Detection of Undiagnosed Liver Cirrhosis via Artificial Intelligence-Enabled Electrocardiogram (DULCE): Rationale and design of a pragmatic cluster randomized clinical trial

Abstract

Background

Cirrhosis is a leading cause of morbidity and mortality worldwide, yet preventable at early stages. Currently, effective approaches for early diagnosis are lacking. A novel electrocardiogram (ECG)-enabled deep learning model trained for detection of advanced chronic liver disease (CLD) has demonstrated promising results and it may be used for screening of advanced CLD in primary care.

Design

A pragmatic, cluster randomized trial (NCT05782283) in 45 Mayo Clinic primary care practices will be conducted over a period of 6 months with 6 months of follow up. Care teams will be randomized 1:1 to intervention or usual care, stratified by region and patient volume. Patients from providers enrolled in the trial who undergo an ECG during the study period will be included. In the intervention arm, consenting providers to patients identified as higher risk of advanced CLD based on their ECG will be notified with a recommendation for noninvasive fibrosis assessment. The primary endpoint will be detection of advanced CLD (defined as stage 3–4 on blood- or imaging-based noninvasive liver disease assessment or liver biopsy). Secondary outcomes will include completion of fibrosis assessment tests within 180 days of ECG, new diagnosis of liver disease stratified by etiology and risk factors for CLD, and detection of any liver fibrosis (stages 1–4). Post-study surveys to participating clinicians will be conducted.

Summary

Preliminary findings suggest outstanding potential for the use of an ECG-enabled machine learning algorithm for detection of advanced CLD in the primary care community.