Development and prospective validation of a machine learning model to predict mortality in cirrhosis with esophageal variceal bleeding

BACKGROUND Acute esophageal variceal bleeding (AEVB) is a critical complication in patients with cirrhosis, associated with high mortality despite advancements in management. Traditional prognostic scores often lack predictive accuracy in this context. AIM To develop, internally validate, and prospectively validate a machine learning (ML) model to predict 1-year mortality in patients with cirrhosis presenting with AEVB. METHODS A retrospective cohort of 94 patients treated between 2010 and 2016 was used to train ML models, incorporating 36 clinical, laboratory, and imaging variables. Four algorithms (generalized linear models, boosted generalized linear models, naive Bayes, random forests) were evaluated, and the best-performing model was prospectively validated in a cohort of 24 patients treated between 2017 and 2018. Performance metrics included the area under the curve (AUC), sensitivity, specificity, and calibration via Brier scores. Data preprocessing involved k-nearest neighbor imputation, one-hot encoding, and scaling. RESULTS The random forest model achieved the highest AUC (0.91, 95% confidence interval [CI]: 0.85-0.96) during internal validation and demonstrated robust performance in the prospective cohort (AUC 0.88, 95%CI: 0.80-0.94). Calibration was excellent, with a low Brier score (0.12). The model was deployed as an online prediction tool. CONCLUSION This ML model shows promise in improving mortality prediction for AEVB, potentially aiding timely clinical interventions and decision-making. Prospective validation underscores its generalizability and clinical utility. Future research should explore external validation in diverse settings.