Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival.

Autor:	Lindow T; Kolling Institute, Royal North Shore Hospital, University of Sydney, Sydney, Australia.; Department of Clinical Physiology, Research and Development, Växjö Central Hospital, Region Kronoberg, Sweden.; Clinical Physiology, Clinical Sciences, Lund University, Sweden., Maanja M; Department of Clinical Physiology, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden., Schelbert EB; Minneapolis Heart Institute East, United Hospital, MN, USA., Ribeiro AH; Department of Information Technology, Uppsala University, Uppsala, Sweden., Ribeiro ALP; Telehealth Center, Hospital das Clínicas, and Internal Medicine Department, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil., Schlegel TT; Department of Clinical Physiology, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden.; Nicollier-Schlegel SARL, Trélex, Switzerland., Ugander M; Kolling Institute, Royal North Shore Hospital, University of Sydney, Sydney, Australia.; Department of Clinical Physiology, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden.
Jazyk:	angličtina
Zdroj:	European heart journal. Digital health [Eur Heart J Digit Health] 2023 Jul 25; Vol. 4 (5), pp. 384-392. Date of Electronic Publication: 2023 Jul 25 (Print Publication: 2023).
DOI:	10.1093/ehjdh/ztad045
Abstrakt:	Aims: Deep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference between an electrocardiogram (ECG)-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age. Methods and Results: Both A-ECG and DNN-AI Heart Age were applied to patients who had undergone clinical cardiovascular magnetic resonance imaging. The association between A-ECG or DNN-AI Heart Age Gap and cardiovascular risk factors was evaluated using logistic regression. The association between Heart Age Gaps and death or heart failure (HF) hospitalization was evaluated using Cox regression adjusted for clinical covariates/comorbidities. Among patients [ n = 731, 103 (14.1%) deaths, 52 (7.1%) HF hospitalizations, median (interquartile range) follow-up 5.7 (4.7-6.7) years], A-ECG Heart Age Gap was associated with risk factors and outcomes [unadjusted hazard ratio (HR) (95% confidence interval) (5 year increments): 1.23 (1.13-1.34) and adjusted HR 1.11 (1.01-1.22)]. DNN-AI Heart Age Gap was associated with risk factors and outcomes after adjustments [HR (5 year increments): 1.11 (1.01-1.21)], but not in unadjusted analyses [HR 1.00 (0.93-1.08)], making it less easily applicable in clinical practice. Conclusion: A-ECG Heart Age Gap is associated with cardiovascular risk factors and HF hospitalization or death. Explainable A-ECG Heart Age Gap has the potential for improving clinical adoption and prognostic performance compared with existing DNN-AI-type methods. Competing Interests: Conflict of interest: T.T.S. is owner and founder of Nicollier-Schlegel SARL, which performs ECG interpretation consultancy using software that can quantify the advanced ECG measures used in the current study. T.T.S. and M.U. are owners and founders of Advanced ECG Systems, a company that is developing commercial applications of advanced ECG technology used in the current study. (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)
Databáze:	MEDLINE
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