Next Article in Journal
Panoramic on Epigenetics in Coronary Artery Disease and the Approach of Personalized Medicine
Next Article in Special Issue
Brain Endothelial Cells Activate Neuroinflammatory Pathways in Response to Early Cerebral Small Vessel Disease (CSVD) Patients’ Plasma
Previous Article in Journal
Investigating the Pathogenic Interplay of Alpha-Synuclein, Tau, and Amyloid Beta in Lewy Body Dementia: Insights from Viral-Mediated Overexpression in Transgenic Mouse Models
Previous Article in Special Issue
Successful Mechanical Thrombectomy for Basilar Artery Occlusion in a Pediatric Patient: A Case Report
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 11
Issue 10
10.3390/biomedicines11102865
biomedicines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prevalence and Impact of Cerebral Microbleeds on Clinical and Safety Outcomes in Acute Ischaemic Stroke Patients Receiving Reperfusion Therapy: A Systematic Review and Meta-Analysis

by
Shraddha Tipirneni
1,2,3,
Peter Stanwell
4,
Robert Weissert
5 and
Sonu M. M. Bhaskar
1,3,6,7,*,†
1
Global Health Neurology Lab, Sydney, NSW 2150, Australia
2
UNSW Medicine and Health, South Western Sydney Clinical Campuses, University of New South Wales (UNSW), Sydney, NSW 2170, Australia
3
Neurovascular Imaging Laboratory, Ingham Institute for Applied Medical Research, Clinical Sciences Stream, Sydney, NSW 2170, Australia
4
School of Health Sciences, University of Newcastle, Newcastle, NSW 2308, Australia
5
Department of Neurology, Regensburg University Hospital, University of Regensburg, 93053 Regensburg, Germany
6
NSW Brain Clot Bank, NSW Health Pathology, Sydney, NSW 2170, Australia
7
Department of Neurology & Neurophysiology, Liverpool Hospital & South Western Sydney Local Health District (SWSLHD), Liverpool, NSW 2170, Australia
*
Author to whom correspondence should be addressed.
†
Current address: Department of Neurology, National Cerebral and Cardiovascular Center (NCVC), 6-1 Kishibeshimmachi, Suita 564-8565, Osaka, Japan.
Biomedicines 2023, 11(10), 2865; https://doi.org/10.3390/biomedicines11102865
Submission received: 27 September 2023 / Revised: 15 October 2023 / Accepted: 16 October 2023 / Published: 23 October 2023
(This article belongs to the Special Issue Advanced Research on Cerebrovascular Diseases)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background: Cerebral microbleeds (CMBs), a notable neuroimaging finding often associated with cerebral microangiopathy, demonstrate a heightened prevalence in patients diagnosed with acute ischemic stroke (AIS), which is in turn linked to less favourable clinical prognoses. Nevertheless, the exact prevalence of CMBs and their influence on post-reperfusion therapy outcomes remain inadequately elucidated. Materials and Methods: Through systematic searches of PubMed, Embase and Cochrane databases, studies were identified adhering to specific inclusion criteria: (a) AIS patients, (b) age ≥ 18 years, (c) CMBs at baseline, (d) availability of comparative data between CMB-positive and CMB-negative groups, along with relevant post-reperfusion therapy outcomes. The data extracted were analysed using forest plots of odds ratios, and random-effects modelling was applied to investigate the association between CMBs and symptomatic intracerebral haemorrhage (sICH), haemorrhagic transformation (HT), 90-day functional outcomes, and 90-day mortality post-reperfusion therapy. Results: In a total cohort of 9776 AIS patients who underwent reperfusion therapy, 1709 had CMBs, with a pooled prevalence of 19% (ES 0.19; 95% CI: 0.16, 0.23, p < 0.001). CMBs significantly increased the odds of sICH (OR 2.57; 95% CI: 1.72; 3.83; p < 0.0001), HT (OR 1.53; 95% CI: 1.25; 1.88; p < 0.0001), as well as poor functional outcomes at 90 days (OR 1.59; 95% CI: 1.34; 1.89; p < 0.0001) and 90-day mortality (OR 1.65; 95% CI: 1.27; 2.16; p < 0.0001), relative to those without CMBs, in AIS patients undergoing reperfusion therapy (encompassing intravenous thrombolysis [IVT], endovascular thrombectomy [EVT], either IVT or EVT, and bridging therapy). Variations in the level of association can be observed among different subgroups of reperfusion therapy. Conclusions: This meta-analysis underscores a significant association between CMBs and adverse postprocedural safety outcomes encompassing sICH, HT, poor functional outcome, and increased mortality in AIS patients undergoing reperfusion therapy. The notable prevalence of CMBs in both the overall AIS population and those undergoing reperfusion therapy emphasizes their importance in post-stroke prognostication.

Graphical Abstract

1. Introduction

Cerebral microbleeds (CMBs), tiny areas of intracerebral bleeding detected through brain imaging scans such as T2*-weighted Gradient Recalled Echo (T2*GRE) [1] and susceptibility weighted-imaging (SWI) [2], have emerged as pivotal factors linked to adverse outcomes in acute ischemic stroke (AIS), particularly when their prevalence is high [3]. Conventional magnetic resonance imaging (MRI) plays a crucial role in the detection and evaluation of cerebral small vessel disease (CSVD). Key MRI findings indicative of CSVD encompass CMBs, leukoaraiosis [4,5], recent clinically symptomatic subcortical lacunar infarcts, clinically silent lacunes, conspicuous perivascular spaces, and atrophy [6,7]. The increased mortality and poor functional outcomes following stroke can be attributed to the underlying pathophysiological mechanisms involving hypertensive arteriopathy and cerebral amyloid angiopathy (CAA) [8]. Given the substantial burden of CMBs in stroke patients, discerning those presenting with stroke symptoms and concomitant CMBs becomes paramount [9]. This holds exceptional importance in the context of stroke therapies, including intravenous thrombolysis (IVT) and more recently endovascular thrombectomy (EVT) [10] as well as bridging therapies combining IVT and EVT [11]. Tailoring treatments based on clinical considerations and the presence of CMBs has the potential to optimise patient outcomes. Nevertheless, there exists a gap in our understanding of the repercussions of CMBs on reperfusion outcomes post-AIS and their prevalence exclusively in AIS patients. Recognising the significance of CMB prevalence in AIS spans several dimensions, encompassing their role as prognostic indicators for poorer clinical outcomes [12], insights into underlying vascular pathologies [13], the ability to construct predictive models for risk stratification [14] and facilitating well-informed decisions regarding appropriate AIS treatment [15]. Addressing these gaps, our meta-analysis endeavours to elucidate the impact of CMBs on reperfusion outcomes among AIS patients and to quantify the prevalence of CMBs in this specific population.

