An integrated approach-demographic, clinical, laboratory and radiological features in predicting mortality of sars-cov-2 patients in critical care medicine

Abstract

Introduction: Now the situation is rapidly evolving with new variants of SARS-Cov-2, and 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. This was the main motivating factor behind this study. Aim: 1) 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-survivor. 2) To propose a model to predict mortality rate in critically ill COVID-19 patients. Materials and Methods: This study was conducted on RT-PCR confirmed COVID-19 patients admitted in Critical Care Medicine Department at Yenepoya Medical College, Mangalore during May and June 2021. The data collected (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 Result: Among the 91 enrolled patents, 65(71.42%) survived and 26 succumbed to death. In the non-survivors mean age was 61.42±13.24, male 18(69.23%). Backward stepwise logistic regression is used to identify the significant predictors of mortality. These parameters were significant in Backward logistic regression model: RR (p:0.008, OR1.164), spO2(p:0.05, OR:0.928), WBC (p:0.001, OR:1.170), D-dimer (p: 0.005, OR:0.999), Urea (p:0.001, OR:0.916) and CT (p:0.000, OR:1.259). The sensitivity of the model is 80.00%% (95% confidence interval is [59.30% 93.17%]), specificity is 92.68%. (95% CI is [80.08% 98.46%]). The overall accuracy is 87.88%. (95% CI is [77.51% 94.62%]). The positive predictive value is 86.96%. (95% CI is [68.79% 95.28%]). The negative predictive value is 88.37%. (95% CI is [77.55% 94.36%]) Conclusion: Involving clinical, laboratory and radiological features has shown to be a good approach in mortality prediction of critically ill COVID-19 patients.