AI-Cirrhosis-ECG (ACE) score for predicting decompensation and liver outcomes.

ABSTRACT:

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.

SUBMITTER: Ahn JC 

PROVIDER: S-EPMC12018547 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature