Machine learning-based predictive model for the perioperative co-occurrence of T-cell-mediated rejection and
Junjie Sun1, Guangyi Zhu1, Qingwen Liang1
1Institute of Transplant Medicine, The Second Affiliated Hospital of Guangxi Medical University, Guangxi Clinical Research Center for Organ Transplantation, Guangxi Key Laboratory of Organ Donation and Transplantation, Nanning, China.
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 recall compared to other models. SHAP analysis identified variables that contributed to the model predictions.
Conclusions:
This study constructed a robust predictive model for the perioperative co-occurrence of TCMR and pneumonia. The SVM model demonstrated superior predictive performance.
Insights
This study developed a machine learning model to predict T-cell-mediated rejection (TCMR) and pneumonia after liver transplantation (LT). The support vector machine (SVM) model showed strong predictive accuracy for these complications.
Area of Science:
- Hepatology
- Transplantation immunology
- Medical informatics
Background:
- Perioperative T-cell-mediated rejection (TCMR) and pneumonia are critical complications following liver transplantation (LT).
- These conditions significantly impact graft function and patient survival.
- Predictive tools are needed to identify high-risk patients for early intervention.
Purpose of the Study:
- To develop and validate a machine learning (ML)-based model for predicting the perioperative co-occurrence of TCMR and pneumonia in LT recipients.
- To identify key predictive variables associated with this co-occurrence.
Main Methods:
- Retrospective collection of recipient data from 717 LT patients.
- Identification of predictive variables using LASSO regression.
- Development of predictive models using five ML algorithms, including support vector machine (SVM).
- Performance evaluation using ROC curves, calibration curves, and SHapley Additive exPlanations (SHAP).
Main Results:
- 93 out of 717 LT recipients experienced perioperative TCMR and pneumonia co-occurrence.
- Key predictors identified include postoperative bilirubin, INR, HDL, ALT, NK cells, tacrolimus levels, Na+, operative time, anhepatic phase, induction regimen, and ICU stay.
- The SVM model achieved an AUC of 0.881 (training) and 0.786 (test), outperforming other models in specificity, sensitivity, F1 score, and recall.
- SHAP analysis provided insights into model predictions.
Conclusions:
- A robust ML model, specifically SVM, was developed for predicting perioperative TCMR and pneumonia co-occurrence in LT.
- The SVM model demonstrated superior predictive performance, offering a valuable tool for risk stratification and clinical decision-making.
Related Concept Videos
01:26Kidney Transplant II: Surgical Procedure
01:40Cell-mediated Immune Responses
01:24Tissue Transplantation
The Biology of Tissue Transplantation
The biology of tissue transplantation hinges on the Major Histocompatibility Complex (MHC) molecules. These molecules...