2. Objectives

This study aims to determine the prevalence of CMBs in AIS patients, both in those undergoing reperfusion therapies like IVT, EVT, or bridging therapy. Additionally, it aims to explore the association of CMBs with clinical outcomes among AIS patients undergoing reperfusion therapy. The research will address the following questions:
  • What is the overall prevalence of CMBs among AIS patients?
  • What is the prevalence of CMBs in patients undergoing reperfusion therapy?
  • Do CMBs in AIS patients associate with 90-day functional outcomes?
  • Do CMBs in AIS patients associate with 90-day mortality?
  • Do CMBs in AIS patients associate with risk of symptomatic intracerebral haemorrhage (sICH) or haemorrhagic transformation (HT)?

3. Materials and Methods

3.1. Literature Search: Study Identification and Selection

Studies were sourced from online databases, including PubMed, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL), spanning the period from 1 January 2000, to June 2023. A comprehensive search strategy was employed, utilising relevant terms such as “cerebral microbleeds”, “microhemorrhages”, “microbleeds”, “microbleed”, and “microhemorrhage”, combined with terms such as “acute ischemic stroke”, “stroke”, “cerebrovascular ischemia”, “brain ischemia”, or “stroke, acute”. The Supplementary Materials provides a detailed overview of the search strategy (Search Strategy). Only research conducted in the English language and involving human participants was considered. Manual screening of reference lists from relevant articles, systematic reviews, and meta-analyses was also conducted to identify further pertinent studies. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flowchart (Figure 1) illustrates the search process and the inclusion of studies and subgroups, while adhering to reporting standards. In adherence to reporting standards, the PRISMA 2020 (Supplemental Table S1) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist is provided in the online supplementary information (Supplemental Table S2).

3.2. Inclusion and Exclusion Criteria

Studies considered eligible for inclusion met the following criteria: (a) patients diagnosed with AIS; (b) patients aged ≥ 18 years; (c) availability of data on AIS patients exhibiting CMBs; (d) consecutive patients undergoing reperfusion therapy, including IVT, EVT or bridging therapy, and (e) studies with robust methodological design deemed as a minimum sample of 20 patients. Exclusion criteria encompassed: (a) systematic reviews, meta-analyses, and case reports; (b) studies involving animal subjects; (c) studies lacking full-text availability; (d) studies lacking baseline CMB data, (e) studies involving intra-arterial thrombolysis, and (f) studies published in languages other than English or duplicate studies.

3.3. Data Extraction

Using Endnote (Clarivate Analytics, London, UK), we meticulously screened titles and abstracts to identify articles meeting our inclusion criteria. Two researchers conducted the screening process independently, and any disagreements were resolved through consensus discussions. Subsequently, a detailed review of these articles was conducted to assess their eligibility based on predefined criteria. Pertinent data from each selected study were extracted, employing a data extraction sheet on Google Sheets. This sheet encompassed essential details, including: (1) baseline demographics, comprising of author names, country of origin, and year of publication; (2) characteristics of the study population, such as the number of patients with CMBs at baseline, overall cohort size, MRI sequence type, age distribution of patients with and without CMBs, and specific characteristics of patients with AIS; (3) the specific type of reperfusion therapy used; (4) outcome measures encompassing various assessments, such as the modified Rankin Scale (mRS) score for functional outcomes at 90 days, 90-day mortality rate, and the incidence of sICH and HT. The definition of sICH followed established criteria, including the European-Australasian Acute Stroke Study (ECASS), the Second European-Australasian Acute Stroke Study (ECASS-II), the Third European-Australasian Acute Stroke Study (ECASS-III), Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST), National Institute of Neurological Disorders and Stroke (NINDS), and Prolyse in Acute Cerebral Thromboembolism trial two (PROACT-II). The reported definitions of HT and its detection timeframes varied across studies. Through the rigorous methodology, we ensured a comprehensive data collection process that underpinned the robustness of our analysis and the validity of our conclusions.

3.4. Methodological Quality Assessment of Included Studies

The methodological quality assessment was performed using the modified Jadad analysis (MJA). The risk funding bias was also noted and was based on author disclosures of funding and any conflicts of interest. These can be accessed in the online supplementary material (Supplemental Tables S3 and S4).

3.5. Statistical Analyses

Statistical analyses were performed using STATA v 13.0 (StataCorp, College Station, TX, USA). Baseline characteristics were extracted from the included studies and means and standard deviations (SD) were derived from medians and interquartile ranges (IQR) when needed, following the method outlined by Wan et al. [16]. The pooled prevalence of cerebral microbleeds (CMBs) was estimated using the Metaprop command in STATA, employing a random-effects model. Refined 95% confidence intervals (95% CI) were calculated using the “cimethod (exact)” and “ftt” commands. To assess the association between CMBs in AIS patients and outcomes such as sICH, HT, poor functional outcome, and 90-day mortality, a DerSimonian and Laird (DL) random effects meta-analysis was performed. This analysis was restricted to studies reporting baseline CMB data and outcome related to CMB presence or absence. Summary effects and measures of heterogeneity were tabulated. Forest plots depicting odds ratios (OR) and risk ratios (RR) were generated for association studies (Supplemental Figures S6–S13) to visually present the data. Heterogeneity among studies was assessed using the I2 statistic and p-value. The metaninf package in STATA was used to evaluate the impact of excluding individual studies on pooled ORs. For potential publication bias, Egger’s test and funnel plots were implemented from the metabias and metafunnel packages. Asymmetry in the funnel plot was considered, supplemented by the p-value from Egger’s test. Subgroup analyses were conducted for IVT, EVT, and bridging therapies, investigating potential treatment specific variations. Cochran’s Q test p-values were also considered, and between-study variances were estimated using Tau-squared. A significance threshold of p < 0.05 was set for meaningful associations. This rigorous approach ensures the robustness of conclusions drawn and the identification of potential trends in the data.

4. Results

4.1. Description of Included Studies

In this meta-analysis, we delved into the prevalence of CMBs in AIS patients who underwent reperfusion therapy, involving data from 26 studies comprising 9776 patients. Among these studies, 21 focused on patients undergoing IVT [11,15,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36], seven on EVT [11,21,22,25,37,38,39,40], 1 on IVT or EVT [21] and one on bridging therapy [11]. Certain studies were excluded from the meta-analysis due to various reasons, such as overlapping patient cohorts, the reporting of CMBs as newly occurring post-reperfusion, and inclusion of patients with Transient Ischemic Attack (TIA). Of the entire AIS patient cohort considered, 6172 individuals exhibited baseline CMBs. Among those undergoing reperfusion therapy, this number was reduced to 1709.
Table 1, Table 2 and Table 3 outline the clinical characteristics and outcomes of the participants across the studies. Moreover, Table 4 provides insights into heterogeneity and summary effects corresponding to specific clinical and safety outcome parameters. An in-depth assessment of the methodological quality and potential funding bias is presented in Supplemental Table S3. Additionally, we examined the effect sizes for key outcomes such as functional outcome, mortality at 90 days, sICH and HT, as encapsulated in Table 4. Studies demonstrated minimal potential for publication bias, and this was further reinforced by Egger’s test, as shown in Supplemental Figure S16.

