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.
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.
Authors: Jonathan Graff-Radford; Jeremiah A Aakre; David S Knopman; Christopher G Schwarz; Kelly D Flemming; Alejandro A Rabinstein; Jeffrey L Gunter; Chadwick P Ward; Samantha M Zuk; A J Spychalla; Gregory M Preboske; Ronald C Petersen; Kejal Kantarci; John Huston; Clifford R Jack; Michelle M Mielke; Prashanthi Vemuri Journal: Mayo Clin Proc Date: 2020-06 Impact factor: 7.616
Authors: 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; Peter A Noseworthy Journal: Circ Arrhythm Electrophysiol Date: 2020-11-13
Authors: Sarah E Vermeer; Niels D Prins; Tom den Heijer; Albert Hofman; Peter J Koudstaal; Monique M B Breteler Journal: N Engl J Med Date: 2003-03-27 Impact factor: 91.245
Authors: Evan L Thacker; Barbara McKnight; Bruce M Psaty; W T Longstreth; Colleen M Sitlani; Sascha Dublin; Alice M Arnold; Annette L Fitzpatrick; Rebecca F Gottesman; Susan R Heckbert Journal: Neurology Date: 2013-06-05 Impact factor: 9.910