Artificial intelligence age prediction using electrocardiogram data: Exploring biological age differences.

Autor:	Evans S; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia., Howson SA; Royal Adelaide Hospital, Adelaide, South Australia, Australia., Booth AEC; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia., Shahmohamadi E; University of Adelaide, Adelaide, South Australia, Australia., Lim M; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia., Bacchi S; Flinders University, Adelaide, South Australia, Australia; Lyell McEwin Hospital, Adelaide, South Australia, Australia., Roberts-Thomson RL; Royal Adelaide Hospital, Adelaide, South Australia, Australia., Middeldorp ME; University of Adelaide, Adelaide, South Australia, Australia., Emami M; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia., Psaltis PJ; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia., Sanders P; Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia. Electronic address: prash.sanders@adelaide.edu.au.
Jazyk:	angličtina
Zdroj:	Heart rhythm [Heart Rhythm] 2024 Sep 27. Date of Electronic Publication: 2024 Sep 27.
DOI:	10.1016/j.hrthm.2024.09.046
Abstrakt:	Background: Biological age can be predicted using artificial intelligence (AI) trained on electrocardiograms (ECGs), which is prognostic for mortality and cardiovascular events. Objective: We developed an AI model to predict age from an ECG and compared baseline characteristics to identify determinants of advanced biological age. Methods: An AI model was trained on ECGs from cardiology inpatients aged 20-90 years. AI analysis used a convolutional neural network with data divided in an 80:20 ratio (development/internal validation), with external validation undertaken using data from the UK Biobank. Performance and subgroup comparison measures included correlation, difference, and mean absolute difference. Results: A total of 63,246 patients with 353,704 total ECGs were included. In internal validation, the correlation coefficient was 0.72, with a mean absolute difference between chronological age and AI-predicted age of 9.1 years. The same model performed similarly in external validation. In patients aged 20-29 years, AI-ECG-predicted biological age was greater than chronological age by a mean of 14.3 ± 0.2 years. In patients aged 80-89 years, biological age was lower by a mean of 10.5 ± 0.1 years. Women were biologically younger than men by a mean of 10.7 months (P = .023), and patients with a single ECG were biologically 1.0 years younger than those with multiple ECGs (P < .0001). Conclusion: There are significant between-group differences in AI-ECG-predicted biological age for patient subgroups. Biological age was greater than chronological age in young hospitalized patients and lower than chronological age in older hospitalized patients. Women and patients with a single ECG recorded were biologically younger than men and patients with multiple recorded ECGs. Competing Interests: Disclosures Dr Roberts-Thomson has received speaking honoraria from Abbott and Edwards Lifesciences. Dr Psaltis has received consulting fees from Amgen, Eli Lilly, and Esperion and speaker honoraria from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Merck, Schering-Plough, Novartis, Novo Nordisk, Pfizer, and Sanofi. Dr Sanders reports serving on the medical advisory boards of Abbott, Medtronic, Boston Scientific, CathRx, and PaceMate. The University of Adelaide has received on behalf of Dr Sanders research funds from Boston Scientific, Medtronic, Abbott, and Becton Dickenson. All other authors declare that there is no conflicts of interest. (Copyright © 2024 Heart Rhythm Society. All rights reserved.)
Databáze:	MEDLINE
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