similar risk levels, and preserve the discriminating information across different risk levels, two constraints are added on SDAE to make the reconstructed feature representations contain more risk information of patients, which contribute to a better clinical risk prediction result. We validate our approach on a real clinical dataset consisting of 3464 ACS patient samples. The performance of our approach for predicting ACS risk remains robust and reaches 0.868 and 0.73 in terms of both AUC and accuracy, respectively. The obtained results show that the proposed approach achieves a competitive performance compared to state-of-the-art models in dealing with the clinical risk prediction problem. In addition, our approach can extract informative risk factors of ACS via a reconstructive learning strategy. Some of these extracted risk factors are not only consistent with existing medical domain knowledge, but also contain suggestive hypotheses that could be validated by further investigations in the medical domain.

New measure of insulin sensitivity predicts cardiovascular disease better than HOMA estimated insulin resistance.

Directory of Open Access Journals (Sweden)

Kavita Venkataraman

Full Text Available CONTEXT: Accurate assessment of insulin sensitivity may better identify individuals at increased risk of cardio-metabolic diseases. OBJECTIVES: To examine whether a combination of anthropometric, biochemical and imaging measures can better estimate insulin sensitivity index (ISI and provide improved prediction of cardio-metabolic risk, in comparison to HOMA-IR. DESIGN AND PARTICIPANTS: Healthy male volunteers (96 Chinese, 80 Malay, 77 Indian, 21 to 40 years, body mass index 18-30 kg/m(2. Predicted ISI (ISI-cal was generated using 45 randomly selected Chinese through stepwise multiple linear regression, and validated in the rest using non-parametric correlation (Kendall's tau τ. In an independent longitudinal cohort, ISI-cal and HOMA-IR were compared for prediction of diabetes and cardiovascular disease (CVD, using ROC curves. SETTING: The study was conducted in a university academic medical centre. OUTCOME MEASURES: ISI measured by hyperinsulinemic euglycemic glucose clamp, along with anthropometric measurements, biochemical assessment and imaging; incident diabetes and CVD. RESULTS: A combination of fasting insulin, serum triglycerides and waist-to-hip ratio (WHR provided the best estimate of clamp-derived ISI (adjusted R(2 0.58 versus 0.32 HOMA-IR. In an independent cohort, ROC areas under the curve were 0.77±0.02 ISI-cal versus 0.76±0.02 HOMA-IR (p>0.05 for incident diabetes, and 0.74±0.03 ISI-cal versus 0.61±0.03 HOMA-IR (p<0.001 for incident CVD. ISI-cal also had greater sensitivity than defined metabolic syndrome in predicting CVD, with a four-fold increase in the risk of CVD independent of metabolic syndrome. CONCLUSIONS: Triglycerides and WHR, combined with fasting insulin levels, provide a better estimate of current insulin resistance state and improved identification of individuals with future risk of CVD, compared to HOMA-IR. This may be useful for estimating insulin sensitivity and cardio-metabolic risk in clinical and

Significant interarm blood pressure difference predicts cardiovascular risk in hypertensive patients: CoCoNet study.

Science.gov (United States)

Kim, Su-A; Kim, Jang Young; Park, Jeong Bae

2016-06-01

There has been a rising interest in interarm blood pressure difference (IAD), due to its relationship with peripheral arterial disease and its possible relationship with cardiovascular disease. This study aimed to characterize hypertensive patients with a significant IAD in relation to cardiovascular risk. A total of 3699 patients (mean age, 61 ± 11 years) were prospectively enrolled in the study. Blood pressure (BP) was measured simultaneously in both arms 3 times using an automated cuff-oscillometric device. IAD was defined as the absolute difference in averaged BPs between the left and right arm, and an IAD ≥ 10 mm Hg was considered to be significant. The Framingham risk score was used to calculate the 10-year cardiovascular risk. The mean systolic IAD (sIAD) was 4.3 ± 4.1 mm Hg, and 285 (7.7%) patients showed significant sIAD. Patients with significant sIAD showed larger body mass index (P < 0.001), greater systolic BP (P = 0.050), more coronary artery disease (relative risk = 1.356, P = 0.034), and more cerebrovascular disease (relative risk = 1.521, P = 0.072). The mean 10-year cardiovascular risk was 9.3 ± 7.7%. By multiple regression, sIAD was significantly but weakly correlated with the 10-year cardiovascular risk (β = 0.135, P = 0.008). Patients with significant sIAD showed a higher prevalence of coronary artery disease, as well as an increase in 10-year cardiovascular risk. Therefore, accurate measurements of sIAD may serve as a simple and cost-effective tool for predicting cardiovascular risk in clinical settings.

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

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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.

Diet and risk of inflammatory bowel disease

DEFF Research Database (Denmark)

Andersen, Vibeke; Olsen, Anja; Carbonnel, Franck

2012-01-01

Background: A better understanding of the environmental factors leading to inflammatory bowel disease should help to prevent occurrence of the disease and its relapses. Aim: To review current knowledge on dietary risk factors for inflammatory bowel disease. Methods: The PubMed, Medline and Cochrane...... Library were searched for studies on diet and risk of inflammatory bowel disease. Results: Established non-diet risk factors include family predisposition, smoking, appendectomy, and antibiotics. Retrospective case–control studies are encumbered with methodological problems. Prospective studies...... on European cohorts, mainly including middle-aged adults, suggest that a diet high in protein from meat and fish is associated with a higher risk of inflammatory bowel disease. Intake of the n-6 polyunsaturated fatty acid linoleic acid may confer risk of ulcerative colitis, whereas n-3 polyunsaturated fatty...

New measure of insulin sensitivity predicts cardiovascular disease better than HOMA estimated insulin resistance.

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Venkataraman, Kavita; Khoo, Chin Meng; Leow, Melvin K S; Khoo, Eric Y H; Isaac, Anburaj V; Zagorodnov, Vitali; Sadananthan, Suresh A; Velan, Sendhil S; Chong, Yap Seng; Gluckman, Peter; Lee, Jeannette; Salim, Agus; Tai, E Shyong; Lee, Yung Seng

2013-01-01

Accurate assessment of insulin sensitivity may better identify individuals at increased risk of cardio-metabolic diseases. To examine whether a combination of anthropometric, biochemical and imaging measures can better estimate insulin sensitivity index (ISI) and provide improved prediction of cardio-metabolic risk, in comparison to HOMA-IR. Healthy male volunteers (96 Chinese, 80 Malay, 77 Indian), 21 to 40 years, body mass index 18-30 kg/m(2). Predicted ISI (ISI-cal) was generated using 45 randomly selected Chinese through stepwise multiple linear regression, and validated in the rest using non-parametric correlation (Kendall's tau τ). In an independent longitudinal cohort, ISI-cal and HOMA-IR were compared for prediction of diabetes and cardiovascular disease (CVD), using ROC curves. The study was conducted in a university academic medical centre. ISI measured by hyperinsulinemic euglycemic glucose clamp, along with anthropometric measurements, biochemical assessment and imaging; incident diabetes and CVD. A combination of fasting insulin, serum triglycerides and waist-to-hip ratio (WHR) provided the best estimate of clamp-derived ISI (adjusted R(2) 0.58 versus 0.32 HOMA-IR). In an independent cohort, ROC areas under the curve were 0.77±0.02 ISI-cal versus 0.76±0.02 HOMA-IR (p>0.05) for incident diabetes, and 0.74±0.03 ISI-cal versus 0.61±0.03 HOMA-IR (pHOMA-IR. This may be useful for estimating insulin sensitivity and cardio-metabolic risk in clinical and epidemiological settings.

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

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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.

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.

Body mass index predicts risk for complications from transtemporal cerebellopontine angle surgery.

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Mantravadi, Avinash V; Leonetti, John P; Burgette, Ryan; Pontikis, George; Marzo, Sam J; Anderson, Douglas

2013-03-01

To determine the relationship between body mass index (BMI) and risk for specific complications from transtemporal cerebellopontine angle (CPA) surgery for nonmalignant disease. Case series with chart review. Tertiary-care academic hospital. Retrospective review of 134 consecutive patients undergoing transtemporal cerebellopontine angle surgery for nonmalignant disease from 2009 to 2011. Data were collected regarding demographics, body mass index, intraoperative details, hospital stay, and complications including cerebrospinal fluid leak, wound complications, and brachial plexopathy. One hundred thirty-four patients were analyzed with a mean preoperative body mass index of 28.58. Statistical analysis demonstrated a significant difference in body mass index between patients with a postoperative cerebrospinal fluid leak and those without (P = .04), as well as a similar significant difference between those experiencing postoperative brachial plexopathy and those with no such complication (P = .03). Logistical regression analysis confirmed that body mass index is significant in predicting both postoperative cerebrospinal fluid leak (P = .004; odds ratio, 1.10) and brachial plexopathy (P = .04; odds ratio, 1.07). Elevated body mass index was not significant in predicting wound complications or increased hospital stay beyond postoperative day 3. Risk of cerebrospinal fluid leak and brachial plexopathy is increased in patients with elevated body mass index undergoing surgery of the cerebellopontine angle. Consideration should be given to preoperative optimization via dietary and lifestyle modifications as well as intraoperative somatosensory evoked potential monitoring of the brachial plexus to decrease these risks.

Accurate Prediction of Coronary Artery Disease Using Bioinformatics Algorithms

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Hajar Shafiee

2016-06-01

Full Text Available Background and Objectives: Cardiovascular disease is one of the main causes of death in developed and Third World countries. According to the statement of the World Health Organization, it is predicted that death due to heart disease will rise to 23 million by 2030. According to the latest statistics reported by Iran’s Minister of health, 3.39% of all deaths are attributed to cardiovascular diseases and 19.5% are related to myocardial infarction. The aim of this study was to predict coronary artery disease using data mining algorithms. Methods: In this study, various bioinformatics algorithms, such as decision trees, neural networks, support vector machines, clustering, etc., were used to predict coronary heart disease. The data used in this study was taken from several valid databases (including 14 data. Results: In this research, data mining techniques can be effectively used to diagnose different diseases, including coronary artery disease. Also, for the first time, a prediction system based on support vector machine with the best possible accuracy was introduced. Conclusion: The results showed that among the features, thallium scan variable is the most important feature in the diagnosis of heart disease. Designation of machine prediction models, such as support vector machine learning algorithm can differentiate between sick and healthy individuals with 100% accuracy.

