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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 6 of 6 Research Studies DisplayedBavishi A, Bruce M, Ning H
Predictive accuracy of heart failure-specific risk equations in an electronic health record-based cohort.
The objective of this study was to assess the predictive accuracy of the Pooled Cohort Equations to Prevent Heart Failure within a primary prevention cohort derived from the electronic health record. Findings showed that a novel sex- and race-specific risk score predicts incident heart failure (HF) in a real-world, electronic health record-based cohort. Recommendations included integration of HF risk into the electronic health record to allow for risk-based discussion, enhanced surveillance, and targeted preventive interventions in order to reduce the public health burden of HF.
AHRQ-funded; HS026385.
Citation: Bavishi A, Bruce M, Ning H .
Predictive accuracy of heart failure-specific risk equations in an electronic health record-based cohort.
Circ Heart Fail 2020 Nov;13(11):e007462. doi: 10.1161/circheartfailure.120.007462..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Prevention, Risk
Wehbe RM, Khan SS, Shah SJ
Predicting high-risk patients and high-risk outcomes in heart failure.
Identifying patients with heart failure at high risk for poor outcomes is important for patient care, resource allocation, and process improvement. Although numerous risk models exist to predict mortality, hospitalization, and patient-reported health status, they are infrequently used for several reasons, including modest performance, lack of evidence to support routine clinical use, and barriers to implementation. The authors discuss the potential of artificial to enhance the performance of risk prediction models.
AHRQ-funded; HS026385.
Citation: Wehbe RM, Khan SS, Shah SJ .
Predicting high-risk patients and high-risk outcomes in heart failure.
Heart Fail Clin 2020 Oct;16(4):387-407. doi: 10.1016/j.hfc.2020.05.002..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Risk, Hospitalization
Khera R, Kondamudi N, Zhong L
Temporal trends in heart failure incidence among Medicare
This retrospective, national cohort study looked at temporal trends in heart failure (HF) incidence among Medicare beneficiaries from 2011 to 2016. There had been a decline in claims during that time period. Five percent of all fee-for-service Medicare beneficiaries with no prior HF diagnosis were followed up from 2011-2016. Annual trends were examined in HF incidence among groups with and without primary HF risk factors (hypertension, diabetes, and obesity) and predisposing cardiovascular conditions (acute myocardial infarction (MI) and atrial fibrillation (AF). Of the approximately 1.8 million Medicare beneficiaries at risk for HF, 249,832 had a new diagnosis of HF. The prevalence of all 5 risk factors had increased during the 5-year study period. There was a relative decline in HF incidence among beneficiaries with primary HF risk factors, but incidence increased among individuals with acute MI and AF.
AHRQ-funded; HS022418.
Citation: Khera R, Kondamudi N, Zhong L .
Temporal trends in heart failure incidence among Medicare
JAMA Netw Open 2020 Oct;3(10):e2022190. doi: 10.1001/jamanetworkopen.2020.22190.
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Keywords: Heart Disease and Health, Cardiovascular Conditions, Medicare, Risk, Elderly, Mortality
O'Halloran JA, Sahrmann J, Butler AM
Brief report: integrase strand transfer inhibitors are associated with lower risk of incident cardiovascular disease in people living with HIV.
AHRQ-funded; HS019455.
Citation: O'Halloran JA, Sahrmann J, Butler AM .
Brief report: integrase strand transfer inhibitors are associated with lower risk of incident cardiovascular disease in people living with HIV.
J Acquir Immune Defic Syndr 2020 Aug 1;84(4):396-99. doi: 10.1097/qai.0000000000002357..
Keywords: Human Immunodeficiency Virus (HIV), Cardiovascular Conditions, Medication, Stroke, Heart Disease and Health, Risk
Prasada S, Rivera A, Nishtala A
Differential associations of chronic inflammatory diseases with incident heart failure.
The purpose of this study was to compare the risks of incident heart failure (HF) among a variety of chronic inflammatory diseases (CIDs) and to determine whether risks varied by severity of inflammation within each CID. Electronic health records from a large urban medical system were examined. Findings showed that systemic sclerosis and systemic lupus erythematosus were associated with the highest risks of HF, followed by rheumatoid arthritis and HIV. Measurements of inflammation were associated with HF risk across different CIDs.
AHRQ-funded; HS026385.
Citation: Prasada S, Rivera A, Nishtala A .
Differential associations of chronic inflammatory diseases with incident heart failure.
JACC Heart Fail 2020 Jun;8(6):489-98. doi: 10.1016/j.jchf.2019.11.013..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Chronic Conditions, Risk
Angraal 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)