Circulation. Arrhythmia and electrophysiology · 2020
Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Christopoulos Georgios, Graff-Radford Jonathan, Lopez Camden L, Yao Xiaoxi, Attia Zachi I, Rabinstein Alejandro A, Petersen Ronald C, Knopman David S, Mielke Michelle M, Kremers Walter, Vemuri Prashanthi, Siontis Konstantinos C, Friedman Paul A, Noseworthy Peter A
Аннотация
BACKGROUND: An 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.
METHODS: We 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.
RESULTS: A 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.
CONCLUSIONS: In 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.
METHODS: We 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.
RESULTS: A 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.
CONCLUSIONS: In 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.