ABSTRACT
INTRODUCTION: Despite an increase in CT studies to evaluate patients with coronavirus disease 2019 (COVID-19), their indication in triage is not well-established. The purpose was to investigate the incidence of lung involvement and analyzed factors related to lung involvement on CT images for establishment of the indication for CT scans in the triaging of COVID-19 patients. METHODS: Included were 192 COVID-19 patients who had undergone CT scans and blood tests for triaging. Two radiologists reviewed the CT images and recorded the incidence of lung involvement. The prediction model for lung involvement on CT images using clinico-laboratory variables [age, gender, body mass index, oxygen saturation of the peripheral artery (SpO2), comorbidities, symptoms, and blood data] were developed by multivariate logistic regression with cross-validation. RESULTS: In 120 of the 192 patients (62.5%), CT revealed lung involvement. The patient age (odds ratio [OR]; 4.95, 95% confidence interval [CI]; 0.93-26.49), albumin (OR; 4.66, 95%CI; 1.37-15.84), lactate dehydrogenase (OR; 5.79, 95%CI; 1.43-23.38) and C-reactive protein (OR; 8.93, 95%CI; 4.13-19.29) were selected for the final prediction model for lung involvement on CT images. The cross-validated area under the receiver operating characteristics (ROC) curve was 0.83. CONCLUSIONS: The high incidence of lung involvement (62.5%) was confirmed on CT images. The proposed prediction model that includes the patient age, albumin, lactate dehydrogenase, and C-reactive protein may be useful for predicting lung involvement on CT images and may assist in deciding whether triaged COVID-19 patients should undergo CT.