To investigate the clinical risk factors for acute gangrenous cholecystitis (AGC), and to develop and validate an AGC risk prediction model based on these factors.

Clinical data of 265 patients with acute cholecystitis (modeling cohort) admitted to Taizhou Fourth People's Hospital from January 2022 to June 2024 were retrospectively analyzed. The informed consents of all patients were obtained and the local ethical committee approval was received. Among them, 125 patients were male and 140 female, aged from 46 to 68 years, with a median age of 57 years. Perioperative clinicopathological data were collected and analyzed using univariate and multivariate logistic regression. An AGC risk prediction model was established in combination with postoperative pathological findings. An independent cohort of 108 patients with acute cholecystitis (validation cohort) enrolled from July 2024 to December 2024 was subsequently collected to validate the prediction model.

Multivariate logistic regression analysis showed that male sex, high body mass index (BMI), gas and fluid accumulation in the gallbladder on CT, gallbladder stones, impacted stones, and elevated white blood cell count were independent risk factors for AGC (OR=3.705, 1.202, 10.005, 9.718, 3.559, and 1.239, respectively; P<0.05). An AGC prediction nomogram was constructed. Nomogram analysis showed that the risk of gallbladder gangrene increased significantly when the total score exceeded 80 points and reached 99% when the total score increased to 280 points. The Hosmer-Lemeshow test showed good model fit. Receiver operating characteristic (ROC) curve analysis showed that the areas under the curve (AUC) for predicting AGC were 0.848 in the modeling cohort and 0.709 in the validation cohort, indicating reliable predictive performance for gallbladder gangrene. The Hosmer-Lemeshow goodness-of-fit test was performed for both datasets, and the calibration curves were highly consistent with the ideal curve, demonstrating the good predictive ability of the model. Clinical application data showed that the nomogram-based prediction model had substantial practical utility in real-world clinical settings.

Male sex, high BMI, gas and fluid accumulation in the gallbladder on CT, gallbladder stones, impacted stones, and elevated white blood cell count are independent risk factors for AGC. The prediction model based on key clinicopathological characteristics has high diagnostic value and can provide a simple and convenient clinical tool for the early diagnosis and intervention of AGC.