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
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.