AbstractObjectives: All over the world, attempts are made at the early prediction of disease severity of ongoing COVID-19 pandemic and catching early those patients who are likely to develop severe disease and may undergo cytokine storm. Though clinical and laboratory parameters are mainstay in diagnosing severe disease and oxygen requirements, high resolution computed tomographic (HRCT) scanning of the chest is one such promising tool to help identify such a subset of patients very early in the course of COVID-19 disease. The purpose of this research is to find an answer to a question can chest CT severity score (CTSS) on HRCT thorax scan forecast clinical requirements of oxygen support in covid-19 patients?
Methods: During the period from May 2020 to October 15, 2020, 250 patients with confirmed RT-PCR diagnosis of COVID-19 on first or repeat sample and who also underwent HRCT scan of the chest, were retrospectively assigned chest CT severity score (CTSS). Patients were categorized into mild and severe score groups and from data obtained, analysis of how many patients from both groups progress to require oxygen support and intubation?
Results: Out of a total of 250 patients, 175 patients were males and 75 patients were females. The average CT severity score (CTSS) was 19.5. 150 patients belong to mild CTSS group while 100 to severe CTSS group. Overall 180 patients required oxygen support, 100 belong to severe CTSS group while 80 belong to mild CTSS group. In mild CTSS group, 80 patients required low-flow oxygen. In severe CTSS group, 5 patients required low-flow oxygen, 75 required high-flow oxygen and 20 patients needed intubation. 8 out of 20 intubated patients succumbed to death. Overall 28 mortalities were reported of which 22 belong to severe CTSS group. With the Receiver operator characteristics (ROC) analysis, we found the cut off of CTS score. At the score of greater than 13 showed the significant effect on oxygen support with area under curve (AUC) 0.996 (95% CI 0.98 to 1; P <0.0001) with 94.4% sensitivity and 100% specificity. We found one another cut off of CTS score (>26) with in-hospital mortality. The Area under curve (AUC) 0.78 (95% CI 0.73 to 0.83; P <0.0001) with 70% sensitivity and 81.4% specificity. Intubation, oxygen requirement and mortality are the strongest predictors of CT score. (Regression coefficients 12.65(95% CI 10.05-15.24; P <0.0001, 11.04(95% CI 9.5-12.58; P <0.0001) and 4.1(95% CI 1.93-6.27; P <0.0001 consecutively).
Conclusion: CTSS may be used as a new decisive tool in triaging in-hospital COVID-19 patients. Currently, clinical and laboratory blood parameters guide the requirements of oxygen support in managing severe COVID-19 pneumonia. In the setting of patients overload, there may be delay in prompt clinical judgment and appropriate therapy may be initiated late and hence the poor outcome. Categorizing patients in mild and severe CTSS early in the disease course, even before the marked worsening of clinical parameters may save energy, health resources, help to triage severe patients, and above all may save many lives.
