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March 29, 2026Open Access

Predictive modeling and risk factor analysis of common bile duct stones after laparoscopic cholecystectomy: implications for postoperative management in minimally invasive surgery

WZWenjie ZhangGuangdong Medical College SWSong WuTongji University DGDuhui GongGuangzhou Medical University

Key Points

The aim is to identify risk factors for recurrent common bile duct stones after laparoscopic cholecystectomy and develop a predictive model for postoperative management.
Data collected from 891 patients post-cholecystectomy over 12 months.
Univariate and multivariate logistic regression analyses performed to find independent predictors.
A nomogram model created integrating risk factors for clinical use.
Model performance evaluated using AUC, calibration curves, and decision curve analysis.
Validation of model robustness through 1,000 bootstrap repetitions.
Identified six independent predictors for common bile duct stones recurrence: age, CRP, ALT, TB, CBD diameter, and liver fluke infection history.
Final predictive model achieved an AUC of 0.835, indicating excellent discrimination.
Demonstrated strong calibration and confirmed robustness with a bootstrap-corrected C-index of 0.835.
The model shows superior clinical utility compared to standard treatment strategies.

Abstract

Minimally invasive surgery, particularly laparoscopic cholecystectomy, has become the standard treatment for gallbladder disease. However, postoperative recurrence of common bile duct stones (CBDS) remains a clinically significant complication that compromises recovery and long-term outcomes. This study aimed to identify independent risk factors associated with CBDS following laparoscopic cholecystectomy and to develop a visual predictive model for early warning and individualized postoperative management. We collected data from 891patients who underwent cholecystectomy at Affiliated Hospital Group of Guangdong Medical University Panyu He Xian Memorial Hospital (Panyu District, Guangzhou) between September 2021and September 2024, Patients were followed for 12 months postoperatively, and 80 developed recurrent CBD stones. Demographic characteristics (age, history of liver fluke infection) and perioperative laboratory indicators (C-reactive protein CRP, alanine aminotransferase ALT, total bilirubin TB, and common bile duct diameter) were compared using SPSS 27.0. Univariate and multivariate logistic regression analyses, combined with LASSO regression, were employed to identify independent predictors, which were incorporated into a nomogram model. Model performance was assessed by discrimination (AUC), calibration (calibration curve and Hosmer–Lem show test), and clinical utility (decision curve analysis), with internal validation via bootstrapping (1,000 repetitions). Multivariate analysis identified six independent predictors of CBD stone recurrence: older age (OR = 1.03 per year, 95% CI: 1.01–1.07, P = 0.019), elevated CRP (OR = 1.03 per mg/L, 95% CI: 1.00–1.07, P = 0.023), higher ALT (OR = 1.02 per U/L, 95% CI: 1.01–1.02, P < 0.001), increased TB (OR = 1.02 per µmol/L, 95% CI: 0.99–1.05, P = 0.14, considered clinically relevant despite marginal statistical significance), larger CBD diameter (OR = 1.60 per mm, 95% CI: 1.27–2.01, P < 0.001), and positive liver fluke infection history (OR = 2.09, 95% CI: 0.85–5.16, P = 0.108, retained due to strong epidemiological and biological plausibility). The final nomogram demonstrated excellent discrimination with an AUC of 0.835 (95% CI: 0.773–0.896) and good calibration (mean absolute error = 0.033). Internal validation confirmed robustness (bootstrap-corrected C-index = 0.835). Decision curve analysis indicated that the model provided superior net clinical benefit compared to treat-all or treat-none strategies across a wide range of risk thresholds (10%–30%). Our predictive model integrates key perioperative factors—including age, CRP, ALT, TB, common bile duct diameter and liver fluke infection—to accurately identify patients at high risk of CBDS recurrence after laparoscopic cholecystectomy. This nomogram enables personalized postoperative surveillance and early intervention in minimally invasive biliary surgery, ultimately supporting enhanced recovery and precision surgical care.

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