AI-derived risk estimates significantly improved physicians' atrial fibrillation risk discrimination from an AUROC of 0.573 to 0.650, with a significant net reclassification improvement of 0.127 for non-AF cases.
Observational (n=112,524)
Yes
Does an AI algorithm for multi-day prediction of incident atrial fibrillation improve physician risk discrimination in simulated clinical settings?
An AI algorithm predicting incident AF from sinus rhythm ECGs significantly improved physician risk discrimination in simulated cases, particularly among non-electrophysiologists.
Effect estimate: NRI 0.127 (95% CI 0.086-0.168)
Absolute Event Rate: 0.65% vs 0.573%
p-value: p=<0.001
Atrial fibrillation (AF) is frequently asymptomatic and often remains undetected until complications arise. Although artificial intelligence (AI)-enabled electrocardiography (ECG) can predict incident AF from sinus rhythm ECGs, its influence on physician risk assessment in simulated clinical settings remains uncertain. We developed a deep learning model to predict multi-day AF risk using non-AF 12-lead ECGs. For ECG-labeled outcomes, the model achieved an AUROC of 0.79 in the internal EUMC cohort and 0.74 in the external BIDMC cohort. For Holter-labeled outcomes, AUROC values reached 0.87 in the internal EUMC subset and 0.75 in the prospective PROVISION-AF cohort. To assess decision-support utility, a multinational survey of 70 physicians evaluated how AI-derived risk estimates influenced physician risk assessment and follow-up decisions in structured simulated cases. AI assistance significantly improved physicians’ AF risk discrimination (AUROC 0.573 to 0.650) and negative predictive value (0.764 to 0.839), with significant net reclassification improvement for non-AF cases (NRI 0.127, p < 0.001). Performance gains were most notable among non-electrophysiologist cardiologists. In conclusion, AI-derived risk estimates improved physician risk discrimination in a structured simulated survey, particularly in non-specialist settings, supporting their potential role as a digital decision-support tool. Further real-world implementation studies are needed to determine whether these effects translate into improved clinical outcomes or healthcare efficiency.
Kim et al. (Mon,) conducted a observational in Incident atrial fibrillation (n=112,524). AI-enabled ECG prediction algorithm vs. Physician assessment without AI assistance (ECG + clinical data) was evaluated on Physician AF risk discrimination (AUROC) (NRI 0.127, 95% CI 0.086-0.168, p=<0.001). AI-derived risk estimates significantly improved physicians' atrial fibrillation risk discrimination from an AUROC of 0.573 to 0.650, with a significant net reclassification improvement of 0.127 for non-AF cases.