Detection of undiagnosed liver cirrhosis via AI-enabled electrocardiogram: a pragmatic, cluster-randomized clinical trial.

Abstract

Advanced chronic liver disease (CLD) affects 2-5% of the general population, and accessible screening tools are needed in primary care. Here we conducted a pragmatic trial to assess whether an electrocardiogram (ECG)-based machine learning (ECG-ML) model enables early detection of advanced CLD. In this trial, 98 primary care teams were cluster randomized to intervention (access to ECG-ML results; 123 clinicians) or usual care (122 clinicians). Clinicians in the intervention arm were notified of a positive ECG-ML result, indicating higher risk of advanced CLD. The primary endpoint was new diagnosis of CLD with advanced fibrosis within 180 days of ECG, confirmed by sequential liver disease assessments. A total of 15,596 adults underwent 12-lead ECGs as part of routine care and met inclusion criteria (N = 8,034 intervention and N = 7,562 control). The intervention significantly increased new diagnoses of advanced CLD in the overall cohort (1.0% versus 0.5% in the control arm; odds ratio (OR) 2.09, 95% confidence interval (CI) 1.22-3.55, P = 0.007). Among ECG-ML-positive patients, advanced CLD was more frequent in the intervention arm (4.4% versus 1.1%; OR 4.37, 95% CI 1.94-9.88, P < 0.001). The intervention also increased the detection of any fibrosis (secondary endpoint) in the overall cohort (1.7% versus 0.5%; OR 3.17, 95% CI 1.86-5.40, P < 0.001) and among ECG-ML-positive patients (8.4% versus 1.1%; OR 8.03, 95% CI 3.50-18.4, P < 0.001). The diagnostic yield below epidemiological estimates probably reflects variable clinician adherence to artificial intelligence-driven recommendations. These results demonstrate that an ECG-based machine learning model, followed by targeted testing based on risk factors, may aid case finding of advanced CLD in routine primary care. ClinicalTrials.gov registration: NCT05782283 .