Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.

Fig. 1. Study workflow.

The flow diagram shows the study population enrollment and observation period.

Fig. 2. Representative chest CT images of patients with COVID-19 pneumonia.

a Subpleural patchy areas of GGO with crazy-paving sign in the right middle lobe. b Multiple patchy areas of consolidation in the right middle lobe, left upper lobe, and bilateral lower lobes and air bronchogram in the right middle lobe. c Multiple patchy areas of organizing pneumonia in the right middle and lower lobes on the sagittal image with CT severity score of 9 for the right lung. d Bilateral and peripheral multiple patchy areas of GGO with reticular and intralobular septal thickening. e Multiple mixed distributed pure GGO, GGO with consolidation, and interlobular septal thickening in bilateral lungs. f Bilateral multiple patchy and thin areas of GGO in the posterior parts of the lungs.

Fig. 3. Development and performance of nomogram.

a A nomogram for the prediction of developing severe COVID-19 pneumonia. Calibration curves of the nomogram in the derivation (b) and validation (c) cohorts, respectively, which depict the calibration of the nomogram in terms of the agreement between the predicted risk of severe COVID-19 pneumonia and observed outcomes. The 45° blue line represents a perfect prediction, and the dotted red lines represent the predictive performance of the nomogram. The closer the dotted red line fit is to the ideal line, the better the predictive accuracy of the nomogram is. Plots show the ROC curves of the nomogram in the derivation (d) and validation (e) cohorts, respectively.

Fig. 4. Correlation between CT characteristics and inflammatory indexes.

Heatmaps depict the correlations between the baseline CT characteristics and inflammatory indexes (within the blue dotted box) on admission (a) and on day 3 after admission (b) showing the correlation coefficients r with P < 0.05 of all pairs.