Abstract
Background
Recurrence of common bile duct stones (CBDs) commonly happens after endoscopic retrograde cholangiopancreatography (ERCP). The clinical prediction models for the recurrence of CBDs after ERCP are lacking.
Aims
We aim to develop high-performance prediction models for the recurrence of CBDS after ERCP treatment using automated machine learning (AutoML) and to assess the AutoML models versus the traditional regression models.
Methods
473 patients with CBDs undergoing ERCP were recruited in the single-center retrospective cohort study. Samples were divided into Training Set (65%) and Validation Set (35%) randomly. Three modeling approaches, including fully automated machine learning (Fully automated), semi-automated machine learning (Semi-automated), and traditional regression were applied to fit prediction models. Models’ discrimination, calibration, and clinical benefits were examined. The Shapley additive explanations (SHAP), partial dependence plot (PDP), and SHAP local explanation (SHAPLE) were proposed for the interpretation of the best model.
Results
The area under roc curve (AUROC) of semi-automated gradient boost machine (GBM) model was 0.749 in Validation Set, better than the other fully/semi-automated models and the traditional regression models (highest AUROC = 0.736). The calibration and clinical application of AutoML models were adequate. Through the SHAP-PDP-SHAPLE pipeline, the roles of key variables of the semi-automated GBM model were visualized. Lastly, the best model was deployed online for clinical practitioners.
Conclusion
The GBM model based on semi-AutoML is an optimal model to predict the recurrence of CBDs after ERCP treatment. In comparison with traditional regressions, AutoML algorithms present significant strengths in modeling, which show promise in future clinical practices.
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Funding
The work was supported by the National Natural Science Foundation of China (82000540), the Youth Program of Suzhou Health Committee (KJXW2019001), Development Project of Clinical Medicine in Jiangsu University (JLY20180134), Science and Technology Plan (Apply Basic Research) of Changzhou City (CJ20210006), and Science and Technology Plan of Suzhou City (SKY2021038).
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CX and JZ took part in the study concept and design. JG, LL, MY, CY, and XL involved in the acquisition of data. JL and YS were involved in statistical analyses. YS, JL, and YW participated in the first draft of the manuscript. JZ made the critical revision of the manuscript for important intellectual content.
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Shi, Y., Lin, J., Zhu, J. et al. Predicting the Recurrence of Common Bile Duct Stones After ERCP Treatment with Automated Machine Learning Algorithms. Dig Dis Sci 68, 2866–2877 (2023). https://doi.org/10.1007/s10620-023-07949-7
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DOI: https://doi.org/10.1007/s10620-023-07949-7