Samuel Brown, Fardad Soltani, Jack Wu, Matthew Ryan, Brett S Bernstein, Brian Tam To, Tom Searle, Maleeha Rizvi, Natalie Fairhurst, George Kaye, Ranu Baral, Dhanushan Vijayakumar, Daksh Mehta, Zeeshan Khawaja, Phil Chowienczyk, James Teo, Richard Jb Dobson, Daniel I Bromage, Gerald Carr-White, Thomas F Lüscher, Ali Vazir, Theresa A McDonagh, Jessica Webb, Christopher A Miller, Ajay M Shah, Dhruva Biswas, Kevin O'Gallagher
Longitudinal NLP-based phenotyping of real-world EHRs identified four reproducible HFpEF phenogroups with distinct trajectories, distinguishing an early progressive disease state from stable advanced phenotypes. Earlier HFpEF detection using artificial intelligence-driven EHR tools could facilitate phenotype-targeted treatment of patients at potentially modifiable stages.
BACKGROUND AND AIMS: 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.
METHODS: 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.
RESULTS: 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 highly stable (3.2% and 12.1% transitioned, respectively). Within young-low comorbidity, those who transitioned had greater baseline cardiac abnormalities, including higher E/e', left ventricle mass and wall thickness. Adjusted mortality risk was highest in cardiovascular-kidney-metabolic (HR 1.53, 95% CI 1.22 to 1.92, p<0.001); current phenogroup status discriminated mortality risk modestly better than baseline classification alone (C-index 0.659 vs 0.648). In an independent external cohort (n=3349), RF predictions agreed strongly with independently derived LCA phenogroup labels (C-statistics 0.891-0.953).
CONCLUSION: Longitudinal NLP-based phenotyping of real-world EHRs identified four reproducible HFpEF phenogroups with distinct trajectories, distinguishing an early progressive disease state from stable advanced phenotypes. Earlier HFpEF detection using artificial intelligence-driven EHR tools could facilitate phenotype-targeted treatment of patients at potentially modifiable stages.