Interpretable machine learning models for predicting New-onset acute organ injury within 48 hours of admission in patients with community-acquired acute cholangitis

Abstract

Community-acquired acute cholangitis (CAAC) can progress to acute organ injury (AOI), increasing poor prognosis risk. This study aimed to develop machine learning models using clinical information obtained before AOI onset to predict new-onset AOI within 48 h after admission in patients with CAAC. This single-center retrospective cohort study screened 487 patients with CAAC between January 2018 and December 2022; 456 without AOI at admission were included. The primary outcome was new-onset AOI within 48 h. Candidate predictors included demographic data, medical history, vital signs, laboratory parameters, and composite indices obtained after admission and before AOI onset. Patients were stratified into training and independent test sets at an 8:2 ratio. In the training set, univariable logistic regression (LR) was used for screening, followed by LR with least absolute shrinkage and selection operator (LASSO) penalty via 5-fold cross-validation. Five models were developed: random forest (RF), support vector machine (SVM), LR, neural network (NN), and adaptive boosting (AdaBoost). Optimization used 5-fold stratified cross-validation and grid search. The optimal model was selected by cross-validated area under the receiver operating characteristic curve (ROC-AUC) and evaluated for discrimination, calibration, and net clinical benefit. SHapley Additive exPlanations (SHAP) was used for interpretation. RF and SVM achieved the highest mean cross-validated ROC-AUC (0.847 each). RF was selected per the prespecified tie-breaking protocol. In the independent test set, RF achieved an ROC-AUC of 0.820 [95% confidence interval (CI): 0.720–0.906], accuracy of 72.8%, sensitivity of 80.6%, specificity of 68.9%, positive predictive value (PPV) of 56.8%, negative predictive value (NPV) of 87.5%, and F1-score of 0.667. The Brier score was 0.156, calibration intercept 0.014, and calibration slope 1.252. Decision curve analysis (DCA) demonstrated a positive net clinical benefit across threshold probabilities ranging from 0.01 to 0.80. The eight key RF predictors were C-reactive protein (CRP), aspartate aminotransferase-to-platelet ratio index (APRI), MAP, prognostic nutritional index (PNI), gamma-glutamyl transferase-to-albumin ratio (GAR), age, neutrophil-to-lymphocyte ratio (NLR), and drink history. The RF model predicted new-onset AOI within 48 h in patients with CAAC and provided interpretable individual risk information through SHAP analysis.