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

Jian CHEN; Kaijian XIA; Fuli GAO; Luojie LIU; Ganhong WANG; Xiaodan XU

doi:10.12449/JCH250319

Volume 41 Issue 3

Mar. 2025

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

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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

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.
- Choledocholithiasis,
- Cholangiopancreatography， Endoscopic Retrograde,
- Machine Learning,
- Predictive Model

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References(22)

References

[1]	MANES G, PASPATIS G, AABAKKEN L, et al. Endoscopic management of common bile duct stones: European Society of Gastrointestinal Endoscopy(ESGE) guideline[J]. Endoscopy, 2019, 51( 5): 472- 491. DOI: 10.1055/a-0862-0346.
[2]	ASGE Standards of Practice Committee, BUXBAUM JL, ABBAS FEHMI SM, et al. ASGE guideline on the role of endoscopy in the evaluation and management of choledocholithiasis[J]. Gastrointest Endosc, 2019, 89( 6): 1075- 1105. e 15. DOI: 10.1016/j.gie.2018.10.001.
[3]	group ERCP, Chinese Society of Digestive Endoscopology; group Biliopancreatic, Chinese Association of Gastroenterologist and hepatologist, National Clinical Research Center for Digestive Diseases. Chinese guidelines for ERCP(2018)[J]. J Clin Hepatol, 2018, 34( 12): 2537- 2554. DOI: 10.3969/j.issn.1001-5256.2018.12.009. 中华医学会消化内镜学分会ERCP学组, 中国医师协会消化医师分会胆胰学组, 国家消化系统疾病临床医学研究中心. 中国经内镜逆行胰胆管造影术指南(2018版)[J]. 临床肝胆病杂志, 2018, 34( 12): 2537- 2554. DOI: 10.3969/j.issn.1001-5256.2018.12.009.
[4]	ASGE Standards of Practice Committee, CHANDRASEKHARA V, KHASHAB MA, et al. Adverse events associated with ERCP[J]. Gastrointest Endosc, 2017, 85( 1): 32- 47. DOI: 10.1016/j.gie.2016.06.051.
[5]	Endoscopic Surgery Group, Digestive Endoscopy Branch, Chinese Medical Association, Endoscopic Surgery Expert Working Group, Chinese College of Surgeons, Professional Committee of Pancreatic Disease, Chinese Medical Doctor Association, et al. Guideline for the management of complications of duodenal perforation associated with ERCP in China(2023 edition)[J]. Chin J Dig Surg, 2024, 23( 1): 1- 9. DOI: 10.3760/cma.j.cn115610-20231025-00166. 中华医学会消化内镜学分会内镜外科学组, 中国医师协会外科医师分会内镜外科专家工作组, 中国医师协会胰腺病专业委员会, 等. 中国ERCP致十二指肠穿孔并发症管理指南(2023版)[J]. 中华消化外科杂志, 2024, 23( 1): 1- 9. DOI: 10.3760/cma.j.cn115610-20231025-00166.
[6]	FAN L, FU Y, LIU Y, et al. Research advances in hemorrhage after endoscopic retrograde cholangiopancreatography[J]. J Clin Hepatol, 2023, 39( 10): 2497- 2505. DOI: 10.3969/j.issn.1001-5256.2023.10.032. 范玲, 傅燕, 刘懿, 等. 内镜逆行胰胆管造影术后出血的研究进展[J]. 临床肝胆病杂志, 2023, 39( 10): 2497- 2505. DOI: 10.3969/j.issn.1001-5256.2023.10.032.
[7]	KHAN OA, BALAJI S, BRANAGAN G, et al. Randomized clinical trial of routine on-table cholangiography during laparoscopic cholecystectomy[J]. Br J Surg, 2011, 98( 3): 362- 367. DOI: 10.1002/bjs.7356.
[8]	WANG GH, CHEN J, SHEN ZJ, et al. Establishing and evaluating a risk prediction model for colonoscopy bowel preparation failure based on automated machine learning[J]. China J Endosc, 2024, 30( 5): 36- 47. DOI: 10.12235/E20230422. 王甘红, 陈健, 沈支佳, 等. 基于自动化机器学习建立结肠镜肠道准备失败风险预测模型及评价[J]. 中国内镜杂志, 2024, 30( 5): 36- 47. DOI: 10.12235/E20230422.
[9]	YAN WX, ZHANG PP, WANG WX, et al. Influencing factors of spontaneous passage of common bile duct stones in gallstones patients[J]. J Chin Pract Diagn Ther, 2023, 37( 10): 1025- 1027. DOI: 10.13507/j.issn.1674-3474.2023.10.011. 阎文心, 张平平, 王万祥, 等. 胆总管结石合并胆囊结石患者胆总管自发排石的影响因素分析[J]. 中华实用诊断与治疗杂志, 2023, 37( 10): 1025- 1027. DOI: 10.13507/j.issn.1674-3474.2023.10.011.
