Development of the AI-Cirrhosis-ECG (ACE) Score: An electrocardiogram-based deep learning model in cirrhosis

Cirrhosis is associated with cardiac dysfunction and distinct ECG abnormalities. This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG and generate an AI-Cirrhosis-ECG (ACE) score that would correlate with disease severity. A review of Mayo Clinic’s electronic health records identified 5,212 patients with advanced cirrhosis ≥18 years of age who underwent liver transplantation (LT) at the three Mayo Clinic transplant centers between 1988 and 2019. The patients were matched by age and sex in a 1:4 ratio to controls without liver disease, then divided into training, validation, and test sets using a 70%-10%-20% split. The primary outcome was the performance of the model in distinguishing patients with cirrhosis from controls using their ECGs. Additionally, the association between the ACE score and the severity of patients’ liver disease was assessed. The model’s AUC in the testing set was 0.908 with 84.9% sensitivity and 83.2% specificity, and this performance remained consistent after additional matching for medical comorbidities. Significant elevations in the ACE scores were seen with increasing MELD-Na. Longitudinal trends in the ACE scores before and after LT mirrored the progression and resolution of liver disease. The ACE score, a deep learning model, can accurately discriminate ECGs from patients with and without cirrhosis. This novel relationship between AI-enabled ECG analysis and cirrhosis holds promise as the basis for future low-cost tools and applications in the care of patients with liver disease.