What predicts depression in cardiac patients: sociodemographic factors, disease severity or theoretical vulnerabilities?

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Doyle, F; McGee, H M; Conroy, R M; Delaney, M

2011-05-01

Depression is associated with increased cardiovascular risk in acute coronary syndrome (ACS) patients, but some argue that elevated depression is actually a marker of cardiovascular disease severity. Therefore, disease indices should better predict depression than established theoretical causes of depression (interpersonal life events, reinforcing events, cognitive distortions, type D personality). However, little theory-based research has been conducted in this area. In a cross-sectional design, ACS patients (n = 336) completed questionnaires assessing depression and psychosocial vulnerabilities. Nested logistic regression assessed the relative contribution of demographic or vulnerability factors, or disease indices or vulnerabilities to depression. In multivariate analysis, all vulnerabilities were independent significant predictors of depression (scoring above threshold on any scale, 48%). Demographic variables accounted for vulnerabilities accounting for significantly more (pseudo R² = 0.16, χ²(change) = 150.9, df = 4, p vulnerabilities increased the overall variance explained to 22% (pseudo R² = 0.22, χ² = 58.6, df = 4, p vulnerabilities predicted depression status better than did either demographic or disease indices. The presence of these proximal causes of depression suggests that depression in ACS patients is not simply a result of cardiovascular disease severity.

The Myocardial Perfusion Scintigraphy in Predicting Risk for Coronary Artery Disease in Patients with Anxiety and Depression Symptoms

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Billur Çalışkan

2016-06-01

Full Text Available INTRODUCTION: An association between psychological factors and cardiovascular disease, has long been suspected. However it is not clear whether chest pain is caused by emotional distress or whether coronary spasms are caused by the onset of coronary artery disease (CAD. We aimed to predict the risk for CAD in patients referred to myocardial perfusion imaging (MPI with chest pain using depression, stress, and anxiety symptoms. METHODS: The emotional status of all patients was evaluated using the Hospital Anxiety and Depression Scale (HADS-A and HADS-D, the State and Trait Anxiety Inventory (STAI-1 and STAI-2, the Perceived Stress Scale (PSS, and the Anxiety Sensitivity Index-3 (ASI. Myocardial perfusion was measured using a 17-segment model and 5-point scoring system (0: normal perfusion; 4: no perfusion. RESULTS: MPI revealed reversible perfusion defects in 24 of 141 patients and no perfusion defects in 117 patients. The STAI-2 and HADS-A and HADS-D scores were significantly higher in patients with myocardial ischemia than in those without (STAI-2: 50.8 ± 7.5 vs. 46.3 ± 7.1, respectively; p = 0.008; HADS-A: 9.5 ± 3.9 vs. 7.8 ± 3.4, respectively; p = 0.033; HADS-D: 8.7 ± 3.0 vs. 7.3 ± 3.0, respectively; p = 0.05. Unadjusted correlation analysis showed that there was statistically significant correlation between reversible perfusion defects and anxiety scores (r=0.186, p= 0.029. DISCUSSION AND CONCLUSION: The patients with symptoms of depression and high-trait anxiety may be at higher risk of myocardial ischemia than patients without such symptoms. Thus, the emotional status of patients should be taken into consideration during clinical evaluation for CAD.

Obesity and Cardiovascular Disease: a Risk Factor or a Risk Marker?

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Mandviwala, Taher; Khalid, Umair; Deswal, Anita

2016-05-01

In the USA, 69 % of adults are either overweight or obese and 35 % are obese. Obesity is associated with an increased incidence of various cardiovascular disorders. Obesity is a risk marker for cardiovascular disease, in that it is associated with a much higher prevalence of comorbidities such as diabetes, hypertension, and metabolic syndrome, which then increase the risk for cardiovascular disease. However, in addition, obesity may also be an independent risk factor for the development of cardiovascular disease. Furthermore, although obesity has been shown to be an independent risk factor for several cardiovascular diseases, it is often associated with improved survival once the diagnosis of the cardiovascular disease has been made, leading to the term "obesity paradox." Several pathways linking obesity and cardiovascular disease have been described. In this review, we attempt to summarize the complex relationship between obesity and cardiovascular disorders, in particular coronary atherosclerosis, heart failure, and atrial fibrillation.

A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.

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Blair, David R; Lyttle, Christopher S; Mortensen, Jonathan M; Bearden, Charles F; Jensen, Anders Boeck; Khiabanian, Hossein; Melamed, Rachel; Rabadan, Raul; Bernstam, Elmer V; Brunak, Søren; Jensen, Lars Juhl; Nicolae, Dan; Shah, Nigam H; Grossman, Robert L; Cox, Nancy J; White, Kevin P; Rzhetsky, Andrey

2013-09-26

Although countless highly penetrant variants have been associated with Mendelian disorders, the genetic etiologies underlying complex diseases remain largely unresolved. By mining the medical records of over 110 million patients, we examine the extent to which Mendelian variation contributes to complex disease risk. We detect thousands of associations between Mendelian and complex diseases, revealing a nondegenerate, phenotypic code that links each complex disorder to a unique collection of Mendelian loci. Using genome-wide association results, we demonstrate that common variants associated with complex diseases are enriched in the genes indicated by this "Mendelian code." Finally, we detect hundreds of comorbidity associations among Mendelian disorders, and we use probabilistic genetic modeling to demonstrate that Mendelian variants likely contribute nonadditively to the risk for a subset of complex diseases. Overall, this study illustrates a complementary approach for mapping complex disease loci and provides unique predictions concerning the etiologies of specific diseases. Copyright © 2013 Elsevier Inc. All rights reserved.

Forecasting the future risk of Barmah Forest virus disease under climate change scenarios in Queensland, Australia.

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Suchithra Naish

Full Text Available BACKGROUND: Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV disease under climate change scenarios in Queensland, Australia. METHODS/PRINCIPAL FINDINGS: We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall, socio-economic and tidal conditions for current period 2000-2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. CONCLUSIONS/SIGNIFICANCE: We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland.

Blunted cyclic variation of heart rate predicts mortality risk in post-myocardial infarction, end-stage renal disease, and chronic heart failure patients.

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Hayano, Junichiro; Yasuma, Fumihiko; Watanabe, Eiichi; Carney, Robert M; Stein, Phyllis K; Blumenthal, James A; Arsenos, Petros; Gatzoulis, Konstantinos A; Takahashi, Hiroshi; Ishii, Hideki; Kiyono, Ken; Yamamoto, Yoshiharu; Yoshida, Yutaka; Yuda, Emi; Kodama, Itsuo

2017-08-01

Cyclic variation of heart rate (CVHR) associated with sleep-disordered breathing is thought to reflect cardiac autonomic responses to apnoeic/hypoxic stress. We examined whether blunted CVHR observed in ambulatory ECG could predict the mortality risk. CVHR in night-time Holter ECG was detected by an automated algorithm, and the prognostic relationships of the frequency (FCV) and amplitude (ACV) of CVHR were examined in 717 patients after myocardial infarction (post-MI 1, 6% mortality, median follow-up 25 months). The predictive power was prospectively validated in three independent cohorts: a second group of 220 post-MI patients (post-MI 2, 25.5% mortality, follow-up 45 months); 299 patients with end-stage renal disease on chronic haemodialysis (ESRD, 28.1% mortality, follow-up 85 months); and 100 patients with chronic heart failure (CHF, 35% mortality, follow-up 38 months). Although CVHR was observed in ≥96% of the patients in all cohorts, FCV did not predict mortality in any cohort. In contrast, decreased ACV was a powerful predictor of mortality in the post-MI 1 cohort (hazard ratio [95% CI] per 1 ln [ms] decrement, 2.9 [2.2-3.7], P < 0.001). This prognostic relationship was validated in the post-MI 2 (1.8 [1.4-2.2], P < 0.001), ESRD (1.5 [1.3-1.8], P < 0.001), and CHF (1.4 [1.1-1.8], P = 0.02) cohorts. The prognostic value of ACV was independent of age, gender, diabetes, β-blocker therapy, left ventricular ejection fraction, sleep-time mean R-R interval, and FCV. Blunted CVHR detected by decreased ACV in a night-time Holter ECG predicts increased mortality risk in post-MI, ESRD, and CHF patients. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

Blunted cyclic variation of heart rate predicts mortality risk in post-myocardial infarction, end-stage renal disease, and chronic heart failure patients

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Hayano, Junichiro; Yasuma, Fumihiko; Watanabe, Eiichi; Carney, Robert M.; Stein, Phyllis K.; Blumenthal, James A.; Arsenos, Petros; Gatzoulis, Konstantinos A.; Takahashi, Hiroshi; Ishii, Hideki; Kiyono, Ken; Yamamoto, Yoshiharu; Yoshida, Yutaka; Yuda, Emi; Kodama, Itsuo

2017-01-01

Abstract Aims Cyclic variation of heart rate (CVHR) associated with sleep-disordered breathing is thought to reflect cardiac autonomic responses to apnoeic/hypoxic stress. We examined whether blunted CVHR observed in ambulatory ECG could predict the mortality risk. Methods and results CVHR in night-time Holter ECG was detected by an automated algorithm, and the prognostic relationships of the frequency (FCV) and amplitude (ACV) of CVHR were examined in 717 patients after myocardial infarction (post-MI 1, 6% mortality, median follow-up 25 months). The predictive power was prospectively validated in three independent cohorts: a second group of 220 post-MI patients (post-MI 2, 25.5% mortality, follow-up 45 months); 299 patients with end-stage renal disease on chronic haemodialysis (ESRD, 28.1% mortality, follow-up 85 months); and 100 patients with chronic heart failure (CHF, 35% mortality, follow-up 38 months). Although CVHR was observed in ≥96% of the patients in all cohorts, FCV did not predict mortality in any cohort. In contrast, decreased ACV was a powerful predictor of mortality in the post-MI 1 cohort (hazard ratio [95% CI] per 1 ln [ms] decrement, 2.9 [2.2–3.7], P < 0.001). This prognostic relationship was validated in the post-MI 2 (1.8 [1.4–2.2], P < 0.001), ESRD (1.5 [1.3–1.8], P < 0.001), and CHF (1.4 [1.1–1.8], P = 0.02) cohorts. The prognostic value of ACV was independent of age, gender, diabetes, β-blocker therapy, left ventricular ejection fraction, sleep-time mean R-R interval, and FCV. Conclusion Blunted CVHR detected by decreased ACV in a night-time Holter ECG predicts increased mortality risk in post-MI, ESRD, and CHF patients. PMID:27789562

Coronary risk stratification of patients undergoing surgery for valvular heart disease.

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Hasselbalch, Rasmus Bo; Engstrøm, Thomas; Pries-Heje, Mia; Heitmann, Merete; Pedersen, Frants; Schou, Morten; Mickley, Hans; Elming, Hanne; Steffensen, Rolf; Køber, Lars; Iversen, Kasper

2017-01-15

Multislice computed tomography (MSCT) is a non-invasive, less expensive, low-radiation alternative to coronary angiography (CAG) prior to valvular heart surgery. MSCT has a high negative predictive value for coronary artery disease (CAD) but previous studies of patients with valvular disease have shown that MSCT, as the primary evaluation technique, lead to re-evaluation with CAG in about a third of cases and it is therefore not recommended. If a subgroup of patients with low- to intermediate risk of CAD could be identified and examined with MSCT, it could be cost-effective, reduce radiation and the risk of complications associated with CAG. The study cohort was derived from a national registry of patients undergoing CAG prior to valvular heart surgery. Using logistic regression, we identified significant risk factors for CAD and developed a risk score (CT-valve score). The score was validated on a similar cohort of patients from another registry. The study cohort consisted of 2221 patients, 521 (23.5%) had CAD. The validation cohort consisted of 2575 patients, 771 (29.9%) had CAD. The identified risk factors were male sex, age, smoking, hyperlipidemia, hypertension, aortic valve disease, extracardiac arteriopathy, ejection fraction <30% and diabetes mellitus. CT-valve score could identify a third of the population with a risk about 10%. A score based on risk factors of CAD can identify patients that might benefit from using MSCT as a gatekeeper to CAG prior to heart valve surgery. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Models to Predict the Burden of Cardiovascular Disease Risk in a Rural Mountainous Region of Vietnam

NARCIS (Netherlands)

Nguyen, Thi Phuong Lan; Schuiling-Veninga, Nynke; Nguyen, Thi Bach Yen; Hang, Vu Thi Thu; Wright, E. Pamela; Postma, M.J.

2014-01-01

Objective: To compare and identify the most appropriate model to predict cardiovascular disease (CVD) in a rural area in Northern Vietnam, using data on hypertension from the communities. Methods: A cross-sectional survey was conducted including all residents in selected communities, aged 34 to 65

Identifying fallers with Parkinson's disease using home-based tests: who is at risk?

NARCIS (Netherlands)

Lim-de Vries, L.I.I.K.; van Wegen, E.E.; Jones, D.; Rochester, L.; Nieuwboer, A.; Willems, A.M.; Baker, K.; Hetherington, V.; Kwakkel, G.

2008-01-01

The objective of this work is to determine risk factors for falling in patients with Parkinson's disease (PD) using home-based assessments and develop a prediction model. Data on falls, balance, gait-related activities, and nonmotor symptoms were obtained from 153 PD patients (Hoehn-Yahr 2-4) in

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 ...

Prognostic Value of High-Sensitivity Cardiac Troponin T Compared with Risk Scores in Stable Cardiovascular Disease.

Science.gov (United States)

Biener, Moritz; Giannitsis, Evangelos; Kuhner, Manuel; Zelniker, Thomas; Mueller-Hennessen, Matthias; Vafaie, Mehrshad; Trenk, Dietmar; Neumann, Franz-Josef; Hochholzer, Willibald; Katus, Hugo A

2017-05-01

Risk stratification of patients with cardiovascular disease remains challenging despite consideration of risk scores. We aimed to evaluate the prognostic performance of high-sensitivity cardiac troponin T in a low-risk outpatient population presenting for nonsecondary and secondary prevention. All-cause mortality, a composite of all-cause mortality, acute myocardial infarction, and stroke (end point 2), and a composite of all-cause mortality, acute myocardial infarction, stroke and rehospitalization for acute coronary syndrome, and decompensated heart failure (end point 3) were defined. The prognostic performance of high-sensitivity cardiac troponin T on index visit was compared with the PROCAM score and 3 FRAMINGHAM subscores. In 693 patients with a median follow-up of 796 days, we observed 16 deaths, 32 patients with end point 2, and 83 patients with end point 3. All risk scores performed better in the prediction of all-cause mortality in nonsecondary prevention (area under the curve [AUC]: PROCAM: 0.922 vs 0.523, P = .001, consistent for all other scores). In secondary prevention, high-sensitivity cardiac troponin T outperformed all risk scores in the prediction of all-cause mortality (ΔAUC: PROCAM: 0.319, P risk scores. Our findings on the prediction of all-cause mortality compared with the FRAMINGHAM-Hard Coronary Heart Disease score were confirmed in an independent validation cohort on 2046 patients. High-sensitivity troponin T provides excellent risk stratification regarding all-cause mortality and all-cause mortality, acute myocardial infarction, and stroke in a secondary prevention cohort in whom risk scores perform poorly. Copyright © 2016 Elsevier Inc. All rights reserved.

Efficacy of NETDC (New England Trophoblastic Disease Center prognostic index score to predict gestational trophoblastic tumor from hydatidiform mole

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Khrismawan Khrismawan

2004-03-01

Full Text Available A prospective longitudinal analytic study assessing the efficacy of NETDC (New England Trophoblastic Disease Center prognostic index score in predicting malignancy after hydatidiform mole had been performed. Of the parameter evaluated; age of patients, type of hydatidiform mole, uterine enlargement, serum hCG level, lutein cyst, and presence of complicating factors were significant risk factors for malignancy after hydatidiform mole were evacuated (p<0.032. The study were done on 50 women diagnosed with hydatidiform mole with 1 year observation (January 2001-December 2002 at the Department of Obstetrics and Gynecology, Mohammad Hoesin Hospital, Palembang. The results showed that the NETDC prognostic index score predicted malignancy in 50% of high risk group and 10% in low risk group (p<0.05. This showed a higher number than that found by the WHO (19%-30%. The risk for incidence of  malignancy after hydatidiform mole in the high risk group is 9.0 times higher compared to that of the low risk group (CI: 1.769-45.786. (Med J Indones 2004; 13: 40-6 Keywords: New England Trophoblastic Disease Center (NETDC, gestational trophoblastic tumor, hydatidiform mole, high and low risk

Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

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Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

2014-05-01

Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

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

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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.

Office-Based Screening for Dementia in Parkinson Disease: The Montreal Parkinson Risk of Dementia Scale in 4 Longitudinal Cohorts.

Science.gov (United States)

Dawson, Benjamin K; Fereshtehnejad, Seyed-Mohammad; Anang, Julius B M; Nomura, Takashi; Rios-Romenets, Silvia; Nakashima, Kenji; Gagnon, Jean-François; Postuma, Ronald B

2018-06-01

Parkinson disease dementia dramatically increases mortality rates, patient expenditures, hospitalization risk, and caregiver burden. Currently, predicting Parkinson disease dementia risk is difficult, particularly in an office-based setting, without extensive biomarker testing. To appraise the predictive validity of the Montreal Parkinson Risk of Dementia Scale, an office-based screening tool consisting of 8 items that are simply assessed. This multicenter study (Montreal, Canada; Tottori, Japan; and Parkinson Progression Markers Initiative sites) used 4 diverse Parkinson disease cohorts with a prospective 4.4-year follow-up. A total of 717 patients with Parkinson disease were recruited between May 2005 and June 2016. Of these, 607 were dementia-free at baseline and followed-up for 1 year or more and so were included. The association of individual baseline scale variables with eventual dementia risk was calculated. Participants were then randomly split into cohorts to investigate weighting and determine the scale's optimal cutoff point. Receiver operating characteristic curves were calculated and correlations with selected biomarkers were investigated. Dementia, as defined by Movement Disorder Society level I criteria. Of the 607 patients (mean [SD] age, 63.4 [10.1]; 376 men [62%]), 70 (11.5%) converted to dementia. All 8 items of the Montreal Parkinson Risk of Dementia Scale independently predicted dementia development at the 5% significance level. The annual conversion rate to dementia in the high-risk group (score, >5) was 14.9% compared with 5.8% in the intermediate group (score, 4-5) and 0.6% in the low-risk group (score, 0-3). The weighting procedure conferred no significant advantage. Overall predictive validity by the area under the receiver operating characteristic curve was 0.877 (95% CI, 0.829-0.924) across all cohorts. A cutoff of 4 or greater yielded a sensitivity of 77.1% (95% CI, 65.6-86.3) and a specificity of 87.2% (95% CI, 84.1-89.9), with a

Predicting clinically unrecognized coronary artery disease: use of two- dimensional echocardiography

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Nagueh Sherif F

2009-03-01

Full Text Available Abstract Background 2-D Echo is often performed in patients without history of coronary artery disease (CAD. We sought to determine echo features predictive of CAD. Methods 2-D Echo of 328 patients without known CAD performed within one year prior to stress myocardial SPECT and angiography were reviewed. Echo features examined were left ventricular and atrial enlargement, LV hypertrophy, wall motion abnormality (WMA, LV ejection fraction (EF 15% LV perfusion defect or multivessel distribution. Severe coronary artery stenosis (CAS was defined as left main, 3 VD or 2VD involving proximal LAD. Results The mean age was 62 ± 13 years, 59% men, 29% diabetic (DM and 148 (45% had > 2 risk factors. Pharmacologic stress was performed in 109 patients (33%. MPA was present in 200 pts (60% of which, 137 were high risk. CAS was present in 166 pts (51%, 75 were severe. Of 87 patients with WMA, 83% had MPA and 78% had CAS. Multivariate analysis identified age >65, male, inability to exercise, DM, WMA, MAC and AS as independent predictors of MPA and CAS. Independent predictors of high risk MPA and severe CAS were age, DM, inability to exercise and WMA. 2-D echo findings offered incremental value over clinical information in predicting CAD by angiography. (Chi square: 360 vs. 320 p = 0.02. Conclusion 2-D Echo was valuable in predicting presence of physiological and anatomical CAD in addition to clinical information.

Cardiovascular disease risk factors and cognitive impairment.

