Development and temporal validation of a predictive model for difficult laparoscopic cholecystectomy

Abstract Background Difficult laparoscopic cholecystectomy (LC) is associated with an increased risk of intraoperative complications. This study aimed to develop and validate a preoperative nomogram for predicting difficult LC. Materials and methods A retrospective study included 284 consecutive patients who underwent LC at Shijiazhuang People’s Hospital between September 2025 and May 2026. Patients were divided into a training cohort ( n = 194) and a temporal validation cohort ( n = 90). Surgical difficulty was graded using the Nassar scale, with grades III–IV defined as difficult LC. Predictors were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression, and a nomogram was constructed. Model performance was assessed by bootstrap internal validation (500 resamples) and temporal validation. Discrimination, calibration, and clinical utility were evaluated using receiver operating characteristic analysis, calibration curves, the Hosmer–Lemeshow test, and decision curve analysis. Results Body roundness index, pericholecystic fluid, and fibrinogen were independent predictors and were incorporated into the nomogram. The model showed good discrimination, with an area under the ROC curve (AUC) of 0.827 (95% CI, 0.756–0.898 ) in the training cohort and 0.849 (95% CI, 0.759–0.939) in the validation cohort. Nested bootstrap internal validation yielded an optimism-corrected C-index of 0.822, with minimal estimated optimism (0.005). The Hosmer–Lemeshow test yielded a χ² value of 6.52 ( P = 0.089). The calibration intercept was 1.0215 (95% CI, 0.0149–2.2827), indicating systematic underestimation of the observed risk. The calibration slope was 1.3357 (95% CI, 0.7483–2.1282), suggesting that the predicted risks had a narrower range than the observed risks. Taken together, these findings indicated some degree of miscalibration in the temporal validation cohort.Decision curve analysis provided exploratory evidence of potential net benefit across selected threshold probabilities. Conclusions The nomogram showed promising predictive performance for difficult LC and may support preoperative risk stratification and perioperative planning. Independent multicenter validation is required before routine clinical application.