Качественная клиническая практика (Feb 2024)

Development of a model for the differential diagnosis of community-acquired bacterial pneumonia and viral lung injury in hospitalized adult patients

  • O. A. Kupriushina,
  • D. A. Strelkova,
  • A. S. Yasneva,
  • S. A. Rachina,
  • S. N. Avdeev,
  • A. E. Vlasenko,
  • L. V. Fedina,
  • O. V. Ivanova,
  • I. V. Kaledina,
  • N. A. Ananicheva

DOI
https://doi.org/10.37489/2588-0519-2023-4-78-85
Journal volume & issue
Vol. 0, no. 4
pp. 78 – 85

Abstract

Read online

Relevance. During and after the COVID-19 pandemic, viruses have become a more common cause of pulmonary infections in adults; therefore, the distinction between viral lung injury and community-acquired bacterial pneumonia is of increasing importance. Aim. Development of a model for differentiating community-acquired bacterial pneumonia and viral lung injury, including COVID-19. Materials and methods. This retrospective case–control study included 300 adult patients with viral lung injury and 100 adult patients with community-acquired bacterial pneumonia. Clinical, laboratory, and instrumental data were analyzed, significant factors were selected by which the samples differed, and a model was developed using logistic regression to distinguish between community-acquired bacterial pneumonia and viral lung damage, including COVID-19. Results. The developed model included the following parameters: total protein level, neutrophil/lymphocyte index, heart rate, unilateral infiltration on CT or chest x-ray, vasopressor prescription in the first 24 h of hospitalization, altered level of consciousness, chills, and fatigue. The model had the following characteristics: AUC = 0.94 (0.92–0.96), AUC_PR = 0.84 (0.76 to 0.92), prediction accuracy — 90%, sensitivity — 76%, specificity — 95%, positive predictive value — 83 %. Conclusion. The use of this model can facilitate the differential diagnosis of community-acquired bacterial pneumonia and viral lung injury, including COVID-19, in adults in general wards and intensive care units.

Keywords