Science.gov (United States)

Nash, David T; Fillit, Howard

2006-04-15

The role of cardiovascular disease risk factors in the occurrence and progression of cognitive impairment has been the subject of a significant number of publications but has not achieved widespread recognition among many physicians and educated laymen. It is apparent that the active treatment of certain of these cardiovascular disease risk factors is accompanied by a reduced risk for cognitive impairment. Patients with hypertension who are treated experience fewer cardiovascular disease events as well as less cognitive impairment than similar untreated patients. Patients who exercise may present with less cognitive impairment, and obesity may increase the risk for cognitive impairment. Lipid abnormalities and genetic markers are associated with an increased risk for cardiovascular disease and cognitive impairment. Autopsy studies have demonstrated a correlation between elevated levels of cholesterol and amyloid deposition in the brain. Research has demonstrated a relation between atherosclerotic obstruction lesions in the circle of Willis and dementia. Diabetes mellitus is associated with an increased risk for cardiovascular disease and cognitive impairment. A number of nonpharmacologic factors have a role in reducing the risk for cognitive impairment. Antioxidants, fatty acids, and micronutrients may have a role, and diets rich in fruits and vegetables and other dietary approaches may improve the outlook for patients considered at risk for cognitive impairment.

LDAP: a web server for lncRNA-disease association prediction.

Science.gov (United States)

Lan, Wei; Li, Min; Zhao, Kaijie; Liu, Jin; Wu, Fang-Xiang; Pan, Yi; Wang, Jianxin

2017-02-01

Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis. Some computational methods have been developed to infer lncRNA-disease associations. However, most of these methods infer lncRNA-disease associations only based on single data resource. In this paper, we propose a new computational method to predict lncRNA-disease associations by integrating multiple biological data resources. Then, we implement this method as a web server for lncRNA-disease association prediction (LDAP). The input of the LDAP server is the lncRNA sequence. The LDAP predicts potential lncRNA-disease associations by using a bagging SVM classifier based on lncRNA similarity and disease similarity. The web server is available at http://bioinformatics.csu.edu.cn/ldap jxwang@mail.csu.edu.cn. Supplementary data are available at Bioinformatics online.

Vitamin D, cardiovascular disease and risk factors

DEFF Research Database (Denmark)

Skaaby, Tea; Thuesen, Betina H.; Linneberg, Allan

2017-01-01

of vitamin D effects from a cardiovascular health perspective. It focuses on vitamin D in relation to cardiovascular disease, i.e. ischemic heart disease, and stroke; the traditional cardiovascular risk factors hypertension, abnormal blood lipids, obesity; and the emerging risk factors hyperparathyroidism......, microalbuminuria, chronic obstructive pulmonary diseases, and non-alcoholic fatty liver disease. Meta-analyses of observational studies have largely found vitamin D levels to be inversely associated with cardiovascular risk and disease. However, Mendelian randomization studies and randomized, controlled trials...... (RCTs) have not been able to consistently replicate the observational findings. Several RCTs are ongoing, and the results from these are needed to clarify whether vitamin D deficiency is a causal and reversible factor to prevent cardiovascular disease....

Serum Vascular Adhesion Protein-1 Predicts End-Stage Renal Disease in Patients with Type 2 Diabetes.

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Hung-Yuan Li

Full Text Available Diabetes is the leading cause of end-stage renal disease (ESRD worldwide. Vascular adhesion protein-1 (VAP-1 participates in inflammation and catalyzes the deamination of primary amines into aldehydes, hydrogen peroxide, and ammonia, both of which are involved in the pathogenesis of diabetic complications. We have shown that serum VAP-1 is higher in patients with diabetes and in patients with chronic kidney disease (CKD, and can predict cardiovascular mortality in subjects with diabetes. In this study, we investigated if serum VAP-1 can predict ESRD in diabetic subjects.In this prospective cohort study, a total of 604 type 2 diabetic subjects were enrolled between 1996 to 2003 at National Taiwan University Hospital, Taiwan, and were followed for a median of 12.36 years. The development of ESRD was ascertained by linking our database with the nationally comprehensive Taiwan Society Nephrology registry. Serum VAP-1 concentrations at enrollment were measured by time-resolved immunofluorometric assay.Subjects with serum VAP-1 in the highest tertile had the highest incidence of ESRD (p<0.001. Every 1-SD increase in serum VAP-1 was associated with a hazard ratio of 1.55 (95%CI 1.12-2.14, p<0.01 for the risk of ESRD, adjusted for smoking, history of cardiovascular disease, body mass index, hypertension, HbA1c, duration of diabetes, total cholesterol, use of statins, ankle-brachial index, estimated GFR, and proteinuria. We developed a risk score comprising serum VAP-1, HbA1c, estimated GFR, and proteinuria, which could predict ESRD with good performance (area under the ROC curve = 0.9406, 95%CI 0.8871-0.9941, sensitivity = 77.3%, and specificity = 92.8%. We also developed an algorithm based on the stage of CKD and a risk score including serum VAP-1, which can stratify these subjects into 3 categories with an ESRD risk of 0.101%/year, 0.131%/year, and 2.427%/year, respectively.In conclusion, serum VAP-1 can predict ESRD and is a useful biomarker to

Avian Cholera emergence in Arctic-nesting northern Common Eiders: using community-based, participatory surveillance to delineate disease outbreak patterns and predict transmission risk

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Samuel A. Iverson

2016-12-01

Full Text Available Emerging infectious diseases are a growing concern in wildlife conservation. Documenting outbreak patterns and determining the ecological drivers of transmission risk are fundamental to predicting disease spread and assessing potential impacts on population viability. However, evaluating disease in wildlife populations requires expansive surveillance networks that often do not exist in remote and developing areas. Here, we describe the results of a community-based research initiative conducted in collaboration with indigenous harvesters, the Inuit, in response to a new series of Avian Cholera outbreaks affecting Common Eiders (Somateria mollissima and other comingling species in the Canadian Arctic. Avian Cholera is a virulent disease of birds caused by the bacterium Pasteurella multocida. Common Eiders are a valuable subsistence resource for Inuit, who hunt the birds for meat and visit breeding colonies during the summer to collect eggs and feather down for use in clothing and blankets. We compiled the observations of harvesters about the growing epidemic and with their assistance undertook field investigation of 131 colonies distributed over >1200 km of coastline in the affected region. Thirteen locations were identified where Avian Cholera outbreaks have occurred since 2004. Mortality rates ranged from 1% to 43% of the local breeding population at these locations. Using a species-habitat model (Maxent, we determined that the distribution of outbreak events has not been random within the study area and that colony size, vegetation cover, and a measure of host crowding in shared wetlands were significantly correlated to outbreak risk. In addition, outbreak locations have been spatially structured with respect to hypothesized introduction foci and clustered along the migration corridor linking Arctic breeding areas with wintering areas in Atlantic Canada. At present, Avian Cholera remains a localized threat to Common Eider populations in the

Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

KAUST Repository

Hou, Siqing

2018-05-21

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.

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...

A utility/cost analysis of breast cancer risk prediction algorithms

Science.gov (United States)

Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

2016-03-01

Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

Lipid measures and cardiovascular disease prediction

NARCIS (Netherlands)

van Wijk, D.F.; Stroes, E.S.G.; Kastelein, J.J.P.

2009-01-01

Traditional lipid measures are the cornerstone of risk assessment and treatment goals in cardiovascular prevention. Whereas the association between total, LDL-, HDL-cholesterol and cardiovascular disease risk has been generally acknowledged, the rather poor capacity to distinguish between patients

[Impact of plasma pro-B-type natriuretic peptide amino-terminal and galectin-3 levels on the predictive capacity of the LIPID Clinical Risk Scale in stable coronary disease].

Science.gov (United States)

Higueras, Javier; Martín-Ventura, José Luis; Blanco-Colio, Luis; Cristóbal, Carmen; Tarín, Nieves; Huelmos, Ana; Alonso, Joaquín; Pello, Ana; Aceña, Álvaro; Carda, Rocío; Lorenzo, Óscar; Mahíllo-Fernández, Ignacio; Asensio, Dolores; Almeida, Pedro; Rodríguez-Artalejo, Fernando; Farré, Jerónimo; López Bescós, Lorenzo; Egido, Jesús; Tuñón, José

2015-01-01

At present, there is no tool validated by scientific societies for risk stratification of patients with stable coronary artery disease (SCAD). It has been shown that plasma levels of monocyte chemoattractant protein-1 (MCP-1), galectin-3 and pro-B-type natriuretic peptide amino-terminal (NT-proBNP) have prognostic value in this population. To analyze the prognostic value of a clinical risk scale published in Long-term Intervention with Pravastatin in Ischemic Disease (LIPID) study and determining its predictive capacity when combined with plasma levels of MCP-1, galectin-3 and NT-proBNP in patients with SCAD. A total of 706 patients with SCAD and a history of acute coronary syndrome (ACS) were analyzed over a follow up period of 2.2 ± 0.99 years. The primary endpoint was the occurrence of an ischemic event (any SCA, stroke or transient ischemic attack), heart failure, or death. A clinical risk scale derived from the LIPID study significantly predicted the development of the primary endpoint, with an area under the ROC curve (Receiver Operating Characteristic) of 0.642 (0.579 to 0.705); Pvalue improved with an area under the curve of 0.744 (0.684 to 0.805); P<0.001 (P=0.022 for comparison). A score greater than 21.5 had a sensitivity of 74% and a specificity of 61% for the development of the primary endpoint (P<0.001, log -rank test). Plasma levels of MCP-1, galectin -3 and NT-proBNP improve the ability of the LIPID clinical scale to predict the prognosis of patients with SCAD. Copyright © 2014 Sociedad Española de Arteriosclerosis. Published by Elsevier España. All rights reserved.

Identifying Risk Factors for the Prediction of Hospital Readmission among Older Persons with Cardiovascular Disease.

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Middleton, Renee Annette

Older persons (55 years and older) with cardiovascular disease are at increased risk for hospital readmission when compared to other subgroups of our population. This issue presents an economic problem, a concern for the quality and type of care provided, and an urgent need to implement innovative strategies designed to reduce the rising cost of…

Autoimmune disease and risk for Parkinson disease A population-based case-control study

DEFF Research Database (Denmark)

Rugbjerg, K.; Friis, S.; Ritz, B.

2009-01-01

Objective: Inflammatory mediators are increased in autoimmune diseases and may activate microglia and might cause an inflammatory state and degeneration of dopaminergic neurons in the brain. Thus, we evaluated whether having an autoimmune disease increases the risk for developing Parkinson disease...... do not support the hypothesis that autoimmune diseases increase the risk for Parkinson disease. The decreased risk observed among patients with rheumatoid arthritis might be explained by underdiagnosis of movement disorders such as Parkinson disease in this patient group or by a protective effect...

