Abstract

Methods

Data Extraction and Quality Assessment

Two authors independently extracted the data about the baseline characteristics: 1) publication time and location of study; 2) type of study design; 3) sample size; 4) gender distribution; 5) age of patients (range and median age); 6) type of cardio-cerebrovascular disease; 7) number of COVID-19 patients with cardio-cerebrovascular disease and number of patients without cardio-cerebrovascular disease; 8) number of severe COVID-19 patients among patients with the cardio-cerebrovascular disease and patients without cardio-cerebrovascular disease; 9) the number of deaths among patients with cardio-cerebrovascular disease and patients without cardio-cerebrovascular disease. Discrepant judgments of the extracted data were resolved after discussion with the third author.

Two authors independently assessed the quality of each included study using the Newcastle-Ottawa Quality Assessment Scale (NOS; Stang, 2010). The included studies were assessed on 3 aspects: selection, comparability, and outcome/exposure. The scores of each study is detailed in Table 3. Discrepancies in the quality scores were resolved by discussion with the third author.

Table 3.

The Qualities of Included Studies by Newcastle-Ottawa Quality Assessment Scale.

Study	Selection				Comparability	Outcome/Exposure			Score
	1	2	3	4	1	1	2	3
Mendy, A.									7
Petrilli, C. M.									7
Buckner, F. S.									7
Romero-Sanchez, C.M.									7
Khamis, F.									6
Li, Y.									7
Xie, Y.									8
Mao, L.									7
Huang, C.									8
Wang, D.									7
Guan, W. J.									7
Yan, S.									7
Zhang, J. J.									7
Zhang, G.									8
Cao, M.									6
Xu, X. W.									8
Wei, Y. Y.									6
Mehra, M. R.									7
Inciardi, R. M.									7
Sousa, G. J. B.									8
Soares, R. C. M.									8
Nikpouraghdam, M.									7
Halvatsiotis, P.									7
Du, R.									7
Yan, Y.									7
Wu, C.									8
Ruan, Q.									6
Chen, T.									7
Tu, W. J.									7
Yuan, M.									6
Zhou, F.									7

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Results

Search Results and Characteristics of the Included Studies

After initially identifying 1,401 studies from databases including PubMed, Web of Science, CNKI (Chinese), MedRxiv and BioRxiv, we excluded 637 duplicate results. Five hundred and fifty-six studies were excluded by the titles and abstracts. After assessing 81 full-text for eligibility, we excluded 50 full-text studies due to the wrong study design (n = 11) or insufficient data for analysis (n = 39). Thus, 31 studies (27 retrospective studies and four prospective studies) were included in this meta-analysis. Figure 1 displays the process of selection of studies. Three studies had not been peer-reviewed.

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Figure 1.

Flow diagram of the study selection and exclusion process.

There was a total of 23,632 patients, and the characteristics of them are summarized in Table 1 (for disease severity) and Table 2 (for mortality). All selected studies were published in 2020, with sample sizes ranging from 27 to 8,910. The majority of studies were from China (n = 20), four from the USA and seven from other countries (Italy, Brazil, Greece, Iran, Spain and Oman). The median age of patients ranged from 41.0 to 67.0 years, and the prevalence of male gender ranged from 41% to 89%. In the group of COVID-19 severity, the average prevalence of cardio-cerebrovascular disease was 16.27%. In the group of COVID-19 mortality, the average prevalence of cardio-cerebrovascular disease was 15.15%. Seventeen studies (Buckner et al., 2020; Cao et al., 2020; Guan et al., 2020; Huang et al., 2020; Khamis et al., 2020; Y. Li et al., 2020; Mao et al., 2020; Mendy et al., 2020; Petrilli et al., 2020; Romero-Sanchez et al., 2020; Wang et al., 2020; Wei et al., 2020; Xie et al., 2020; Xu et al., 2020; S. Yan et al., 2020; G. Zhang et al., 2020; J. J. Zhang et al., 2020) reported the disease severity of COVID-19 patients with cardio-cerebrovascular disease, and 14 studies (T. Chen et al., 2020; Du et al., 2020; Halvatsiotis et al., 2020; Inciardi et al., 2020; Mehra et al., 2020; Nikpouraghdam et al., 2020; Ruan et al., 2020; Soares et al., 2020; Sousa et al., 2020; Tu et al., 2020; Wu et al., 2020; Y. Yan et al., 2020; Yuan et al., 2020; Zhou et al., 2020) reported the mortality of COVID-19 patients with cardio-cerebrovascular disease.

