Data driven heart failure patient segmentation: Identification of underlying patient phenotypes

Abstract

Heart failure is a heterogeneous syndrome where the underlying aetiology remains uncertain. The most common sub-classifications of heart failure are based on left ventricular ejection fraction, forming three subgroups: reduced (HFrEF), mildly reduced (HFmrEF), and preserved (HFpEF). While the development of targeted therapies holds promise for improving outcomes across the heart failure spectrum, current sub-classifications have not yet enabled precision medicine, as they fail to fully capture the underlying pathophysiological mechanisms.

Recent efforts have focused on HFpEF, and thus, patient segmentation in heart failure remains suboptimal. Based on data from four clinical trials in heart failure with a total of 11,140 patients, this project aimed to identify clinically relevant clusters across the full spectrum of ejection fraction. Twelve numerical and categorical patient characteristics were used as input covariates in latent class analysis to model underlying distributions, identifying four distinct clusters: • Cluster 1: Old atrial fibrillation no myocardial infarction (HFpEF, 29.1% of patients) • Cluster 2: Male high NT-ProBNP with myocardial infarction (HFrEF, 29.2% of patients) • Cluster 3: Obese diabetic (HFpEF, 22.0% of patients) • Cluster 4: Young male, with good kidney function (HFrEF, 19.8% of patients) The identified patient sub-groups are clinically meaningful and associated with significantly different hard outcomes, such as cardiovascular and all-cause mortality. A sensitivity analysis using a k-prototypes clustering algorithm for mixed data derived clusters that corresponded to most characteristics of the HFrEF groups. However, cluster separation was relatively low across both latent class analysis and k-prototypes. Further work, such as data-driven feature selection, could improve cluster quality and separation.