Fig. 4: Relationship between AI-ECG age gap and atrial fibrillation burden.

This analysis was conducted in the S-Patch cohort for all subjects with AF burden >0% detected by the wearable device (including both device-detected and clinically adjudicated AF episodes). a Scatter plot showing the relationship between AF burden proportion (x-axis) and the 48-h average AI-ECG age gap during sinus rhythm (y-axis). The solid red line and shaded band represent the fitted linear trend and its 95% confidence interval (Pearson’s r = 0.130, p = 0.048). b Forest plot of average marginal effects (AMEs) and 95% confidence intervals derived from a multivariable fractional logit regression model (generalized linear model with binomial family and logit link) with AF burden proportion as the outcome. Each point estimate represents the absolute change in AF burden (proportion) associated with a one-unit increase in the corresponding predictor. For the AI-ECG age gap, each additional 1-year increase in the gap was associated with an AME of 0.0074 (95% CI 0.001–0.014), corresponding to a 0.74-percentage-point higher AF burden. Covariates include heart failure, diabetes mellitus, chronological age, height, diastolic and systolic blood pressure, body weight, prior myocardial infarction, antihypertensive medication, sex, and smoking status. The dashed vertical line indicates AME = 0; blue markers denote predictors with p < 0.05.