Diagnostic Value of Initial Chest CT Findings for the Need of ICU Treatment/Intubation in Patients with COVID-19

Abstract

Computed tomography (CT) plays an important role in the diagnosis of COVID-19. The aim of this study was to evaluate a simple, semi-quantitative method that can be used for identifying patients in need of subsequent intensive care unit (ICU) treatment and intubation. We retrospectively analyzed the initial CT scans of 28 patients who tested positive for SARS-CoV-2 at our Level-I center. The extent of lung involvement on CT was classified both subjectively and with a simple semi-quantitative method measuring the affected area at three lung levels. Competing risks Cox regression was used to identify factors associated with the time to ICU admission and intubation. Their potential diagnostic ability was assessed with receiver operating characteristic (ROC)/area under the ROC curves (AUC) analysis. A 10% increase in the affected lung parenchyma area increased the instantaneous risk of intubation (hazard ratio (HR) = 2.00) and the instantaneous risk of ICU admission (HR 1.73). The semi-quantitative measurement outperformed the subjective assessment diagnostic ability (AUC = 85.6% for ICU treatment, 71.9% for intubation). This simple measurement of the involved lung area in initial CT scans of COVID-19 patients may allow early identification of patients in need of ICU treatment/intubation and thus help make optimal use of limited ICU/ventilation resources in hospitals.

Keywords: COVID-19; CT; ICU; SARS-CoV-2; intubation; ventilation.

Figure A1

Cause-specific hazard ratio estimates along with 95% confidence intervals (CI) derived from competing risks Cox regression models to identify factors influencing the instantaneous risk of intubation. Events of interest were ICU admission and intubation, respectively; discharge was treated as a competing risk. All displayed variables were included in the model.

Figure A2

Diagnostic performance for prediction of intensive care unit (ICU, dark blue) treatment or intubation (light blue) in patients with COVID-19. Receiver-operating characteristic (ROC) curves based on the affected lung area on the level of the xiphoid (%).

Figure 1

Example of regions of interest (ROI)-based semi-quantitative assessment of lung area involved in COVID-19 findings at the three levels chosen as standard: at the level of the aortic arch, at the level of the tracheal bifurcation, and at the inferior end of the xiphoid. Highlighted in red: area of involved lung parenchyma; the blue line outlines the total lung area in this section.

Figure 2

Boxplots of the extent of affected lung area on initial CT in relation to the two clinical endpoints—need of ICU treatment and intubation. Median lung involvement in initial CT scans is more severe in ICU (left) patients compared to patients in conventional wards. The same tendency can be seen for intubated patients (right).

Figure 3

Cause-specific hazard ratio estimates along with 95% confidence intervals (CI) derived from competing risks Cox regression models to identify factors associated with the instantaneous risk of ICU admission and intubation, respectively. Event of interest was intubation; discharge and death were treated as competing risks. All displayed variables were included in the model.

Figure 4

Receiver operating characteristic (ROC) curves to evaluate the diagnostic performance of affected lung area (%) for prediction of intensive care unit (ICU) treatment or intubation in patients with COVID-19. For the endpoint of ICU treatment (dark blue), the area under the curve (AUC) is 85.6% (95% CI: 72.1–100%). For the endpoint of intubation (light blue), the AUC is 71.9% (95% CI: 52.5–91.3%).

Figure 5

Receiver operating characteristic (ROC) curves to evaluate the diagnostic performance of affected lung area (light blue) and the subjective assessment (dark blue) for prediction of intensive care unit (ICU) treatment in patients with COVID-19 (left). Receiver operating characteristic (ROC) curves to evaluate the diagnostic performance of affected lung area (light blue) and the subjective assessment (dark blue) for prediction of intubation in patients with COVID-19 (right).