Clinical and Imaging Characteristics to Discriminate Between Complicated and Uncomplicated Acute Cholecystitis: A Regression Model and Decision Tree Analysis
<b>Background</b>: 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 sco...
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2025-07-01
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| author | Yu Chen Ning Kuo Hui-An Lin Chun-Chieh Chao Suhwon Lee Cheng-Han Tsai Sheng-Feng Lin Sen-Kuang Hou |
| author_facet | Yu Chen Ning Kuo Hui-An Lin Chun-Chieh Chao Suhwon Lee Cheng-Han Tsai Sheng-Feng Lin Sen-Kuang Hou |
| author_sort | Yu Chen |
| collection | DOAJ |
| description | <b>Background</b>: 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). <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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. |
| format | Article |
| id | doaj-art-ad80acb7551f4d1f86f28fe9c1926e1d |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-ad80acb7551f4d1f86f28fe9c1926e1d2025-08-20T03:32:26ZengMDPI AGDiagnostics2075-44182025-07-011514177710.3390/diagnostics15141777Clinical and Imaging Characteristics to Discriminate Between Complicated and Uncomplicated Acute Cholecystitis: A Regression Model and Decision Tree AnalysisYu Chen0Ning Kuo1Hui-An Lin2Chun-Chieh Chao3Suhwon Lee4Cheng-Han Tsai5Sheng-Feng Lin6Sen-Kuang Hou7Department of Emergency Medicine, Taipei Medical University Hospital, Taipei 110, TaiwanDepartment of Emergency Medicine, Taipei Medical University Hospital, Taipei 110, TaiwanDepartment of Emergency Medicine, Taipei Medical University Hospital, Taipei 110, TaiwanDepartment of Emergency Medicine, Taipei Medical University Hospital, Taipei 110, TaiwanDepartment of Emergency Medicine, Taipei Medical University Hospital, Taipei 110, TaiwanDepartment of Emergency Medicine, Taipei Medical University Hospital, Taipei 110, TaiwanDepartment of Emergency Medicine, Taipei Medical University Hospital, Taipei 110, TaiwanDepartment of Emergency Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan<b>Background</b>: 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). <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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.https://www.mdpi.com/2075-4418/15/14/1777complicated cholecystitisHounsfield unitgallbladder wall thicknessgallbladder gangrenous changegallbladder volume |
| spellingShingle | Yu Chen Ning Kuo Hui-An Lin Chun-Chieh Chao Suhwon Lee Cheng-Han Tsai Sheng-Feng Lin Sen-Kuang Hou Clinical and Imaging Characteristics to Discriminate Between Complicated and Uncomplicated Acute Cholecystitis: A Regression Model and Decision Tree Analysis Diagnostics complicated cholecystitis Hounsfield unit gallbladder wall thickness gallbladder gangrenous change gallbladder volume |
| title | Clinical and Imaging Characteristics to Discriminate Between Complicated and Uncomplicated Acute Cholecystitis: A Regression Model and Decision Tree Analysis |
| title_full | Clinical and Imaging Characteristics to Discriminate Between Complicated and Uncomplicated Acute Cholecystitis: A Regression Model and Decision Tree Analysis |
| title_fullStr | Clinical and Imaging Characteristics to Discriminate Between Complicated and Uncomplicated Acute Cholecystitis: A Regression Model and Decision Tree Analysis |
| title_full_unstemmed | Clinical and Imaging Characteristics to Discriminate Between Complicated and Uncomplicated Acute Cholecystitis: A Regression Model and Decision Tree Analysis |
| title_short | Clinical and Imaging Characteristics to Discriminate Between Complicated and Uncomplicated Acute Cholecystitis: A Regression Model and Decision Tree Analysis |
| title_sort | clinical and imaging characteristics to discriminate between complicated and uncomplicated acute cholecystitis a regression model and decision tree analysis |
| topic | complicated cholecystitis Hounsfield unit gallbladder wall thickness gallbladder gangrenous change gallbladder volume |
| url | https://www.mdpi.com/2075-4418/15/14/1777 |
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