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Updated: May 13, 2026

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Predicting biliary complications after liver transplantation: a machine learning and nomogram-based exploratory

Han Zhang1, Jinxi Chen1, Minghang Zhang1

  • 1Liver Transplantation Center, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.

BMC Gastroenterology
|May 12, 2026
PubMed
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This summary is machine-generated.

This study developed a machine learning (ML) model and nomogram to predict biliary complications after liver transplantation (LT). The ML model showed promising accuracy, aiding proactive, risk-stratified patient surveillance.

Area of Science:

  • Hepatobiliary Surgery
  • Transplant Surgery
  • Medical Informatics

Background:

  • Accurate risk stratification for biliary complications (BCs) after liver transplantation (LT) is crucial but challenging.
  • Existing methods may lack precision in identifying patients at high risk for post-LT BCs.

Purpose of the Study:

  • To develop and validate a machine learning (ML) and nomogram framework for predicting BCs post-LT.
  • To create a web calculator and a clinically interpretable nomogram for risk stratification.

Main Methods:

  • Retrospective analysis of 133 LT patients, split into training and validation sets.
  • Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression for predictor identification.
  • Trained eight ML algorithms, evaluated using AUC, Brier score, and DCA; SHAP for feature importance.
Keywords:
Biliary complicationsLightGBMLiver transplantationMachine learningNomogramRisk prediction

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Main Results:

  • Identified four predictors: hepatocellular carcinoma (protective), intraoperative crystalloid infusion, preoperative partial hepatectomy, and antiviral therapy (risk factors).
  • Light Gradient Boosting Machine (LightGBM) achieved the highest AUC (0.753) in the validation set, outperforming logistic regression (AUC 0.701).
  • The LightGBM model demonstrated satisfactory calibration and clinical utility, with a web calculator offering precise risk estimation.

Conclusions:

  • The LightGBM-based web calculator provides promising predictive accuracy for post-LT BCs.
  • The nomogram aids bedside decision-making through visual interpretability.
  • This framework supports a shift towards proactive, risk-stratified surveillance for post-transplant biliary complications.