Abstract
Background: This study aimed to evaluate the prediction capabilities of clinical laboratory biomarkers to the prognosis of COVID-19 patients. Methods: Observational studies reporting at least 30 cases of COVID-19 describing disease severity or mortality were included. Meta-data of demographics, clinical symptoms, vital signs, comorbidities, and 14 clinical laboratory biomarkers on initial hospital presentation were extracted. Taking the outcome group as the analysis unit, meta-regression analysis with the generalized estimating equations (GEE) method for clustered data was performed sequentially. The unadjusted effect of each potential predictor of the three binary outcome variables (i.e., severe vs. non-severe, critically severe vs. non-critically severe, and dead vs. alive) was examined one by one by fitting three series of simple GEE logistic regression models due to missing data. The worst one was dropped one at a time. Then, a final multiple GEE logistic regression model for each of the three outcome variables was obtained. Findings: Meta-data was extracted from 76 articles, reporting a total of 26,627 cases of COVID-19. Patients were recruited across 16 countries. The number of studies (patients) included in the final models of the analysis for severity, critical severity, and mortality was 38 studies (9,764 patients), 21 studies (4,792 patients), and 24 studies (14,825 patients), respectively. After adjusting for the effect of age, lymphocyte count mean or median ≤ 1.03 (estimated hazard ratio [HR] = 46.2594, p Interpretation: Lymphocyte count should be closely watched for COVID-19 patients in clinical practice.Funding: None to declare. Declaration of Interest: None to declare.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.