4.2. Prevalence of CMBs in AIS

In the context of reperfusion therapy, the overall prevalence of CMBs was found to be 19%. Subgroup analysis based on the type of reperfusion therapy (as shown in Figure 2) revealed that patients undergoing IVT had the highest pooled prevalence at 20% (95% CI: 0.16; 0.25). EVT patients followed with a prevalence of 18% (95% CI: 0.14; 0.23), while bridging therapy exhibited a prevalence of 9% (95% CI: 0.03; 0.18), albeit relying on a limited dataset from a single study, thus warranting cautious interpretation. Notably, significant heterogeneity persisted within these subgroups, resulting in an overall I2 of 94.02%. These findings shed light on the variations in CMB prevalence at baseline across different continents and types of reperfusion therapies, providing valuable insights into the epidemiology of CMBs in AIS patients.

4.3. Association of CMBs with sICH Post-Reperfusion Therapy

The meta-analysis included a total of 13 studies [11,15,18,19,21,23,25,26,27,28,29,36,40], comprising 5499 patients, aiming to explore the association between the presence of CMBs and sICH. Various criteria were used to define sICH, including ECASS-I, ECASS-II, ECASS-III, NINDS, PROACT-II, and SITS-MOST. The prevalence of sICH using these criteria is shown in Supplemental Figure S2. In cases where one study utilised multiple definitions, they were considered as separate studies, labelled as ‘a’, ‘b’, ‘c’, and so on. When using the ECASS-III definition, the association of sICH with CMBs in AIS following IVT was the highest, with an OR of 4.12 (95% CI: 1.04; 16.40, p < 0.0001). When using the PROACT-II definition, the association of sICH with CMBs in AIS following IVT was the lowest, with an OR of 1.42 (95% CI: 0.21; 9.79, p = 0.724), but failed to reach statistical significance. The overall OR for the association between CMB presence and sICH was found to be 2.57 (95% CI: 1.72; 3.83, p < 0.0001). Further stratification was performed based on the type of reperfusion therapy as shown in Figure 3. Among these, CMBs in IVT patients demonstrated the strongest association with sICH, with an OR of 2.57 (95% CI: 1.82; 3.61, p = 0.045). On the other hand, EVT showed increased odds of sICH, but this result was not statistically significant, with an OR of 1.14 (95% CI: 0.40; 3.21, p = 0.805). For CMBs in those who underwent bridging therapy, the association with sICH was not statistically significant, with an OR of 0.67 (95% CI: 0.03; 13.28, p = 0.792). For a study with either EVT or IVT, CMBs were highly associated with sICH, with an OR of 8.96 (95% CI: 4.82; 16.63, p < 0.0001) [21]. It is crucial to note that only one study on bridging therapy and CMBs was available, which limits the certainty of this finding. The overall heterogeneity in the meta-analysis was low, with an I2 of 0.0%, however, this result was not statistically significant (p = 0.079). Minimal publication bias was observed from the inspection of the funnel plot (Supplemental Figure S17A) and confirmed by Egger’s test (Supplemental Figure S16A). These findings support a significant association between CMB presence and sICH, particularly with IVT, while the results for EVT and bridging therapy were not statistically significant due to limited data availability and lower sample sizes. The low heterogeneity and minimal publication bias enhance the reliability and validity of the results.

4.4. Association of CMBs with HT Post-Reperfusion Therapy

The meta-analysis included a total of 10 studies [19,20,21,24,26,27,28,29,31,38,39], encompassing 3882 patients. The presence of CMBs was associated with higher odds of HT for those undergoing reperfusion therapy as shown in Figure 3 (OR 1.53; 95% CI: 1.25; 1.88; p < 0.0001). Similarly, within the subgroups of patients who received IVT or EVT, there were increased odds of HT, but only IVT and IVT/EVT reached statistical significance (IVT: OR 1.46; 95% CI: 1.03; 2.07; p = 0.034; IVT/EVT: OR 1.92; 95% CI: 1.37; 2.68; p < 0.0001; EVT: OR 1.19; 95% CI: 0.81; 1.76; p = 0.373). The overall heterogeneity in the meta-analysis was low, with an I2 of 0.0%, and this finding was not statistically significant (p = 0.768). Moreover, minimal publication bias was observed (Supplemental Figure S17C). While the presence of CMBs showed a trend towards higher odds of HT, the association did not reach statistical significance patients receiving EVT only. The low heterogeneity and minimal publication bias enhance the reliability and validity of these findings. However, further research with larger sample sizes may be needed to establish the significance of the observed trends.

4.5. Association of CMBs with 90-Day Functional Outcomes Post-Reperfusion

The meta-analysis included a total of seven studies [11,15,18,19,21,25,38], involving 4199 participants. Poor functional outcome at 90 days was uniformly defined as an mRS score of 3–6 across all studies. Overall, the presence of CMBs was associated with significantly increased odds of poor functional outcome at 90 days for those undergoing reperfusion therapy (OR 1.59; 95% CI: 1.34; 1.89; p < 0.0001). However, upon further examination of the subgroups as shown in Figure 4, bridging therapy was associated with increased odds of poor functional outcome (OR 2.77; 95% CI: 0.47; 16.27; p = 0.260) although this association did not reach statistical significance. It is crucial to note that this finding was based on data from only one study, which may limit its validity. In contrast, both IVT and EVT demonstrated statistically significant associations with poor functional outcome at 90 days. For IVT, the odds ratio was 1.70 (95% CI: 1.28; 2.25; p < 0.0001), while for EVT, it was 1.70 (95% CI: 1.26; 2.29; p = 0.001). For IVT/EVT, it was 1.32 (95% CI: 0.95; 1.84; p = 0.103), the association was not statistically significant. The overall heterogeneity in the meta-analysis was low, with an I2 of 0.0%, and this result was not statistically significant (p = 0.626). Furthermore, minimal publication bias was observed (Supplemental Figure S17D). More research is needed, especially with larger sample sizes, to further elucidate the significance of these associations.

