Covid Visual Assessment Scale (“Co.V.A.Sc.”): quantification of COVID-19 disease extent on admission chest computed tomography (CT) in the prediction of clinical outcome—a retrospective analysis of 273 patients

ABSTRACT

Background Admission chest CT is often included in COVID-19 patient management. Purpose To evaluate the inter- and intraobserver variability of the Covid Visual Assessment Scale (“Co.V.A.Sc.”) used for stratifying chest CT disease extent and to estimate its prospect to predict clinical outcomes. Materials and methods This single-center, retrospective observational cohort study included all RT-PCR-confirmed COVID-19 adult patients undergoing admission chest CT, between 01/03/2021 and 17/03/2021. CTs were independently evaluated by two radiologists according to the “Co.V.A.Sc.” (0 0%, 1 1–10%, 2 11–25%, 3 26–50%, 4 51–75%, 5 > 75%). Patient demographics, laboratory, clinical, and hospitalization data were retrieved and analyzed in relation to the “Co.V.A.Sc.” evaluations. Results Overall, 273 patients (mean age 60.7 ± 14.8 years;50.9% male) were evaluated. Excellent inter- and intraobserver variability was noted between the two independent radiologists’ “Co.V.A.Sc.” evaluations. “Co.V.A.Sc.” classification (Exp(B) 0.391, 95%CI 0.212–0.719;p = 0.025) and patient age (Exp(B) 0.947, 95%CI 0.902–0.993;p = 0.25) were the only variables correlated with ICU admission, while age (Exp(B) 1.111, p = 0.0001), “Co.V.A.Sc.” (Exp(B) 2.408;p = 0.002), and male gender (Exp(B) 3.213;p = 0.028) were correlated with in-hospital mortality. Specifically, for each “Co.V.A.Sc.” unit increase, the probability of ICU admission increased by 1.47 times, and the probability of death increased by 11.1 times. According to ROC analysis, “Co.V.A.Sc.” could predict ICU admission and in-hospital death with an optimal cutoff value of unit 3 (sensitivity 56.0%, specificity 84.3%) and unit 4 (sensitivity 41.9%, specificity 93.6%), respectively. Conclusion “Co.V.A.Sc.” upon hospital admittance seems to predict ICU admission and in-hospital death and could aid in optimizing risk-stratification and patient management.