  The controversy of computed tomography (CT) use in COVID-19 screening is
associated with ambiguous characteristics of chest CT as a diagnostic test. The
reported values of CT sensitivity and specificity calculated using RT-PCR as a
reference standard vary widely. The objective of this study was to reevaluate
the diagnostic and prognostic value of CT using an alternative approach. This
study included 973 symptomatic COVID-19 patients aged 42 $\pm$ 17 years, 56%
females. We reviewed the disease dynamics between the initial and follow-up CT
studies using a "CT0-4" grading system. Sensitivity and specificity were
calculated as conditional probabilities that a patient's condition would
improve or deteriorate relative to the initial CT study results. For the
calculation of negative (NPV) and positive (PPV) predictive values, we
estimated the COVID-19 prevalence in Moscow. We used several ARIMA and EST
models with different parameters to fit the data on total cases of COVID-19
from March 6, 2020, to July 20, 2020, and forecast the incidence. The "CT0-4"
grading scale demonstrated low sensitivity (28%) but high specificity (95%).
The best statistical model for describing the pandemic in Moscow was ETS with
multiplicative trend, error, and season type. According to our calculations,
with the predicted prevalence of 2.1%, the values of NPV and PPV would be 98%
and 10%, correspondingly. We associate the low sensitivity and PPV values with
the small sample size of the patients with severe symptoms and non-optimal
methodological setup for measuring these specific characteristics. The "CT0-4"
grading scale was highly specific and predictive for identifying admissions to
hospitals of COVID-19 patients. Despite the ambiguous accuracy, chest CT proved
to be an effective practical tool for patient management during the pandemic,
provided that the necessary infrastructure and human resources are available.
