Relationship between chest computed tomography findings and clinical conditions of coronavirus disease (COVID-19): A multicentre experience

Aims This study aimed to investigate the clinical and chest computed tomography (CT) features associated with clinical parameters for coronavirus disease (COVID-19) in the capital of Turkey, Ankara. Materials and methods Epidemiological, clinical features, laboratory findings and radiological characteristics of 1563 hospitalised patients with COVID-19 in Ankara were collected, reviewed and analysed in this study. The risk factors associated with disease severity were investigated. Results Non-severe (1214; 77.7%) and severe cases (349; 22.3%) were enrolled in the study. Compared with the non-severe group, the severe group were significantly older and had more comorbidities (ie, hypertension, diabetes mellitus, cardiovascular disease and chronic kidney disease). Smoking was more common in the severe group. Severe patients had higher respiratory rates and higher incidences of cough and dyspnoea compared with non-severe patients. Compared with the non-severe patients, the severe patients had increased C-reactive protein (CRP), procalcitonin, neutrophil to lymphocyte ratio (NLR) and CRP/albumin ratio and decreased albumin. The occurrence rates of consolidation, subpleural sparing, crazy-paving pattern, cavity, halo sign, reversed halo sign, air bronchogram, pleural thickening, micronodule, subpleural curvilinear line and multilobar and bilateral involvement in the CT finding of the severe patients were significantly higher than those of the non-severe patients. Conclusions Many factors are related to the severity of COVID-19, which can help clinicians judge the severity of the patient and evaluate the prognosis. This cohort study revealed that male sex, age (>= 55 years), patients with any comorbidities, especially those with cardiovascular disease, dyspnoea, increased CRP, D-dimer and NLR, and decreased lymphocyte count and CT findings of consolidation and multilobar involvement were predictors of severe COVID-19.