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

被引:6
|
作者
Weil, Erika L. [1 ]
Noseworthy, Peter A. [1 ,2 ]
Lopez, Camden L. [3 ]
Rabinstein, Alejandro A. [1 ,6 ]
Friedman, Paul A. [2 ,5 ]
Attia, Zachi I. [2 ]
Yao, Xiaoxi [2 ,4 ]
Siontis, Konstantinos C. [2 ]
Kremers, Walter K. [3 ]
Christopoulos, Georgios [2 ]
Mielke, Michelle M. [1 ,3 ,6 ]
Vemuri, Prashanthi
Jack, Clifford R., Jr. [5 ]
Gersh, Bernard J. [2 ]
Machulda, Mary M. [5 ]
Knopman, David S. [1 ,2 ]
Petersen, Ronald C. [1 ,3 ,6 ]
Graff-Radford, Jonathan [1 ,4 ,7 ]
机构
[1] Mayo Clin, Dept Neurol, Coll Med, 200 1st St SW, Rochester, MN 55905 USA
[2] Mayo Clin, Robert D & Patricia E Kern Ctr Sci Hlth Care Deli, Rochester, MN USA
[3] Mayo Clin, Dept Radiol, Rochester, MN USA
[4] Mayo Clin, Dept Psychiat & Psychol, Rochester, MN USA
[5] Mayo Clin, Dept Neurol, Rochester, MN USA
[6] Mayo Clin, Dept Cardiovasc Med, Rochester, MN USA
[7] Mayo Clin, Dept Quantitat Hlth Sci, Rochester, MN USA
基金
美国国家卫生研究院;
关键词
MEDICAL-RECORDS-LINKAGE; SILENT BRAIN INFARCTS; STROKE PREVENTION; DEMENTIA; MICROINFARCTS; ASSOCIATION; POPULATION; IMPAIRMENT; PREVALENCE; PROFILE;
D O I
10.1016/j.mayocp.2022.01.026
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
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 APOE4-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. (c) 2022 Mayo Foundation for Medical Education and Research
引用
收藏
页码:871 / 880
页数:10
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