Chest CT severity score: assessment of COVID‑19 severity and short-term prognosis in hospitalized Iranian patients

BACKGROUND: The aim of this study was to evaluate the value of chest computed tomography (CT) severity score in the assessment of coronavirus disease 2019 (COVID‑19) severity and short-term prognosis. METHODS: In this cross-sectional study, we evaluated all patients who were referred to our university hospital, from 21 May 2020 to 22 June 2020 with positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcription-polymerase chain reaction (RT-PCR) test. The patients suspected of having other respiratory diseases including influenza, according to an infectious disease specialist, and those without chest CT scan were excluded. A chest CT was obtained for all patients between days 4 and 7 days after symptom onset. Chest CT severity score was also calculated based on the degree of involvement of the lung lobes as 0%, (0 points), 1–25% (1 point), 26–50% (2 points), 51–75% (3 points), and 76–100% (4 points). The CT severity score was quantified by summing the 5 lobe indices (range 0–20). The ROC curve analysis was performed for the clinical value of CT scores in distinguishing the patients based on the severity of disease (mild/moderate group versus severe group), ICU admission, intubation requirement, and mortality. RESULTS: Of the 148 patients included, 93 patients recovered, while 55 patients died (mortality rate 37%). The area under the curve of CT score for discriminating of recovered patients from deceased individuals was 0.726, and the optimal CT score threshold was 15.5 with 61.8% sensitivity and 76.3% specificity. The best CT score cut-off for discriminating of patients based on the severity of disease was 12.5 with 68.3% sensitivity and 72.7% specificity. In addition, with CT score cut-off of 15.5, sensitivities of 70.8% and 51.6% and specificities of 78% and 72.6% were observed for intubation and ICU admission, respectively. CONCLUSION: CT scan and semiquantitative scoring method could be beneficial and applicable in predicting the patient’s condition.