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.