0747 Polysomnogram characteristics associated with artificial Intelligence enabled electrocardiogram algorithm predicted probability of atrial fibrillation in patients with obstructive sleep apnea

Abstract Introduction Obstructive sleep apnea (OSA) is associated with atrial fibrillation (AF). Several features of OSA are thought to play role in the development of AF. A recently developed artificial intelligence (AI) enabled electrocardiogram (ECG) algorithm predicts the probability of future development of AF from a single ECG. We sought to examine the relationship between polysomnogram (PSG) and clinical features of patients with OSA and the probability of future AF (P-AF). Methods Consecutive adults (age ≥ 18) with OSA and with an ECG obtained within 2 years of their attended polysomnograms (PSG) were examined. We excluded patients with AF, left ventricular ejection fraction < 50%, implanted pacemaker or cardioverter defibrillator. We recorded demographics, apnea-hypopnea index (AHI), respiratory arousal threshold (RAT), percent sleep time with SpO2 < 89%, arousal index, and a comorbidity score composed of presence of coronary artery disease (CAD), hypertension (HTN), diabetes mellitus (DM), stroke, and chronic lung disease. One and two-way ANOVA was used to examine the relationship of OSA severity (mild, moderate, or severe) with P-AF. Multiple linear regression further characterized associations with P-AF. Results Mean AI determined P- AF was 0.052±0.012 (mean±SE) in mild, 0.064±0.014 in moderate, and 0.105 + 0.014 in severe OSA, demonstrating a significant association of OSA severity with P-AF (p = 0.01). Post-hoc pairwise tests showed a significant difference in P-AF between mild and severe (p = 0.008) but not between moderate and severe OSA (p = 0.083). Multiple linear regression with all the variables listed above showed the association between OSA severity and P-AF remains significant (P = 0.044). Age (p<0.001) and comorbidity score (p<0.001) were the only other variables significantly associated with P-AF. Conclusion OSA severity determined by AHI was significantly associated with P-AF as determined by a novel ECG-based AI algorithm independent of other variables. Given the strength of association with age and the comorbidity score, OSA severity may have been associated as it is known to increase with age and comorbid conditions. Future directions include incorporating AI enabled algorithms to identify individuals with the highest risk of AF in whom the role of OSA treatment in reducing risk of AF may be examined. Support (If Any)