A novel approach to distinguish complicated and non-complicated acute cholecystitis: Decision tree method

It is difficult to differentiate between non-complicated acute cholecystitis (NCAC) and complicated acute cholecystitis (CAC) preoperatively, which are two separate pathologies with different management. The aim of this study was to create an algorithm that distinguishes between CAC and NCAC using the decision tree method, which includes simple examinations. In this retrospective study, the patients were divided into 2 groups: CAC (149 patients) and NCAC (885 patients). Parameters such as patient demographic data, American Society of Anesthesiologists (ASA) score, Tokyo grade, comorbidity findings, white blood cell (WBC) count, neutrophil/lymphocyte ratio, C-reactive protein (CRP) level, albumin level, CRP/albumin ratio (CAR), and gallbladder wall thickness (GBWT) were evaluated. In this algorithm, the CRP value became a very important parameter in the distinction between NCAC and CAC. Age was an important predictive factor in patients with CRP levels >57 mg/L, and the critical value for age was 42. After the age factor, the important parameters in the decision tree were WBC and GBWT. In patients with a CRP value of <= 57 mg/L, GBWT is decisive and the critical value is 4.85 mm. Age, neutrophil/lymphocyte ratio, and WBC count were among the other important factors after GBWT. Sex, ASA score, Tokyo grade, comorbidity, CAR, and albumin value did not have an effect on the distinction between NCAC and CAC. In statistical analysis, significant differences were found groups in terms of gender (34.8% vs 51.7% male), ASA score (P < .001), Tokyo grade (P < .001), comorbidity (P < .001), albumin (4 vs 3.4 g/dL), and CAR (2.4 vs 38.4). By means of this algorithm, which includes low-cost examinations, NCAC and CAC distinction can be made easily and quickly within limited possibilities. Preoperative prediction of pathologies that are difficult to manage, such as CAC, can minimize patient morbidity and mortality.

Automated summary

The aim of this study was to create an algorithm that distinguishes between complicated acute cholecystitis (CAC) and non-complicated acute cholecystitis (NCAC) using the decision tree method. The algorithm identified parameters such as CRP value, age, WBC count, and gallbladder wall thickness as important factors in the distinction between CAC and NCAC. The algorithm provides a quick and easy way to differentiate between CAC and NCAC, minimizing patient morbidity and mortality.