Predictive modeling of coral disease distribution within a reef system.

Directory of Open Access Journals (Sweden)

Gareth J Williams

2010-02-01

Full Text Available Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1 coral diseases show distinct associations with multiple environmental factors, 2 incorporating interactions (synergistic collinearities among environmental variables is important when predicting coral disease spatial patterns, and 3 modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA, Porites tissue loss (PorTL, Porites trematodiasis (PorTrem, and Montipora white syndrome (MWS, and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response, led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to

Gait Rather Than Cognition Predicts Decline in Specific Cognitive Domains in Early Parkinson's Disease.

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Morris, Rosie; Lord, Sue; Lawson, Rachael A; Coleman, Shirley; Galna, Brook; Duncan, Gordon W; Khoo, Tien K; Yarnall, Alison J; Burn, David J; Rochester, Lynn

2017-11-09

Dementia is significant in Parkinson's disease (PD) with personal and socioeconomic impact. Early identification of risk is of upmost importance to optimize management. Gait precedes and predicts cognitive decline and dementia in older adults. We aimed to evaluate gait characteristics as predictors of cognitive decline in newly diagnosed PD. One hundred and nineteen participants recruited at diagnosis were assessed at baseline, 18 and 36 months. Baseline gait was characterized by variables that mapped to five domains: pace, rhythm, variability, asymmetry, and postural control. Cognitive assessment included attention, fluctuating attention, executive function, visual memory, and visuospatial function. Mixed-effects models tested independent gait predictors of cognitive decline. Gait characteristics of pace, variability, and postural control predicted decline in fluctuating attention and visual memory, whereas baseline neuropsychological assessment performance did not predict decline. This provides novel evidence for gait as a clinical biomarker for PD cognitive decline in early disease. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America.

Density of calcium in the ascending thoracic aorta and risk of incident cardiovascular disease events.

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Thomas, Isac C; McClelland, Robyn L; Michos, Erin D; Allison, Matthew A; Forbang, Nketi I; Longstreth, W T; Post, Wendy S; Wong, Nathan D; Budoff, Matthew J; Criqui, Michael H

2017-10-01

The volume and density of coronary artery calcium (CAC) both independently predict cardiovascular disease (CVD) beyond standard risk factors, with CAC density inversely associated with incident CVD after accounting for CAC volume. We tested the hypothesis that ascending thoracic aorta calcium (ATAC) volume and density predict incident CVD events independently of CAC. The Multi-Ethnic Study of Atherosclerosis (MESA) is a prospective cohort study of participants without clinical CVD at baseline. ATAC and CAC were measured from baseline cardiac computed tomography (CT). Cox regression models were used to estimate the associations of ATAC volume and density with incident coronary heart disease (CHD) events and CVD events, after adjustment for standard CVD risk factors and CAC volume and density. Among 6811 participants, 234 (3.4%) had prevalent ATAC and 3395 (49.8%) had prevalent CAC. Over 10.3 years, 355 CHD and 562 CVD events occurred. One-standard deviation higher ATAC density was associated with a lower risk of CHD (HR 0.48 [95% CI 0.29-0.79], pdensity was inversely associated with incident CHD and CVD after adjustment for CVD risk factors and CAC volume and density. Copyright © 2017 Elsevier B.V. All rights reserved.

Usefulness of Genetic Polymorphisms and Conventional Risk Factors to Predict Coronary Heart Disease in Patients With Familial Hypercholesterolemia

NARCIS (Netherlands)

van der Net, Jeroen B.; Janssens, A. Cecile J. W.; Defesche, Joep C.; Kastelein, John J. P.; Sijbrands, Eric J. G.; Steyerberg, Ewout W.

2009-01-01

Familial hypercholesterolemia (FH) is an autosomal dominant disorder with an associated high risk of coronary heart disease (CHD). The considerable variation in age of onset of CHD in patients with FH is believed to arise from conventional risk factors, as well as genetic variation other than in the

Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa.

Science.gov (United States)

Redding, David W; Tiedt, Sonia; Lo Iacono, Gianni; Bett, Bernard; Jones, Kate E

2017-07-19

Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources.This article is part of the themed issue 'One Health for a changing world: zoonoses, ecosystems and human well-being'. © 2017 The Authors.

Donor genotype in the Interleukin-7 receptor α-chain predicts risk of graft-versus-host disease and cytomegalovirus infection after allogeneic hematopoietic stem cell transplantation

DEFF Research Database (Denmark)

Kielsen, Katrine; Enevold, Christian; Heilmann, Carsten

2018-01-01

The efficacy of allogeneic hematopoietic stem cell transplantation (HSCT) is challenged by acute and chronic graft-versus-host disease (aGVHD and cGVHD) and viral infections due to long-lasting immunodeficiency. Interleukin-7 (IL-7) is a cytokine essential for de novo T cell generation in thymus.......1-3.8, P = 0.034) and with significantly increased risk of extensive cGVHD (HR = 2.0, 95% CI = 1.1-3.6, P = 0.025) after adjustment for potential risk factors. In addition, the TT genotype was associated with a higher risk of cytomegalovirus (CMV) infection post-transplant (HR = 2.4, 95% CI = 1.2-4.3, P.......7, 95% CI = 1.2-2.3, P = 0.0027) and increased treatment-related mortality (HR = 2.3, 95% CI = 1.3-4.0, P = 0.0047), but was not associated with the risk of relapse (P = 0.35). In conclusion, the IL-7Rα rs6897932 genotype of the donor is predictive of aGVHD and cGVHD, CMV infection, and mortality...

GIMDA: Graphlet interaction-based MiRNA-disease association prediction.

Science.gov (United States)

Chen, Xing; Guan, Na-Na; Li, Jian-Qiang; Yan, Gui-Ying

2018-03-01

MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures. © 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

Receiver Operating Characteristic Curve-Based Prediction Model for Periodontal Disease Updated With the Calibrated Community Periodontal Index.

Science.gov (United States)

Su, Chiu-Wen; Yen, Amy Ming-Fang; Lai, Hongmin; Chen, Hsiu-Hsi; Chen, Sam Li-Sheng

2017-12-01

The accuracy of a prediction model for periodontal disease using the community periodontal index (CPI) has been undertaken by using an area under a receiver operating characteristics (AUROC) curve. How the uncalibrated CPI, as measured by general dentists trained by periodontists in a large epidemiologic study, and affects the performance in a prediction model, has not been researched yet. A two-stage design was conducted by first proposing a validation study to calibrate CPI between a senior periodontal specialist and trained general dentists who measured CPIs in the main study of a nationwide survey. A Bayesian hierarchical logistic regression model was applied to estimate the non-updated and updated clinical weights used for building up risk scores. How the calibrated CPI affected performance of the updated prediction model was quantified by comparing AUROC curves between the original and updated models. Estimates regarding calibration of CPI obtained from the validation study were 66% and 85% for sensitivity and specificity, respectively. After updating, clinical weights of each predictor were inflated, and the risk score for the highest risk category was elevated from 434 to 630. Such an update improved the AUROC performance of the two corresponding prediction models from 62.6% (95% confidence interval [CI]: 61.7% to 63.6%) for the non-updated model to 68.9% (95% CI: 68.0% to 69.6%) for the updated one, reaching a statistically significant difference (P prediction model was demonstrated for periodontal disease as measured by the calibrated CPI derived from a large epidemiologic survey.

Implications of host genetic variation on the risk and prevalence of infectious diseases transmitted through the environment.

Science.gov (United States)

Doeschl-Wilson, Andrea B; Davidson, R; Conington, J; Roughsedge, T; Hutchings, M R; Villanueva, B

2011-07-01

Previous studies have shown that host genetic heterogeneity in the response to infectious challenge can affect the emergence risk and the severity of diseases transmitted through direct contact between individuals. However, there is substantial uncertainty about the degree and direction of influence owing to different definitions of genetic variation, most of which are not in line with the current understanding of the genetic architecture of disease traits. Also, the relevance of previous results for diseases transmitted through environmental sources is unclear. In this article a compartmental genetic-epidemiological model was developed to quantify the impact of host genetic diversity on epidemiological characteristics of diseases transmitted through a contaminated environment. The model was parameterized for footrot in sheep. Genetic variation was defined through continuous distributions with varying shape and degree of dispersion for different disease traits. The model predicts a strong impact of genetic heterogeneity on the disease risk and its progression and severity, as well as on observable host phenotypes, when dispersion in key epidemiological parameters is high. The impact of host variation depends on the disease trait for which variation occurs and on environmental conditions affecting pathogen survival. In particular, compared to homogeneous populations with the same average susceptibility, disease risk and severity are substantially higher in populations containing a large proportion of highly susceptible individuals, and the differences are strongest when environmental contamination is low. The implications of our results for the recording and analysis of disease data and for predicting response to selection are discussed.

Development of a questionnaire to measure heart disease risk knowledge in people with diabetes: the Heart Disease Fact Questionnaire.

Science.gov (United States)

Wagner, Julie; Lacey, Kimberly; Chyun, Deborah; Abbott, Gina

2005-07-01

This paper describes a paper and pencil questionnaire that measures heart disease risk knowledge in people with diabetes. The Heart Disease Fact Questionnaire (HDFQ) is a 25-item questionnaire that was developed to tap into respondents' knowledge of major risk factors for the development of CHD. Approximately half of these items specifically address diabetes-related CHD risk factors. Based on extensive pilot data, the current study analyzed responses from 524 people with diabetes to assess the psychometric properties. The HDFQ is readable to an average 13-year old and imposes little burden. It shows good content and face validity. It demonstrates adequate internal consistency, with Kuder-Richardson-20 formula = 0.77 and good item-total correlations. Item analysis showed a desirable range in P-values. In discriminant function analyses, HDFQ scores differentiated respondents by knowledge of their own cardiovascular health, use of lipid lowering medications, health insurance status, and educational attainment, thus indicating good criterion related validity. This measure of heart disease risk knowledge is brief, understandable to respondents, and easy to administer and score. Its potential for use in research and practice is discussed. Future research should establish norms as well as investigate its test-retest reliability and predictive validity.

Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate.

