key: cord-0779797-h4ufpzca
authors: Xu, Jie; Xiao, Wenwei; Liang, Xuan; Zhang, Peihua; Shi, Li; Wang, Ying; Wang, Yadong; Yang, Haiyan
title: The association of cerebrovascular disease with adverse outcomes in COVID-19 patients: a meta-analysis based on adjusted effect estimates
date: 2020-08-28
journal: J Stroke Cerebrovasc Dis
DOI: 10.1016/j.jstrokecerebrovasdis.2020.105283
sha: 06dc71300fae832d9e1d123ed133b0b990af2222
doc_id: 779797
cord_uid: h4ufpzca

OBJECTIVE: The aim of this study was to address the association between cerebrovascular disease and adverse outcomes in coronavirus disease 2019 (COVID-19) patients by using a quantitative meta-analysis based on adjusted effect estimates. METHOD: A systematic search was performed in PubMed, Web of Science, and EMBASE up to August 10(th), 2020. The adjusted effect estimates were extracted and pooled to evaluate the risk of the unfavorable outcomes in COVID-19 patients with cerebrovascular disease. Subgroup analysis and meta-regression were also carried out. RESULTS: There were 12 studies with 10,304 patients included in our meta-analysis. A significant trend was observed when evaluating the association between cerebrovascular disease and adverse outcomes (pooled effect = 2.05, 95% confidence interval (CI): 1.34-3.16). In addition, the pooled effects showed that patients with a history of cerebrovascular disease had more likelihood to progress fatal outcomes than patients without a history of cerebrovascular disease (pooled effect = 1.78, 95% CI: 1.04-3.07). CONCLUSION: This study for the first time indicated that cerebrovascular disease was an independent risk factor for predicting the adverse outcomes, particularly fatal outcomes, in COVID-19 patients on the basis of adjusted effect estimates. Well-designed studies with larger sample size are needed for further verification.

The disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been named coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO). Both the morbidity and mortality of COVID-19 were so high that the health system of many countries was on the verge of collapse 1 . To help clinicians better allocate health resources, many researchers devoted themselves to explore the biomarkers and clinic features which might be useful predictors for disease progression 2-4 . Recently, an article published in the International Journal of Stroke by Aggarwal et al. 5 indicated that cerebrovascular disease was associated with increased disease severity in COVID-19 patients. The authors evaluated the common risk estimates by unadjusted effects and found that there was no significant association between cerebrovascular disease and fatal outcomes in COVID-19 patients (odds ratio (OR) = 2.33, 95% confidence interval (CI): 0.77-7.04).

Coincidentally, a paper reported by Chen et al. 6 illustrated that cerebrovascular disease might increase the risk of death in COVID-19 patients in univariable analysis (OR = 3.258, 95% CI: 1.658-6.402), but in multivariable analysis, it might not be associated with fatal outcome in COVID-19 patients (OR = 1.379, 95% CI: 0.650-2.926). This suggested that the association between cerebrovascular disease and adverse outcomes in COVID-19 patients might be affected by many confounders such as age, gender, and other comorbidities [7] [8] [9] [10] . Therefore, this meta-analysis was performed to evaluate the association between cerebrovascular disease and adverse outcomes in COVID-19 patients on the basis of adjusted effect estimates.

A comprehensive retrieve was conducted in PubMed, Web of Science, and EMBASE up to August 10 th , 2020 using the following search terms: "COVID-19", "coronavirus", "SARS-CoV-2", "2019-nCoV", "cerebrovascular disease", "stroke", "cerebral infarction", "brain infarction", "ICD-9", "ICD-10", "fatality", "mortality", "death", "severe", "critical", "severity", and "outcome". References to previous similar studies were also considered.

Studies were taken into account if they met all of the following criteria: (1) the included population should be patients with confirmed COVID-19; (2) the method must include multivariate analysis such as cox regression, logistic regression, and so on; (3) the results of the study included the association between cerebrovascular disease (stroke, cerebral infarction, brain infarction) and the adverse outcomes (severe, critical, and fatal outcomes) in patients with COVID-19; (4) complete and available data were reported in those studies. Studies with larger sample sizes were selected if the exposed population of them originated from the same hospital within an overlapping period. Duplicates, comments, letters, conference records, and editorial were excluded.

Two investigators extracted the following information independently: first author, country, date of data collection, source of data, percentage of cerebrovascular disease patients, adjusted effect estimates and confounders. When a paper reported both multivariable adjusted hazard ratio (HR) and OR, it was preferred to include HR because cox regression took time into account. Two researchers negotiated to resolve it in case of disagreement. The quality of included studies was assessed by investigators according to the Newcastle-Ottawa Scale 11 . The studies with a score above 7 were considered to be high quality.

The pooled effects were calculated by multivariable adjusted effect estimates (OR and HR) and 95% CI, assessing the association between cerebrovascular disease and adverse outcomes in COVID-19 patients. Cochran's Q-statistic and I² test were conducted to evaluate the heterogeneity among studies. The random-effects model was applied if heterogeneity existed across studies (I² ≥ 50%, P < 0.1), otherwise, the fixed-effects model was adopted 12 . Subgroup analysis and meta-regression were performed to explore the source of heterogeneity. The robustness of the results was assessed by sensitivity analyses 13 . Publication bias was evaluated by Begg's test, Egger's test and Deek's funnel plot [14] [15] [16] . Stata V.12.0 software was used to conduct all analyses.

