Machine Learning Model for Predicting Risk Factor Analysis and a Mortality Prediction Model of Acute Cholangitis Complicated with Sepsis.

Abstract

This study examined factors associated with sepsis in acute cholangitis and developed outcome-specific models for sepsis and mortality. We retrospectively analyzed data from 1,999 patients across two centers, including 1,561 admitted to general wards and 438 to the ICU. For each prediction task, the data were divided into training and internal-validation cohorts at a 7:3 ratio. Logistic regression (LR), random forest (RF), support vector machine (SVM), and Extreme Gradient Boosting (XGBoost) models were developed and compared, and Shapley Additive Explanations (SHAP) were used for interpretation. Sepsis occurred in 544 general-ward patients (34.85%) and 299 ICU patients (68.3%). LR achieved the highest internal-validation AUC for general-ward sepsis prediction (0.826; training AUC, 0.893). Sepsis was associated with poorer in-hospital survival in the general-ward cohort and poorer 28-day survival in the ICU cohort. The ICU 28-day mortality nomogram incorporated Acute Physiology Score III (APS III), hemoglobin (Hb), alanine aminotransferase (ALT), lactate (Lac), total bilirubin (TBil), albumin, sepsis status, and kidney injury and yielded an AUC of 0.840. The general-ward in-hospital mortality nomogram incorporated albumin, kidney injury, sepsis status, blood urea nitrogen (BUN), TBil, and aspartate aminotransferase (AST), yielding an AUC of 0.904. The internally validated models showed preliminary discriminatory ability for outcome-specific risk stratification. Prospective multicenter external validation is required before clinical implementation.