Department of Epidemiology, School of Public Health, Zhengzhou University, No. 100 of Science Avenue, Zhengzhou, 450001, China.
Department of Toxicology, Henan Center for Disease Control and Prevention, Zhengzhou, 450016, China.
Neurol Sci. 2022 Jul;43(7):4049-4059. doi: 10.1007/s10072-022-06024-9. Epub 2022 Mar 24.
To investigate the association between stroke and the risk for mortality among coronavirus disease 2019 (COVID-19) patients.
We performed systematic searches through electronic databases including PubMed, Embase, Scopus, and Web of Science to identify potential articles reporting adjusted effect estimates on the association of stroke with COVID-19-related mortality. To estimate pooled effects, the random-effects model was applied. Subgroup analyses and meta-regression were performed to explore the possible sources of heterogeneity. The stability of the results was assessed by sensitivity analysis. Publication bias was evaluated by Begg's test and Egger's test.
This meta-analysis included 47 studies involving 7,267,055 patients. The stroke was associated with higher COVID-19 mortality (pooled effect = 1.30, 95% confidence interval (CI): 1.16-1.44; I = 89%, P < 0.01; random-effects model). Subgroup analyses yielded consistent results among area, age, proportion of males, setting, cases, effect type, and proportion of severe COVID-19 cases. Statistical heterogeneity might result from the different effect type according to the meta-regression (P = 0.0105). Sensitivity analysis suggested that our results were stable and robust. Both Begg's test and Egger's test indicated that potential publication bias did not exist.
Stroke was independently associated with a significantly increased risk for mortality in COVID-19 patients.
研究中风与 2019 年冠状病毒病(COVID-19)患者死亡率之间的关系。
我们通过电子数据库(包括 PubMed、Embase、Scopus 和 Web of Science)进行了系统检索,以确定报告中风与 COVID-19 相关死亡率之间关联的调整效果估计值的潜在文章。为了估计汇总效应,应用了随机效应模型。进行了亚组分析和meta 回归,以探索可能的异质性来源。通过敏感性分析评估结果的稳定性。通过 Begg 检验和 Egger 检验评估发表偏倚。
这项荟萃分析包括 47 项研究,涉及 7267055 名患者。中风与 COVID-19 死亡率升高相关(汇总效应=1.30,95%置信区间(CI):1.16-1.44;I=89%,P<0.01;随机效应模型)。亚组分析在地域、年龄、男性比例、环境、病例数、效应类型和严重 COVID-19 病例比例方面得出了一致的结果。根据 meta 回归,统计异质性可能源于不同的效应类型(P=0.0105)。敏感性分析表明,我们的结果是稳定和可靠的。Begg 检验和 Egger 检验均表明不存在潜在的发表偏倚。
中风与 COVID-19 患者的死亡率显著增加独立相关。