Predictive factors of severe coronavirus disease 2019 in previously healthy young adults: a single-center, retrospective study

Abstract

Background: Several previously healthy young adults have developed Coronavirus Disease 2019 (COVID-19), and a few of them progressed to the severe stage. However, the factors are not yet determined.

Method: We retrospectively analyzed 123 previously healthy young adults diagnosed with COVID-19 from January to March 2020 in a tertiary hospital in Wuhan. Patients were classified as having mild or severe COVID-19 based on their respiratory rate, SpO₂, and PaO₂/FiO₂ levels. Patients' symptoms, computer tomography (CT) images, preadmission drugs received, and the serum biochemical examination on admission were compared between the mild and severe groups. Significant variables were enrolled into logistic regression model to predict the factors affecting disease severity. A receiver operating characteristic (ROC) curve was applied to validate the predictive value of predictors.

Result: Age; temperature; anorexia; and white blood cell count, neutrophil percentage, platelet count, lymphocyte count, C-reactive protein, aspartate transaminase, creatine kinase, albumin, and fibrinogen values were significantly different between patients with mild and severe COVID-19 (P < 0.05). Logistic regression analysis confirmed that lymphopenia (P = 0.010) indicated severe prognosis in previously healthy young adults with COVID-19, with the area under the curve (AUC) was 0.791(95% Confidence Interval (CI) 0.704-0.877)(P < 0.001).

Conclusion: For previously healthy young adults with COVID-19, lymphopenia on admission can predict severe prognosis.

Keywords: COVID-19; Predictive factors; Retrospective study; SARS-CoV-2.

Fig. 1

Recruitment flowchart of patients for the study

Fig. 2

Lymphocyte count for severe progressed coronavirus disease in previously healthy adults using receiver operating characteristic curves. The area under the curve of lymphocyte count is 0.791(95% confidence interval: 0.704–0.877) (P < 0.001). The best cutoff for lymphocyte count for prediction 0.905*10⁹/L is with a specificity of 64.3% and a sensitivity of 84.2% (P < 0.001)