The timeline and risk factors of clinical progression of COVID-19 in Shenzhen, China

J Transl Med. 2020 Jul 3;18(1):270. doi: 10.1186/s12967-020-02423-8.

Abstract

Background: The novel coronavirus disease 2019 (COVID-19) broke out globally. Early prediction of the clinical progression was essential but still unclear. We aimed to evaluate the timeline of COVID-19 development and analyze risk factors of disease progression.

Methods: In this retrospective study, we included 333 patients with laboratory-confirmed COVID-19 infection hospitalized in the Third People's Hospital of Shenzhen from 10 January to 10 February 2020. Epidemiological feature, clinical records, laboratory and radiology manifestations were collected and analyzed. 323 patients with mild-moderate symptoms on admission were observed to determine whether they exacerbated to severe-critically ill conditions (progressive group) or not (stable group). We used logistic regression to identify the risk factors associated with clinical progression.

Results: Of all the 333 patients, 70 (21.0%) patients progressed into severe-critically ill conditions during hospitalization and assigned to the progressive group, 253 (76.0%) patients belonged to the stable group, another 10 patients were severe before admission. we found that the clinical features of aged over 40 (3.80 [1.72, 8.52]), males (2.21 [1.20, 4.07]), with comorbidities (1.78 [1.13, 2.81]) certain exposure history (0.38 [0.20, 0.71]), abnormal radiology manifestations (3.56 [1.13, 11.40]), low level of T lymphocytes (0.99 [0.997, 0.999]), high level of NLR (0.99 [0.97, 1.01]), IL-6 (1.05 [1.03, 1.07]) and CRP (1.67 [1.12, 2.47]) were the risk factors of disease progression by logistic regression.

Conclusions: The potential risk factors of males, older age, with comorbidities, low T lymphocyte level and high level of NLR, CRP, IL-6 can help to predict clinical progression of COVID-19 at an early stage.

Keywords: COVID-19; Clinical progression; Pneumonia; Retrospective analysis; Risk factor.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Age Distribution
  • Aged
  • Aged, 80 and over
  • Betacoronavirus / physiology*
  • COVID-19
  • Child
  • Child, Preschool
  • China / epidemiology
  • Coronavirus Infections / diagnosis
  • Coronavirus Infections / epidemiology*
  • Coronavirus Infections / pathology*
  • Disease Progression*
  • Female
  • Hospitalization
  • Humans
  • Infant
  • Logistic Models
  • Male
  • Middle Aged
  • Pandemics
  • Pneumonia, Viral / diagnosis
  • Pneumonia, Viral / epidemiology*
  • Pneumonia, Viral / pathology*
  • ROC Curve
  • Risk Factors
  • SARS-CoV-2
  • Time Factors
  • Treatment Outcome
  • Young Adult