4.6. Association of CMBs with 90-Day Mortality Post-Reperfusion Therapy

Overall, five studies [11,15,18,21,25] were included in the meta-analysis mortality at 90 days, consisting of 3330 patients. There was a strong association between CMBs and mortality at 90 days for those undergoing reperfusion therapy (OR 1.65; 95% CI: 1.27; 2.16; p < 0.0001). Within the subgroups as shown in Figure 4, bridging therapy showed no association with mortality at 90 days (OR 0.67; 95% CI: 0.07; 6.37; p = 0.740) although this association did not reach statistical significance. IVT/EVT showed an association with mortality at 90 days (OR 1.40; 95% CI: 0.89; 2.20; p = 0.142) but also failed to reach statistical significance. It is crucial to note that both findings were based on data from only one study, which may limit their validity. Similarly, for IVT, the OR was 1.52 (95% CI: 0.91; 2.54; p = 0.104) meaning the association did not reach statistical significance. In contrast, EVT demonstrated a statistically significant association with mortality at 90 days. OR: 2.14 (95% CI: 1.37; 3.34; p = 0.001). The overall heterogeneity in the meta-analysis was low, with an I2 of 0.0%, and this result was not statistically significant (p = 0.721). Some publication bias was observed in the meta-analysis (Supplemental Figure S17E). However, it is essential to acknowledge that further research is warranted to gain a deeper understanding of the significance of these associations.

5. Discussion

The findings of this meta-analysis provide valuable insights into the association between CMBs and post-thrombolysis outcomes in AIS patients as well as prevalence based on several studies. The results indicate that CMBs are significantly associated with increased odds of sICH, HT, and poor functional outcomes and death at 90 days post-reperfusion. Notably, the significance of the association between CMBs and HT appeared to differ between IVT and EVT subgroups, with slightly increased odds of HT after IVT compared to EVT. In summary, this meta-analysis highlights the importance of personalised and evidence-based treatment decisions in AIS patients. It underscores the significance of considering individual patient characteristics, such as the presence of CMBs, when selecting the most appropriate treatment strategy. By identifying patients at higher risk of adverse outcomes, clinicians can optimise therapeutic approaches, improve patient outcomes and minimise complications.
Pathophysiological reasons for the observed outcomes could be multifactorial. The presence of CMBs may indicate underlying vascular pathology such as CAA [13], hypertensive arteriopathy [13] or other cerebral small vessel disease [41], which could predispose patients to a higher risk of haemorrhagic complications after thrombolysis [42]. Blood brain barrier (BBB) dysfunction can occur with CMB presence due to inflammation and microvascular injury [43]. Damage to the BBB causes hyperpermeability which allows the entry of inflammatory cells, cytokines which can damage brain parenchyma [43]. After acute ischaemic stroke, BBB dysfunction can increase the risk of HT due to inflammatory molecules and blood leaking into the brain tissue [44]. This can worsen the functional outcome after stroke by concurrently increasing oedema, causing neurological deficits and mass effect [45]. Reperfusion therapy can activate inflammatory responses [46] and due to pre-existing inflammation in those who have CMBs, this could lead to greater parenchymal damage and poorer outcomes [21]. As CMBs are suggestive of underlying small vessel disease, the compromised vessels after stroke may be at greater risk of rupture after reperfusion therapy [47], leading to sICH [48] or HT [49]. Moreover, CMBs might serve as a marker for more severe or advanced cerebrovascular disease [50], which could contribute to poorer functional outcomes [51] and higher mortality rates [52]. Understanding the underlying pathophysiology of CMBs and their implications in the context of reperfusion therapies is a vital area for future research.
The pooled prevalence of CMBs in AIS patients receiving reperfusion therapy overall was 19%. This is within the range of other studies such as the Egyptian elderly with CMBs having a prevalence of 29.4% [53], Chinese population with 24% [54] and from other studies that were used in the prevalence meta-analysis [11,15,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84]. A previous meta-analysis had a crude prevalence of 23.4% and a prevalence of 18.9% for 1–10 CMBs and 0.8% for >10 CMBs [85]. This is slightly higher than our analysis however, our meta-analysis had looked at CMBs in patients specifically undergoing reperfusion therapy. When stratified by continent, the prevalence was the highest in Asian countries, with the majority of the data coming from East Asia. Genetics are unlikely to explain the ethnic difference as other factors such as age and comorbidities would contribute to heterogeneity. Data on CMB prevalence comparing ethnicities is limited. In less developed countries, it is difficult to estimate CMB prevalence in AIS due to the paucity of studies and likely less access to diagnostic equipment for CMBs such as SWI or T2*GRE MRI [86].
We observed variations in impacts of different reperfusion therapies on the relationship between CMBs and outcomes. IVT was significantly associated with sICH, HT, poor functional outcomes, and mortality. Conversely, there were statistically significant association between CMBs and outcomes in EVT, specifically in terms of poor functional outcomes and 90-day mortality. However, no such significant associations were found for sICH and HT. The treatment of AIS patients with CMBs with bridging therapy was not associated with clinical and safety outcomes, possibly due to the limited availability of data for these subgroups. It is essential to recognise the limitation of having only one study providing data on CMBs and bridging therapy outcomes, which may affect the accuracy and generalisability of this specific finding.
Our study confirms that CMBs are associated with worse clinical outcomes following reperfusion therapy, consistent with findings reported in previous meta-analyses [85,87]. Our findings show that there is a negative association between CMBs in AIS patients who underwent reperfusion therapy and worse clinical outcomes such as 90-day poor functional outcome (mRS 3–6) and 90-day mortality. However, more data on EVT and bridging therapy is needed as most of the studies included IVT patients.
In terms of adverse outcomes, our study suggests that AIS patients with CMBs are associated with increased odds of sICH and HT. This aligns with previous studies, in which the presence of CMBs were associated with sICH and HT [49,87]. Prior CMBs are generally associated with microangiopathy in the brain, making it prone to haemorrhage and thus sICH or HT [88]. One of the studies included in our meta-analysis found that CMB presence alone was not significantly associated with an increased risk of HT or sICH [28]. In five of the 10 studies included in the meta-analysis, CMB presence was not associated with the increased risk of HT [20,27,28,39,40]. However, studies such as Charidimou et al. (2015) [89], Tsivgoulis et al. (2016) [85] and Charidimou et al. (2017) [12] found that CMB presence was associated with HT. This could be explained due to the low numbers of patients with high CMB burden included in the studies for the meta-analysis, as high CMB burden is associated with adverse outcomes such as sICH [19]. In Yan et al. (2015), CMB presence was not associated with increased risk of HT after IVT but was associated with parenchymal haemorrhage (PH) and poor functional outcomes for ≥3 CMBs [35]. As there are discrepancies in the data, further studies on reperfusion therapy in patients with high CMB burdens are needed to assess post-stroke outcomes in order to guide decision making.
The future implications of these findings lie in the potential optimisation of thrombolysis strategies in AIS patients with CMBs. The significant association between CMBs and adverse outcomes, particularly following IVT, underscores the importance of careful consideration and risk stratification when determining the appropriateness of thrombolysis as a treatment option. Neurologists may need to carefully weigh the potential benefits of reperfusion therapy against the increased risks of sICH and unfavorable functional outcomes in patients with CMBs. However, CMBS should not serve as a barrier to treatment. Additionally, further research with larger sample sizes is crucial to solidify the significance of the associations between CMBs and outcomes for EVT and bridging therapy. While this meta-analysis provides valuable insights, further research is needed to establish the significance of these observed associations, particularly for EVT and bridging therapy. Further investigations into the underlying pathophysiological mechanisms and larger-scale studies can offer a more comprehensive understanding of the relationship between CMBs and post-thrombolysis outcomes, guiding clinicians towards more effective and personalised treatment strategies for AIS patients. Given the distinct demographics and risk factors in very old age segment (aged 80 years or older) of stroke patients, future clinical studies are warranted to investigate the prevalence and effects of CMBs on clinical and safety outcomes in the specific subgroup of AIS patients [90]. Resuming anticoagulation, and its optimal timing, after an ICH in patients with atrial fibrillation (AF) poses an ongoing clinical conundrum [91]. Individualized treatment decision-making that factors in the potential for future bleeding events and the risk of thromboembolic complications is suggested that takes into account various risk factors, including blood pressure control, age, the location of the ICH, the presence of CSVD markers (such as CMBs, leukoaraiosis, and cortical superficial siderosis), and the indication for antiplatelet therapy [92].