Science.gov (United States)

Grams, Morgan E; Sang, Yingying; Ballew, Shoshana H; Carrero, Juan Jesus; Djurdjev, Ognjenka; Heerspink, Hiddo J L; Ho, Kevin; Ito, Sadayoshi; Marks, Angharad; Naimark, David; Nash, Danielle M; Navaneethan, Sankar D; Sarnak, Mark; Stengel, Benedicte; Visseren, Frank L J; Wang, Angela Yee-Moon; Köttgen, Anna; Levey, Andrew S; Woodward, Mark; Eckardt, Kai-Uwe; Hemmelgarn, Brenda; Coresh, Josef

2018-03-24

Patients with chronic kidney disease and severely decreased glomerular filtration rate (GFR) are at high risk for kidney failure, cardiovascular disease (CVD) and death. Accurate estimates of risk and timing of these clinical outcomes could guide patient counseling and therapy. Therefore, we developed models using data of 264,296 individuals in 30 countries participating in the international Chronic Kidney Disease Prognosis Consortium with estimated GFR (eGFR)s under 30 ml/min/1.73m 2 . Median participant eGFR and urine albumin-to-creatinine ratio were 24 ml/min/1.73m 2 and 168 mg/g, respectively. Using competing-risk regression, random-effect meta-analysis, and Markov processes with Monte Carlo simulations, we developed two- and four-year models of the probability and timing of kidney failure requiring kidney replacement therapy (KRT), a non-fatal CVD event, and death according to age, sex, race, eGFR, albumin-to-creatinine ratio, systolic blood pressure, smoking status, diabetes mellitus, and history of CVD. Hypothetically applied to a 60-year-old white male with a history of CVD, a systolic blood pressure of 140 mmHg, an eGFR of 25 ml/min/1.73m 2 and a urine albumin-to-creatinine ratio of 1000 mg/g, the four-year model predicted a 17% chance of survival after KRT, a 17% chance of survival after a CVD event, a 4% chance of survival after both, and a 28% chance of death (9% as a first event, and 19% after another CVD event or KRT). Risk predictions for KRT showed good overall agreement with the published kidney failure risk equation, and both models were well calibrated with observed risk. Thus, commonly-measured clinical characteristics can predict the timing and occurrence of clinical outcomes in patients with severely decreased GFR. Copyright © 2018 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

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

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Erin E. Wood

2017-01-01

Full Text Available We investigated how mothers’ parenting behaviors and personal characteristics were related to risk-taking by young children. We tested contrasting predictions from evolutionary and social role theories with the former predicting higher risk-taking by boys compared to girls and the latter predicting that mothers would influence children’s gender role development with risk-taking occurring more in children parented with higher levels of harshness (i.e., authoritarian parenting style. In our study, mothers reported their own gender roles and parenting styles as well as their children’s risk-taking and activities related to gender roles. The results were only partially consistent with the two theories, as the amount of risk-taking by sons and daughters did not differ significantly and risk-taking by daughters, but not sons, was positively related to mothers’ use of the authoritarian parenting style and the girls’ engagement in masculine activities. Risk-taking by sons was not predicted by any combination of mother-related variables. Overall, mothers who were higher in femininity used more authoritative and less authoritarian parenting styles. Theoretical implications as well as implications for predicting and reducing children’s risk-taking are discussed.

Inductive matrix completion for predicting gene-disease associations.

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Natarajan, Nagarajan; Dhillon, Inderjit S

2014-06-15

Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive. Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better-it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has bigdata.ices.utexas.edu/project/gene-disease. © The Author 2014. Published by Oxford University Press.

The Identification of a Threshold of Long Work Hours for Predicting Elevated Risks of Adverse Health Outcomes.

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Conway, Sadie H; Pompeii, Lisa A; Gimeno Ruiz de Porras, David; Follis, Jack L; Roberts, Robert E

2017-07-15

Working long hours has been associated with adverse health outcomes. However, a definition of long work hours relative to adverse health risk has not been established. Repeated measures of work hours among approximately 2,000 participants from the Panel Study of Income Dynamics (1986-2011), conducted in the United States, were retrospectively analyzed to derive statistically optimized cutpoints of long work hours that best predicted three health outcomes. Work-hours cutpoints were assessed for model fit, calibration, and discrimination separately for the outcomes of poor self-reported general health, incident cardiovascular disease, and incident cancer. For each outcome, the work-hours threshold that best predicted increased risk was 52 hours per week or more for a minimum of 10 years. Workers exposed at this level had a higher risk of poor self-reported general health (relative risk (RR) = 1.28; 95% confidence interval (CI): 1.06, 1.53), cardiovascular disease (RR = 1.42; 95% CI: 1.24, 1.63), and cancer (RR = 1.62; 95% CI: 1.22, 2.17) compared with those working 35-51 hours per week for the same duration. This study provides the first health risk-based definition of long work hours. Further examination of the predictive power of this cutpoint on other health outcomes and in other study populations is needed. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Type 2 diabetes risk alleles demonstrate extreme directional differentiation among human populations, compared to other diseases.

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Rong Chen

Full Text Available Many disease-susceptible SNPs exhibit significant disparity in ancestral and derived allele frequencies across worldwide populations. While previous studies have examined population differentiation of alleles at specific SNPs, global ethnic patterns of ensembles of disease risk alleles across human diseases are unexamined. To examine these patterns, we manually curated ethnic disease association data from 5,065 papers on human genetic studies representing 1,495 diseases, recording the precise risk alleles and their measured population frequencies and estimated effect sizes. We systematically compared the population frequencies of cross-ethnic risk alleles for each disease across 1,397 individuals from 11 HapMap populations, 1,064 individuals from 53 HGDP populations, and 49 individuals with whole-genome sequences from 10 populations. Type 2 diabetes (T2D demonstrated extreme directional differentiation of risk allele frequencies across human populations, compared with null distributions of European-frequency matched control genomic alleles and risk alleles for other diseases. Most T2D risk alleles share a consistent pattern of decreasing frequencies along human migration into East Asia. Furthermore, we show that these patterns contribute to disparities in predicted genetic risk across 1,397 HapMap individuals, T2D genetic risk being consistently higher for individuals in the African populations and lower in the Asian populations, irrespective of the ethnicity considered in the initial discovery of risk alleles. We observed a similar pattern in the distribution of T2D Genetic Risk Scores, which are associated with an increased risk of developing diabetes in the Diabetes Prevention Program cohort, for the same individuals. This disparity may be attributable to the promotion of energy storage and usage appropriate to environments and inconsistent energy intake. Our results indicate that the differential frequencies of T2D risk alleles may

Borrelia infection and risk of celiac disease.

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Alaedini, Armin; Lebwohl, Benjamin; Wormser, Gary P; Green, Peter H; Ludvigsson, Jonas F

2017-09-15

Environmental factors, including infectious agents, are speculated to play a role in the rising prevalence and the geographic distribution of celiac disease, an autoimmune disorder. In the USA and Sweden where the regional variation in the frequency of celiac disease has been studied, a similarity with the geographic distribution of Lyme disease, an emerging multisystemic infection caused by Borrelia burgdorferi spirochetes, has been found, thus raising the possibility of a link. We aimed to determine if infection with Borrelia contributes to an increased risk of celiac disease. Biopsy reports from all of Sweden's pathology departments were used to identify 15,769 individuals with celiac disease. Through linkage to the nationwide Patient Register, we compared the rate of earlier occurrence of Lyme disease in the patients with celiac disease to that in 78,331 matched controls. To further assess the temporal relationship between Borrelia infection and celiac disease, we also examined the risk of subsequent Lyme disease in patients with a diagnosis of celiac disease. Twenty-five individuals (0.16%) with celiac disease had a prior diagnosis of Lyme disease, whereas 79 (0.5%) had a subsequent diagnosis of Lyme disease. A modest association between Lyme disease and celiac disease was seen both before (odds ratio, 1.61; 95% confidence interval (CI), 1.06-2.47) and after the diagnosis of celiac disease (hazard ratio, 1.82; 95% CI, 1.40-2.35), with the risk of disease being highest in the first year of follow-up. Only a minor fraction of the celiac disease patient population had a prior diagnosis of Lyme disease. The similar association between Lyme disease and celiac disease both before and after the diagnosis of celiac disease is strongly suggestive of surveillance bias as a likely contributor. Taken together, the data indicate that Borrelia infection is not a substantive risk factor in the development of celiac disease.

A systematic review of the factors associated with interest in predictive genetic testing for obesity, type II diabetes and heart disease.

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Collins, J; Ryan, L; Truby, H

2014-10-01

In the future, it may be possible for individuals to take a genetic test to determine their genetic predisposition towards developing lifestyle-related chronic diseases. A systematic review of the literature was undertaken to identify the factors associated with an interest in having predictive genetic testing for obesity, type II diabetes and heart disease amongst unaffected adults. Ovid Medline, PsycINFO and EMBASE online databases were searched using predefined search terms. Publications meeting the inclusion criteria (English language, free-living adult population not selected as a result of their disease diagnosis, reporting interest as an outcome, not related to a single gene inherited disease) were assessed for quality and content. Narrative synthesis of the results was undertaken. From the 2329 publications retrieved, eight studies met the inclusion criteria and were included in the review. Overall, the evidence base was small but of positive quality. Interest was associated with personal attitudes towards disease risk and the provision of information about genetic testing, shaped by perceived risk of disease and expected outcomes of testing. The role of demographic factors was investigated with largely inconclusive findings. Interest in predictive genetic testing for obesity, type II diabetes or heart disease was greatest amongst those who perceived the risk of disease to be high and/or the outcomes of testing to be beneficial. © 2013 The British Dietetic Association Ltd.

Are gastroenterologists less tolerant of treatment risks than patients? Benefit-risk preferences in Crohn's disease management.

