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oai:figshare.com:article/34008300 2026-09-28T04:30:04Z category_19 portal_316 item_type_3 month_year_09_2026

Data Sheet 1_Machine-learning phenotyping and exploratory medication-outcome associations in heart failure with preserved ejection fraction: a multicohort retrospective study.pdf Qianli Ma (28901) Shaotong Zhang (13041849) Chengjian Guan (14097420) Yitong Liu (5162333) Xiuchun Yang (498132) Xiong Chen (555744) Sheng Jin (7135568) Bing Xiao (158836) Pharmacology HFpEF medication–mortality associations phenotyping TabPFN classifier unsupervised machine learning Background<p>The marked clinical and pathophysiological heterogeneity of HFpEF has repeatedly undermined uniform treatment strategies. Consequently, identifying distinct phenotypes and medication-associated patterns across phenotypes may inform future precision-medicine research.</p>Methods<p>In this multicohort retrospective study, patients with HFpEF were included from MIMIC-IV (derivation cohort, n = 2,511), the MIMIC-III CareVue subset (internal validation cohort, n = 1,524), and the Second Hospital of Hebei Medical University (external application cohort, n = 340). Unsupervised K-prototypes clustering was used to derive phenotypic groups, with K = 2 evaluated as a lower-resolution sensitivity solution. All phenotype-stratified medication analyses were exploratory. In the derivation and internal validation cohorts, overlap weighting was the primary weighted analysis and stabilized inverse probability of treatment weighting was a sensitivity analysis; external medication estimates were reported descriptively because adequate within-group covariate balance was not achieved. 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> 2026-09-28T04:30:04Z Dataset Dataset 10.3389/fphar.2026.1838338.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Machine-learning_phenotyping_and_exploratory_medication-outcome_associations_in_heart_failure_with_preserved_ejection_fraction_a_multicohort_retrospective_study_pdf/34008300 CC BY 4.0