Why the study?
Building on prior results, the authors hypothesized that an electrocardiogram-enabled machine learning model could detect advanced chronic liver disease.
Does an ECG-enabled machine learning model accurately detect advanced chronic liver disease?
Does an ECG-enabled machine learning model accurately detect advanced chronic liver disease?
An ECG-enabled machine learning model demonstrates strong discriminative ability for detecting advanced chronic liver disease, offering a potential diagnostic tool for low-resource areas.
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.
No takes yet. Share an insight, caveat, or question.
Rattan et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: