Abstract and Introduction
Objective: This research aims to develop a laboratory model that can accurately distinguish pneumonia from nonpneumonia in patients with COVID-19 and to identify potential protective factors against lung infection.
Methods: We recruited 50 patients diagnosed with COVID-19 infection with or without pneumonia. We selected candidate predictors through group comparison and punitive least absolute shrinkage and selection operator (LASSO) analysis. A stepwise logistic regression model was used to distinguish patients with and without pneumonia. Finally, we used a decision-tree method and randomly selected 50% of the patients 1000 times from the same specimen to verify the effectiveness of the model.
Results: We found that the percentage of eosinophils, a high–fluorescence-reticulocyte ratio, and creatinine had better discriminatory power than other factors. Age and underlying diseases were not significant for discrimination. The model correctly discriminated 77.1% of patients. In the final validation step, we observed that the model had an overall predictive rate of 81.3%.
Conclusion: We developed a laboratory model for COVID-19 pneumonia in patients with mild to moderate symptoms. In the clinical setting, the model will be able to predict and differentiate pneumonia vs nonpneumonia before any lung computed tomography findings. In addition, the percentage of eosinophils, a high–fluorescence-reticulocyte ratio, and creatinine were considered protective factors against lung infection in patients without pneumonia.
Since the outbreak of COVID-19 in Wuhan, China, in December 2019, the COVID-19 epidemic has developed rapidly. By April 2020, the epidemic had affected most countries and regions in the world. The disease has caused serious global health and social problems.
Patients with COVID-19 with severe symptoms usually die of pneumonia within a short period of time after infection, whereas a small proportion of patients die of other causes. Mild acute respiratory infection symptoms, such as fever, dry cough, and fatigue, usually occur in the early stages of COVID-19, but those who develop acute respiratory distress syndrome, acute respiratory failure, multiple organ failure, and other fatal complications die rapidly. Generally, patients infected with COVID-19 without pneumonia recover, and asymptomatic infection is not life-threatening. However, a specific treatment method for COVID-19 has not been fully developed.
Because of decreased immunity and underlying diseases, the symptoms and mortality associated with COVID-19 in older adults are more serious. Older adult patients are more susceptible to viral infections and death and have more underlying diseases, such as hypertension, hyperlipidemia, diabetes, and rheumatoid arthritis.[10–12] However, it is not clear whether age and underlying disease can predict pneumonia. Current research on COVID-19 has focused on the epidemiology and clinical characteristics of patients, but information on the susceptibility to pneumonia has not been clear.
Pneumonia clearly plays a vital role in the prognosis of COVID-19. Therefore, we were committed to finding a way to identify whether a patient was susceptible to pneumonia before a chest computed tomography (CT) scan or before symptoms of pneumonia appear. Quickly identifying such patients will help prevent more serious cases of infection. It is a way to fight the death caused by COVID-19 infection. This research aimed to develop a model that can accurately distinguish patients with pneumonia from those without pneumonia in patients with COVID-19 and determine the factors that are significant in fighting infections in the lungs. The study investigated a group of patients at Hefei Second People's Hospital in China. Herein, we report our epidemiological, clinical, radiological, and laboratory examination results.
Lab Med. 2021;52(4):e104-e114. © 2021 American Society for Clinical Pathology