Risk Stratification for Postoperative Infection Following Laparoscopic-to-Open Cholecystectomy Conversion: Construction and Evaluation of a Clinical Prediction Model

Abstract

AIM: This study focused on identifying independent risk factors for surgical site infection (SSI) following conversion from laparoscopic to open cholecystectomy and on developing a predictive model for preoperative risk stratification.

METHODS: A total of 214 patients who underwent conversion from laparoscopic cholecystectomy to open cholecystectomy at the People's Hospital of Pingyang between January 2021 and June 2024 were included in this study. The patients were divided into two groups based on the occurrence of SSI within 30 days after surgery: the SSI group and the non-SSI group. Clinical data, including demographic information, laboratory test results, and medical history, were collected for both groups. Patients were randomly assigned to a training set (n = 151) and a validation set (n = 63) in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for SSI. A nomogram model was constructed based on these variables. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate the model's performance and predictive value.

RESULTS: Multivariate logistic regression analysis identified C-reactive protein (CRP), gallbladder wall thickness, preoperative endoscopic retrograde cholangio-pancreatography (p-ERCP), and preoperative percutaneous transhepatic biliary drainage (p-PTBD) as independent risk factors for SSI, while albumin was identified as an independent protective factor. The nomogram showed satisfactory predictive performance, with an area under the curve (AUC) of 0.78 (95% CI: 0.691–0.869) in the training set and 0.831 (95% CI: 0.702–0.959) in the validation set. Calibration curves demonstrated good agreement between predicted and observed probabilities, with Hosmer–Lemeshow test p-values of 0.693 and 0.585 for the training and validation sets, respectively. DCA revealed a net clinical benefit when the threshold probability was below 80%.

CONCLUSIONS: This predictive model, incorporating routinely available clinical variables, exhibited robust discrimination and calibration in identifying SSI risk following conversion to open cholecystectomy. External validation and prospective studies are warranted to further assess its clinical applicability and utility in surgical decision-making.