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Introduction: Gangrenous cholecystitis is difficult to diagnose clinically. There are no distinctive signs, symptoms or laboratory findings to distinguish it from acute cholecystitis. Furthermore, the Computed Tomography (CT) scans have a low discriminative value to distinguish between the two. To improve the diagnostic accuracy and dependability of CT scan, radiologic parameters should be analysed with clinical variables. Aim: To develop models for prediction of gangrenous cholecystitis according to pre-operative patient-dependent and clinical risk factors. Materials and Methods: This retrospective, observational study included patients who underwent laparoscopic cholecystectomy for acute and gangrenous cholecystitis between February 2015 and November 2017. The study population was stratified into two groups based on surgical and histopathological diagnosis: acute cholecystitis and gangrenous cholecystitis. Predictive models for gangrenous cholecystitis were developed based on multivariate analysis of pre-operative clinical and radiological variables. Results: A total of 437 patients (mean age 54.7±15.5 years, 260 males) were included in the study. Of the included patients 65.4% exhibited acute cholecystitis and 34.6% exhibited gangrenous cholecystitis. Multivariate analysis identified independent factors associated with gangrenous cholecystitis: presence of diabetes increased white blood cells >10000/mm³, gallbladder wall thickness >3 mm and pericholecystic fluid collection. Based on the aforementioned results of multivariate analysis; two predictive models were developed to predict gangrenous cholecystitis: 1) A standard predictive model for symptomatic patients (82% of sensitivity and 82.2% of specificity); 2) Quick-and-easy predictive model for asymptomatic patients (78.2% of sensitivity and 78% of specificity). Conclusion: Clinical application of standard, quick-and-easy predictive models is expected to help and improve pre-operative diagnosis of gangrenous cholecystitis in symptomatic and asymptomatic patients. Moreover, application of models can assist the surgeons to prioritise patients for urgent surgical intervention. [ABSTRACT FROM AUTHOR]