Development of a machine learning model to predict bleed in esophageal varices in compensated advanced chronic liver disease: A proof of concept.

ABSTRACT

BACKGROUND AND AIM: Risk stratification beyond the endoscopic classification of esophageal varices (EVs) to predict first episode of variceal bleeding (VB) is currently limited in patients with compensated advanced chronic liver disease (cACLD). We aimed to assess if machine learning (ML) could be used for predicting future VB more accurately. METHODS: In this retrospective analysis, data from patients of cACLD with EVs, laboratory parameters and liver stiffness measurement (LSM) were used to generate an extreme-gradient boosting (XGBoost) algorithm to predict the risk of VB. The performance characteristics of ML and endoscopic classification were compared in internal and external validation cohorts. Bleeding rates were estimated in subgroups identified upon risk stratification with combination of model and endoscopic classification. RESULTS: Eight hundred twenty-eight patients of cACLD with EVs, predominantly related to non-alcoholic fatty liver disease (28.6%), alcohol (23.7%) and hepatitis B (23.1%) were included, with 455 (55%) having the high-risk varices. Over a median follow-up of 24 (12-43) months, 163 patients developed VB. The accuracy of machine learning (ML) based model to predict future VB was 98.7 (97.4-99.5)%, 93.7 (88.8-97.2)%, and 85.7 (82.1-90.5)% in derivation (n = 497), internal validation (n = 149), and external validation (n = 182) cohorts, respectively, which was better than endoscopic classification [58.9 (55.5-62.3)%] alone. Patients stratified high risk on both endoscopy and model had 1-year and 3-year bleeding rates of 31-43% and 64-85%, respectively, whereas those stratified as low risk on both had 1-year and 3-year bleeding rates of 0-1.6% and 0-3.4%, respectively. Endoscopic classification and LSM were the major determinants of model's performance. CONCLUSION: Application of ML model improved the performance of endoscopic stratification to predict VB in patients with cACLD with EVs.