SRS376 - Development and validation of a predictive equation for conversion from laparoscopic to open cholecystectomy: a retrospective cohort study

Abstract Background Conversion from laparoscopic to open cholecystectomy represents a critical clinical event associated with increased morbidity and longer hospital stay. Despite advancements in minimally invasive surgery, global conversion rates remain around 5–15%. This study aimed to develop and validate a predictive equation for estimating conversion risk using routinely available preoperative variables. Methods We retrospectively analyzed 677 laparoscopic cholecystectomy cases at the Royal Free Hospital between 2022 and 2025. A multivariable logistic regression model was constructed using conversion to open surgery as the binary outcome. Predictor variables included age, white blood cell count (WBC), emergency status, prior abdominal surgery, and radiological evidence of pericholecystic fluid. Internal validation was performed using bootstrap resampling (1000 iterations), and performance was assessed via AUC and calibration. Results Conversion occurred in 54 cases (8.0%). The final model demonstrated fair discriminative ability with an AUC of 0.662. Significant predictors included pericholecystic fluid (OR 4.10), previous surgery (OR 1.62), and age (OR 1.02 per year). The predictive equation is as follows: log(P / (1−P)) = −4.65 + (0.021 × Age) + (0.041 × WBC) − (0.276 × Emergency) + (0.483 × Previous Surgery) + (1.411 × Pericholecystic Fluid) The model achieved fair discriminative performance with an AUC of 0.662 (95% c.i.: 0.610–0.714) in the validation cohort, demonstrating clinically meaningful risk stratification capabilities. Conclusions This predictive equation provides a simple, quantitative method for estimating conversion risk in laparoscopic cholecystectomy. It can support surgical planning, informed consent, and resource allocation. Future work will focus on external validation and implementation in clinical decision tools.