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. 2022 Mar 24;43(7):4049–4059. doi: 10.1007/s10072-022-06024-9

The association between stroke and COVID-19-related mortality: a systematic review and meta-analysis based on adjusted effect estimates

Shuwen Li 1, Jiahao Ren 1, Hongjie Hou 1, Xueya Han 1, Jie Xu 1, Guangcai Duan 1, Yadong Wang 2, Haiyan Yang 1,✉
PMCID: PMC8943353  PMID: 35325320

Abstract

Objective

To investigate the association between stroke and the risk for mortality among coronavirus disease 2019 (COVID-19) patients.

Methods

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.

Results

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; I2 = 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.

Conclusion

Stroke was independently associated with a significantly increased risk for mortality in COVID-19 patients.

Keywords: COVID-19, Stroke, Mortality, Adjusted effect estimates, Meta-analysis

Introduction

A series of studies have explored the relationship between comorbid stroke and the risk of mortality in coronavirus disease 2019 (COVID-19), but the conclusions were inconsistent or conflicting. Two previous meta-analyses on the basis of un-adjusted effect estimates failed to observe that comorbid stroke was significantly related to an increased risk of mortality in COVID-19 patients [1, 2]. To our knowledge, several risk factors including age, gender, and other underlying conditions (diabetes, hypertension, chronic kidney disease, and chronic obstructive pulmonary disease and others) have been reported to have a significant impact on the clinical progression of patients with COVID-19 [3–8]. This meant that these factors might affect the association between stroke and COVID-19 mortality. For example, the results of univariate analysis in Muhammad et al.’s study [9] showed that stroke was a risk factor for the mortality of COVID-19 (odds ratio (OR) = 2.73, 95% confidence interval (CI): 1.08–6.88), while the results of multivariate analysis demonstrated that stroke was not significantly associated with COVID-19 mortality (adjusted OR = 1.34, 95% CI: 0.45–4.02). Similar findings were also observed by Lee et al. [10]. Therefore, a meta-analysis based on adjusted effect estimates was performed to investigate the association of stroke with fatal outcome in COVID-19 patients.

Methods

The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) Checklist was used to improve the reporting of this meta‐analysis. Databases including PubMed, Web of Science, Scopus, and Embase were searched up to November 22, 2021, to find the relevant studies. The following search terms were used: (“COVID-19” OR “coronavirus disease 2019” OR “SARS-CoV-2” OR “novel coronavirus” OR “2019-nCoV”) AND (“stroke” OR “strokes” OR “cerebral stroke” OR “acute cerebrovascular accident” OR “cerebral infarction”) AND (“mortality” OR “death” OR “fatality” OR “deceased” OR “non-survivor”). Moreover, to find more grey literature on this topic, relevant references of the included studies were also taken into account.

The following inclusion criteria were defined for the selection of the papers: (1) articles should be published in English; (2) COVID-19 patients were diagnosed according to the World Health Organization (WHO) standards; (3) articles reported adjusted effect estimates on the association between stroke and the risk of mortality among COVID-19 patients. Repeated articles, case reports, reviews, comments, protocols, errata, and articles without sufficient information were excluded. Only the articles with the most complete data could be included if studies were based on the same data sources.

Two authors selected eligible studies independently and screened the titles and abstracts on the basis of the inclusion criteria. Any disagreement would be resolved through discussion. The primary message included the following data: author, country, cohort type, cases, numbers of stroke, mean or median age, proportion of males, setting, proportion of severe COVID-19 cases, adjusted effect estimate, and 95% CI.

