Initial Chest CT Scan as a Marker for Clinical Severity and Predictor for Outcome in COVID-19 Patients

Abstract Background: Chest computed tomography (CT) is a con-sidered best imaging modalityfor COVID-19 infection diag-nosis, even in asymptomatic patients. Apart from being a diagnostic tool, CT can also potentially help in evaluating the disease progression and monitoring the response to therapy. Early and accurate diagnosis of the disease helps in accurate management of the patients and good prognosis. Newly emerging radiological classifications have been published in literature trying to classify chest radiological findings as Coronavirus disease 2019 (COVID-19) imaging reporting and data system (CO-RADS) to help in early diagnosis of the disease. Also, other CT chest severity scoring (CT-SS) systems were reported, aiming to quantify disease severity through radiological findings aiming to triage, along with clinical and laboratory findings, patients who need intensive care. Aim of Study: The goal of our study is to evaluate the role of initial CT imaging in the diagnosis of the disease, and prediction of patients' outcome as it is a rapid, easy and available method for investigation. Patients and Methods: This a prospective cohort study of 70 patients with PCR-confirmed COVID-19 who underwent chest CT. CT Radiological findings and CT-SS were compared between patients according clinical severity and the need for ICU. Results: Seventy patients were included in the study. The mean was 44.9±20.71 (5 months-82 years), 34 females (48.6%) and 36 males (51.4%). Severe and critical COVID-19 group was associated with statistically significant increase in the incidence of GGO, consolidation, crazy paving, vascular thickening, bronchial dilatation , nodules and Median CT-SS (p=0.045, 0.008, <0.001, 0.016, 0.031, <0.001 and <0.001 respectively with cut off value >7.5 showing 81.8% sen. and 70.8 spes. and AUC 0.855. The cases who were admitted into ICU were associated with statistically significant increase in the incidence of GGO, consolidation, crazy paving, vascular thickening, atelectatic bands, bronchial dilatation, nodules and The median CT-SS (p=0.049, 0.001, 0.002, 0.04, 0.021, 0.003 and <0.001 respectively) best cutoff point of chest CT score for detection of cases who were admitted into ICU was >8 with 90.9% sen. and 75% spe. and AUC 0.908.
Conclusion: CT chest along with CT-SScan be used as rapid, widely available and accurate imaging tool in detection of disease severity and in predicting for ICU admission which could help clinicians to treat the diseaseearly and accurately.