Interpretable machine learning models for pre- and postoperative prediction of early intra-abdominal infections after liver transplantation: a multicenter retrospective cohort study.

Abstract

Background: Early intra-abdominal infections (EIAIs) are among the most frequent and life-threatening complications following liver transplantation (LT). Early identification of high-risk patients remains challenging, and no standardized risk stratification tool is currently available.

Objective: To develop and externally validate interpretable machine learning (ML) models for preoperative and postoperative prediction of EIAIs after LT.

Design: A multicenter retrospective cohort study.

Methods: A total of 363 adult LT recipients were included (Center 1: n = 285; Center 2: n = 78). EIAIs were defined as intra-abdominal infections occurring within 30 days after LT. From 120 candidate variables, predictors were selected using random forest, LASSO regression, and univariate logistic regression. Seven ML algorithms were evaluated, and stacking ensemble models were selected as the final preoperative (pre-liver transplantation early intra-abdominal infection forecast tool (LIFT)) and postoperative (post-LIFT) models. Internal testing and external validation were performed. Model interpretability was assessed using SHapley Additive exPlanations (SHAP).

Results: The stacking models demonstrated superior performance. For pre-LIFT, the areas under receiver operator characteristic curve (ROC) curves (AUCs) were 0.995 ± 0.004 (training), 0.818 ± 0.056 (testing), and 0.796 ± 0.024 (external validation). For post-LIFT, AUCs were 0.996 ± 0.003, 0.847 ± 0.055, and 0.858 ± 0.026, respectively. Both models significantly outperformed model for end-stage liver disease and Child-Turcotte-Pugh scores (all p < 0.05). SHAP analysis revealed that baseline liver dysfunction, inflammatory markers, and immune status were key determinants in the pre-LIFT model, whereas perioperative factors such as intraoperative blood loss, ICU stay, and drainage duration predominated in the post-LIFT model.

Conclusion: We developed and externally validated interpretable ML-based models (LIFT) for predicting EIAIs after LT. These models enable individualized risk stratification at both preoperative and postoperative stages and may support personalized infection surveillance and management strategies. Prospective validation is warranted.