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Meta-Analysis
. 2023 Jun 21:14:1212998.
doi: 10.3389/fimmu.2023.1212998. eCollection 2023.

Systemic inflammation index, disease severity, and mortality in patients with COVID-19: a systematic review and meta-analysis

Arduino A Mangoni  1   2 , Angelo Zinellu  3
Affiliations
Meta-Analysis

Systemic inflammation index, disease severity, and mortality in patients with COVID-19: a systematic review and meta-analysis

Arduino A Mangoni et al. Front Immunol. .

Abstract

Introduction: An excessive systemic pro-inflammatory state increases the risk of severe disease and mortality in patients with coronavirus disease 2019 (COVID-19). However, there is uncertainty regarding whether specific biomarkers of inflammation can enhance risk stratification in this group. We conducted a systematic review and meta-analysis to investigate an emerging biomarker of systemic inflammation derived from routine hematological parameters, the systemic inflammation index (SII), in COVID-19 patients with different disease severity and survival status.

Methods: A systematic literature search was conducted in PubMed, Web of Science, and Scopus, between the 1st of December 2019 and the 15th of March 2023. Risk of bias and certainty of evidence were assessed using the Joanna Briggs Institute Critical Appraisal Checklist and the Grades of Recommendation, Assessment, Development and Evaluation, respectively (PROSPERO registration number: CRD42023420517).

Results: In 39 studies, patients with a severe disease or non-survivor status had significantly higher SII values on admission compared to patients with a non-severe disease or survivor status (standard mean difference (SMD)=0.91, 95% CI 0.75 to 1.06, p<0.001; moderate certainty of evidence). The SII was also significantly associated with the risk of severe disease or death in 10 studies reporting odds ratios (1.007, 95% CI 1.001 to 1.014, p=0.032; very low certainty of evidence) and in six studies reporting hazard ratios (1.99, 95% CI 1.01 to 3.92, p=0.047; very low certainty of evidence). Pooled sensitivity, specificity, and area under the curve for severe disease or mortality were 0.71 (95% CI 0.67 to 0.75), 0.71 (95% CI 0.64 to 0.77), and 0.77 (95% CI 0.73 to 0.80), respectively. In meta-regression, significant correlations were observed between the SMD and albumin, lactate dehydrogenase, creatinine, and D-dimer.

Discussion: Our systematic review and meta-analysis has shown that the SII on admission is significantly associated with severe disease and mortality in patients with COVID-19. Therefore, this inflammatory biomarker derived from routine haematological parameters can be helpful for early risk stratification in this group.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420517.

Keywords: COVID-19; biomarkers; disease severity; inflammation; mortality; risk stratification; systemic inflammation index.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
PRISMA 2020 flow chart of study selection.
Figure 2
Figure 2
Forest plot of studies reporting SII values in COVID-19 patients with different disease severity and survival status.
Figure 3
Figure 3
Forest plot of studies examining the association between the SII and disease severity or survival status in patients with COVID-19 by means of odds ratio.
Figure 4
Figure 4
Forest plot of studies examining association between SII and disease severity or survival status in patients with COVID-19 through HR.
Figure 5
Figure 5
Forest plot for the pooled estimates of sensitivity and specificity of the SII towards disease severity or mortality.
Figure 6
Figure 6
SROC curve with 95% confidence region and prediction region for the SII towards prediction of severe disease or mortality.
Figure 7
Figure 7
Empirical Bayes (posterior prediction) estimates hierarchical receiving operating characteristic (HSROC) curve with 95% confidence region and prediction region for SII towards prediction of severe disease or mortality.
Figure 8
Figure 8
Graphical illustration of residual-based goodness-of-fit (A), bivariate normality (B), influence (C), and outlier detection (D) analyses.
Figure 9
Figure 9
Fagan’s nomogram for the SII towards prediction of severe disease or mortality.
See this image and copyright information in PMC

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