RESUMO
Introduction: The COVID19 pandemic has posed a serious threat to global health, with developing nations like India being amongst the worst affected. Chest CT scans play a pivotal role in the diagnosis and evaluation of COVID19, and certain CT features may aid in predicting the prognosis of COVID19 illness. Methods: This was a single center, hospital based, cross sectional study conducted at a tertiary care center in Northern India during the second wave of the COVID19 pandemic from May June 2021. The study included 473 patients who tested positive for COVID-19. A high resolution chest CT scan was performed within five days of hospitalization, and patientrelated information was extracted retrospectively from medical records. Univariable and Multivariable analysis was done to study the predictors of poor outcome. Results: A total of 473 patients were included in the study, with 75.5% being males. The mean total CT score was 29.89 {+/-} 9.06. Fibrosis was present in 17.1% of patients, crazy paving in 3.6%, pneumomediastinum in 8.9%, and pneumothorax in 3.6%. Males had a significantly higher total score, while the patients who survived (30.00 {+/-} 9.55 vs 35.00 v 6.21, p value <.001), received Steroids at day 2 (28.04 {+/-} 9.71 vs 31.66 {+/-} 7.12, p value 0.002) or Remdesivir had lower total scores (28.04 {+/-} 9.71 vs 31.66 {+/-} 7.12, p value 0.002). Total CT score (aHR 1.05, 95% CI 1.02 1.08, p 0.001), pneumothorax (aHR 1.38, 95 % CI 0.67 2.87, p 0.385), pneumomediastinum (aHR 1.20, 95% CI 0.71 2.03, p 0.298) and cardiovascular accident (CVA, aHR 4.75, 95% CI 0.84 26.72, p 0.077) were associated with increased mortality, but the results were not significant after adjusting with other variables on multiple regression analysis. Conclusion: This study identifies several radiological parameters, including fibrosis, crazy paving, pneumomediastinum, and pneumothorax, that are associated with poor prognosis in COVID19. These findings highlight the role of CT thorax in COVID19 illness and the importance of timely identification and interventions in severe and critical cases of COVID19 to reduce mortality and morbidity.