• Georgios Christopoulos, Jonathan Graff-Radford, Camden L Lopez, Xiaoxi Yao, Zachi I Attia, Alejandro A Rabinstein, Ronald C Petersen, David S Knopman, Michelle M Mielke, Walter Kremers, Prashanthi Vemuri, Konstantinos C Siontis, Paul A Friedman, and Peter A Noseworthy.
• Department of Cardiovascular Medicine (G.C., X.Y., Z.I.A., K.C.S., P.A.F., P.A.N.), Mayo Clinic, Rochester, MN.
• Circ Arrhythm Electrophysiol. 2020 Dec 1; 13 (12): e009355.

BackgroundAn artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI-enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology-AF) in a population-based sample.MethodsWe calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score.ResultsA total of 1936 participants with median age 75.8 (interquartile range, 70.4-81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2-14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51-2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58-2.28]) independently predicted future AF without significant interaction (P=0.54). C statistics were 0.69 (95% CI, 0.66-0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66-0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69-0.75) for combined AI-ECG and CHARGE-AF score.ConclusionsIn the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.

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