External validation of artificial intelligence for detection of heart failure with preserved ejection fraction
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...
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Nature Portfolio
2025-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58283-7 |
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| author | 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 Jordan B. Strom |
| author_facet | 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 Jordan B. Strom |
| author_sort | Ashley P. Akerman |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-13cb174a6eab41fea182964fe867d92d |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-13cb174a6eab41fea182964fe867d92d2025-08-20T02:28:09ZengNature PortfolioNature Communications2041-17232025-03-0116111210.1038/s41467-025-58283-7External validation of artificial intelligence for detection of heart failure with preserved ejection fractionAshley P. Akerman0Nora Al-Roub1Constance Angell-James2Madeline A. Cassidy3Rasheed Thompson4Lorenzo Bosque5Katharine Rainer6William Hawkes7Hania Piotrowska8Paul Leeson9Gary Woodward10Patricia A. Pellikka11Ross Upton12Jordan B. Strom13Ultromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park SouthRichard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical CenterRichard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical CenterRichard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical CenterHoward University College of MedicineDrexel University College of MedicineRichard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical CenterUltromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park SouthUltromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park SouthUltromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park SouthUltromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park SouthDepartment of Cardiovascular Medicine, Mayo ClinicUltromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park SouthRichard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical CenterAbstract 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.https://doi.org/10.1038/s41467-025-58283-7 |
| spellingShingle | 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 Jordan B. Strom External validation of artificial intelligence for detection of heart failure with preserved ejection fraction Nature Communications |
| title | External validation of artificial intelligence for detection of heart failure with preserved ejection fraction |
| title_full | External validation of artificial intelligence for detection of heart failure with preserved ejection fraction |
| title_fullStr | External validation of artificial intelligence for detection of heart failure with preserved ejection fraction |
| title_full_unstemmed | External validation of artificial intelligence for detection of heart failure with preserved ejection fraction |
| title_short | External validation of artificial intelligence for detection of heart failure with preserved ejection fraction |
| title_sort | external validation of artificial intelligence for detection of heart failure with preserved ejection fraction |
| url | https://doi.org/10.1038/s41467-025-58283-7 |
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