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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedAngraal S, Mortazavi BJ, Gupta A
Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction.
This study developed models to predict the risk of death and hospitalization in patients with heart failure (HF) with preserved ejection fraction (HFpEF). Data was used from the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) clinical trial. Five methods: logistic regression with a forward selection of variables; logistic regression with a lasso regularization for variable selection; random forest (RF); gradient descent boosting; and support vector machine, were used to train models for assessing risks of mortality and HF hospitalization through 3 years of follow-up and were validated using 5-fold cross-validation. RF was found to be the best performing model for predicting mortality and HF hospitalization. Blood urea nitrogen levels, body mass index, and Kansas City Cardiomyopathy Questionnaire (KCCQ) subscale scores were strongly associated with mortality, while hemoglobin level, blood urea nitrogen, time since previous HF hospitalization, and KCCQ scores were the most significant predictors of HF hospitalization.
AHRQ-funded; HS023000.
Citation: Angraal S, Mortazavi BJ, Gupta A .
Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction.
JACC Heart Fail 2020 Jan;8(1):12-21. doi: 10.1016/j.jchf.2019.06.013..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Mortality, Hospitalization, Risk, Health Status, Health Information Technology (HIT)
Angraal S, Gupta A, Khera R
Association of access to exercise opportunities and cardiovascular mortality.
The purpose of this study is to examine the patterns of cardiovascular disease (CVD) mortality in varying degrees of access within the U.S. at the county level. The results indicate that access to exercise opportunities has a significant association with adjusted CVD mortality--higher access correlates with lower CVD mortality. Counties that have lower access to exercise facilities show a higher prevalence of obesity and diabetes in comparison with counties that have higher access. States with fewer people living in close proximity to a park have higher percentage of people who do not engage in any leisure physical activity. These results suggest means by which opportunities to increase access may be developed.
AHRQ-funded; HS023000.
Citation: Angraal S, Gupta A, Khera R .
Association of access to exercise opportunities and cardiovascular mortality.
Am Heart J 2019 Jun;212:152-56. doi: 10.1016/j.ahj.2019.02.010..
Keywords: Cardiovascular Conditions, Health Status, Mortality, Social Determinants of Health