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

Puru Rattan, Joseph C Ahn, Beatriz Sordi Chara,Aidan F Mullan, Kan Liu, Zachi I Attia,Paul A Friedman,Alina Allen,Vijay H Shah,Patrick S Kamath,Peter A Noseworthy,Douglas A Simonetto

The American journal of gastroenterology（2025）

Division of Gastroenterology and Hepatology | Division of Biomedical Statistics and Informatics | Department of Cardiovascular Medicine

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Abstract

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.

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【要点】：本研究提出了一种基于心电图（ECG）的机器学习模型，能够有效检测晚期慢性肝病（CLD），展示了在资源匮乏区域中的应用潜力。

【方法】：通过电子健康记录匹配CLD患者和对照者的12导联ECG数据，训练了一种作为二元分类器的机器学习模型。

【实验】：在包含12,930名CLD患者和64,577名对照者的队列中，所训练模型对CLD的分类显示出0.858的曲线下面积（AUC），在选定阈值下，CLD心电图被分类为CLD的可能性高出12倍。