Predicting gangrenous cholecystitis.

Background: Gangrenous cholecystitis (GC) is often challenging to treat. The objectives of this study were to determine the accuracy of pre-operative diagnosis, to assess the rate of post-cholecystectomy complications and to assess models to predict GC.

Methods: A retrospective single-institution review identified patients undergoing a cholecystectomy. Logistic regression models were used to examine the association of variables with GC and to build risk-assessment models.

Results: Of 5812 patients undergoing a cholecystectomy, 2219 had acute, 4837 chronic and 351 GC. Surgeons diagnosed GC pre-operatively in only 9% of cases. Patients with GC had more complications, including bile-duct injury, increased estimated blood loss (EBL) and more frequent open cholecystectomies. In unadjusted analyses, variables significantly associated with GC included: age >45 years, male gender, heart rate (HR) >90, white blood cell count (WBC) >13,000/mm(3), gallbladder wall thickening (GBWT) ≥ 4 mm, pericholecystic fluid (PCCF) and American Society of Anesthesiology (ASA) >2. In adjusted analyses, age, WBC, GBWT and HR, but not gender, PCCF or ASA remained statistically significant. A 5-point scoring system was created: 0 points gave a 2% probability of GC and 5 points a 63% probability.

Conclusion: Using models can improve a diagnosis of GC pre-operatively. A prediction of GC pre-operatively may allow surgeons to be better prepared for a difficult operation.