Clinical and Imaging Characteristics to Discriminate Between Complicated and Uncomplicated Acute Cholecystitis: A Regression Model and Decision Tree Analysis

Abstract

Background: Acute complicated cholecystitis (ACC) is associated with prolonged hospitalization, increased morbidity, and higher mortality. However, objective imaging-based criteria to guide early clinical decision-making remain limited. This study aimed to develop a predictive scoring system integrating clinical characteristics, laboratory biomarkers, and computed tomography (CT) findings to facilitate the early identification of ACC in the emergency department (ED). Methods: We conducted a retrospective study at an urban tertiary care center in Taiwan, screening 729 patients who presented to the ED with suspected cholecystitis between 1 January 2018 and 31 December 2020. Eligible patients included adults (≥18 years) with a confirmed diagnosis of acute cholecystitis based on the Tokyo Guidelines 2018 (TG18) and who were subsequently admitted for further management. Exclusion criteria included (a) the absence of contrast-enhanced CT imaging, (b) no hospital admission, (c) alternative final diagnosis, and (d) incomplete clinical data. A total of 390 patients met the inclusion criteria. Demographic data, laboratory results, and CT imaging features were analyzed. Logistic regression and decision tree analyses were used to construct predictive models. Results: Among the 390 included patients, 170 had mild, 170 had moderate, and 50 had severe cholecystitis. Key predictors of ACC included gangrenous changes, gallbladder wall attenuation > 80 Hounsfield units, CRP > 3 mg/dL, and WBC > 11,000/μL. A novel scoring system incorporating these variables demonstrated good diagnostic performance, with an area under the curve (AUC) of 0.775 and an optimal cutoff score of ≥2 points. Decision tree analysis similarly identified these four predictors as critical determinants in stratifying disease severity. Conclusions: This CT- and biomarker-based scoring system, alongside a decision tree model, provides a practical and robust tool for the early identification of complicated cholecystitis in the ED. Its implementation may enhance diagnostic accuracy and support timely clinical intervention.

Keywords: Hounsfield unit; complicated cholecystitis; gallbladder gangrenous change; gallbladder volume; gallbladder wall thickness.

Figure 1

Image example. (a) Normal thickness of GB wall (≤3 mm), cholelithiasis of hyperattenuation of the GB wall; (b) thickening of the GB wall (>3 mm) with no pericholecystic fat stranding; (c) thin linear or ground glass pattern of pericholecystic fat stranding; (d) reticular pattern of pericholecystic fat stranding, abscess formation, GB perforation, and GB gangrenous change.

Figure 2

Flow chart illustrating the inclusion and exclusion criteria of the retrospective study.

Figure 3

ROC of Model 1 and Model 2.

Figure 4

New scoring system represented by Model 3.

Figure 5

Decision tree analysis of Model 3.