Diagnostic accuracy of computed tomography for identifying hospitalizations for patients with COVID-19

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Abstract

BACKGROUND: In Russia, a semi-quantitative CT 0–4 scoring system is used in the analysis of thoracic computed tomography (CT) scans of COVID-19 patients to grade the severity of lung lesions. Despite the widespread use of this approach, the scoring system’s diagnostic accuracy for identification hospitalizations for patients with the disease is currently unknown.

AIM: To evaluate the sensitivity, specificity, positive (PPV) and negative (NPV) predictive value of the CT 0–4 system for the triage of COVID-19 patients.

MATERIALS AND METHODS: This retrospective study enrolled 575 patients of Moscow clinics with laboratory-verified COVID-19, aged 57.2±13.9 years, 55% females. All patients were examined with four consecutive chest CT scans, and the disease severity was assessed using the CT 0–4 scoring system. Sensitivity and specificity were calculated as conditional probabilities that a patient would experience clinical improvement or deterioration, depending on the preceding CT examination results. For the calculation of the NPV and PPV, we estimated the COVID-19 prevalence in Moscow. The data on total cases of COVID-19 from March 6 to November 28, 2020, were taken from the Rospotrebnadzor website. We used several ARIMA and EST models with different parameters to fit the data and forecast the incidence.

RESULTS: The median specificity of the CT 0–4 scoring system was 69% (95% CI 32%, 100%), and the sensitivity was 92% (95% CI 74%, 100%). The best statistical model describing the epidemiological situation in Moscow was ARIMA (0,2,1). According to our calculations, with the predicted point prevalence of 9.6%, the values of PPV and NPV were 56% and 97%, correspondingly.

CONCLUSION: The maximum Youden’s index was observed for the period between the first and the second chest CT examinations when the majority of the included patients experienced clinical deterioration. The CT 0–4 scoring system makes it possible to safely exclude the development of pathological changes in patients with mild and moderate disease (categories CT-0 and CT-1), thereby optimizing the burden on hospitals in an unfavorable epidemic situation.

About the authors

Sergey P. Morozov

Moscow Center for Diagnostics and Telemedicine

Email: morozov@npcmr.ru
ORCID iD: 0000-0001-6545-6170
SPIN-code: 8542-1720

MD, Dr.Sci. (Med), Professor

Russian Federation, Moscow

Roman V. Reshetnikov

Moscow Center for Diagnostics and Telemedicine; I.M. Sechenov First Moscow State Medical University (Sechenov University)

Author for correspondence.
Email: reshetnikov@fbb.msu.ru
ORCID iD: 0000-0002-9661-0254
SPIN-code: 8592-0558

Cand.Sci. (Phys-Math)

Russian Federation, Moscow

Victor A. Gombolevskiy

Moscow Center for Diagnostics and Telemedicine

Email: gombolevskiy@npcmr.ru
ORCID iD: 0000-0003-1816-1315
SPIN-code: 6810-3279

MD, Cand.Sci. (Med)

Russian Federation, Moscow

Natalya V. Ledikhova

Moscow Center for Diagnostics and Telemedicine

Email: n.ledikhova@npcmr.ru
SPIN-code: 6907-5936
Russian Federation, Moscow

Ivan A. Blokhin

Moscow Center for Diagnostics and Telemedicine

Email: i.blokhin@npcmr.ru
ORCID iD: 0000-0002-2681-9378
SPIN-code: 3306-1387
Russian Federation, Moscow

Olesya A. Mokienko

Moscow Center for Diagnostics and Telemedicine

Email: o.mokienko@npcmr.ru
ORCID iD: 0000-0002-7826-5135
SPIN-code: 8088-9921

MD, Cand.Sci. (Med)

Russian Federation, Moscow

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Supplementary files

Supplementary Files
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1. JATS XML
2. Fig. 1. Scheme of the examination of the study participants.

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3. Fig. 2. Dynamics of the distribution of the number of patients according to the degree of changes in the lung tissue.

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4. Fig. 3. Prediction of the prevalence of COVID-19 in Moscow: actual data (black curve); ETS MMM model (yellow curve); ARIMA model (0,2,1) (red curve). The prognosis of the ETS ZZZ model is not displayed as it coincides with ARIMA (0,2,1). For each of the models, the 95% confidence intervals are shown in the corresponding dimmed color.

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Copyright (c) 2021 Morozov S.P., Reshetnikov R.V., Gombolevskiy V.A., Ledikhova N.V., Blokhin I.A., Mokienko O.A.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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