Key result
Machine learning improves HFpEF diagnostic accuracy over H2FPEF to a median AUC of ~0.88.
Why the study?
Traditional HFpEF diagnostic scores (H2FPEF, HFA-PEFF) use categorical cutoffs that limit precision across distinct phenotypes. This study evaluated the diagnostic accuracy, prognostic discrimination, and clinical implementation readiness of machine learning phenotyping versus established clinical scores.
Does machine learning phenotyping improve diagnostic accuracy and prognostic outcome discrimination (HF-hospitalization/mortality) compared to established clinical scores in patients with HFpEF?
Meta-Analysis (n=8,456)
Yes
Does machine learning phenotyping improve diagnostic accuracy and prognostic outcome discrimination (HF-hospitalization/mortality) compared to established clinical scores in patients with HFpEF?
Machine learning phenotyping significantly outperforms traditional clinical scores like H2FPEF in diagnosing HFpEF and provides robust, externally validated prognostic stratification for heart failure hospitalization and mortality.
Machine learning phenotyping may improve HFpEF diagnosis and risk stratification over H2FPEF; extends scores by identifying high-risk phenogroups for targeted trials.
Background HFpEF encompasses 3–5 mechanistically distinct phenotypes (hypertensive, metabolic, restrictive, senile) requiring differential therapeutic approaches. Traditional diagnostic scores (H2FPEF, HFA-PEFF) use categorical cutoffs, limiting precision. Machine learning (ML) phenotyping enables superior patient subtyping with prognostic stratification potential. Objective To evaluate ML phenotyping diagnostic accuracy versus established clinical scores, assess prognostic outcome discrimination by phenogroup, and characterize clinical implementation readiness. Methods Parallel search of MEDLINE, Embase, IEEE Xplore (2018–January 2026) for observational/prospective studies applying supervised ML (random forests, gradient boosting) or unsupervised clustering to HFpEF diagnosis/phenotyping. Inclusion: baseline LVEF ≥50%, N≥100, follow-up ≥6 months, reported diagnostic accuracy (AUC) or prognostic metrics (c-statistic). Primary outcomes: diagnostic AUC, phenogroup-stratified outcome discrimination (c-statistic for HF-hospitalization/mortality). QUADAS-AI bias assessment applied. Results Twenty-eight studies (n=8,456 HFpEF; 1,248 events; median follow-up 2.5 years) included. ML achieved diagnostic AUC 0.82–0.94 (median 0.88) versus H2FPEF 0.76–0.84 (p<0.001). Three–four distinct phenogroups identified; highest-risk demonstrated 3.0-fold increased HF-hospitalization/mortality versus lowest-risk (p<0.001). External multicenter validation c-statistic 0.75 (range 0.71–0.79), supporting real-world implementation. Conclusions ML phenotyping significantly enhances HFpEF diagnostic accuracy and enables robust prognostic stratification. External validation demonstrates clinically meaningful discrimination.
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Gohri et al. (2026) conducted a meta-analysis in Heart failure with preserved ejection fraction (HFpEF) (n=8,456). Machine learning phenotyping (supervised ML or unsupervised clustering) vs. Established clinical scores (H2FPEF, HFA-PEFF) and lowest-risk phenogroup was evaluated on Diagnostic AUC and phenogroup-stratified outcome discrimination (c-statistic for HF-hospitalization/mortality) (p=<0.001). Machine learning phenotyping achieved higher HFpEF diagnostic accuracy (AUC 0.82-0.94, median 0.88) than H2FPEF (AUC 0.76-0.84; p<0.001) and stratified mortality/hospitalization risk.
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