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

Respir Res. 2020 Jun 22;21(1):157. doi: 10.1186/s12931-020-01412-1.

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.

MeSH terms

  • Age Factors
  • COVID-19
  • COVID-19 Testing
  • China / epidemiology
  • Clinical Laboratory Techniques / methods
  • Cohort Studies
  • Coronavirus Infections / diagnosis
  • Coronavirus Infections / epidemiology*
  • Female
  • Hospital Mortality*
  • Hospitalization / statistics & numerical data
  • Humans
  • Intensive Care Units / statistics & numerical data
  • Logistic Models
  • Male
  • Pandemics / statistics & numerical data*
  • Pneumonia, Viral / diagnosis
  • Pneumonia, Viral / epidemiology*
  • Predictive Value of Tests
  • Prognosis
  • ROC Curve
  • Radiography, Thoracic / methods
  • Retrospective Studies
  • Risk Assessment
  • Survival Rate
  • Tertiary Care Centers
  • Tomography, X-Ray Computed / methods
  • Young Adult