Association between AI-Based Electrocardiographic Age from Wearable Devices and Atrial Fibrillation: The PROPHECG-Age Single Study

Background and Aims : Artificial-intelligence (AI)-derived electrocardiographic (ECG) age is a promising marker of atrial fibrillation (AF) risk, yet it has been evaluated only in hospital-based 12-lead recordings. We aimed to develop PROPHECG-Age Single—an AI model that estimates ECG-age from wearable single-lead ECGs—and to examine whether the resulting ECG-age is associated with AF risk in a real-world self-monitoring setting. Methods : One million 12-lead ECGs (academic tertiary hospital, Jan 2006–Sep 2021) were converted into synthetic single-lead data via a pre-trained Cycle-Consistent Generative Adversarial Network and used to train a ResNet-1D age-prediction network. The age-prediction model was validated in the S-Patch registry (1,980 participants; Sep 2021–Aug 2024; NCT05119725) and externally in the Memo Patch registry (582 participants; Sep 2022–Nov 2023; NCT05355948). Multivariable logistic (AF presence) and fractional-logit (AF burden) models, adjusted for sex, age, and comorbidities, generated cohort-specific effect estimates that were pooled with fixed-effect meta-analysis. Results : PROPHECG-Age Single achieved mean absolute errors of 10.01 years (S-Patch) and 11.88 years (Memo Patch). Participants with AF demonstrated significantly larger AI-ECG age gaps than those without AF (–1.2 vs –4.1 years; p < 0.001), a difference that persisted after adjustment (odds ratio 1.02 per year; 95% CI 1.01–1.04). Each additional year of AI-ECG age gap showed a 0.74 percentage-point increase in AF burden (p = 0.030) after adjustment. Meta-analysis confirmed significant associations with both AF presence (pooled adjusted OR = 1.03 per year; 95% CI 1.01–1.04) and AF burden (pooled marginal effect = 0.008 per year; 95% CI 0.002–0.014). Conclusions : PROPHECG-Age Single provides ECG-age estimates from wearable devices and robustly associates with AF presence and burden. Wearable-based AI-ECG age is a potential digital biomarker for proactive cardiovascular monitoring in a patient-centred context.