Associations Between Cardio-Cerebrovascular Disease and COVID-19

The analysis of the relationship between cardio-cerebrovascular disease and patients with COVID-19 severity included 7,094 participants in 17 studies. The analysis of the relationship between cardio-cerebrovascular disease and patients with COVID-19 mortality included 16,638 participants in 14 studies. Pooled data from the random-effects model showed that the cardio-cerebrovascular disease was associated with increased risk of severe COVID-19 (OR = 3.004; 95% CI = 2.097–4.303; z = 6.00; p < 0.001; I² = 65.3%) and death (OR = 5.587; 95% CI = 2.810–11.112; z = 4.91; p < 0.001; I² = 93.5%), as shown in Figure 2.

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Figure 2.

Forest plot for cardio-cerebrovascular disease and COVID-19.

Publication Bias and Subgroup Analyses

The funnel-plot analyses with Begg’s and Egger’s test were used to analyze potential publication bias, as shown in Figure 3. However, the funnel-plot for severity was asymmetry, which showed evidence of publication bias for studies reporting on COVID-19 severity and cardio-cerebrovascular disease (Begg’s test: p > | z | = 0.469; Egger’s test: p > | t | = 0.015) There was no evidence of publication bias for COVID-19 mortality and cardio-cerebrovascular disease (Begg’s test, p > | z | = 0.444; Egger’s test, p > | t | = 0.216).

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Figure 3.

Funnel plot of cardio-cerebrovascular disease and COVID-19.

We undertook subgroup analyses by different types of cardio-cerebrovascular disease (cardiovascular disease, cerebrovascular disease and cardio-cerebrovascular disease) for each outcome. It showed similar results to those of the main analysis (Figure 4). The random-effects model showed that cardio-cerebrovascular disease was associated with an increased risk of severe COVID-19 for cardiovascular disease (n = 13, I² = 70.1%; OR = 2.903; 95% CI = 1.906–4.420; z = 4.97; p < 0.001) and for cerebrovascular disease (n = 7, I² = 52.5%; OR = 3.400; 95% CI = 1.569–7.368; z = 3.10; p = 0.002). It also provided evidence that cardio-cerebrovascular disease was associated with an increased risk of death; for those with cardiovascular disease (n = 11, I² = 95.3%; OR = 4.220; 95% CI = 1.854–9.604; z = 3.43; p = 0.001); for those with cerebrovascular disease (n = 2, I² = 0.0%; OR = 23.477; 95% CI = 3.050–180.735; z = 3.03; p = 0.002); and for those with cardio-cerebrovascular disease (n = 3, I² = 0.0%; OR = 9.710; 95% CI = 5.271−17.889; z = 7.29; p < 0.001.

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Figure 4.

Subgroup analysis by the type of cardio-cerebrovascular disease.

Sensitivity Analyses and Meta-Regression

We conducted a sensitivity analysis by excluding one of the studies and found no significant heterogeneity among included studies for COVID-19 severity (OR = 3.004, 95% CI = 2.097–4.303) and COVID-19 mortality (OR = 5.587, 95% CI = 2.810–11.112), shown in Figure 5.

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Figure 5.

Sensitivity analysis for cardio-cerebrovascular disease and COVID-19.

Finally, we did meta-regression to evaluate the influence of median age on the heterogeneity among the included studies, and found that the median age was not the source of the heterogeneity (for severity, p = 0.181; for mortality, p = 0.090), shown in Figure 6.

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Figure 6.

Meta-regression analysis for patients’ median age (years).

