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患有心血管疾病和高血压的患者的新冠病毒病结局

COVID-19 Outcomes Amongst Patients With Pre-existing Cardiovascular Disease and Hypertension.

作者信息

Chakinala Raja Chandra, Shah Chail D, Rakholiya Jigisha H, Martin Mehwish, Kaur Nirmaljot, Singh Harmandeep, Okafor Toochukwu L, Nwodika Chika, Raval Payu, Yousuf Salma, Lakhani Komal, Yogarajah Angelina, Malik Preeti, Singh Jagmeet, Kichloo Asim, Patel Urvish K

机构信息

Medicine, Geisinger Commonwealth School of Medicine, Danville, USA.

Medicine, Guthrie Robert Packer Hospital, Sayre, USA.

出版信息

Cureus. 2021 Feb 18;13(2):e13420. doi: 10.7759/cureus.13420.

DOI:10.7759/cureus.13420
PMID:33763316
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7980770/
Abstract

INTRODUCTION

Coronavirus disease 2019 (COVID-19) has multiorgan involvement and its severity varies with the presence of pre-existing risk factors like cardiovascular disease (CVD) and hypertension (HTN). Therefore, it is important to evaluate their effect on outcomes of COVID-19 patients. The objective of this meta-analysis and meta-regression is to evaluate outcomes of COVID-19 amongst patients with CVD and HTN.

METHODS

English full-text observational studies having data on epidemiological characteristics of patients with COVID-19 were identified searching PubMed from December 1, 2019, to July 31, 2020, following Meta-analysis Of Observational Studies in Epidemiology (MOOSE) protocol. Studies having pre-existing CVD and HTN data that described outcomes including mortality and invasive mechanical ventilation (IMV) utilization were selected. Using random-effects models, risk of composite poor outcomes (meta-analysis) and isolated mortality and IMV utilization (meta-regression) were evaluated. Pooled prevalence of CVD and HTN, correlation coefficient (r) and odds ratio (OR) were estimated. The forest plots and correlation plots were created using random-effects models.

RESULTS

Out of 29 studies (n=27,950) that met the criteria, 28 and 27 studies had data on CVD and HTN, respectively. Pooled prevalence of CVD was 18.2% and HTN was 32.7%. In meta-analysis, CVD (OR: 3.36; 95% CI: 2.29-4.94) and HTN (OR: 1.94; 95% CI: 1.57-2.40) were associated with composite poor outcome. In age-adjusted meta-regression, pre-existing CVD was having significantly higher correlation of IMV utilization (r: 0.28; OR: 1.3; 95% CI: 1.1-1.6) without having any association with mortality (r: -0.01; OR: 0.9; 95% CI: 0.9-1.1) among COVID-19 hospitalizations. HTN was neither correlated with higher IMV utilization (r: 0.01; OR: 1.0; 95% CI: 0.9-1.1) nor correlated with higher mortality (r: 0.001; OR: 1.0; 95% CI: 0.9-1.1).

CONCLUSION

In age-adjusted analysis, though we identified pre-existing CVD as a risk factor for higher utilization of mechanical ventilation, pre-existing CVD and HTN had no independent role in increasing mortality.

摘要

引言

2019冠状病毒病(COVID-19)会累及多个器官,其严重程度因心血管疾病(CVD)和高血压(HTN)等既往存在的风险因素而有所不同。因此,评估它们对COVID-19患者预后的影响很重要。本荟萃分析和荟萃回归的目的是评估患有CVD和HTN的COVID-19患者的预后。

方法

按照流行病学观察性研究的荟萃分析(MOOSE)方案,在2019年12月1日至2020年7月31日期间检索PubMed,以确定具有COVID-19患者流行病学特征数据的英文全文观察性研究。选择那些有既往CVD和HTN数据且描述了包括死亡率和有创机械通气(IMV)使用情况等预后的研究。使用随机效应模型,评估复合不良预后的风险(荟萃分析)以及单独的死亡率和IMV使用情况(荟萃回归)。估计CVD和HTN的合并患病率、相关系数(r)和比值比(OR)。使用随机效应模型创建森林图和相关图。

结果

在符合标准的29项研究(n = 27,950)中,分别有28项和27项研究有关于CVD和HTN的数据。CVD的合并患病率为18.2%,HTN的合并患病率为32.7%。在荟萃分析中,CVD(OR:3.36;95% CI:2.29 - 4.94)和HTN(OR:1.94;95% CI:1.57 - 2.40)与复合不良预后相关。在年龄调整的荟萃回归中,在COVID-19住院患者中,既往存在的CVD与IMV使用的相关性显著更高(r:0.28;OR:1.3;95% CI:1.1 - 1.6),但与死亡率无关联(r: - 0.01;OR:0.9;95% CI:0.9 - 1.1)。HTN与更高的IMV使用既无相关性(r:0.01;OR:1.0;95% CI:0.9 - 1.1),也与更高的死亡率无相关性(r:0.001;OR:1.0;95% CI:0.9 - 1.1)。

结论

在年龄调整分析中,虽然我们确定既往存在的CVD是机械通气使用率较高的一个风险因素,但既往存在的CVD和HTN在增加死亡率方面没有独立作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/02f723f411d0/cureus-0013-00000013420-i07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/247bd4010c54/cureus-0013-00000013420-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/8860a020802f/cureus-0013-00000013420-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/412058ecc442/cureus-0013-00000013420-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/43101e5193cf/cureus-0013-00000013420-i04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/e8bcd15d0f7e/cureus-0013-00000013420-i05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/27efec390b56/cureus-0013-00000013420-i06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/02f723f411d0/cureus-0013-00000013420-i07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/247bd4010c54/cureus-0013-00000013420-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/8860a020802f/cureus-0013-00000013420-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/412058ecc442/cureus-0013-00000013420-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/43101e5193cf/cureus-0013-00000013420-i04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/e8bcd15d0f7e/cureus-0013-00000013420-i05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/27efec390b56/cureus-0013-00000013420-i06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3987/7980770/02f723f411d0/cureus-0013-00000013420-i07.jpg

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