CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients

Abstract

Rationale: Some patients with coronavirus disease 2019 (COVID-19) rapidly develop respiratory failure or even die, underscoring the need for early identification of patients at elevated risk of severe illness. This study aims to quantify pneumonia lesions by computed tomography (CT) in the early days to predict progression to severe illness in a cohort of COVID-19 patients. Methods: This retrospective cohort study included confirmed COVID-19 patients. Three quantitative CT features of pneumonia lesions were automatically calculated using artificial intelligence algorithms, representing the percentages of ground-glass opacity volume (PGV), semi-consolidation volume (PSV), and consolidation volume (PCV) in both lungs. CT features, acute physiology and chronic health evaluation II (APACHE-II) score, neutrophil-to-lymphocyte ratio (NLR), and d-dimer, on day 0 (hospital admission) and day 4, were collected to predict the occurrence of severe illness within a 28-day follow-up using both logistic regression and Cox proportional hazard models. Results: We included 134 patients, of whom 19 (14.2%) developed any severe illness. CT features on day 0 and day 4, as well as their changes from day 0 to day 4, showed predictive capability. Changes in CT features from day 0 to day 4 performed the best in the prediction (area under the receiver operating characteristic curve = 0.93, 95% confidence interval [CI] 0.87~0.99; C-index=0.88, 95% CI 0.81~0.95). The hazard ratios of PGV and PCV were 1.39 (95% CI 1.05~1.84, P=0.023) and 1.67 (95% CI 1.17~2.38, P=0.005), respectively. CT features, adjusted for age and gender, on day 4 and in terms of changes from day 0 to day 4 outperformed APACHE-II, NLR, and d-dimer. Conclusions: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.

Chest CT Features of 182 Patients with Mild Coronavirus Disease 2019 (COVID-19) Pneumonia: A Longitudinal, Retrospective and Descriptive Study

“…First, this was a single-center study, and the results require verification through a multicenter study. Second, the CT scores of this study may not be comparable with the quantitative scores determined using artificial intelligence [33], although visual scores are easier to determine. Finally, only patients with mild COVID-19 pneumonia were included, and the longitudinal CT performance of severe pneumonia must be determined using a larger sample size.…”

Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients

“…The %HAA − 700, %HAA − 700–250, and %HAA − 250 showed the highest correlation with overall pneumonia, the sum of ground-glass opacity and crazy-paving pattern, and consolidation extent, respectively. Similar density thresholds were used by Liu et al, who demonstrated that CT features extensions were significant predictors of progression to severe illness in COVID-19 pneumonia [ 7 ].…”

“…The use of CT led to understanding that visual quantification of COVID-19 pneumonia extent and several features (predominant crazy-paving pattern or presence of consolidations) at chest CT are associated with increased risk of death [ 4 – 6 ]. In addition, CT quantitative parameters obtained by software predict the severity of COVID-19 pneumonia and its progression [ 7 , 8 ].…”

Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT

“…Similar to other coronavirus pneumonia, COVID-19 mainly causes alveolar edema with hemorrhage, bronchiolitis, alveolitis, and pulmonary interstitial fibrosis [2]. With the gradual understanding of the disease, CT is reserved for hospitalized, symptomatic patients for assessing the progression course of COVID-19 [3][4][5][6]. Recent studies have used semi-quantitative methods to observe the lung changes during COVID-19 from the onset of symptoms to recovery with CT images [7,8].…”