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Published on: May 9, 2018
Factors influencing recurrence and model development for recurrence of minimally invasive percutaneous transhepatic
Yong-Rong Liang1, Jia-Peng Zhou1, Yong-Xiang Luo1
1Department of Hepatobiliary and Pancreatic Surgery, The First People's Hospital of Qinzhou Qinzhou 535000, Guangxi, China.
Objective:
To identify factors influencing recurrence after percutaneous transhepatic choledochoscopic lithotripsy (PTCSL) and to develop a predictive model.
Methods:
We retrospectively analyzed clinical data from 354 patients with intrahepatic and extrahepatic bile duct stones treated with PTCSL at Qinzhou First People's Hospital between February 2018 and January 2020. Patients were followed for three years and categorized into non-recurrence and recurrence groups based on postoperative outcome. Univariate analysis identified possible predictors of stone recurrence. Data were split using the gradient boosting machine (GBM) algorithm, assigning 70% as the training set and 30% as the test set. The predictive performance of the GBM model was assessed using the receiver operating characteristic (ROC) curve and calibration curve, and compared with a logistic regression model.
Results:
Six factors were identified as significant predictors of recurrence: age, diabetes, total bilirubin, biliary stricture, number of stones, and stone diameter. The GBM model, developed based on these factors, showed high predictive accuracy. The area under the ROC curve (AUC) was 0.763 (95% CI: 0.695-0.830) for the training set and 0.709 (95% CI: 0.596-0.822) for the test set. Optimal cutoff values were 0.286 and 0.264, with sensitivities of 62.30% and 66.70%, and specificities of 77.20% and 68.50%, respectively. Calibration curves indicated good agreement between predicted probabilities and observed recurrence rates in both sets. DeLong's test revealed no significant differences between the GBM and logistic regression models in predictive performance (training set: D = 0.003, P = 0.997 > 0.05; test set: D = 0.075, P = 0.940 > 0.05).
Conclusion:
Biliary stricture, stone diameter, diabetes, stone number, age, and total bilirubin significantly influence stone recurrence after PTCSL. The GBM model, based on these factors, demonstrates robust accuracy and discrimination. Both GBM and logistic regression models effectively predicted stone recurrence post-PTCSL.
