Data Sheet 1_Machine-learning phenotyping and exploratory medication-outcome associations in heart failure with preserved ejection fraction: a multicohort retrospective study.pdf

A TabPFN phenotype-assignment classifier was developed after recursive feature elimination, evaluated in internal validation against independently reclustered labels aligned by prespecified one-to-one Hungarian assignment on cluster-profile distances, and applied without refitting in the external cohort.</p>Results<p>The primary three-cluster solution identified clinically interpretable but partially overlapping cardiorenal-metabolic, hypertensive-pulmonary, and low-blood-pressure/arrhythmia profiles.

K = 2 had a higher mean silhouette width than K = 3 (0.083 versus 0.060), whereas both solutions showed high median resampling stability (ARI, 0.940 versus 0.924). A graded 365-day mortality difference was observed across the three clusters in the derivation cohort, but between-cluster survival differences were not statistically significant in the internal validation or external application cohorts. In the derivation and internal validation cohorts, no weighted cluster-specific medication association remained statistically significant after FDR correction, and all medication-by-cluster interaction tests were nonsignificant.

External medication estimates were descriptive and were not used for confirmatory inference. The fixed TabPFN classifier achieved one-versus-rest AUCs of 0.951–0.969 in internal validation (10-bin multiclass ECE = 0.028).</p>Conclusion<p>Routinely collected ICU data supported an exploratory framework for describing overlapping HFpEF phenotypes and reproducibly assigning machine-derived labels. The analyses did not establish phenotype-specific medication effects, and the classifier should be regarded as a research tool pending prospective multicenter validation.</p>