The factors predicting pneumonia in COVID-19 patients: preliminary results from a university hospital in Turkey

Turk J Med Sci. 2020 Dec 17;50(8):1810-1816. doi: 10.3906/sag-2005-385.


Background/aim: Pneumonia is the most serious clinical presentation of COVID-19. This study aimed to determine the demographic, clinical, and laboratory findings that can properly predict COVID-19 pneumonia.

Materials and methods: This study was conducted in the Gazi University hospital. All hospitalized patients with confirmed and suspected SARS-CoV-2 infection between 16 March 2020 and 30 April 2020 were analyzed retrospectively. COVID-19 patients were separated into two groups, pneumonia and nonpneumonia, and then compared to determine predicting factors for COVID-19 pneumonia. Variables that had a P-value of less than 0.20 and were not correlated with each other were included in the logistic regression model.

Results: Of the 247 patients included in the study 58% were female, and the median age was 40. COVID-19 was confirmed in 70.9% of these patients. Among the confirmed COVID-19 cases, 21.4% had pneumonia. In the multivariate analysis male sex (P = 0.028), hypertension (P = 0.022), and shortness of breath on hospital admission (P = 0.025) were significant factors predicting COVID-19 pneumonia.

Conclusion: Shortness of breath, male sex, and hypertension were significant for predicting COVID-19 pneumonia on admission. Patients with these factors should be evaluated more carefully for diagnostic procedures, such as thorax CT.

Keywords: COVID-19; pneumonia; predicting factors.

MeSH terms

  • Adult
  • COVID-19* / diagnosis
  • COVID-19* / epidemiology
  • COVID-19* / physiopathology
  • Causality
  • Comorbidity
  • Dyspnea* / diagnosis
  • Dyspnea* / etiology
  • Female
  • Humans
  • Hypertension / epidemiology*
  • Lung / diagnostic imaging*
  • Male
  • Pneumonia, Viral* / diagnosis
  • Pneumonia, Viral* / etiology
  • Retrospective Studies
  • SARS-CoV-2 / metabolism
  • Sex Factors
  • Tomography, X-Ray Computed / methods
  • Tomography, X-Ray Computed / statistics & numerical data
  • Turkey / epidemiology