Prediction for Acute Biliary Pancreatitis After Laparoscopic Cholecystectomy.

Background: Acute biliary pancreatitis (ABP), caused by biliary stones, is a severe inflammatory condition with high mortality rates. ABP recurrence is often linked to gallstones, necessitating effective treatment strategies. Despite recent advancements, the prediction of ABP occurrence following LC continues to present challenges, indicating the need for ongoing research and model refinement.

Purpose: This study aims to develop a predictive model for assessing the risk of post- Laparoscopic cholecystectomy pancreatitis in patients with gallstones.

Methods: A retrospective cohort study was conducted on 968 patients who underwent LC. The patients were divided into the training set and validation set to develop and validate the predictive model. Demographic, clinical, and laboratory data were collected, and univariate and multivariate logistic regression analyses identified risk factors for ABP. A nomogram was constructed, and model performance was assessed using ROC curves, calibration, and decision curve analysis.

Results: The incidence of ABP was 9.07% in the training set and 14.43% in the validation set. Significant predictors of post-LC pancreatitis included baseline APACHE II score, choledocholithiasis, number of intubation attempts, timing of cholecystectomy, and biochemical markers (C-reactive protein, white blood cell, red cell distribution width, D-dimer, neutrophils, triglycerides). The predictive model demonstrated high discriminative ability with a receiver operating characteristic value of 0.949 (training set), of folds 1-5 ranged from 0.855 to 0.962 (5-fold cross-validation), and 0.922, (external validation set). Calibration curves confirmed stable prediction performance, and decision curve analysis indicated high net benefit across a range of threshold probabilities.

Conclusion: The developed model effectively predicts the risk of post-LC pancreatitis in patients with gallstones, offering valuable guidance for clinical decision-making. Early identification of high-risk patients could improve treatment outcomes and reduce recurrence rates.