Journal article
External validation of artificial intelligence for detection of heart failure with preserved ejection fraction
Nature communications, v 16(1), 2915
25 Mar 2025
PMID: 40133291
Featured in Collection : UN Sustainable Development Goals @ Drexel
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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Details
- Title
- External validation of artificial intelligence for detection of heart failure with preserved ejection fraction
- Creators
- Ashley P Akerman - Ultromics, Ltd. (United Kingdom)Nora Al-Roub - Beth Israel Deaconess Medical CenterConstance Angell-James - Beth Israel Deaconess Medical CenterMadeline A Cassidy - Beth Israel Deaconess Medical CenterRasheed Thompson - Howard UniversityLorenzo Bosque - Drexel UniversityKatharine Rainer - Beth Israel Deaconess Medical CenterWilliam Hawkes - Ultromics, Ltd. (United Kingdom)Hania Piotrowska - Ultromics, Ltd. (United Kingdom)Paul Leeson - Ultromics, Ltd. (United Kingdom)Gary Woodward - Ultromics, Ltd. (United Kingdom)Patricia A Pellikka - Mayo Clinic in ArizonaRoss Upton - Ultromics, Ltd. (United Kingdom)Jordan B Strom (Corresponding Author) - Beth Israel Deaconess Medical Center
- Publication Details
- Nature communications, v 16(1), 2915
- Publisher
- Nature Publishing
- Grant note
- R01 AG063937 / NIA NIH HHS R01 HL173998 / NHLBI NIH HHS R01 HL169517 / NHLBI NIH HHS 1R01HL173998 / U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI) 1R01AG063937 / U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Medicine
- Web of Science ID
- WOS:001452497500021
- Scopus ID
- 2-s2.0-105000885959
- Other Identifier
- 991022197308504721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Cardiac & Cardiovascular Systems