6. Limitations

This study has several limitations that warrant consideration. Firstly, the inclusion of the studies involving patients receiving IVT in conjunction with other therapies such as antithrombotic therapies. This could potentially impact outcomes after AIS. Notably, the varying definitions of sICH across studies, employing criteria such as ECASS-I, ECASS-II, ECASS-III, NINDS, PROACT-II, or SITS-MOST, introduces substantial variability. Different MRI imaging sequences also contributes to variability. SWI outperforms T2*GRE in the detection of CMBs, especially when higher magnetic field strengths are employed [93,94,95]. Variability in results may partly stem from variations in SWI usage and the utilization of higher magnetic field strengths, which enhance CMB detection capabilities. Furthermore, there remains a scarcity of studies specifically reporting post-thrombolytic outcomes for patients undergoing only EVT or bridging therapy. More specifically, bridging therapy outcomes vis a vis the prevalence of CMBs and their implications have scarcely been reported, including outcomes such as sICH, mortality at 90 days, functional status assessed via mRS at 90 days, HT, and recurrent stroke incidence. Definitions of HT were not always specified, and follow-up imaging times were variable, contributing to uncertainties in results. Ambiguities emerged in some studies regarding the precise number of participants who underwent IVT, EVT, or both. Additionally, inconsistencies in reporting CMB location and burden parameters were evident, where certain studies combined infratentorial and deep CMBs, while others grouped CMBs under broader classifications such as “1–10 CMBs” rather than distinct categories (1, 2–4, or ≥5). The precise location of CMBs was largely underreported. Gender disparities may exist in the distribution of risk factors, stroke subtype, stroke severity, and post-stroke outcomes [96]. Nonetheless, the present study did not explore this and it merits further investigation. Furthermore, substantial heterogeneity persisted across study cohorts in terms of sample sizes, clinical risk factors, and ethnic backgrounds. These limitations should be considered when interpreting the study results and forming conclusive insights.

7. Conclusions

In conclusion, CMBs play a crucial role as prognostic indicators in patients with AIS undergoing reperfusion therapy. This meta-analysis reveals a strong association between CMBs and adverse clinical outcomes, encompassing poor functional outcomes at 90 days, increased 90-day mortality, and an elevated risk of sICH and HT. These consistent findings traverse diverse reperfusion therapies, except for bridging therapy, which lacks substantial data, limited to a single study. The evidence robustly underscores the integration of CMB assessment in clinical decision-making for AIS patients undergoing reperfusion therapy. It is, however, vital to emphasize that the presence of CMBs should not deter the use of thrombolytic therapies. This meta-analysis accentuates the significance of identifying CMBs as pivotal indicators for patient prognosis in the context of reperfusion therapy. Clinicians and healthcare providers may take cognisance of these findings when formulating clinical guidelines to optimise treatment strategies for individuals with AIS. Considering the high prevalence of CMBs in AIS patients receiving reperfusion therapy, it is important to acknowledge CMBs as a prognostic indicator in AIS patients.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines11102865/s1, Search strategy; Supplemental Table S1. Preferred Reporting Items for Systematic Reviews and Meta-analyses PRISMA (2020) Checklist; Supplemental Table S2. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) Checklist; Supplemental Table S3. Modified Jadad Analysis for Methodological Quality; Supplemental Table S4. Funding Bias scores for studies; Supplemental Figure S1. Forest plot showing estimated pooled prevalence of sICH in AIS patients with CMBs who underwent reperfusion therapy; Supplemental Figure S2. Forest plot showing estimated pooled prevalence of sICH in AIS patients with CMBs who underwent IVT; Supplemental Figure S3. Forest plot showing estimated pooled prevalence of HT in AIS patients with CMBs undergoing IVT; Supplemental Figure S4. Forest plot showing estimated pooled prevalence of poor functional outcomes at 90 days in AIS patients with CMBs undergoing IVT; Supplemental Figure S5. Forest plot showing estimated pooled prevalence of mortality at 90 days in AIS patients with CMBs who underwent reperfusion therapy; Supplemental Figure S6. Forest plot of OR of sICH in AIS patients with CMBs who underwent reperfusion therapy; Supplemental Figure S7. Forest plot of OR of sICH in AIS patients with CMBs who underwent IVT; Supplemental Figure S8. Forest plot of OR of HT in AIS patients with CMBs who underwent reperfusion therapy; Supplemental Figure S9. Forest plot of OR of poor functional outcome at 90 days in AIS patients with CMBs who underwent reperfusion therapy; Supplemental Figure S10. Forest plot of OR of 90 day mortality in AIS patients with CMBs who underwent reperfusion therapy; Supplemental Figure S11. Forest plot of Risk Ratios (RR) of sICH in AIS patients with CMBs who underwent reperfusion therapy; Supplemental Figure S12. Forest plot of Risk Ratios (RR) of sICH in AIS patients with CMBs who underwent IVT; Supplemental Figure S13. Forest plot of RR of HT in AIS patients with CMBs who underwent reperfusion therapy; Supplemental Figure S14. Forest plot of RR of poor functional outcome at 90 days in AIS patients with CMBs who underwent reperfusion therapy; Supplemental Figure S15. Forest plot of RR of 90-day mortality in AIS patients with CMBs who underwent reperfusion therapy; Supplemental Figure S16. Egger’s test for the meta-analyses on the association of CMBs in AIS patients who underwent reperfusion therapy with various clinical outcomes; Supplemental Figure S17. Funnel plots of meta-analyses on the association between CMBs in AIS who underwent reperfusion therapy with various clinical outcomes. Supplemental Figure S18. Sensitivity analyses for meta-analyses on the association between CMBs in AIS patients and various clinical outcomes following reperfusion therapy.

