Training and Performance of an Electrocardiogram-Enabled Machine Learning Model for Detection of Advanced Chronic Liver Disease

作者：Puru Rattan, Joseph Ahn, Beatriz Sordi Chara, Aidan F. Mullan, Kan Liu, Zachi I. Attia, Paul A. Friedman, Alina M. Allen, Vijay H. Shah, Patrick S. Kamath, Peter A. Noseworthy, Douglas A. Simonetto · 发表于：The American Journal of Gastroenterology · 年份：2025 · DOI：10.14309/ajg.0000000000003433 · 被引用次数：4 · 研究领域：ECG Monitoring and Analysis、Heart Rate Variability and Autonomic Control、Non-Invasive Vital Sign Monitoring

INTRODUCTION: Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD). METHODS: A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier. RESULTS: There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850-0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16-13.63). DISCUSSION: An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.