Multimodal data-driven clustering analysis of heart failure patients identifies two distinct patterns of heart failure with preserved ejection fraction: Results from the PACIFIC-PRESERVED study
Heart failure with preserved ejection fraction (HFpEF) accounts for over half of heart failure cases but remains poorly defined due to its clinical heterogeneity. In the prospective PACIFIC-Preserved cohort (NCT04189029, n = 155), we applied multimodal deep phenotyping, including high-throughput proteomics, and unsupervised machine learning to identify biologically distinct HFpEF subgroups. Multiview clustering revealed two major HFpEF phenogroups: PEF1, characterized by inflammatory signaling, TNF receptor activation, cardio-skeletal injury, and greater renal dysfunction; and PEF2, marked by endothelial stress and milder perturbations. Conventional diagnostics alone could not reliably distinguish these subgroups, but a multimodal classifier integrating clinical, imaging, and proteomic variables achieved high predictive accuracy (F1-score 0.87). External validation in the MEDIA-DHF cohort (n = 456) confirmed that PEF1 was independently associated with adverse outcomes (adjusted HR 2.27, 95% CI 1.04–4.94). These findings establish a classifier-driven, biologically informed stratification of HFpEF, providing a framework for precision medicine in heart failure.