Early prediction of severe acute cholecystitis using routine admission parameters: a study of 1,330 patients

Area of Science:

• Gastroenterology and Hepatology
• Surgical Critical Care
• Clinical Chemistry

Background:

• Acute cholecystitis requires accurate risk stratification for optimal patient outcomes.
• Current diagnostic criteria may not fully capture all patients at high risk for severe disease progression.
• Early identification of severe acute cholecystitis is crucial for timely and appropriate clinical management.

Purpose of the Study:

• To identify independent predictors of severe acute cholecystitis among routine clinical, demographic, and laboratory admission parameters.
• To develop and validate a predictive model for early risk stratification of severe acute cholecystitis.
• To assess the clinical utility of identified predictors in guiding patient management.

Main Methods:

• Retrospective analysis of 1,330 adult patients diagnosed with acute cholecystitis (Tokyo Guidelines 2018).
• Logistic regression and ROC analyses were used to identify independent predictors, excluding direct TG18 Grade 3 indicators.
• Internal validation via bootstrap resampling and clinical utility assessment using decision curve analysis (DCA).

Main Results:

• Severe acute cholecystitis was present in 12.9% of the cohort.
• Independent predictors identified: CRP/albumin ratio (CAR), age, diabetes mellitus, and hemoglobin level.
• The predictive model showed moderate discriminatory power (AUC = 0.766); a CAR cut-off > 1.10 significantly increased severe disease likelihood.

Conclusions:

• Routine admission parameters including age, diabetes, hemoglobin, and CAR can effectively predict severe acute cholecystitis.
• This multivariable approach enhances early bedside risk stratification beyond standard Tokyo Guidelines 2018 assessment.
• The findings support improved clinical vigilance and timely intervention for high-risk patients.