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Johnson, F Reed; Hauber, Brett; Özdemir, Semra; Siegel, Corey A; Hass, Steven; Sands, Bruce E

2010-10-01

treatment risk that exactly offsets the hypothetical increase in treatment benefit), was calculated using preference weights (parameter marginal log odds ratios) that were estimated with conjoint analysis (random parameters logit models). Gastroenterologists' and patients' mean MARs for 3 SAE risks were calculated for 6 improvements in Crohn's disease symptoms, and gastroenterologists' preference weights for each of the 3 patient profiles were compared. Gastroenterologists' MARs for a hypothetical middle-aged patient were then compared with predicted MARs derived using data from the patient study for male patients aged 40 to 50 years with 1 surgery. After exclusion of nonrespondents (n = 4,021 of 4,422 gastroenterologists; n = 681 of 1,285 patients) and nonevaluable respondents (n = 86 gastroenterologists; n = 24 patients), 315 gastroenterologists and 580 patients were included in the final analytic samples. There were no statistically significant differences in gastroenterologists' preference weights for the middle-aged versus young patient profiles. However, preference weights indicated that gastroenterologists are more concerned about 5% side-effect risks for the older patient profile than for the middle-aged patient profile. For symptomatic improvements from severe symptoms to remission, gastroenterologists' highest MARs were for lymphoma: 6.21%, 8.99%, and 25.00% for the young, middle-aged, and older patient types, respectively. In analyses of improvements from severe to moderate symptoms and from moderate symptoms to remission for hypothetical middle-aged patients, gastroenterologists' 10-year risk tolerance ranged between 1.96% lymphoma risk in return for an improvement from moderate symptoms to remission and 4.93% lymphoma risk for an improvement from severe to moderate symptoms; patients' 10-year risk tolerance for middle-aged patients ranged between 1.52% PML risk in return for an improvement from severe to moderate symptoms and 5.86% infection risk for an

Prediction of disease course in inflammatory bowel diseases.

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Lakatos, Peter Laszlo

2010-06-07

Clinical presentation at diagnosis and disease course of both Crohn's disease (CD) and ulcerative colitis are heterogeneous and variable over time. Since most patients have a relapsing course and most CD patients develop complications (e.g. stricture and/or perforation), much emphasis has been placed in the recent years on the determination of important predictive factors. The identification of these factors may eventually lead to a more personalized, tailored therapy. In this TOPIC HIGHLIGHT series, we provide an update on the available literature regarding important clinical, endoscopic, fecal, serological/routine laboratory and genetic factors. Our aim is to assist clinicians in the everyday practical decision-making when choosing the treatment strategy for their patients suffering from inflammatory bowel diseases.

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

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Zefeng Zhang

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

A clinically useful risk-score for chronic kidney disease in HIV infection

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Mocroft, Amanda; Lundgren, Jens; Ross, Michael

2014-01-01

baseline eGFR, female gender, lower CD4 nadir, hypertension, diabetes and cardiovascular disease predicted CKD and were included in the risk score (Figure 1). The incidence of CKD in those at low, medium and high risk was 0.8/1000 PYFU (95% CI 0.6-1.0), 5.6 (95% CI 4.5-6.7) and 37.4 (95% CI 34.......0-40.7) (Figure 1). The risk score showed good discrimination (Harrell's c statistic 0.92, 95% CI 0.90-0.93). The number needed to harm (NNTH) in patients starting ATV or LPV/r was 1395, 142 or 20, respectively, among those with low, medium or high risk. NNTH were 603, 61 and 9 for those with a low, medium...

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

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King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin

2011-01-01

Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

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Michael King

Full Text Available Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL for the development of hazardous drinking in safe drinkers.A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women.69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873. The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51. External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846 and Hedge's g of 0.68 (95% CI 0.57, 0.78.The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

The PER (Preoperative Esophagectomy Risk) Score: A Simple Risk Score to Predict Short-Term and Long-Term Outcome in Patients with Surgically Treated Esophageal Cancer.

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Reeh, Matthias; Metze, Johannes; Uzunoglu, Faik G; Nentwich, Michael; Ghadban, Tarik; Wellner, Ullrich; Bockhorn, Maximilian; Kluge, Stefan; Izbicki, Jakob R; Vashist, Yogesh K

2016-02-01

Esophageal resection in patients with esophageal cancer (EC) is still associated with high mortality and morbidity rates. We aimed to develop a simple preoperative risk score for the prediction of short-term and long-term outcomes for patients with EC treated by esophageal resection. In total, 498 patients suffering from esophageal carcinoma, who underwent esophageal resection, were included in this retrospective cohort study. Three preoperative esophagectomy risk (PER) groups were defined based on preoperative functional evaluation of different organ systems by validated tools (revised cardiac risk index, model for end-stage liver disease score, and pulmonary function test). Clinicopathological parameters, morbidity, and mortality as well as disease-free survival (DFS) and overall survival (OS) were correlated to the PER score. The PER score significantly predicted the short-term outcome of patients with EC who underwent esophageal resection. PER 2 and PER 3 patients had at least double the risk of morbidity and mortality compared to PER 1 patients. Furthermore, a higher PER score was associated with shorter DFS (P PER score was identified as an independent predictor of tumor recurrence (hazard ratio [HR] 2.1; P PER score allows preoperative objective allocation of patients with EC into different risk categories for morbidity, mortality, and long-term outcomes. Thus, multicenter studies are needed for independent validation of the PER score.

Prediction of acute respiratory disease in current and former smokers with and without COPD.

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Bowler, Russell P; Kim, Victor; Regan, Elizabeth; Williams, André A A; Santorico, Stephanie A; Make, Barry J; Lynch, David A; Hokanson, John E; Washko, George R; Bercz, Peter; Soler, Xavier; Marchetti, Nathaniel; Criner, Gerard J; Ramsdell, Joe; Han, MeiLan K; Demeo, Dawn; Anzueto, Antonio; Comellas, Alejandro; Crapo, James D; Dransfield, Mark; Wells, J Michael; Hersh, Craig P; MacIntyre, Neil; Martinez, Fernando; Nath, Hrudaya P; Niewoehner, Dennis; Sciurba, Frank; Sharafkhaneh, Amir; Silverman, Edwin K; van Beek, Edwin J R; Wilson, Carla; Wendt, Christine; Wise, Robert A

2014-10-01

The risk factors for acute episodes of respiratory disease in current and former smokers who do not have COPD are unknown. Eight thousand two hundred forty-six non-Hispanic white and black current and former smokers in the Genetic Epidemiology of COPD (COPDGene) cohort had longitudinal follow-up (LFU) every 6 months to determine acute respiratory episodes requiring antibiotics or systemic corticosteroids, an ED visit, or hospitalization. Negative binomial regression was used to determine the factors associated with acute respiratory episodes. A Cox proportional hazards model was used to determine adjusted hazard ratios (HRs) for time to first episode and an acute episode of respiratory disease risk score. At enrollment, 4,442 subjects did not have COPD, 658 had mild COPD, and 3,146 had moderate or worse COPD. Nine thousand three hundred three acute episodes of respiratory disease and 2,707 hospitalizations were reported in LFU (3,044 acute episodes of respiratory disease and 827 hospitalizations in those without COPD). Major predictors included acute episodes of respiratory disease in year prior to enrollment (HR, 1.20; 95% CI, 1.15-1.24 per exacerbation), airflow obstruction (HR, 0.94; 95% CI, 0.91-0.96 per 10% change in % predicted FEV1), and poor health-related quality of life (HR, 1.07; 95% CI, 1.06-1.08 for each 4-unit increase in St. George's Respiratory Questionnaire score). Risks were similar for those with and without COPD. Although acute episode of respiratory disease rates are higher in subjects with COPD, risk factors are similar, and at a population level, there are more episodes in smokers without COPD.

Prediction of Acute Respiratory Disease in Current and Former Smokers With and Without COPD

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Kim, Victor; Regan, Elizabeth; Williams, André A. A.; Santorico, Stephanie A.; Make, Barry J.; Lynch, David A.; Hokanson, John E.; Washko, George R.; Bercz, Peter; Soler, Xavier; Marchetti, Nathaniel; Criner, Gerard J.; Ramsdell, Joe; Han, MeiLan K.; Demeo, Dawn; Anzueto, Antonio; Comellas, Alejandro; Crapo, James D.; Dransfield, Mark; Wells, J. Michael; Hersh, Craig P.; MacIntyre, Neil; Martinez, Fernando; Nath, Hrudaya P.; Niewoehner, Dennis; Sciurba, Frank; Sharafkhaneh, Amir; Silverman, Edwin K.; van Beek, Edwin J. R.; Wilson, Carla; Wendt, Christine; Wise, Robert A.; Curtis, Jeffrey; Kazerooni, Ella; Hanania, Nicola; Alapat, Philip; Bandi, Venkata; Guntupalli, Kalpalatha; Guy, Elizabeth; Lunn, William; Mallampalli, Antara; Trinh, Charles; Atik, Mustafa; DeMeo, Dawn; Hersh, Craig; Jacobson, Francine; Graham Barr, R.; Thomashow, Byron; Austin, John; MacIntyre, Neil; Washington, Lacey; Page McAdams, H.; Rosiello, Richard; Bresnahan, Timothy; McEvoy, Charlene; Tashjian, Joseph; Wise, Robert; Hansel, Nadia; Brown, Robert; Casaburi, Richard; Porszasz, Janos; Fischer, Hans; Budoff, Matt; Sharafkhaneh, Amir; Niewoehner, Dennis; Allen, Tadashi; Rice, Kathryn; Foreman, Marilyn; Westney, Gloria; Berkowitz, Eugene; Bowler, Russell; Friedlander, Adam; Meoni, Eleonora; Criner, Gerard; Kim, Victor; Marchetti, Nathaniel; Satti, Aditi; James Mamary, A.; Steiner, Robert; Dass, Chandra; Bailey, William; Dransfield, Mark; Gerald, Lynn; Nath, Hrudaya; Ramsdell, Joe; Ferguson, Paul; Friedman, Paul; McLennan, Geoffrey; van Beek, Edwin JR; Martinez, Fernando; Han, MeiLan; Thompson, Deborah; Kazerooni, Ella; Wendt, Christine; Allen, Tadashi; Sciurba, Frank; Weissfeld, Joel; Fuhrman, Carl; Bon, Jessica; Anzueto, Antonio; Adams, Sandra; Orozco, Carlos; Santiago Restrepo, C.; Mumbower, Amy; Crapo, James; Silverman, Edwin; Make, Barry; Regan, Elizabeth; Samet, Jonathan; Willis, Amy; Stinson, Douglas; Beaty, Terri; Klanderman, Barbara; Laird, Nan; Lange, Christoph; Ionita, Iuliana; Santorico, Stephanie; Silverman, Edwin; Lynch, David; Schroeder, Joyce; Newell, John; Reilly, John; Coxson, Harvey; Judy, Philip; Hoffman, Eric; San Jose Estepar, Raul; Washko, George; Leek, Rebecca; Zach, Jordan; Kluiber, Alex; Rodionova, Anastasia; Mann, Tanya; Crapo, Robert; Jensen, Robert; Farzadegan, Homayoon; Murphy, James; Everett, Douglas; Wilson, Carla; Hokanson, John