Author Contributions

S.M.M.B. conceived the study, contributed to the planning, drafting, and revision of the manuscript; and supervision of the student. S.M.M.B. encouraged S.T. to investigate and supervised the findings of this work. S.T. and S.M.M.B. wrote the first draft of this paper. All authors (S.T., P.S., R.W. and S.M.M.B.) contributed to the revision of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research study received no funding.

Institutional Review Board Statement

Not applicable. All analyses were based on previously published studies; thus, no ethical approval or patient consent was required.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article and online supplemental information, and further inquiries can be directed to the corresponding author.

Acknowledgments

We acknowledge the financial support received from the Grant-in-Aid for Scientific Research (KAKENHI) (PI: SMMB) by the Japan Society for the Promotion of Science (JSPS), Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT). We also extend our gratitude for the JSPS International Fellowship, supported by MEXT and the Australian Academy of Science, awarded to SMMB for the period 2023–2025. The funding body has no role in the study design, data collection, analysis, interpretation of findings and manuscript preparation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the affiliated/funding organisation/s.

Conflicts of Interest

The authors declare that they have no conflict of interest.

References

  1. Lee, J.; Sohn, E.H.; Oh, E.; Lee, A.Y. Characteristics of Cerebral Microbleeds. Dement. Neurocognitive Disord. 2018, 17, 73–82. [Google Scholar] [CrossRef] [PubMed]
  2. Haller, S.; Vernooij, M.W.; Kuijer, J.P.A.; Larsson, E.M.; Jager, H.R.; Barkhof, F. Cerebral Microbleeds: Imaging and Clinical Significance. Radiology 2018, 287, 11–28. [Google Scholar] [CrossRef] [PubMed]
  3. Schlemm, L.; Endres, M.; Werring, D.J.; Nolte, C.H. Benefit of Intravenous Thrombolysis in Acute Ischemic Stroke Patients with High Cerebral Microbleed Burden. Stroke 2020, 51, 232–239. [Google Scholar] [CrossRef]
  4. Rastogi, A.; Weissert, R.; Bhaskar, S.M.M. Emerging role of white matter lesions in cerebrovascular disease. Eur. J. Neurosci. 2021, 54, 5531–5559. [Google Scholar] [CrossRef] [PubMed]
  5. Rastogi, A.; Weissert, R.; Bhaskar, S.M.M. Leukoaraiosis severity and post-reperfusion outcomes in acute ischaemic stroke: A meta-analysis. Acta Neurol. Scand. 2022, 145, 171–184. [Google Scholar] [CrossRef]
  6. Rastogi, A.; Weissert, R.; Bhaskar, S.M. Brain atrophy in acute ischaemic stroke patients treated with reperfusion therapy: A systematic review. Acta Radiol. 2023, 64, 257–266. [Google Scholar] [CrossRef] [PubMed]
  7. Rudilosso, S.; Rodríguez-Vázquez, A.; Urra, X.; Arboix, A. The Potential Impact of Neuroimaging and Translational Research on the Clinical Management of Lacunar Stroke. Int. J. Mol. Sci. 2022, 23, 1497. [Google Scholar] [CrossRef]
  8. Yakushiji, Y. Cerebral Microbleeds: Detection, Associations and Clinical Implications. Front. Neurol. Neurosci. 2015, 37, 78–92. [Google Scholar] [CrossRef]
  9. Puy, L.; Pasi, M.; Rodrigues, M.; van Veluw, S.J.; Tsivgoulis, G.; Shoamanesh, A.; Cordonnier, C. Cerebral microbleeds: From depiction to interpretation. J. Neurol. Neurosurg. Psychiatry 2021, 92, 598–607. [Google Scholar] [CrossRef]
  10. Wach-Klink, A.; Izycka-Swieszewska, E.; Kozera, G.; Sobolewski, P. Cerebral microbleeds in neurological practice: Concepts, diagnostics and clinical aspects. Neurol. Neurochir. Pol. 2021, 55, 450–461. [Google Scholar] [CrossRef]
  11. Gratz, P.P.; El-Koussy, M.; Hsieh, K.; von Arx, S.; Mono, M.L.; Heldner, M.R.; Fischer, U.; Mattle, H.P.; Zubler, C.; Schroth, G.; et al. Preexisting cerebral microbleeds on susceptibility-weighted magnetic resonance imaging and post-thrombolysis bleeding risk in 392 patients. Stroke 2014, 45, 1684–1688. [Google Scholar] [CrossRef] [PubMed]
  12. Charidimou, A.; Turc, G.; Oppenheim, C.; Yan, S.; Scheitz, J.F.; Erdur, H.; Klinger-Gratz, P.P.; El-Koussy, M.; Takahashi, W.; Moriya, Y.; et al. Microbleeds, Cerebral Hemorrhage, and Functional Outcome After Stroke Thrombolysis. Stroke 2017, 48, 2084–2090. [Google Scholar] [CrossRef] [PubMed]
  13. Akoudad, S.; Portegies, M.L.; Koudstaal, P.J.; Hofman, A.; van der Lugt, A.; Ikram, M.A.; Vernooij, M.W. Cerebral Microbleeds Are Associated with an Increased Risk of Stroke: The Rotterdam Study. Circulation 2015, 132, 509–516. [Google Scholar] [CrossRef] [PubMed]
  14. Li, Y.; Gao, H.; Zhang, D.; Gao, X.; Lu, L.; Liu, C.; Li, Q.; Miao, C.; Ma, H.; Li, Y. Clinical Prediction Model for Screening Acute Ischemic Stroke Patients with More Than 10 Cerebral Microbleeds. Front. Neurol. 2022, 13, 833952. [Google Scholar] [CrossRef]
  15. Schlemm, L.; Braemswig, T.B.; Boutitie, F.; Vynckier, J.; Jensen, M.; Galinovic, I.; Simonsen, C.Z.; Cheng, B.; Cho, T.H.; Fiehler, J.; et al. Cerebral Microbleeds and Treatment Effect of Intravenous Thrombolysis in Acute Stroke: An Analysis of the WAKE-UP Randomized Clinical Trial. Neurology 2022, 98, e302–e314. [Google Scholar] [CrossRef]
  16. Wan, X.; Wang, W.; Liu, J.; Tong, T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med. Res. Methodol. 2014, 14, 135. [Google Scholar] [CrossRef]
  17. Bai, Q.; Zhao, Z.; Sui, H.; Xie, X.; Chen, J.; Yang, J.; Zhang, L. Susceptibility-weighted imaging for cerebral microbleed detection in super-acute ischemic stroke patients treated with intravenous thrombolysis. Neurol. Res. 2013, 35, 586–593. [Google Scholar] [CrossRef]
  18. Capuana, M.L.; Lorenzano, S.; Caselli, M.C.; Paciaroni, M.; Toni, D. Hemorrhagic risk after intravenous thrombolysis for ischemic stroke in patients with cerebral microbleeds and white matter disease. Neurol. Sci. 2021, 42, 1969–1976. [Google Scholar] [CrossRef]
  19. Chacon-Portillo, M.A.; Llinas, R.H.; Marsh, E.B. Cerebral microbleeds shouldn’t dictate treatment of acute stroke: A retrospective cohort study evaluating risk of intracerebral hemorrhage. BMC Neurol. 2018, 18, 33. [Google Scholar] [CrossRef]
  20. Chatzikonstantinou, A.; Willmann, O.; Szabo, K.; Hennerici, M.G. Cerebral microbleeds are uncommon in ischemic stroke associated with nonvalvular atrial fibrillation. J. Neuroimaging 2011, 21, 103–107. [Google Scholar] [CrossRef]
  21. Choi, K.H.; Kim, J.H.; Kang, K.W.; Kim, J.T.; Choi, S.M.; Lee, S.H.; Park, M.S.; Kim, B.C.; Kim, M.K.; Cho, K.H. Impact of Microbleeds on Outcome Following Recanalization in Patients with Acute Ischemic Stroke. Stroke 2019, 50, 127–134. [Google Scholar] [CrossRef] [PubMed]
  22. Choi, K.H.; Kim, J.H.; Lee, C.; Kim, J.M.; Kang, K.W.; Kim, J.T.; Choi, S.M.; Park, M.S.; Cho, K.H. Microbleeds and Outcome in Patients with Acute Ischemic Stroke and Atrial Fibrillation Taking Anticoagulants. Stroke 2020, 51, 3514–3522. [Google Scholar] [CrossRef] [PubMed]
  23. Dannenberg, S.; Scheitz, J.F.; Rozanski, M.; Erdur, H.; Brunecker, P.; Werring, D.J.; Fiebach, J.B.; Nolte, C.H. Number of cerebral microbleeds and risk of intracerebral hemorrhage after intravenous thrombolysis. Stroke 2014, 45, 2900–2905. [Google Scholar] [CrossRef] [PubMed]
  24. Derex, L.; Nighoghossian, N.; Hermier, M.; Adeleine, P.; Philippeau, F.; Honnorat, J.; Yilmaz, H.; Dardel, P.; Froment, J.C.; Trouillas, P. Thrombolysis for ischemic stroke in patients with old microbleeds on pretreatment MRI. Cerebrovasc. Dis. 2004, 17, 238–241. [Google Scholar] [CrossRef] [PubMed]
  25. Derraz, I.; Cagnazzo, F.; Gaillard, N.; Morganti, R.; Dargazanli, C.; Ahmed, R.; Lefevre, P.H.; Riquelme, C.; Mourand, I.; Gascou, G.; et al. Microbleeds, Cerebral Hemorrhage, and Functional Outcome After Endovascular Thrombectomy. Neurology 2021, 96, e1724–e1731. [Google Scholar] [CrossRef]
  26. Fiehler, J.; Albers, G.W.; Boulanger, J.M.; Derex, L.; Gass, A.; Hjort, N.; Kim, J.S.; Liebeskind, D.S.; Neumann-Haefelin, T.; Pedraza, S.; et al. Bleeding risk analysis in stroke imaging before thromboLysis (BRASIL): Pooled analysis of T2*-weighted magnetic resonance imaging data from 570 patients. Stroke 2007, 38, 2738–2744. [Google Scholar] [CrossRef]
  27. Kakuda, W.; Thijs, V.N.; Lansberg, M.G.; Bammer, R.; Wechsler, L.; Kemp, S.; Moseley, M.E.; Marks, M.P.; Albers, G.W.; Investigators, D. Clinical importance of microbleeds in patients receiving IV thrombolysis. Neurology 2005, 65, 1175–1178. [Google Scholar] [CrossRef]
  28. Kidwell, C.S.; Saver, J.L.; Villablanca, J.P.; Duckwiler, G.; Fredieu, A.; Gough, K.; Leary, M.C.; Starkman, S.; Gobin, Y.P.; Jahan, R.; et al. Magnetic resonance imaging detection of microbleeds before thrombolysis: An emerging application. Stroke 2002, 33, 95–98. [Google Scholar] [CrossRef]
  29. Kim, H.S.; Lee, D.H.; Ryu, C.W.; Lee, J.H.; Choi, C.G.; Kim, S.J.; Suh, D.C. Multiple cerebral microbleeds in hyperacute ischemic stroke: Impact on prevalence and severity of early hemorrhagic transformation after thrombolytic treatment. AJR Am. J. Roentgenol. 2006, 186, 1443–1449. [Google Scholar] [CrossRef]
  30. Kimura, K.; Aoki, J.; Shibazaki, K.; Saji, N.; Uemura, J.; Sakamoto, Y. New appearance of extraischemic microbleeds on T2*-weighted magnetic resonance imaging 24 hours after tissue-type plasminogen activator administration. Stroke 2013, 44, 2776–2781. [Google Scholar] [CrossRef]
  31. Moriya, Y.; Takahashi, W.; Kijima, C.; Yutani, S.; Iijima, E.; Mizuma, A.; Honma, K.; Uesugi, T.; Ohnuki, Y.; Nagata, E.; et al. Predictors for hemorrhagic transformation with intravenous tissue plasminogen activator in acute ischemic stroke. Tokai J. Exp. Clin. Med. 2013, 38, 24–27. [Google Scholar] [PubMed]
  32. Nighoghossian, N.; Hermier, M.; Adeleine, P.; Blanc-Lasserre, K.; Derex, L.; Honnorat, J.; Philippeau, F.; Dugor, J.F.; Froment, J.C.; Trouillas, P. Old microbleeds are a potential risk factor for cerebral bleeding after ischemic stroke: A gradient-echo T2*-weighted brain MRI study. Stroke 2002, 33, 735–742. [Google Scholar] [CrossRef] [PubMed]
  33. Prats-Sanchez, L.; Camps-Renom, P.; Sotoca-Fernandez, J.; Delgado-Mederos, R.; Martinez-Domeno, A.; Marin, R.; Almendrote, M.; Dorado, L.; Gomis, M.; Codas, J.; et al. Remote Intracerebral Hemorrhage After Intravenous Thrombolysis: Results From a Multicenter Study. Stroke 2016, 47, 2003–2009. [Google Scholar] [CrossRef] [PubMed]
