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Artificial Intelligence–Enabled Electrocardiogram for Atrial Fibrillation Identifies Cognitive Decline Risk and Cerebral Infarcts

医学 心脏病学 窦性心律 心房颤动 内科学 逻辑回归 磁共振成像 认知 人口 认知功能衰退 痴呆 放射科 精神科 环境卫生 疾病
作者
Erika L. Weil,Peter A. Noseworthy,Camden Lopez,Alejandro A. Rabinstein,Paul A. Friedman,Zachi I. Attia,Xiaoxi Yao,Konstantinos C. Siontis,Walter K. Kremers,Georgios Christopoulos,Michelle M. Mielke,Prashanthi Vemuri,Clifford R. Jack,Bernard J. Gersh,Mary M. Machulda,David S. Knopman,Ronald C. Petersen,Jonathan Graff‐Radford
出处
期刊:Mayo Clinic Proceedings [Elsevier]
卷期号:97 (5): 871-880 被引量:10
标识
DOI:10.1016/j.mayocp.2022.01.026
摘要

Objective To investigate whether artificial intelligence–enabled electrocardiogram (AI-ECG) assessment of atrial fibrillation (AF) risk predicts cognitive decline and cerebral infarcts. Patients and Methods This population-based study included sinus-rhythm ECG participants seen from November 29, 2004 through July 13, 2020, and a subset with brain magnetic resonance imaging (MRI) (October 10, 2011, through November 2, 2017). The AI-ECG score of AF risk calculated for participants was 0-1. To determine the AI-ECG-AF relationship with baseline cognitive dysfunction, we compared linear mixed-effects models with global and domain-specific cognitive z-scores from longitudinal neuropsychological assessments. The AI-ECG-AF score was logit transformed and modeled with cubic splines. For the brain-MRI subset, logistic regression evaluated correlation of the AI-ECG-AF score and the high-threshold, dichotomized AI-ECG-AF score with infarcts. Results Participants (N=3729; median age, 74.1 years) underwent cognitive analysis. Adjusting for age, sex, education, and APOE ɛ4-carrier status, the AI-ECG-AF score correlated with lower baseline and faster decline in global-cognitive z-scores (P=.009 and P=.01, respectively, non–linear-based spline-models tests) and attention z-scores (P<.001 and P=.01, respectively). Sinus-rhythm-ECG participants (n=1373) underwent MRI. As a continuous measure, the AI-ECG-AF score correlated with infarcts but not after age and sex adjustment (P=.52). For dichotomized analysis, an AI-ECG-AF score greater than 0.5 correlated with infarcts (OR, 4.61; 95% CI, 2.45-8.55; P<.001); even after age and sex adjustment (OR, 2.09; 95% CI, 1.06-4.07; P=.03). Conclusion The AI-ECG-AF score correlated with worse baseline cognition and gradual global cognition and attention decline. High AF probability by AI-ECG-AF score correlated with MRI cerebral infarcts. However, most infarcts observed in our cohort were subcortical, suggesting that AI-ECG not only predicts AF but also detects other non-AF cardiac disease markers and correlates with small vessel cerebrovascular disease and cognitive decline.

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