2014-01-01

BACKGROUND: The risk factors for acute episodes of respiratory disease in current and former smokers who do not have COPD are unknown. METHODS: Eight thousand two hundred forty-six non-Hispanic white and black current and former smokers in the Genetic Epidemiology of COPD (COPDGene) cohort had longitudinal follow-up (LFU) every 6 months to determine acute respiratory episodes requiring antibiotics or systemic corticosteroids, an ED visit, or hospitalization. Negative binomial regression was used to determine the factors associated with acute respiratory episodes. A Cox proportional hazards model was used to determine adjusted hazard ratios (HRs) for time to first episode and an acute episode of respiratory disease risk score. RESULTS: At enrollment, 4,442 subjects did not have COPD, 658 had mild COPD, and 3,146 had moderate or worse COPD. Nine thousand three hundred three acute episodes of respiratory disease and 2,707 hospitalizations were reported in LFU (3,044 acute episodes of respiratory disease and 827 hospitalizations in those without COPD). Major predictors included acute episodes of respiratory disease in year prior to enrollment (HR, 1.20; 95% CI, 1.15-1.24 per exacerbation), airflow obstruction (HR, 0.94; 95% CI, 0.91-0.96 per 10% change in % predicted FEV1), and poor health-related quality of life (HR, 1.07; 95% CI, 1.06-1.08 for each 4-unit increase in St. George’s Respiratory Questionnaire score). Risks were similar for those with and without COPD. CONCLUSIONS: Although acute episode of respiratory disease rates are higher in subjects with COPD, risk factors are similar, and at a population level, there are more episodes in smokers without COPD. PMID:24945159

What does my patient's coronary artery calcium score mean? Combining information from the coronary artery calcium score with information from conventional risk factors to estimate coronary heart disease risk

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Pletcher Mark J

2004-08-01

Full Text Available Abstract Background The coronary artery calcium (CAC score is an independent predictor of coronary heart disease. We sought to combine information from the CAC score with information from conventional cardiac risk factors to produce post-test risk estimates, and to determine whether the score may add clinically useful information. Methods We measured the independent cross-sectional associations between conventional cardiac risk factors and the CAC score among asymptomatic persons referred for non-contrast electron beam computed tomography. Using the resulting multivariable models and published CAC score-specific relative risk estimates, we estimated post-test coronary heart disease risk in a number of different scenarios. Results Among 9341 asymptomatic study participants (age 35–88 years, 40% female, we found that conventional coronary heart disease risk factors including age, male sex, self-reported hypertension, diabetes and high cholesterol were independent predictors of the CAC score, and we used the resulting multivariable models for predicting post-test risk in a variety of scenarios. Our models predicted, for example, that a 60-year-old non-smoking non-diabetic women with hypertension and high cholesterol would have a 47% chance of having a CAC score of zero, reducing her 10-year risk estimate from 15% (per Framingham to 6–9%; if her score were over 100, however (a 17% chance, her risk estimate would be markedly higher (25–51% in 10 years. In low risk scenarios, the CAC score is very likely to be zero or low, and unlikely to change management. Conclusion Combining information from the CAC score with information from conventional risk factors can change assessment of coronary heart disease risk to an extent that may be clinically important, especially when the pre-test 10-year risk estimate is intermediate. The attached spreadsheet makes these calculations easy.

A coronary heart disease risk model for predicting the effect of potent antiretroviral therapy in HIV-1 infected men

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May, Margaret; Sterne, Jonathan A C; Shipley, Martin

2007-01-01

Many HIV-infected patients on highly active antiretroviral therapy (HAART) experience metabolic complications including dyslipidaemia and insulin resistance, which may increase their coronary heart disease (CHD) risk. We developed a prognostic model for CHD tailored to the changes in risk factors...

Improving the estimation of celiac disease sibling risk by non-HLA genes.

Directory of Open Access Journals (Sweden)

Valentina Izzo

Full Text Available Celiac Disease (CD is a polygenic trait, and HLA genes explain less than half of the genetic variation. Through large GWAs more than 40 associated non-HLA genes were identified, but they give a small contribution to the heritability of the disease. The aim of this study is to improve the estimate of the CD risk in siblings, by adding to HLA a small set of non-HLA genes. One-hundred fifty-seven Italian families with a confirmed CD case and at least one other sib and both parents were recruited. Among 249 sibs, 29 developed CD in a 6 year follow-up period. All individuals were typed for HLA and 10 SNPs in non-HLA genes: CCR1/CCR3 (rs6441961, IL12A/SCHIP1 and IL12A (rs17810546 and rs9811792, TAGAP (rs1738074, RGS1 (rs2816316, LPP (rs1464510, OLIG3 (rs2327832, REL (rs842647, IL2/IL21 (rs6822844, SH2B3 (rs3184504. Three associated SNPs (in LPP, REL, and RGS1 genes were identified through the Transmission Disequilibrium Test and a Bayesian approach was used to assign a score (BS to each detected HLA+SNPs genotype combination. We then classified CD sibs as at low or at high risk if their BS was respectively < or ≥ median BS value within each HLA risk group. A larger number (72% of CD sibs showed a BS ≥ the median value and had a more than two fold higher OR than CD sibs with a BS value < the median (O.R = 2.53, p = 0.047. Our HLA+SNPs genotype classification, showed both a higher predictive negative value (95% vs 91% and diagnostic sensitivity (79% vs 45% than the HLA only. In conclusion, the estimate of the CD risk by HLA+SNPs approach, even if not applicable to prevention, could be a precious tool to improve the prediction of the disease in a cohort of first degree relatives, particularly in the low HLA risk groups.

Estimating cross-validatory predictive p-values with integrated importance sampling for disease mapping models.

Science.gov (United States)

Li, Longhai; Feng, Cindy X; Qiu, Shi

2017-06-30

An important statistical task in disease mapping problems is to identify divergent regions with unusually high or low risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is the gold standard for estimating predictive p-values that can flag such divergent regions. However, actual LOOCV is time-consuming because one needs to rerun a Markov chain Monte Carlo analysis for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called integrated importance sampling (iIS), for estimating LOOCV predictive p-values with only Markov chain samples drawn from the posterior based on a full data set. The key step in iIS is that we integrate away the latent variables associated the test observation with respect to their conditional distribution without reference to the actual observation. By following the general theory for importance sampling, the formula used by iIS can be proved to be equivalent to the LOOCV predictive p-value. We compare iIS and other three existing methods in the literature with two disease mapping datasets. Our empirical results show that the predictive p-values estimated with iIS are almost identical to the predictive p-values estimated with actual LOOCV and outperform those given by the existing three methods, namely, the posterior predictive checking, the ordinary importance sampling, and the ghosting method by Marshall and Spiegelhalter (2003). Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Development of a risk prediction model for Barrett's esophagus in an Australian population.

Science.gov (United States)

Ireland, C J; Fielder, A L; Thompson, S K; Laws, T A; Watson, D I; Esterman, A

2017-11-01

Esophageal adenocarcinoma has poor 5-year survival rates. Increased survival might be achieved with earlier treatment, but requires earlier identification of the precursor, Barrett's esophagus. Population screening is not cost effective, this may be improved by targeted screening directed at individuals more likely to have Barrett's esophagus. To develop a risk prediction tool for Barrett's esophagus, this study compared individuals with Barrett's esophagus against population controls. Participants completed a questionnaire comprising 35 questions addressing medical history, symptom history, lifestyle factors, anthropomorphic measures, and demographic details. Statistical analysis addressed differences between cases and controls, and entailed initial variable selection, checking of model assumptions, and establishing calibration and discrimination. The area under the curve (AUC) was used to assess overall accuracy. One hundred and twenty individuals with Barrett's esophagus and 235 population controls completed the questionnaire. Significant differences were identified for age, gender, reflux history, family reflux history, history of hypertension, alcoholic drinks per week, and body mass index. These were used to develop a risk prediction model. The AUC was 0.82 (95% CI 0.78-0.87). Good calibration between predicted and observed risk was noted (Hosmer-Lemeshow test P = 0.67). At the point minimizing false positives and false negatives, the model achieved a sensitivity of 84.96% and a specificity of 66%. A well-calibrated risk prediction model with good discrimination has been developed to identify patients with Barrett's esophagus. The model needs to be externally validated before consideration for clinical practice. © The Authors 2017. Published by Oxford University Press on behalf of International Society for Diseases of the Esophagus. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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.

Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment

NARCIS (Netherlands)

Danaei, Goodarz; Lu, Yuan; Singh, Gitanjali M.; Carnahan, Emily; Stevens, Gretchen A.; Cowan, Melanie J.; Farzadfar, Farshad; Lin, John K.; Finucane, Mariel M.; Rao, Mayuree; Khang, Young-Ho; Riley, Leanne M.; Mozaffarian, Dariush; Lim, Stephen S.; Ezzati, Majid; Aamodt, Geir; Abdeen, Ziad; Abdella, Nabila A.; Rahim, Hanan F. Abdul; Addo, Juliet; Aekplakorn, Wichai; Afifi, Mustafa M.; Agabiti-Rosei, Enrico; Salinas, Carlos A. Aguilar; Agyemang, Charles; Ali, Mohammed K.; Ali, Mohamed M.; Al-Nsour, Mohannad; Al-Nuaim, Abdul R.; Ambady, Ramachandran; Di Angelantonio, Emanuele; Aro, Pertti; Azizi, Fereidoun; Babu, Bontha V.; Bahalim, Adil N.; Barbagallo, Carlo M.; Barbieri, Marco A.; Barceló, Alberto; Barreto, Sandhi M.; Barros, Henrique; Bautista, Leonelo E.; Benetos, Athanase; Bjerregaard, Peter; Björkelund, Cecilia; Bo, Simona; Bobak, Martin; Bonora, Enzo; Botana, Manuel A.; Bovet, Pascal; Breckenkamp, Juergen

2014-01-01

Background High blood pressure, blood glucose, serum cholesterol, and BMI are risk factors for cardiovascular diseases and some of these factors also increase the risk of chronic kidney disease and diabetes. We estimated mortality from cardiovascular diseases, chronic kidney disease, and diabetes

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...