Objective To explore the risk factors of ineffective conservative treatment for acute calculous cholecystitis and to construct a prediction model for ineffective conservative treatment of acute calculous cholecystitis. MethodsThe clinical data of 471 patients with acute calculous cholecystitis admitted to the Department of General Surgery, Suzhou Ninth People’s Hospital, Suzhou Ninth Hospital Affiliated to Soochow University from December 1, 2019 to February 8, 2023 were retrospectively collected. Patients admitted from December 1, 2019 to June 18, 2022 were included in the modeling group (n=362), and those admitted from June 21, 2022 to February 8, 2023 were included in the validation group (n=109). Univariate and multivariate logistic regression analysis were performed in the modeling group, and a nomogram was constructed based on the results of the multivariate analysis. Area under the receiver operating characteristic curve was used to evaluate the discrimination of the model in both the modeling and validation groups, and the calibration curve was used to assess the consistency of the model. The decision curve analysis was used to evaluate the clinical value of the model. ResultsThe multivariate logistic regression analysis in the modeling group showed that increased body temperature (OR=2.327, P=0.003), white blood cell count (OR=1.185, P<0.001), gallbladder long diameter (OR=1.030, P=0.008), gallbladder wall thickness (OR=1.533, P<0.001) as well as right hepatic renal space effusion (OR=3.314, P=0.003), gallstone impaction (OR=2.679, P=0.025) were the risk factors for ineffective conservative treatment of acute calculous cholecystitis. Based on these results, a nomogram model for ineffective conservative treatment of acute calculous cholecystitis was constructed. The area under the curve of the model in the modeling and validation groups was 0.907 [95%CI (0.862, 0.952)] and 0.897 [95%CI (0.834, 0.961)], respectively. Hosmer-Lemeshow goodness-of-fit test results showed that the prediction model had good predictive performance in both the modeling and validation groups (P=0.645, P=0.137). The decision curve analysis showed that in the modeling and validation groups, the net benefit of the prediction model was the greatest when the threshold probability was within the range of 2% to 98% and 1% to 89%, respectively. ConclusionThe prediction model including body temperature, white blood cell count, gallbladder long diameter, gallbladder wall thickness, right hepatic renal space effusion, gallstone impaction has good predictive value for ineffective conservative treatment of acute calculous cholecystitis, providing a basis for early identification of such patients and clinical decision-making.