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

Table 4 Performance comparison of the random forest model vs traditional clinical scoring systems for predicting 1-year mortality in patients with cirrhosis with acute esophageal variceal bleeding (retrospective cohort, n = 97)

Model	AUC (95%CI)	Sensitivity	Specificity	Accuracy	Brier score
Random forest	0.915 (0.856-0.961)	0.800	0.860	0.830	0.124
MELD-Na	0.742 (0.651-0.823)	0.688	0.717	0.702	0.186
MELD	0.726 (0.634-0.809)	0.667	0.696	0.681	0.194
Child-Pugh	0.685 (0.591-0.771)	0.625	0.674	0.649	0.217
Glasgow-Blatchford	0.598 (0.502-0.690)	0.542	0.609	0.574	0.248

A Brier score ranges from 0 to 1, with lower values indicating better calibration (0 = perfect calibration). Random forest model used 100 trees, bootstrap aggregation, and isotonic calibration. P < 0.001 compared to random forest model (DeLong test). All metrics except area under the receiver operating characteristic curve were calculated at the optimal threshold determined by maximizing the Youden index (sensitivity + specificity - 1). Confidence intervals (CIs) for area under the receiver operating characteristic curve were calculated using 1000 bootstrap iterations. AUC: Area under the curve; MELD: Model for end-stage liver disease; MELD-Na: MELD-sodium.