Meta-analysis was performed using the R statistical software (Version 4.1.1, The R Foundation, Vienna, Austria) with “meta” package (Version 4.19-0) to calculate the pooled effect and 95% CI on the association between stroke and COVID-19 mortality. The I2 statistic was used to assess heterogeneity, which was considered significant if I2 > 50%. A random-effects model was adopted if heterogeneity was significant; otherwise, a fixed-effects model would be implemented. Subgroup analyses were conducted according to the area, age, cohort type, effect type, setting, cases, proportion of severe COVID-19 cases, and the proportion of males to explore the sources of heterogeneity. The robustness of the results was tested using sensitivity analysis by eliminating data one at a time. The authors also used the Begg’s and Egger’s tests to detect publication bias. Statistically significant difference was considered when P value was less than 0.05.

Results

The literature search identified 4252 articles through database mining. A total of 4177 of them were excluded after screening the titles and abstracts, and 75 reports were reviewed for full-text evaluation. As a result, we included 47 studies of 7,267,055 patients [9–55]. A flowchart of the study search and selection was shown in Fig. 1. Basic characteristics of the included studies were presented in Table 1.

Fig. 1.

Fig. 1

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Flow chart of the process of study selection

Table 1.

Characteristics of the eligible studies included in this meta-analysis

Author Country Cohort type Cases (n) No. of stroke (%) Age (years) Severe COVID-19 (%) Male (%) Setting Adjusted-effect (95% CI)
Atkins JL UK Retrospective 507 23 (4.5) 74.3 ± 4.5 NA NA Hospitalized OR = 1.16 (0.75–1.81)
Perez-Guzman PN UK Retrospective 614 NA 69 ± 25 13.03 62.21 Hospitalized OR = 0.89 (0.48–1.65)
Kummer BR USA Retrospective 3,248 387 (11.9) 67.0 ± 16.2 NA 85.19 Hospitalized OR = 1.28 (1.01–1.63)
Gupta A USA Retrospective 1,296 135 (10.4) 69.8 ± 13.9 22.22 56.48 Hospitalized OR = 1.177 (0.821–1.688)
Nimkar A USA Retrospective 327 91 (27.8) 71 (59–82) NA 55.7 Hospitalized OR = 1.1 (0.6–1.9)
Reilev M Denmark Retrospective 11,122 542 (4.9) 48 (33–62) 2.82 42 Hospitalized OR = 1.4 (1.1–1.8)
Miller J USA Retrospective 3,633 1,758 (51.8) 58.4 ± 18.1 NA 46.2 All patients OR = 0.94 (0.71–1.24)
Graziani D Spain Retrospective 793 27 (3.4) 75 ± 12 NA 83 All patients OR = 0.76 (0.58–1.71)
Clift AK* UK Prospective 6,083,102 129,699 (2.13) 48.2 ± 18.6 NA 49.9 All patients HR = 1.28 (1.19–1.39)
Liu J China Retrospective 774 43 (5.6) 64 (54–73) 100 58.4 Hospitalized HR = 0.99 (0.58–1.7)
Sheshah E Saudi Arabia Retrospective 300 5 (1.7) 49.7 ± 13.2 7.0 86.3 Hospitalized OR = 1.5 (0.2–10.8)
Rossi L Italy Retrospective 590 21 (3.6) 76.2 (68.2–82.6) 14.24 67.6 All patients HR = 1.721 (0.985–3.008)
FAI2R** France Retrospective 694 25 (3.7) 55.9 12.54 33.4 All patients OR = 1.52 (0.51–4.56)
Alguwaihes AM Saudi Arabia Retrospective 439 17 (3.9) 55 (19–101) 28.02 68.3 Hospitalized HR = 1.3 (0.5–3.8)
Ling SF UK Retrospective 444 40 (9.0) 74 (63–83) 7.6 55.2 Hospitalized OR = 1.09 (0.54–2.19)
Hobbs ALV USA Retrospective 502 57 (11.4) 62 (49–71) 35.66 55.2 Hospitalized OR = 1.25 (0.55–2.72)
Ahlstrom B Sweden Retrospective 1,981 59 (3.0) 61 (52–69) 100 74 Hospitalized HR = 1.04 (0.75–1.44)
Eskandar EN USA Retrospective 4,711 27 (2.4) 63.4 NA 53.3 Hospitalized HR = 1.75 (1.4–2.1)
Muhammad R USA Retrospective 200 22 (11.0) 58.9 ± 15.1 35.5 60.5 Hospitalized OR = 1.34 (0.45–4.02)
Kvale R Norway Retrospective 8,809 125 (1.4) NA NA 49.5 All patients OR = 1.5 (1.1–2.2)
Gonzalez-Fajardo JA Spain Prospective 106 15 (14.15) 65.66 ± 15.49 8.87 67.92 Hospitalized HR = 8.812 (2.25–34.516)
Efros O Israel Retrospective 320 29 (9.06) 63.66 ± 16.87 NA 64.06 Hospitalized HR = 1.90 (0.83–4.32)
Aoun M Lebanon Retrospective 231 14 (6.1) 61.46 ± 13.99 19.91 55.4 Hospitalized OR = 2.71 (0.89–8.28)
Caro-Codon J Spain Retrospective 918 41 (4.5) 63.2 ± 15.5 16.9 60.1 Hospitalized HR = 1.61 (0.89–2.9)
Galvez-Barron C Spain Retrospective 103 13 (12.6) 86.75 ± 4.65 57.28 40.8 Hospitalized OR = 0.85 (0.26–2.91)
Alwafi H Saudi Arabia Retrospective 706 88 (12.5) 48.0 ± 15.6 35.98 68.5 Hospitalized OR = 0.25 (0.06–1.01)
Fan FSY China Retrospective 3,164 NA NA NA NA Hospitalized HR = 2.31 (1.35–3.96)
Cummins L UK Retrospective 1,781 138 (7.7) 59.77 8.53 55.2 Hospitalized OR = 1.93 (1.29–2.88)
Vogels Y Netherlands Retrospective 114 7 (6.1) 68.0 (59.0–73.3) 100 76.3 Hospitalized HR = 1.82 (0.62–5.33)
Azarkar Z Iran Retrospective 364 13(3.57) 54.28 ± 18.81 18.13 56.9 Hospitalized OR = 5.56 (1.5–21.3)
Kelly JD USA Retrospective 27,640 757 (2.7) 57.59 NA 88.6 All patients OR = 0.98 (0.75–1.29)
Panagides V France Retrospective 2,806 249 (8.98) 66.4 ± 16.9 12.94 57.6 Hospitalized HR = 0.92 (0.67–1.26)
Lugon JR Brazil Retrospective 741 26 (3.5) 57 ± 16 28.48 61 All patients HR = 1.37 (0.67–2.82)
Bonnet G France Retrospective 28,778 253 (8.9) 66.6 ± 17.0 0 57.9 Hospitalized HR = 1.96 (1.47–2.62)
Semenzato L France Retrospective 87,809 5,620 (6.4) 67 ± 19 NA 53.3 Hospitalized HR = 1.39 (1.32–1.47)
Chai C China Ambispective 166 4 (2.4) 65 (59–70) NA 49 Hospitalized HR = 3.7 (1.1–12.9)
Işık F Turkey Retrospective 1,897 80 (4.2) 62 (50–72) 25.83 52.7 Hospitalized OR = 1.23 (0.608–2.486)
Bushman D USA Retrospective 1,029 50 (4.9) 56 (23–64) 32.8 65.5 Hospitalized OR = 1.43 (0.78–2.62)
Bandera A Italy Retrospective 1,018 NA 65 ± 16 NA NA Hospitalized OR = 1.15 (0.6–2.19)
Lee JH Korea Retrospective 7,162 200 (2.79) 47.7 ± 18.7 11.45 40.1 All patients OR = 0.894 (0.634–1.262)
Zerbo O USA Retrospective 219,001 1,125 (0.5) 37.45 1.1 47.3 All patients HR = 1.59 (1.29–1.97)
Wang B USA Retrospective 16,504 2,301 (13.9) 67.6 ± 12.0 17.89 47.7 All patients HR = 1.06 (0.96–1.18)
Zagidullin NS Russia Retrospective 386 4 (1.04) 59 (49–66) 12.5 40.16 Hospitalized OR = 1.09 (0.55–2.18)
Puebla Neira DA USA Retrospective 31,526 9,308 (29.52) 66.23 ± 12.27 23.2 53.36 Hospitalized OR = 1.12 (1.01–1.21)
Lu Y USA Retrospective 608,251 10,949 (1.8) 83 2.46 32.7 All patients HR = 0.94 (0.87–1.01)
Marques M Spain Retrospective 2,112 132 (6.3) 66.6 ± 17.4 16.29 42.9 Hospitalized OR = 0.8 (0.51–1.24)
Ouattara E France Retrospective 98,336 6,192 (6.3) 71 (56–83) 26.19 53.8 All patients HR = 2.91 (2.62–3.23)
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* indicates combined effects based on subgroups; ** published by FAI2R /SFR/SNFMI/SOFREMIP/CRI/IMIDIATE consortium and contributors; The age (years) was presented as mean ± standard deviation or median (interquartile range, IQR); COVID-19, coronavirus disease 2019; CI, confidence interval; NA, not available; OR, odds radio; HR, hazard radio; USA, the United States of America; UK, the United Kingdom

Overall, comorbid stroke was significantly linked to an increased risk of mortality in patients with COVID-19 (pooled effect = 1.30, 95% CI: 1.16–1.44, random-effects model, Fig. 2). This significant association was also observed in the subgroup analyses based on cases (23 studies, pooled effect = 1.27, 95% CI: 1.08–1.48 for < 1000 cases; 24 studies, pooled effect = 1.29, 95% CI: 1.13–1.46 for ≥ 1000 cases), mean age (16 studies, pooled effect = 1.26, 95% CI: 1.19–1.34 for < 60 years old; 29 studies, pooled effect = 1.30, 95% CI: 1.13–1.51 for ≥ 60 years old), proportion of males (13 studies, pooled effect = 1.16, 95% CI: 1.00–1.33 for < 50%; 31 studies, pooled effect = 1.36, 95% CI: 1.18–1.57 for ≥ 50%), setting (34 studies, pooled effect = 1.31, 95% CI: 1.18–1.45 for hospitalized patients; 13 studies, pooled effect = 1.25, 95% CI: 1.01–1.56 for all patients), area (11 studies, pooled effect = 1.48, 95% CI: 1.01–2.18 for Asia; 22 studies, pooled effect = 1.34, 95% CI: 1.14–1.58 for Europe; 14 studies, pooled effect = 1.18, 95% CI: 1.04–1.35 for Americas), effect estimates (20 studies, pooled hazard ratio (HR) = 1.51, 95% CI: 1.26–1.80; 27 studies, pooled OR = 1.14, 95% CI: 1.07–1.21), and proportion of severe COVID-19 cases (13 studies, pooled effect = 1.25, 95% CI: 1.03–1.51 for < 14%; 20 studies, pooled effect = 1.32, 95% CI: 1.08–1.62 for ≥ 14%) (Table 2). As we implemented subgroup analysis by cohort type, the association was still significant in the subgroups of retrospective studies (44 studies, pooled effect = 1.27, 95% CI: 1.14–1.42) and ambispective study (1 study, pooled effect = 3.70, 95% CI: 1.08–12.67), while no significant association was found in the subgroup of prospective studies (2 studies, pooled effect = 2.96, 95% CI: 0.45–19.31) (Table 2).

Fig. 2.

Fig. 2

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Forest plot on the association of stroke with mortality of COVID-19 patients

Table 2.

