Artificial intelligence-predicted ECG age gap as a biomarker: bias-adjusted correlation with mortality and cardiovascular risk factors
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Le résumé fourni par la source
Abstract Aims Artificial intelligence models can estimate a person’s age from ECG. The gap between the predicted ECG age and chronological age, predicted age deviation (PAD), has been associated with cardiovascular risk factors and mortality. However, regression bias causes PAD to correlate with chronological age itself, potentially distorting these associations. Objectives To investigate the bias introduced by age on PAD by comparing associations between PAD and a bias-corrected PAD (PADbc) with cardiovascular risk factors and survival outcomes. Methods and results ECG and cardiovascular risk data from Ziekenhuis Oost-Limburg (2002–23) were linked to mortality data from the Belgian National Registry. A neural network was trained to predict age from ECGs. PADbc corresponded to the residual of PAD regressed on chronological age. Associations with risk factors were tested using χ2 and ANOVA. Survival was analysed with Kaplan–Meier curves and Cox proportional hazards models. We included 1 258 993 ECGs from 234 586 patients, split 40:10:50 into training, validation, and test sets by patient. In the test set [mean age 56.4 ± 16.9 years, mean absolute error (MAE) 7.9], PAD correlated with age (r = −0.54) and showed inverse associations with most risk factors; conversely, higher PADbc (r = 0.00) was associated with higher prevalence of risk factors. Kaplan–Meier revealed that PADbc above its MAE was linked to lower survival, whereas PAD showed the opposite. Multivariate Cox showed each 1-year increase in both PAD and PADbc was associated with a 1.4% increased mortality hazard. Conclusion PADbc is associated with cardiovascular risk factors and mortality, offering an age-independent biomarker of biological ageing.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Artificial intelligence-predicted ECG age gap as a biomarker: bias-adjusted correlation with mortality and cardiovascular risk factors
- Date Crossref
- 28/11/2025
- Éditeur
- Oxford University Press (OUP)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Ziekenhuis Oost-Limburg pays non établi dans la noticeÉtablissement de santé
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Hasselt University Data Science Institute pays non établi dans la noticeUniversité ou école supérieure
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KU Leuven pays non établi dans la noticeUniversité ou école supérieure
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Department Future Health pays non établi dans la noticeInstitution
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Faculty of Medicine and Life Sciences Limburg Clinical Research Centre/Mobile Health Unit pays non établi dans la noticeUniversité ou école supérieure
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Qompium NV pays non établi dans la noticeInstitution
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Department of Cardiology pays non établi dans la noticeInstitution
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University of Leuven Department of Cardiovascular Sciences pays non établi dans la noticeUniversité ou école supérieure
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Hospital East-Limburg Department of Cardiology pays non établi dans la noticeÉtablissement de santé
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Data Science Department pays non établi dans la noticeInstitution
Ziekenhuis Oost-Limburg, Data Science Institute — Hasselt University et KU Leuven, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.