Computed tomography severity score as a predictor of disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis

Background: Prediction of outcomes in severe COVID-19 patients using chest computed tomography severity score (CTSS) may enable more effective clinical management and early, timely ICU admission. We conducted a systematic review and meta-analysis to determine the predictive accuracy of the CTSS for disease severity and mortality in severe COVID-19 subjects. Methods: The electronic databases PubMed, Google Scholar, Web of Science, and the Cochrane Library were searched to find eligible studies that investigated the impact of CTSS on disease severity and mortality in COVID-19 patients between 7 January 2020 and 15 June 2021. Two independent authors looked into the risk of bias using the Quality in Prognosis Studies (QUIPS) tool. Results: Seventeen studies involving 2788 patients reported the pre-dictive value of CTSS for disease severity. The pooled sensitivity, speci-ficity, and summary area under the curve (sAUC) of CTSS were 0.85 (95% CI 0.78-0.90, I 2 = 83), 0.86 (95% CI 0.76-0.92, I 2 = 96) and 0.91 (95% CI 0.89-0.94), respectively. Six studies involving 1403 pa-tients reported the predictive values of CTSS for COVID-19 mortal-ity. The pooled sensitivity, specificity, and sAUC of CTSS were 0.77 (95% CI 0.69-0.83, I 2 = 41), 0.79 (95% CI 0.72-0.85, I 2 = 88), and 0.84 (95% CI 0.81-0.87), respectively. Discussion: Early prediction of prognosis is needed to deliver the better care to patients and stratify them as soon as possible. Because different CTSS thresholds have been reported in various studies, clin-icians are still determining whether CTSS thresholds should be used to define disease severity and predict prognosis. Conclusion: Early prediction of prognosis is needed to deliver opti-mal care and timely stratification of patients. CTSS has strong dis-criminating power for the prediction of disease severity and mortality in patients with COVID-19.