ORIGINAL RESEARCH article
Front. Immunol.
Sec. Alloimmunity and Transplantation
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1648993
This article is part of the Research TopicEnhancing Kidney Transplant Outcomes through Machine Learning InnovationsView all 5 articles
Machine Learning-based Predictive Model for the Perioperative Co-occurrence of T-cell-mediated Rejection and Pneumonia in Liver Transplantation
Provisionally accepted
- Second Affiliated Hospital of Guangxi Medical University, Nanning, China
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Objective: Perioperative T-cell-mediated rejection (TCMR) and pneumonia occurrence significantly impair graft function and patient survival following liver transplantation (LT). This article aims to develop a machine learning (ML)-based model to predict perioperative co-occurrence of TCMR and pneumonia. Methods: Recipient-related data were retrospectively collected. Predictive Variables were identified through LASSO regression analysis. Five machine learning algorithms, including support vector machine (SVM), were employed to develop predictive models. Model performance was appraised via the receiver operating characteristic (ROC) curve, and calibration curve. SHapley Additive exPlanations (SHAP) method was employed to visualize model characteristics and individual predictions. Results: This study enrolled 717 LT recipients, including 93 patients with perioperative co-occurrence of TCMR and pneumonia. LASSO regression identified postoperative direct bilirubin, postoperative international normalized ratio, high-density lipoprotein, postoperative alanine aminotransferase, natural killer cell, tacrolimus (FK506) concentration, Na+, operative time, anhepatic phase, induction regimen, and ICU stay as significant predictors. The SVM model demonstrated superior predictive performance, with area under the curve values of 0.881 (95% CI: 0.83–0.93) and 0.786 (95% CI: 0.69–0.88) in the training and test sets, respectively. The calibration curve showed high agreement between the predicted and observed risks. The SVM model demonstrated superior specificity, sensitivity, F1 score, and
Keywords: machine learning, Liver Transplantation, T-cell-mediated rejection, Pneumonia, Perioperative Period, predictive model
Received: 18 Jun 2025; Accepted: 03 Sep 2025.
Copyright: © 2025 Sun, Sun, Zhu, Liang, Wen and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xuyong Sun, Second Affiliated Hospital of Guangxi Medical University, Nanning, China
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