Predictive utility comparable for CHARGE AF clinical risk score, artificial intelligence-enabled analysis of 12-lead electrocardiogram<\/p>\n<\/h3>\n
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WEDNESDAY, Nov. 24, 2021 (HealthDay News) — Artificial intelligence (AI)-based analysis of 12-lead electrocardiograms (ECGs) has similar predictive ability for incident atrial fibrillation (AF) as a clinical risk score, according to a study published online Nov. 8 in Circulation<\/em>.<\/p>\n Shaan Khurshid, M.D., M.P.H., from Massachusetts General Hospital (MGH) in Boston, and colleagues trained a convolutional neural network (ECG-AI) to infer five-year incident AF risk using 12-lead ECGs in patients receiving care at MGH. Three hazard models were fit and included: ECG-AI five-year AF probability; the Cohorts for Heart and Aging in Genomic Epidemiology AF (CHARGE-AF) clinical risk score; and terms for both ECG-AI and CHARGE-AF (CH-AI). Model performance was assessed in an internal test set and two external test sets (Brigham and Women’s Hospital [BWH] and U.K. Biobank). The training set and test sets included 45,770 and 83,162 individuals, respectively.<\/p>\n The researchers found that the area under the receiver operating characteristic curve (AUROC) was comparable using CHARGE-AF (0.802, 0.752, and 0.732 for MGH, BWH, and U.K. Biobank, respectively) and ECG-AI (0.823, 0.747, and 0.705 for MGH, BWH, and U.K. Biobank, respectively). The highest AUROC was seen with CH-AI (0.838, 0.777, and 0.746 for MGH, BWH, and UK Biobank, respectively). Low calibration error was seen with ECG-AI and CH-AI. The ECG P-wave had the greatest influence on AI model predictions in saliency analyses.<\/p>\n “The application of such algorithms could prompt clinicians to modify important risk factors for atrial fibrillation that may reduce the risk of developing the disease altogether,” Khurshid said in a statement.<\/p>\n Several authors disclosed financial ties to the pharmaceutical industry.<\/p>\n Abstract\/Full Text (subscription or payment may be required)<\/a><\/p>\n <\/i><\/p>\n