Background & aims: Accurate prediction of disease severity and prognosis are challenging in patients with cirrhosis. We evaluated whether the deep learning-based AI-Cirrhosis-ECG (ACE) score could detect hepatic decompensation and predict clinical outcomes in cirrhosis.
Methods: We analyzed 2,166 ECGs from 472 patients in a retrospective Mayo Clinic cohort, 420 patients in a prospective Mayo transplant cohort, and 341 patients in an external validation cohort from Hospital Clínic de Barcelona. The ACE score's performance was assessed using receiver-operating characteristic analysis for decompensation detection and competing risks Cox regression for outcome prediction.
Results: The ACE score showed high accuracy in detecting hepatic decompensation (area under the curve 0.933, 95% CI: 0.923-0.942) with 88.0% sensitivity and 84.3% specificity at an optimal threshold of 0.25. In multivariable analysis, each 0.1-point increase in ACE score was independently associated with increased risk of liver-related death (hazard ratio [HR] 1.44, 95% CI 1.32-1.58, p <0.001). Adding ACE to model for end-stage liver disease-sodium significantly improved prediction of adverse outcomes across all cohorts (c-statistics: retrospective cohort 0.903 vs. 0.844; prospective cohort 0.779 vs. 0.735; external validation 0.744 vs. 0.732; all p <0.001).
Conclusions: The ACE score accurately identifies hepatic decompensation and independently predicts liver-related outcomes in cirrhosis. This non-invasive tool enhances current prognostic models and may improve risk stratification in cirrhosis management.
Impact and implications: This study demonstrates the potential of artificial intelligence to enhance prognostication in liver disease, addressing the critical need for improved risk stratification in cirrhosis management. The AI-Cirrhosis-ECG (ACE) score, derived from widely available ECGs, shows promise as a non-invasive tool for detecting hepatic decompensation and predicting liver-related outcomes, which could significantly impact clinical decision-making and resource allocation in hepatology. These findings are particularly important for hepatologists, transplant surgeons, and patients with cirrhosis, as they offer a novel approach to complement existing prognostic models such as model for end-stage liver disease-sodium. In practical terms, the ACE score could be integrated into routine clinical assessments to provide more accurate risk predictions, potentially improving the timing of interventions, optimizing transplant listing decisions, and ultimately enhancing patient outcomes. However, further validation in diverse populations and integration with other established predictors is necessary before widespread clinical implementation.
Keywords: Artificial intelligence; Deep learning; Hepatic decompensation; Liver transplant; Mortality.
© 2025 The Author(s).