Flow diagram of the publication search and selection process was presented in Figure   1 . Total 1,429 documents were initially retrieved, and 850 studies were remained after removing duplications. 379 studies were identified after screening titles and abstracts, and 12 studies with 10,304 cases were included conclusively after full-text review 6, 17-27 . Of those, 8 studies reported OR and 4 reported HR, and 8 studies reported the association between cerebrovascular disease and the fatal outcomes. Seven studies came from China, two from America, two from Korea, and one from UK. The main characteristics of the included studies are summarized in Table 1 .

The pooled effects in our analysis showed that COVID-19 patients with a history of cerebrovascular disease were more likely to progress to adverse outcomes than patients without a history of cerebrovascular disease (pooled effect = 2.05, 95% CI:

1.34-3.16; I² = 50%, Cochran's Q, P = 0.024, random-effects model; Figure 2A ). The results of subgroup analysis grouped by effect values were in keeping with it (OR = 2.19, 95% CI: 1.19-4.05; HR = 2.01, 95% CI: 1.15-3.53; Figure 2A ). In addition, we also conducted a pooled analysis of mortality studies, and the data indicated that an obvious association was also observed between cerebrovascular disease and fatal outcomes (pooled effect = 1.78, 95% CI: 1.04-3.07; I² = 60.7%, Cochran's Q, P = 0.013, random-effects model; Figure 2B ). Sensitivity analysis demonstrated that the results were robust ( Figure 3A and B). No source of heterogeneity was found by meta-regression (all P > 0.05). No publication bias was found either in Begg's test (P = 0.631, Figure 4A ), Egger's test (P = 0.327, Figure 4B ) or Deek's plot (P = 0.846, Figure 4C ).

COVID-19 has caused a worldwide pandemic with its high mortality and infection rates since January, 2020 28 . Identifying risk factors associated with disease progression of COVID-19 is essential to guide clinicians in the use of targeted medications. It has been reported that COVID-19 patients diagnosed in intensive care units have a higher mortality rate than patients confirmed in the ordinary ward, which appeared to be associated with some comorbidities such as hypertension, diabetes, cardiovascular disease, and so on 29 . Cerebrovascular disease, as a common disease with high mortality, caught the attention of researchers 30 . To our knowledge, several meta-analyses have investigated the association of cerebrovascular disease with the adverse outcomes in COVID-19 patients 5, 31, 32 , however, the data were uniformly estimated based on unadjusted effect estimates. As reported in previous studies, age, gender and pre-existing disease conditions could affected disease progression of COVID-19 7-10 , and might modulate the association between cerebrovascular disease and the adverse outcomes in COVID-19 patients. Therefore, it is an urgent need to verify this association by performing a quantitative meta-analysis based on adjusted effect estimates.

Our current meta-analysis based on adjusted effect estimates showed that there was a positive correlation between cerebrovascular disease and unfavorable outcomes, especially fatal outcomes in COVID-19 patients. This suggests that cerebrovascular disease was an independent risk factor for predicting the unfavorable outcomes in COVID-19 patients. Moreover, studies concluded that critically ill COVID-19 patients experiencing cytokine storm and thrombotic complication had a poorer prognosis and increased fatality rate 33, 34 , and that stroke might be an expression of a severe form of COVID-19. Therefore, in clinical practice, clinicians should pay more attention to COVID-19 patients with co-existing cerebrovascular disease and timely medications were applied to prevent worse outcomes.

There are still some important limitations in our study. First, our meta-analysis included only 12 studies due to the limitation of the number of published articles.

Second, the heterogeneity of our study cannot be ignored. The subgroup analysis and meta-regression were conducted to explore the source of heterogeneity, and it was found in the subgroup analysis that the heterogeneity might originate from different effect values used in different studies, but in the meta-regression, no source of heterogeneity was found. Third, although the selected studies presented the adjusted effect estimates, the adjusted confounders are not completely uniform across studies. In summary, our study retrieved all multivariate analyses of the relationship between cerebrovascular disease history and poor prognosis in COVID-19 patients in three databases, and it is the first systematic review and meta-analysis that evaluated the relationship between cerebrovascular disease and adverse outcomes in COVID-19 patients using adjusted effect estimates. The pooled effects suggest that cerebrovascular disease was an independent risk factor for predicting the adverse outcomes, particularly fatal outcomes, in COVID-19 patients. Well-designed with larger sample size are needed for further verification. Age, gender, HTN, diabetes, CVD, malignancy, CKD, COPD, days from onset to clinics, days from onset to admission

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Age, gender, ethnicity, education, prevalent disease (CHD, atrial fibrillation, HTN, diabetes (type 2), CKD, depression, dementia, asthma, COPD, osteoporosis, osteoarthritis)

HTN, hemoglobin, oxygenation index on admission

NLR, neutrophil to lymphocyte ratio; NR, not reported

CI: confidence interval; CHD, coronary heart disease

CAD, coronary artery disease

CKD, chronic kidney diseases

CLD, chronic liver diseases

COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein

BMI, body mass index; SOFA, Sequential Organ Failure Assessment; SAT, subcutaneous adipose tissue area

VAT, visceral adipose tissue area

SMA, skeletal muscle area

Cooperative Oncology Group performance status; LDH, lactate dehydrogenase; NOS