Subgroup analysis and meta-regression

Variables No. of studies Meta-regression Subgroup analysis Heterogeneity
Tau2 z value P value Pooled ES (95% CI) I2 Tau2 P value
Cases - 0.0759  − 0.2204 0.8255 - - - -
  < 1000 23 - - - 1.27 (1.08–1.48) 32% 0.0126 0.07
  ≥ 1000 24 - - - 1.29 (1.13–1.46) 94% 0.0825  < 0.01
Age (years) - 0.0743 - 0.3530 - - - -
  < 60 16 - - - 1.26 (1.19–1.34) 45% 0.0217 0.03
  ≥ 60 29 - 0.5354 0.5924 1.30 (1.13–1.51) 93% 0.0948  < 0.01
     NA 2 - 1.4386 0.1503 1.70 (1.27–2.28) 43% 0.0399 0.19
Male (%) - 0.0714 - 0.3402 - - - -
  < 50 13 - - - 1.16 (1.00–1.33) 80% 0.0356  < 0.01
  ≥ 50 31 - 1.3648 0.1723 1.36 (1.18–1.57) 89% 0.0882  < 0.01
     NA 3 - 0.9429 0.3457 1.44 (1.06–1.94) 54% 0.0874 0.11
Area - 0.0743 - 0.4816 - - - -
  Asia 11 - 0.9885 0.3229 1.48 (1.01–2.18) 60% 0.2077  < 0.01
  Europe 22 - 1.0192 0.3081 1.34 (1.14–1.58) 91% 0.0964  < 0.01
  America 14 - - - 1.18 (1.04–1.35) 76% 0.0340  < 0.01
Cohort type - 0.0780 - 0.2055 - - - -
  Retrospective 44 -  − 1.5469 0.1219 1.27 (1.14–1.42) 89% 0.0736  < 0.01
  Prospective 2 -  − 1.1170 0.2640 2.96 (0.45–19.31) 87% 1.6176  < 0.01
  Ambispective 1 - - - 3.70 (1.08–12.67) - - -
Effect - 0.0645  − 2.5578 0.0105 - - - -
  HR 20 - - - 1.51 (1.26–1.80) 95% 0.1112  < 0.01
  OR 27 - - - 1.14 (1.07–1.21) 17% 0.0027 0.21
Setting - 0.0766 0.4118 0.6805 - - - -
  Hospitalized 34 - - - 1.31 (1.18–1.45) 56% 0.0290  < 0.01
  All patients 13 - - - 1.25 (1.01–1.56) 96% 0.1257  < 0.01
Severe COVID-19 (%) - 0.0782 - 0.9113 - - - -
  < 14 13 - - - 1.25 (1.03–1.51) 81% 0.0663  < 0.01
   ≥ 14 20 - 0.4223 0.6728 1.32 (1.08–1.62) 93% 0.1140  < 0.01
  NA 14 - 0.3038 0.7613 1.30 (1.13–1.49) 62% 0.0326  < 0.01
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ES, effect sizes; CI, confidence interval; NA, not available; OR, odds radio; HR, hazard radio; COVID-19, coronavirus disease 2019

And the outputs of meta-regression demonstrated that effect type might be the source of heterogeneity (P = 0.0105) (Table 2). Sensitivity analysis by omitting each eligible study one by one demonstrated that our findings were stable and robust (Fig. 3). Publication bias was not found in the Begg’s test (P = 0.0864) and Egger’s test (P = 0.8776) (Fig. 4).

Fig. 3.

Fig. 3

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Sensitivity analysis

Fig. 4.

Fig. 4

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Publication bias based on funnel plot and radial plot

Discussion

Two previous meta-analyses did not observe the significant relationship between comorbid stroke and the increased risk of mortality in COVID-19 patients on the basis of un-adjusted effect estimates [1, 2]. In order to exclude the influence of factors such as gender, age, and comorbidities, we conducted a meta-analysis of 47 articles involving 7,267,055 patients that contained the results of multivariate analysis. The results showed that comorbid stroke was significantly associated with an increased risk of mortality in COVID-19 patients on the basis of confounders-adjusted effect estimates, which demonstrated that stroke might be an independent risk factor for COVID-19-related mortality. The outputs of subgroup analyses stratified by age, proportion of male patients, area, cases, setting, effect type, and proportion of severe COVID-19 infection were in consistence with the conclusion above. According to the results of meta-regression, heterogeneity may originate from the different effect indicators used in the included studies.

