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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)
Baik D, Reading M, Jia H
Measuring health status and symptom burden using a web-based mHealth application in patients with heart failure.
This cross-sectional study was conducted at an urban academic medical center to measure health status and symptom burdens of heart failure patients using a mHealth application called mi.Symptoms. Patients were diverse, with a mean age of 58.7, and were 37% women, 36% Black, and 36% Hispanic/Latino. Almost half were classified as New York Heart Association class III, and 44% reported not having enough income to make ends meet. Health status was measured with the Kansas City cardiomyopathy questionnaire clinical summary score. Predictors of better health status included higher physical function and ability to participate in social functions and activities. Predictors of poorer health status was New York Heart Association class IV status and dyspnea.
AHRQ-funded; HS021816.
Citation: Baik D, Reading M, Jia H .
Measuring health status and symptom burden using a web-based mHealth application in patients with heart failure.
Eur J Cardiovasc Nurs 2019 Apr;18(4):325-31. doi: 10.1177/1474515119825704..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Health Status, Telehealth, Health Information Technology (HIT)