Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method

Abstract

Background: So far, only a few studies evaluated the correlation between CT features and clinical outcome in patients with COVID-19 pneumonia.

Purpose: To evaluate CT ability in differentiating critically ill patients requiring invasive ventilation from patients with less severe disease.

Methods: We retrospectively collected data from patients admitted to our institution for COVID-19 pneumonia between March 5th-24th. Patients were considered critically ill or non-critically ill, depending on the need for mechanical ventilation. CT images from both groups were analyzed for the assessment of qualitative features and disease extension, using a quantitative semiautomatic method. We evaluated the differences between the two groups for clinical, laboratory and CT data. Analyses were conducted on a per-protocol basis.

Results: 189 patients were analyzed. PaO2/FIO2 ratio and oxygen saturation (SaO2) were decreased in critically ill patients. At CT, mixed pattern (ground glass opacities (GGO) and consolidation) and GGO alone were more frequent respectively in critically ill and in non-critically ill patients (p < 0.05). Lung volume involvement was significantly higher in critically ill patients (38.5 % vs. 5.8 %, p < 0.05). A cut-off of 23.0 % of lung involvement showed 96 % sensitivity and 96 % specificity in distinguishing critically ill patients from patients with less severe disease. The fraction of involved lung was related to lactate dehydrogenase (LDH) levels, PaO2/FIO2 ratio and SaO2 (p < 0.05).

Conclusion: Lung disease extension, assessed using quantitative CT, has a significant relationship with clinical severity and may predict the need for invasive ventilation in patients with COVID-19.

Keywords: COVID-19; Invasive mechanical ventilation; Lung volume; Pneumonia; Quantitative CT; SARS- CoV-2.

Copyright © 2020 Elsevier B.V. All rights reserved.

Figures

Fig. 1

CT quantitative assessment of disease extension in a non-critically ill Patient with SARS-CoV-2 pneumonia. (A-C) CT image viewed at lung window, on the axial plane. On the same slice, the operator manually drew the contour of the left lower lobe (red line) and the portion of involved parenchyma in it (yellow line). The final volume was automatically computed and measured afterwards. (D,E) Volumetric representation of lung involvement (Vitrea software, version 7.10.1.20), with frontal and lateral view.

Fig. 2

CT quantitative assessment of disease extension in a critically ill Patient with SARS-CoV-2 pneumonia. (A-C) CT image viewed at lung window, on the axial plane. On the same slice, the operator manually drew the contour of the left superior lobe (red line) and the portion of involved parenchyma in it (yellow line). The final volume was automatically computed and measured afterwards. (D,E) Volumetric representation of lung involvement (Vitrea software, version 7.10.1.20), with frontal and lateral view.

Fig. 3

ROC curve analysis evaluating the differential diagnosis ability of quantitative CT in critically ill and non-critically ill patients. The area under the curve (AUC) of affected lung (%) for diagnosing critically ill disease was 0.982 (95 %CI 0.953–1.000). The cut-off of 23.0 % had 96 % sensitivity and 96 % specificity.