CT-Based Risk Stratification for Intensive Care Need and Survival in COVID-19 Patients-A Simple Solution

Abstract

We evaluated a simple semi-quantitative (SSQ) method for determining pulmonary involvement in computed tomography (CT) scans of COVID-19 patients. The extent of lung involvement in the first available CT was assessed with the SSQ method and subjectively. We identified risk factors for the need of invasive ventilation, intensive care unit (ICU) admission and for time to death after infection. Additionally, the diagnostic performance of both methods was evaluated. With the SSQ method, a 10% increase in the affected lung area was found to significantly increase the risk for need of ICU treatment with an odds ratio (OR) of 1.68 and for invasive ventilation with an OR of 1.35. Male sex, age, and pre-existing chronic lung disease were also associated with higher risks. A larger affected lung area was associated with a higher instantaneous risk of dying (hazard ratio (HR) of 1.11) independently of other risk factors. SSQ measurement was slightly superior to the subjective approach with an AUC of 73.5% for need of ICU treatment and 72.7% for invasive ventilation. SSQ assessment of the affected lung in the first available CT scans of COVID-19 patients may support early identification of those with higher risks for need of ICU treatment, invasive ventilation, or death.

Keywords: COVID-19; CT; SARS-CoV-2; developing countries; intensive care; quantification; resource allocation; risk assessment.

Figure 1

Patient population. We retrospectively enrolled 265 patients admitted to our central European level-1 university center. All patients included underwent a chest CT examination and tested positive for SARS-CoV-2 (RT-PCR of nasopharyngeal and oropharyngeal swab samples); patients with a negative swab test were excluded.

Figure 2

Example of region of interest (ROI)-based simple semi-quantitative (SSQ) determination of lung area involved in chest CT scans of COVID-19 patients at three predefined levels: aortic arch, tracheal bifurcation, and inferior end of xiphoid. In the axial image from each level, red indicates the area of involved lung parenchyma while the blue line outlines the total lung area at that level. Reprinted from Büttner et al. [16] with kind permission of MDPI.

Figure 3

Diagnostic performance of subjective assessment and of the SSQ method for the two endpoints: (a) need for ICU treatment, (b) need for invasive ventilation.

Figure 4

Odds ratio estimates along with 95% confidence intervals (CIs) derived from logistic regression model to identify factors associated with the risk for ICU admission (left column) and invasive ventilation (right column). The model included all variables displayed.

Figure 5

Hazard ratio estimates along with 95% confidence intervals derived from Cox regression model to identify factors associated with the instantaneous risk of death. All variables displayed were included in the model.