Logistic regression analysis of the value of biomarkers, clinical symptoms, and imaging examinations in COVID-19 for SARS-CoV-2 nucleic acid detection

Abstract

The detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) nucleic acid detection provides a direct basis for diagnosing Coronavirus Disease 2019. However, nucleic acid test false-negative results are common in practice and may lead to missed diagnosis. Certain biomarkers, clinical symptoms, and imaging examinations are related to SARS-CoV-2 nucleic acid detection and potential predictors. We examined nucleic acid test results, biomarkers, clinical symptoms, and imaging examination data for 116 confirmed cases and asymptomatic infections in Zhuhai, China. Patients were divided into nucleic acid-positive and -false-negative groups. Predictive values of biomarkers, symptoms, and imaging for the nucleic acid-positive rate were calculated by Least Absolute Shrinkage and Selection Operators regression analysis and binary logistic regression analysis, and areas under the curve of these indicators were calculated. Hemoglobin (OR = 1.018, 95% CI: 1.006-1.030; P = .004) was higher in the respiratory tract-positive group than the nucleic acid-negative group, but platelets (OR = 0.996, 95% CI: 0.993-0.999; P = .021) and eosinophils (OR = 0.013, 95% CI: 0.001-0.253; P = .004) were lower; areas under the curve were 0.563, 0.614, and 0.642, respectively. Some biomarkers can predict SARS-CoV-2 viral nucleic acid detection rates in Coronavirus Disease 2019 and are potential auxiliary diagnostic tests.