Bhyravajosyula, S C

Abstract Introduction Atrial fibrillation (AF) is a major contributor to stroke risk, yet many cases remain undiagnosed due to its silent nature. Deep learning-based electrocardiogram (DL-ECG) analysis can streamline AF risk stratification, providing a more reliable and accessible method to stratify risk using only a standard resting ECG. However, this tool has been validated only on AF detected clinically or with short ECG event monitors. Purpose This study aimed to validate the predictive accuracy of a DL-ECG model for identifying AF in normal sinus rhythm (NSR) ECGs among patients undergoing continuous cardiac monitoring with implantable loop recorders (ILRs). Methods We conducted a historical cohort study across three major U.S. academic medical centers, enrolling patients with an ILR and no prior diagnosis or documented ECG evidence of AF. Pre-implantation 10-second, 12-lead ECGs in NSR were analyzed using the DL-ECG model. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with the CHARGE-AF score. Kaplan-Meier survival analysis and Cox proportional hazards models assessed the association between DL-ECG risk categories and incident AF, defined as any episode lasting ≥6 minutes. Results We included 2,523 patients (mean age 66 ± 17 years, 52% male). AF was detected in 658 (26%) over a median follow-up of 19 months (IQR 8-37), Table 1. The DL-ECG model classified 1,634 patients (65%) as at low-risk and 889 (35%) as at high-risk. AF developed in 242 (14.8%) of the low-risk group and 416 (46.8%) of the high-risk group, yielding an AUC of 0.73 (95% CI 0.70-0.75), surpassing the CHARGE-AF AUC of 0.69 (95% CI 0.66-0.71). A combined DL-ECG and CHARGE-AF model yielded an AUC of 0.74 (95% CI 0.72-0.76), Figure 1 presents the AUC, Net Reclassification Index, and DeLong test comparisons among the three models. High-risk patients had a significantly higher incidence of AF, which remained significant after adjusting for CHARGE-AF variables, Figure 2. AF incidence increased as DL-ECG quartiles increased, with 61% of patients at the top quartile developing AF during follow-up, Figure 3. False negatives (patients initially classified as low-risk but in whom later AF was detected) had a longer median time to AF detection (127 days [IQR 28-425] vs. 101 days [IQR 20-318] for true positives, p = 0.01), and where found to have at least one positive ECG during follow-up in 36%, compared to 32% of true negatives, indicating eventual risk reclassification. Conclusions Among patients undergoing ILR monitoring, the DL-ECG model demonstrated superior predictive performance compared to CHARGE-AF, offering a promising and accessible alternative for early detection of silent AF. This approach could aid early AF detection, particularly in resource-limited settings where access to specialized rhythm monitoring services is limited.Table 1 and Figure 1 Figure 2 and 3