External validation of artificial intelligence for detection of heart failure with preserved ejection fraction

Ashley P. Akerman, Nora Al-Roub, Constance Angell-James, Madeline A. Cassidy, Rasheed Thompson, Lorenzo Bosque, Katharine Rainer, William Hawkes, Hania Piotrowska, Paul Leeson, Gary Woodward, Patricia A. Pellikka, Ross Upton and Jordan B. Strom ()
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Ashley P. Akerman: Oxford Business Park South
Nora Al-Roub: Beth Israel Deaconess Medical Center
Constance Angell-James: Beth Israel Deaconess Medical Center
Madeline A. Cassidy: Beth Israel Deaconess Medical Center
Rasheed Thompson: Howard University College of Medicine
Lorenzo Bosque: Drexel University College of Medicine
Katharine Rainer: Beth Israel Deaconess Medical Center
William Hawkes: Oxford Business Park South
Hania Piotrowska: Oxford Business Park South
Paul Leeson: Oxford Business Park South
Gary Woodward: Oxford Business Park South
Patricia A. Pellikka: Mayo Clinic
Ross Upton: Oxford Business Park South
Jordan B. Strom: Beth Israel Deaconess Medical Center

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58283-7

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