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Prognostic value of serum amyloid A in COVID-19: A meta-analysis
Yongkai Li1, He Xiaojing2, Li Zhuanyun1
1Emergency Trauma Center, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Background:
There is still a lack of large-scale clinical studies and evidence-based evidence to prove the relationship between serum amyloid A (SAA) and the severity and prognosis of patients with new coronavirus pneumonia (COVID-19).
Methods:
We searched PubMed, Cochrane Library, Excerpta Medica Database, and Web of Science for original articles from December 1, 2019 to December 19, 2020. Search criteria include free text search, explosive MESH/EMTREE terms, and all synonyms for SAA and COVID-19. There are no language restrictions on the searched documents. Statistical methods were performed using Stata 14.0 software, and RevMan 5.4 software provided by the Cochrane Collaboration for meta-analysis. The 10 included studies in the literature were classified according to the severity of the novel coronavirus treatment guidelines, with mild/moderate categorized as nonsevere and severe/critical as severe, and the data were meta-analyzed using multiple subgroup standard deviations combined. Severe and nonsevere were finally divided into 2 groups, and the combined data were meta-analyzed according to the standardized mean difference.
Results:
The results of the meta-analysis given by random effects showed that SAA levels were significantly higher in severe vs nonsevere (standardized mean difference 1.20 [95% confidence interval 0.91-1.48]), which was statistically significant (P < .001). The 3 literatures studied (random effect size 0.11 [95% confidence interval 0.05-0.19]; I2 = 56.68%) and were statistically significant, z = 5.46 P < .01, suggesting that the risk of death occurs at higher levels with increasing SAA values, with the risk of death in the severe group being 11% higher than in the nonsevere group.
Conclusion:
SAA can be considered as a biomarker for predicting the severity and prognosis of COVID-19. SAA can be used for early warning of the poor prognosis of COVID-19 and for monitoring the recovery process, which has important clinical value.
