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Volume 41 Issue 3
Mar.  2025
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Article Navigation >  Journal of Clinical Hepatology  > 2025  >  41(3): 518-527

Development of a predictive model and application for spontaneous passage of common bile duct stones based on automated machine learning

DOI: 10.12449/JCH250319
  • 1.

    Department of Gastroenterology，Changshu First People’s Hospital，Changshu，Jiangsu 215500，China

  • 2.

    Changshu Key Laboratory of Medical Artificial Intelligence and Big Data，Changshu，Jiangsu 215500，China

  • 3.

    Department of Gastroenterology，Changshu Traditional Chinese Medicine Hospital，Changshu，Jiangsu 215500，China

Research funding:

Gusu Health Talent Training Project (GSWS2020109);

Suzhou 23rd Science and Technology Development Plan Project (SLT2023006);

Suzhou Clinical Key Disease Diagnosis and Treatment Technology Special Project (LCZX202334);

Changshu Science and Technology Development Plan Projects (CS202019);

Changshu Science and Technology Development Plan Projects (CSWS202316)

More Information
  • Corresponding author: XU Xiaodan， xxddocter@gmail.com （ORCID： 0009-0005-1947-3339）
  • Received Date: 2024-08-16
  • Accepted Date: 2024-09-06
  • Published Date: 2025-03-25
    Abstract
  •   Objective  To develop a predictive model and application for spontaneous passage of common bile duct stones using automated machine learning algorithms given the complexity of treatment decision-making for patients with common bile duct stones， and to reduce unnecessary endoscopic retrograde cholangiopancreatography （ERCP） procedures.  Methods  A retrospective analysis was performed for the data of 835 patients who were scheduled for ERCP after a confirmed diagnosis of common bile duct stones based on imaging techniques in Changshu First People’s Hospital （dataset 1） and Changshu Traditional Chinese Medicine Hospital （dataset 2）. The dataset 1 was used for the training and internal validation of the machine learning model and the development of an application， and the dataset 2 was used for external testing. A total of 22 potential predictive variables were included for the establishment and internal validation of the LASSO regression model and various automated machine learning models. The area under the receiver operating characteristic curve （AUC）， sensitivity， specificity， and accuracy were used to assess the performance of models and identify the best model. Feature importance plots， force plots， and SHAP plots were used to interpret the model. The Python Dash library and the best model were used to develop a web application， and external testing was conducted using the dataset 2. The Kolmogorov-Smirnov test was used to examine whether the data were normally distributed， and the Mann-Whitney U test was used for comparison between two groups， while the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups.  Results  Among the 835 patients included in the study， 152 （18.20%） experienced spontaneous stone passage. The LASSO model achieved an AUC of 0.875 in the training set （n=588） and 0.864 in the validation set （n=171）， and the top five predictive factors in terms of importance were solitary common bile duct stones， non-dilated common bile duct， diameter of common bile duct stones， a reduction in serum alkaline phosphatase （ALP）， and a reduction in gamma-glutamyl transpeptidase （GGT）. A total of 55 models were established using automated machine learning， among which the gradient boosting machine （GBM） model had the best performance， with an AUC of 0.891 （95% confidence interval： 0.859‍ ‍—‍ ‍0.927）， outperforming the extreme randomized tree mode， the deep learning model， the generalized linear model， and the distributed random forest model. The GBM model had an accuracy of 0.855， a sensitivity of 0.846， and a specificity of 0.857 in the test set （n=76）. The variable importance analysis showed that five factors had important influence on the prediction of spontaneous stone passage， i.e.， were solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， a reduction in serum ALP， and a reduction in GGT. The SHAP analysis of the GBM model showed a significant increase in the probability of spontaneous stone passage in patients with solitary common bile duct stones， non-dilated common bile duct， a stone diameter of <8 mm， and a reduction in serum ALP or GGT.  Conclusion  The GBM model and application developed using automated machine learning algorithms exhibit excellent predictive performance and user-friendliness in predicting spontaneous stone passage in patients with common bile duct stones. This application can help avoid unnecessary ERCP procedures， thereby reducing surgical risks and healthcare costs.

     

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