Key result
Machine learning models diagnose HFpEF more accurately than traditional HFA-PEFF and H2FPEF scores.
Why the study?
Diagnosing HFpEF remains challenging, particularly in older individuals, leading to the hypothesis that machine learning approaches could improve diagnostic accuracy compared with traditional HFpEF scores.
Do machine learning algorithms improve diagnostic accuracy for HFpEF compared to traditional scoring systems in older adults?
Do machine learning algorithms improve diagnostic accuracy for HFpEF compared to traditional scoring systems in older adults?
Machine learning models, particularly random forest and XGBoost, provide superior diagnostic accuracy for identifying HFpEF in older adults compared to established clinical scores.
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May improve HFpEF diagnosis in older adults over traditional scores; extends ML evidence but should not yet change practice.
Aims Diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging, particularly in older individuals. We hypothesized that machine learning (ML) approaches could improve diagnostic accuracy compared with HFpEF scores. Methods We evaluated the diagnostic performance of four supervised ML algorithms (random forest [RF], extreme gradient boosting [XGBoost], support vector machines, and decision trees) to identify HFpEF in individuals aged 60 to 80 years. The models were trained on three derivation cohorts (N = 1474; HFpEF: KaRen, MEDIA cohorts; community-based without HF: Malmö Preventive Project) and validated in two independent cohorts (N = 542; HFpEF: HF-Nancy cohort; community-based without HF: STANISLAS cohort). Performance metrics included accuracy, F-measure, area under the receiver operating characteristic curve (AUC), and C-index. ML models were also compared with HFA-PEFF, H2FPEF, and HFpEF-ABA scores. Results Among 2017 participants, RF and XGBoost demonstrated the highest diagnostic value, outperforming traditional HFpEF scores (AUC: RF, 0.98; XGBoost, 0.96; HFA-PEFF, 0.86; H2FPEF, 0.79). RF and XGBoost also showed the greatest gain in discriminative capacity among ML algorithms when compared with H2FPEF (ΔC-index: RF +0.20, XGBoost +0.18), HFA-PEFF (ΔC-index: RF +0.12, XGBoost +0.10), and HFpEF-ABA score (ΔC-index: RF +0.17, XGBoost +0.15). Elevated natriuretic peptides were by far the most influential feature in both RF and XGBoost models (36% of model explainability). Conclusions Machine learning algorithms, particularly RF and XGBoost, demonstrated superior diagnostic accuracy compared to established HFpEF scoring systems. These findings support the potential integration of ML-based tools into clinical workflows to facilitate earlier identification of HFpEF and prompt initiation of guideline-recommended therapies.
Monzo et al. (2026) studied this question. Random forest and XGBoost machine learning models diagnosed HFpEF in older adults with higher accuracy (AUC 0.98 and 0.96) than traditional HFA-PEFF (AUC 0.86) and H2FPEF (AUC 0.79) scores.
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