Longitudinal phenotyping of heart failure with preserved ejection fraction identifies early- and end-stage disease states.

Published in Open heart. Abstract: Heart failure with preserved ejection fraction (HFpEF) phenotypes have been characterised as static entities in trial populations yet their temporal dynamics remain unexplored in routine clinical practice. We aimed to identify HFpEF phenogroups from real-world electronic health records (EHRs), characterise their longitudinal stability and determine whether phenogroup trajectories were associated with differences in mortality. A natural language processing (NLP) pipeline identified HFpEF cases from multisite EHRs (2010-2022). Latent class analysis (LCA) defined baseline phenogroups and annual supervised random forest (RF) classification using time-updated values of the same baseline LCA variables tracked individual transitions over five years. Competing risks regression quantified transition versus death probabilities. Time-varying Cox models assessed mortality risk associated with current (vs baseline) phenogroup status. Phenogroups were independently re-derived by LCA in an external cohort and compared with RF predictions. Among 2223 patients (60% female, median age 75 years (IQR 63-83); left ventricular ejection fraction (LVEF) 60.1%; follow-up 4.0 years (IQR 2.2-6.1), we identified four phenogroups: young-low comorbidity (22%), obesity-predominant (24%), elderly atrial dysfunction (32%) and cardiovascular-kidney-metabolic (23%). Young-low comorbidity had the highest transition probability (47.7%), while cardiovascular-kidney-metabolic and elderly-atrial dysfunction were hi...