Risk factors for heart failure with preserved or reduced ejection fraction among Medicare beneficiaries: Applications of competing risks analysis and gradient boosted model

Abstract

AbstractBackgroundThe differential impact of various demographic characteristics and comorbid conditions on development of heart failure (HF) with preserved (pEF) and reduced ejection fraction (rEF) is not well studied among the elderly.Methods and ResultsUsing Medicare claims data linked to electronic health records, we conducted an observational cohort study of individuals ≥ 65 years of age without HF. A Cox proportional hazards model accounting for competing risk of HFrEF and HFpEF incidence was constructed. A gradient boosted model (GBM) assessed the relative influence (RI) of each predictor in development of HFrEF and HFpEF. Among 138,388 included individuals, 9,701 developed HF (IR= 20.9 per 1,000 person-year). Males were more likely to develop HFrEF than HFpEF (HR = 2.07, 95% CI: 1.81-2.37 vs. 1.11, 95% CI: 1.02-1.20, P for heterogeneity < 0.01). Atrial fibrillation and pulmonary hypertension had stronger associations with the risk of HFpEF (HR = 2.02, 95% CI: 1.80-2.26 and 1.66, 95% CI: 1.23-2.22) while cardiomyopathy and myocardial infarction were more strongly associated with HFrEF (HR = 4.37, 95% CI: 3.21-5.97 and 1.94, 95% CI: 1.23-3.07). Age was the strongest predictor across all HF subtypes with RI from GBM >35%. Atrial fibrillation was the most influential comorbidity for development of HFpEF (RI = 8.4%) while cardiomyopathy was most influential for HFrEF (RI = 20.7%).ConclusionsThese findings of heterogeneous relationships between several important risk factors and heart failure types underline the potential differences in the etiology of HFpEF and HFrEF.Key QuestionsWhat is already known about this subject?Previous epidemiologic studies describe the differences in risk factors involved in developing heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF), however, there has been no large study in an elderly population.What does this study add?This study provides further insights into the heterogeneous impact of various clinical characteristics on the risk of developing HFpEF and HFrEF in a population of elderly individuals.Employing an advanced machine learning technique allows assessing the relative importance of each risk factor on development of HFpEF and HFrEF.How might this impact on clinical practice?Our findings provide further insights into the potential differences in the etiology of HFpEF and HFrEF, which are critical in prioritizing populations for close monitoring and targeting prevention efforts.