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Meta-Analysis
. 2021 Oct 8:2021:2571912.
doi: 10.1155/2021/2571912. eCollection 2021.

Value of the Neutrophil-Lymphocyte Ratio in Predicting COVID-19 Severity: A Meta-analysis

Ying Wang  1 , Jingyi Zhao  2 , Lan Yang  3 , Junhui Hu  1 , Yinhui Yao  1
Affiliations
Meta-Analysis

Value of the Neutrophil-Lymphocyte Ratio in Predicting COVID-19 Severity: A Meta-analysis

Ying Wang et al. Dis Markers. .

Abstract

Background: Coronavirus disease 2019 (COVID-19) is highly contagious and continues to spread rapidly. However, there are no simple and timely laboratory techniques to determine the severity of COVID-19. In this meta-analysis, we assessed the potential of the neutrophil-lymphocyte ratio (NLR) as an indicator of severe versus nonsevere COVID-19 cases.

Methods: A search for studies on the NLR in severe and nonsevere COVID-19 cases published from January 1, 2020, to July 1, 2021, was conducted on the PubMed, EMBASE, and Cochrane Library databases. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and area under the curve (AUC) analyses were done on Stata 14.0 and Meta-disc 1.4 to assess the performance of the NLR.

Results: Thirty studies, including 5570 patients, were analyzed. Of these, 1603 and 3967 patients had severe and nonsevere COVID-19, respectively. The overall sensitivity and specificity were 0.82 (95% confidence interval (CI), 0.77-0.87) and 0.77 (95% CI, 0.70-0.83), respectively; positive and negative correlation ratios were 3.6 (95% CI, 2.7-4.7) and 0.23 (95% CI, 0.17-0.30), respectively; DOR was 16 (95% CI, 10-24), and the AUC was 0.87 (95% CI, 0.84-0.90).

Conclusion: The NLR could accurately determine the severity of COVID-19 and can be used to identify patients with severe disease to guide clinical decision-making.

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Conflict of interest statement

No conflicts of interest declared.

Figures

Figure 1
Figure 1
Study selection flow chart.
Figure 2
Figure 2
Risk of bias and applicability concerns in the included studies.
Figure 3
Figure 3
Forest plots for the sensitivity and specificity of the NLR in predicting COVID-19 severity.
Figure 4
Figure 4
Symmetrical summary receiver operator characteristic curve of the NLR in all 30 studies.
Figure 5
Figure 5
Fagan nomogram of the NLR for the prediction of COVID-19 severity.
Figure 6
Figure 6
Univariable metaregression and subgroup analyses.
Figure 7
Figure 7
Stability and robustness analysis of the included studies: (a) goodness-of-fit; (b) bivariate normality; (c) influence analyses; (d) outlier detection.
Figure 8
Figure 8
Deeks' funnel plot.
See this image and copyright information in PMC

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