Can Lung Imaging Scores and Clinical Variables Predict Severe Course and Fatal Outcome in COVID-19 Pneumonia Patients? A Single-Center Observational Study

Life

Irena Jelicic¹  Josipa Domjanovic²  Ivan Skopljanac³  Ognjen Barcot⁴  Danijela Budimir Mrsic⁵  Mirela Pavicic Ivelja⁵  Kresimir Dolic⁵ 

[1] Department of Infectious Diseases, University Hospital of Split, 21000 Split, Croatia;Department of Nephrology, University Hospital of Split, 21000 Split, Croatia;Department of Pulmology, University Hospital of Split, 21000 Split, Croatia;Department of Surgery, University Hospital of Split, 21000 Split, Croatia;School of Medicine, University of Split, 21000 Split, Croatia;

关键词: lung ultrasound;    COVID-19;    prognostic;    pneumonia;    CT;    chest X-ray;   

DOI  :  10.3390/life12050735

来源: DOAJ

【 摘 要 】

COVID-19 prediction models mostly consist of combined clinical features, laboratory parameters, and, less often, chest X-ray (CXR) findings. Our main goal was to propose a prediction model involving imaging methods, specifically ultrasound. This was a single-center, retrospective cohort observational study of patients admitted to the University Hospital Split from November 2020 to May 2021. Imaging protocols were based on the assessment of 14 lung zones for both lung ultrasound (LUS) and computed tomography (CT), correlated to a CXR score assessing 6 lung zones. Prediction models for the necessity of mechanical ventilation (MV) or a lethal outcome were developed by combining imaging, biometric, and biochemical parameters. A total of 255 patients with COVID-19 pneumonia were included in the study. Four independent predictors were added to the regression model for the necessity of MV: LUS score, day of the illness, leukocyte count, and cardiovascular disease (χ2 = 29.16, p < 0.001). The model accurately classified 89.9% of cases. For the lethal outcome, only two independent predictors contributed to the regression model: LUS score and patient’s age (χ2 = 48.56, p < 0.001, 93.2% correctly classified). The predictive model identified four key parameters at patient admission which could predict an adverse outcome.

【 授权许可】

Unknown