Stroke has been confirmed increasing the risk of pulmonary complications like pneumonia, which could lead to the fatal outcome of COVID-19 patients [56–58]. It might partly explain why COVID-19 patients with stroke displayed a higher mortality in our analysis. From another perspective, infection was a vital risk factor for stroke, especially systemic upper respiratory disease [59, 60]. The spike protein surface unit of SARS-CoV-2 highly binds to human ACE-2 receptor, which affects the normal physiological function of the ACE-2 to degrade ANG II , causing neuronal damage and endothelial cell apoptosis [61]. The dysfunction of endothelial cell, leading to fibrinolysis inhibition and excessive thrombin production [62], plays an important role in the occurrence of thrombotic events [63]. The human immune system will produce amounts of proinflammatory cytokines such as interleukin (IL)-1β, tumor necrosis factor-alpha (TNF-α), and IL-6 when SARA-CoV-2 infects the body [64]. This phenomenon referred to as “cytokine storm” results in the degradation of the extracellular matrix (ECM), which is responsible for maintaining the integrity and stability of vascular endothelial cells. Degradation of the ECM contributes to the extravasation of blood components, and leads to hemorrhagic brain injury [65]. Worse still, stroke patients suffering from COVID-19 were more susceptible to undesirable outcomes and greater mortality rates compared with those without [66, 67]. A higher risk of large vessel occlusion and hemorrhagic transformation among ischemic stroke patients with COVID-19, combined with COVID-19 patients being more prone to intracerebral hemorrhage, might contribute to this difference [68–70]. Previous study has shown a past history of cerebrovascular disease might be associated with an increased stroke risk in COVID-19 patients [71]. Thus, we suspected that patients with a history of stroke are more likely to trigger above-mentioned mechanisms, leading to higher mortality of COVID-19 patients with pre-existing stroke. During clinical treatment, more attention should be paid to the possible two-way relationship between stroke and COVID-19 infection.

However, several limitations should be acknowledged. First, our study was limited due to lack of explanation of how stroke was defined in the literature and therefore we could not explore the relationship between different stroke types and COVID-19 mortality. Furthermore, it needs to be acknowledged that most of the selected articles were retrospective observational studies, which were not able to completely explain the casual link between the stroke and COVID-19 mortality, implying that more prospective studies with larger samples are necessary to verify the findings. Lastly, the pooled effects were estimated based on risk factors-adjusted effects, but the adjusted risk factors were not fully consistent across the included researches.

In conclusion, our data indicated that stroke was related to a significantly increased risk for COVID-19 mortality. These findings were supposed to provide a basis for risk scales in the care of patients with COVID-19 in emergency services or intensive care units.

Acknowledgements

We would like to thank Ruiying Zhang, Mengke Hu, Xuan Liang, Wenwei Xiao, Ying Wang, Yang Li, Peihua Zhang, and Jian Wu (all are from Department of Epidemiology, School of Public Health, Zhengzhou University) for their kind help in searching articles and collecting data, and valuable suggestions for data analysis.

Author contribution

Yadong Wang, Haiyan Yang, and Guangcai Duan designed the study. Shuwen Li, Jiahao Ren searched articles and extracted the data. Shuwen Li, Hongjie Hou, Jie Xu, and Xueya Han analyzed the data. Shuwen Li wrote and reviewed the manuscript. All the authors approved the final manuscript.

Funding

This study was supported by a grant from the National Natural Science Foundation of China (grant number 81973105).

Data availability

All data relevant to this study are included in this article.

Declarations

Ethics approval

Not applicable.

Conflict of interest

The authors declare no competing interests.

Informed consent

Not applicable.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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