ArticleNature communications2025
External validation of artificial intelligence for detection of heart failure with preserved ejection fraction.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- CardioMOD-Net: A Modal Decomposition-Neural Network Framework for Diagnosis and Prognosis of Heart Failure with Preserved Ejection Fraction from Echocardiography Cine Loops.Journal of cardiovascular translational research · 2026Article
- Review
- Heart Failure with Preserved Ejection Fraction in Women: Sex-Specific Insights Across the Continuum from Diagnosis to Outcomes.Current cardiology reports · 2026Review
- Explainable machine learning-driven identification of heart failure biomarkers: a multi-model feature selection approach with SHAP-based interpretability.Scientific reports · 2026Article
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Current concepts and challenges in pediatric diastolic dysfunction and heart failure with preserved ejection fraction.Pediatric research · 2026Review
- Revolutionizing Heart Failure Management With Artificial Intelligence: A Narrative Review of Diagnostic, Prognostic, and Therapeutic Innovations.Health science reports · 2026Article
- Heart failure with preserved ejection fraction for the clinician - highlights from the 5th annual Charleston HFpEF conference.Heart failure reviews · 2026Article
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Echocardiographic Diastolic Function Grading in HFpEF: Testing the Updated 2025 ASE Criteria.Journal of the American College of Cardiology · 2026Article
- Invasive haemodynamic validation of an artificial intelligence echocardiographic tool for detecting heart failure with preserved ejection fraction.ESC heart failure · 2026Article
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Large language model detects previously undiagnosed heart failure with preserved ejection fraction in patients with metabolic-associated fatty liver disease: A multicenter cohort study.PLOS digital health · 2026Article
- Extent and predictors of guideline-directed medical therapy optimization during cardiac rehabilitation in patients with heart failure.International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- E/e' ratio combined with left ventricular mass index predicts HFpEF rehospitalization risk in stage 3-4 chronic kidney disease patients: a retrospective cohort study.Frontiers in cardiovascular medicine · 2026Article
- The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.Current cardiology reviews · 2026Review
- Facilitators, barriers, and insights using an HFA-PEFF-based clinical decision support system: A proof-of-concept study.ESC heart failure · 2025Article
- Evaluating the J Wave as a Marker of Diastolic Dysfunction in Heart Failure With Preserved Ejection Fraction.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2025Article
- Gender differences in outcomes among patients with heart failure and preserved ejection fraction: A retrospective cohort study.Bioinformation · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
Abstract
Artificial intelligence (AI) models to identify heart failure (HF) with preserved ejection fraction (HFpEF) based on deep-learning of echocardiograms could help address under-recognition in clinical practice, but they require extensive validation, particularly in representative and complex clinical cohorts for which they could provide most value. In this study enrolling patients with HFpEF (cases; n = 240), and age, sex, and year of echocardiogram matched controls (n = 256), we compare the diagnostic performance (discrimination, calibration, classification, and clinical utility) and prognostic associations (mortality and HF hospitalization) between an updated AI HFpEF model (EchoGo Heart Failure v2) and existing clinical scores (H2FPEF and HFA-PEFF). The AI HFpEF model and H2FPEF score demonstrate similar discrimination and calibration, but classification is higher with AI than H2FPEF and HFA-PEFF, attributable to fewer intermediate scores, due to discordant multivariable inputs. The continuous AI HFpEF model output adds information beyond the H2FPEF, and integration with existing scores increases correct management decisions. Those with a diagnostic positive result from AI have a two-fold increased risk of the composite outcome. We conclude that integrating an AI HFpEF model into the existing clinical diagnostic pathway would improve identification of HFpEF in complex clinical cohorts, and patients at risk of adverse outcomes.
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