Discussion

This systematic meta-analysis demonstrated that COVID-19 patients with cardio-cerebrovascular disease might be at increased risk of developing severe and mortal disease. Patients with cardio-cerebrovascular disease were 3.004 times (95% CI = 2.097–4.303) more likely to be in severe condition than patients without the cardio-cerebrovascular disease and 5.587 times (95% CI = 2.810–11.112) more likely to die than patients without cardio-cerebrovascular disease. Furthermore, based on the results from subgroup analysis, we noticed that those with cerebrovascular disease had greater chance of disease severity and death than those with cardiovascular disease.

Although the exact pathophysiological mechanisms of how cardio-cerebrovascular disease affects COVID-19 patients are still unclear, it can be inferred through some previous studies. First, current studies demonstrated that patients with cardio-cerebrovascular disease were more likely to develop myocardial injury during the course of COVID-19 (T. Chen et al., 2020; Guo et al., 2020). For patients with cardio-cerebrovascular disease, cardiomyocytes could be directly damaged by the SAR-CoV-2 virus, causing systemic inflammatory responses. Furthermore, Huang’s study showed that the imbalanced responses of T helper factor 1 and T helper factor 2 led to a cytokine storm in COVID-19 patients (Huang et al., 2020), resulting in the release of inflammatory post-infection inflammation with a reduction in coronary blood flow, a decrease in the oxygen supply, and the formation of amphibolic coronary plaques and microthrombus. Therefore, patients with cardio-cerebrovascular disease were more likely to develop myocardial injury after SARS-CoV-2 infection, increasing the risk of being critically ill and died. Second, the elevated levels of ACE 2 may be one of the mechanisms by which cardio-cerebrovascular disease affects COVID-19 patients. ACE 2 is a functional receptor for SARS-CoV-2 and has been regarded as the entry point for SARS-CoV-2. Medication that blocks the renin-angiotensin-aldosterone system (RAAS), including angiotensin-converting enzyme inhibitors (ACEI) and angiotensin Ⅱ receptor blockers (ARBs), are commonly used. They increase ACE 2 levels abstractly and lead to more severe diseases in patients (Kow et al., 2020; Mali et al., 2020). However, the role of ACE 2 in coronavirus infection remains controversial and there are insufficient clinical data to prove it (Gross et al., 2020). Third, some of the newly studied drugs for COVID-19 may interact with other cardio-cerebrovascular drugs, which may cause adverse effects and aggravate the patients’ conditions. For example, chloroquine and hydroxychloroquine may interact with antiarrhythmic agents and affect the intracellular pH, which can lead to cardiotoxicity, electrolyte abnormalities, prolonged QT intervals (Driggin et al., 2020; Long et al., 2020). Fourth, due to their low cardiac reserve capacity, once patients with cardio-cerebrovascular disease are infected, the disease is accelerated and lead to poor prognosis (Guo et al., 2020).

This meta-analysis suggested that COVID-19 patients with pre-existing cardio-cerebrovascular disease were associated with poor outcomes. Nurses should be encouraged to assist cardio-cerebrovascular patient to take highly preventive measures. Nurses should not only to pay attention to the occurrence of respiratory dysfunction, but also to pay special attention to signs of cardio-cerebrovascular complications. Meanwhile, the treatment’s safety with ACE inhibitors (ACEI) and angiotensin receptor blockers (ARBs) should be concerned. What’s more, they need to raise awareness of the side effects of various antiviral therapies on cardiovascular-cerebrovascular disease and closely observe the changes in the patient’s condition. Nurses should report the patient’s condition to the clinicians in time so that they can choose correct drugs and formulate the optimal treatment option.

The main limitation of this study is that most of the included studies are from China and there is a lack of COVID-19 patients’ data from other countries. Second, there is a heterogeneity among the included studies. There are 3 articles from MedRxiv that have not been peer-reviewed and we do not rule out the potential bias of them. A larger number of studies are needed to perform more analyses and better confirm our findings.

Conclusions

In summary, this meta-analysis showed that cardio-cerebrovascular disease had a significant association with the severity and mortality of COVID-19. More careful precautions were suggested for patients with cardio-cerebrovascular. In clinical work, nurses should pay more attention to such patients, so as to better cooperate with doctors and help patients recover.

Acknowledgments

Footnotes

References