Background: With the rapidly evolving new variants of SARS-Cov-2, the scientific community is still learning to identify patients with higher risks for effective triaging and better resource allocation as there is no effective specific therapeutics for COVID-19 patients. Aim: To analyse the demographic, laboratory, clinical and radiological features in COVID -19 patients admitted in critical care medicine and to study their association with survivors and non survivors and to propose a model to predict mortality rate in critically ill COVID -19 patients. Methods: The data of RT-PCR confirmed COVID-19 patients (age, gender, RR, PR, BP, SpO2, DM, HTN, WBC, Hb, Platelet, CRP, LDH, D-dimer, Creatinine, Urea, CT Score, lung involvement pattern and distribution) was retrospectively evaluated and compared between survivors and non-survivors. Results: Among the 91 enrolled patients, 65(71.42%) survived and 26 (28.58%) succumbed to death. In the non-survivors mean age was 61.42±13.24, male 18(69.23%), female 8(30.76%). Backward stepwise logistic regression is used to identify the significant predictors of mortality. These parameters were significant in our Backward logistic regression model: RR