Prognostic Value of Early Stage Biomarkers on COVID-19 Severity: A Systematic Review and Meta-Analysis
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Abstract
Background: The surge in morbidity and mortality rate of COVID-19 is happening worldwide. Identifying COVID-19 severity prognosis in patients at an early stage became imperative as it could help to monitor and prevent disease progression towards severe form.
Purpose of Study: To comprehend the variation and profile of specific biomarkers to have the preferred modality to monitor the severity outcomes of COVID-19. Furthermore, this study is expected to help the clinicians to identify the probability of disease progression towards severe in COVID-19 patients on admission.
Methods: This study was reported based on criteria from PRISMA. The literature search was conducted in databases such as MEDLINE, Scopus, and ScienceDirect. RevMan 5.4 were used to provide pooled estimates for standardized mean differences with 95% confidence intervals. Random effect model was used based on heterogeneity level. Risk of Bias was assessed for each study using QUIPS tools.
Results: Fifty studies were included in the qualitative synthesis and twenty three from it were included in the quantitative meta-analysis. These studies consist of a total of 12720 cohort size, 2965 and 9755 for severe patients and non-severe patients respectively. Plasma suPAR (pooled SMD = 1.54, 95% CI = 0.85-2.22, p <0.0001), serum KL-6 (pooled SMD = 1.21, 95% CI = 0.85-1.57, p <0.00001), and SP-D (pooled SMD = 1.75, 95% CI = 0.79-2.70, p = 0.0003) were shown to have significantly high prognostic value for COVID-19 severity.
Conclusion: This study provides evidence that plasma suPAR, serum KL-6, and SP-D can be used as an excellent early stage biomarkers for COVID-19 severity.
Keywords: COVID-19, Biomarker, Severity, Prognosis
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