[10]	XU ZW, MEI Q, HONG JL, et al. Application of endoscopic ultrasonography combined with ALP and GGT in the diagnosis of spontaneous migration of choledocholithiasis[J]. J Hepatobiliary Surg, 2023, 31( 2): 106- 110. DOI: 10.3969/j.issn.1006-4761.2023.02.010. 徐张巍, 梅俏, 洪江龙, 等. 超声内镜联合ALP、GGT在诊断胆总管结石自发排石中的应用研究[J]. 肝胆外科杂志, 2023, 31( 2): 106- 110. DOI: 10.3969/j.issn.1006-4761.2023.02.010.
[11]	NOHARA Y, MATSUMOTO K, SOEJIMA H, et al. Explanation of machine learning models using shapley additive explanation and application for real data in hospital[J]. Comput Methods Programs Biomed, 2022, 214: 106584. DOI: 10.1016/j.cmpb.2021.106584.
[12]	FAHMY AS, CSECS I, ARAFATI A, et al. An explainable machine learning approach reveals prognostic significance of right ventricular dysfunction in nonischemic cardiomyopathy[J]. JACC Cardiovasc Imaging, 2022, 15( 5): 766- 779. DOI: 10.1016/j.jcmg.2021.11.029.
[13]	ANDREOZZI P, de NUCCI G, DEVANI M, et al. The high rate of spontaneous migration of small size common bile duct stones may allow a significant reduction in unnecessary ERCP and related complications: Results of a retrospective, multicenter study[J]. Surg Endosc, 2022, 36( 5): 3542- 3548. DOI: 10.1007/s00464-021-08676-8.
[14]	FROSSARD JL, HADENGUE A, AMOUYAL G, et al. Choledocholithiasis: A prospective study of spontaneous common bile duct stone migration[J]. Gastrointest Endosc, 2000, 51( 2): 175- 179. DOI: 10.1016/s0016-5107(00)70414-7.
[15]	HERBST MK, LI C, BLOMSTROM S. Point-of-care ultrasound assists diagnosis of spontaneously passed common bile duct stone[J]. J Emerg Med, 2021, 60( 4): 517- 519. DOI: 10.1016/j.jemermed.2020.11.008.
[16]	KHOURY T, ADILEH M, IMAM A, et al. Parameters suggesting spontaneous passage of stones from common bile duct: A retrospective study[J]. Can J Gastroenterol Hepatol, 2019, 2019: 5382708. DOI: 10.1155/2019/5382708.
[17]	JIANG L, LIU Z, YU JF, et al. On factors related to spontaneous passage of common bile duct stones leading to unnecessary endoscopic retrograde cholangiopancreatography[J]. Chin J Minim Invasive Surg, 2024, 24( 6): 409- 414. DOI: 10.3969/j.issn.1009-6604.2024.06.002. 姜蕾, 刘振, 于剑锋, 等. 胆总管结石自然排石致非必要治疗性内镜下逆行胰胆管造影的影响因素[J]. 中国微创外科杂志, 2024, 24( 6): 409- 414. DOI: 10.3969/j.issn.1009-6604.2024.06.002.
[18]	LEFEMINE V, MORGAN RJ. Spontaneous passage of common bile duct stones in jaundiced patients[J]. Hepatobiliary Pancreat Dis Int, 2011, 10( 2): 209- 213. DOI: 10.1016/s1499-3872(11)60033-7.
[19]	ZHANG RF, YIN MY, JIANG AQ, et al. Automated machine learning for early prediction of acute kidney injury in acute pancreatitis[J]. BMC Med Inform Decis Mak, 2024, 24( 1): 16. DOI: 10.1186/s12911-024-02414-5.
[20]	LIU LJ, ZHANG RF, SHI DT, et al. Automated machine learning to predict the difficulty for endoscopic resection of gastric gastrointestinal stromal tumor[J]. Front Oncol, 2023, 13: 1190987. DOI: 10.3389/fonc.2023.1190987.
[21]	LIU LJ, ZHANG RF, SHI Y, et al. Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: A SEER-based analysis[J]. Sci Rep, 2024, 14( 1): 12415. DOI: 10.1038/s41598-024-62311-9.
[22]	MURPHREE DH, QUEST DJ, ALLEN RM, et al. Deploying predictive models in A healthcare environment-an open source approach[C]// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC). Honolulu, HI, USA, 2018: 6112- 6116. DOI: 10.1109/EMBC.2018.8513689.

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

DOI: 10.12449/JCH250319

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

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