  34. Turc, G.; Sallem, A.; Moulin, S.; Tisserand, M.; Machet, A.; Edjlali, M.; Baron, J.C.; Leclerc, X.; Leys, D.; Mas, J.L.; et al. Microbleed Status and 3-Month Outcome After Intravenous Thrombolysis in 717 Patients with Acute Ischemic Stroke. Stroke 2015, 46, 2458–2463. [Google Scholar] [CrossRef]
  35. Yan, S.; Chen, Y.; Zhang, X.; Liebeskind, D.S.; Lou, M. New microbleeds after thrombolysis: Contiguous thin-slice 3T MRI. Medicine 2014, 93, e99. [Google Scholar] [CrossRef]
  36. Zand, R.; Tsivgoulis, G.; Singh, M.; McCormack, M.; Goyal, N.; Ishfaq, M.F.; Shahripour, R.B.; Nearing, K.; Elijovich, L.; Alexandrov, A.W.; et al. Cerebral Microbleeds and Risk of Intracerebral Hemorrhage Post Intravenous Thrombolysis. J. Stroke Cerebrovasc. Dis. 2017, 26, 538–544. [Google Scholar] [CrossRef]
  37. Brauner, R.; Gory, B.; Lapergue, B.; Sibon, I.; Richard, S.; Kyheng, M.; Labreuche, J.; Desilles, J.P.; Blanc, R.; Piotin, M.; et al. Effect of small vessel disease severity on blood pressure management after endovascular therapy in the BP TARGET trial. Eur. J. Neurol. 2023, 30, 1676–1685. [Google Scholar] [CrossRef]
  38. Lee, S.J.; Hwang, Y.H.; Hong, J.M.; Choi, J.W.; Park, J.H.; Park, B.; Kang, D.H.; Kim, Y.W.; Kim, Y.S.; Hong, J.H.; et al. Influence of cerebral microbleeds on mechanical thrombectomy outcomes. Sci. Rep. 2022, 12, 3637. [Google Scholar] [CrossRef]
  39. Shi, Z.S.; Duckwiler, G.R.; Jahan, R.; Tateshima, S.; Gonzalez, N.R.; Szeder, V.; Saver, J.L.; Kim, D.; Ali, L.K.; Starkman, S.; et al. Mechanical thrombectomy for acute ischemic stroke with cerebral microbleeds. J. Neurointerv. Surg. 2016, 8, 563–567. [Google Scholar] [CrossRef]
  40. Soo, Y.O.; Siu, D.Y.; Abrigo, J.; Yu, S.; Ng, N.; Ahuja, A.T.; Wong, L.K.; Leung, T.W. Risk of intracerebral hemorrhage in patients with cerebral microbleeds undergoing endovascular intervention. Stroke 2012, 43, 1532–1536. [Google Scholar] [CrossRef]
  41. Demchuk, A.M.; Khan, F.; Hill, M.D.; Barber, P.A.; Silver, B.; Patel, S.; Levine, S.R.; Group, N.r.-P.S.S. Importance of leukoaraiosis on CT for tissue plasminogen activator decision making: Evaluation of the NINDS rt-PA Stroke Study. Cerebrovasc. Dis. 2008, 26, 120–125. [Google Scholar] [CrossRef]
  42. Charidimou, A.; Kakar, P.; Fox, Z.; Werring, D.J. Cerebral microbleeds and recurrent stroke risk: Systematic review and meta-analysis of prospective ischemic stroke and transient ischemic attack cohorts. Stroke 2013, 44, 995–1001. [Google Scholar] [CrossRef]
  43. Wang, H.L.; Zhang, C.L.; Qiu, Y.M.; Chen, A.Q.; Li, Y.N.; Hu, B. Dysfunction of the Blood-brain Barrier in Cerebral Microbleeds: From Bedside to Bench. Aging Dis. 2021, 12, 1898–1919. [Google Scholar] [CrossRef]
  44. Bernardo-Castro, S.; Sousa, J.A.; Bras, A.; Cecilia, C.; Rodrigues, B.; Almendra, L.; Machado, C.; Santo, G.; Silva, F.; Ferreira, L.; et al. Pathophysiology of Blood-Brain Barrier Permeability Throughout the Different Stages of Ischemic Stroke and Its Implication on Hemorrhagic Transformation and Recovery. Front. Neurol. 2020, 11, 594672. [Google Scholar] [CrossRef] [PubMed]
  45. Dostovic, Z.; Dostovic, E.; Smajlovic, D.; Ibrahimagic, O.C.; Avdic, L. Brain Edema After Ischaemic Stroke. Med. Arch. 2016, 70, 339–341. [Google Scholar] [CrossRef] [PubMed]
  46. Mizuma, A.; Yenari, M.A. Anti-Inflammatory Targets for the Treatment of Reperfusion Injury in Stroke. Front. Neurol. 2017, 8, 467. [Google Scholar] [CrossRef]
  47. Kim, B.J.; Lee, S.H. Prognostic Impact of Cerebral Small Vessel Disease on Stroke Outcome. J. Stroke 2015, 17, 101–110. [Google Scholar] [CrossRef] [PubMed]
  48. Shoamanesh, A.; Kwok, C.S.; Lim, P.A.; Benavente, O.R. Postthrombolysis intracranial hemorrhage risk of cerebral microbleeds in acute stroke patients: A systematic review and meta-analysis. Int. J. Stroke 2013, 8, 348–356. [Google Scholar] [CrossRef]
  49. Chen, J.; Duris, K.; Yang, X. Effect of cerebral microbleeds on hemorrhagic transformation and functional prognosis after intravenous thrombolysis of cerebral infarction. Brain Hemorrhages 2022, 3, 117–119. [Google Scholar] [CrossRef]
  50. Martinez-Ramirez, S.; Greenberg, S.M.; Viswanathan, A. Cerebral microbleeds: Overview and implications in cognitive impairment. Alzheimers Res. Ther. 2014, 6, 33. [Google Scholar] [CrossRef]
  51. Xu, T.; Wang, Y.; Yuan, J.; Chen, Y.; Luo, H. Small Vessel Disease Burden and Outcomes of Mechanical Thrombectomy in Ischemic Stroke: A Systematic Review and Meta-Analysis. Front. Neurol. 2021, 12, 602037. [Google Scholar] [CrossRef] [PubMed]
  52. Akoudad, S.; Ikram, M.A.; Koudstaal, P.J.; Hofman, A.; van der Lugt, A.; Vernooij, M.W. Cerebral microbleeds and the risk of mortality in the general population. Eur. J. Epidemiol. 2013, 28, 815–821. [Google Scholar] [CrossRef] [PubMed]
  53. Ibrahim, A.A.; Ibrahim, Y.A.; Darwish, E.A.; Khater, N.H. Prevalence of cerebral microbleeds and other cardiovascular risk factors in elderly patients with acute ischemic stroke. Egypt. J. Radiol. Nucl. Med. 2019, 50, 38. [Google Scholar] [CrossRef]
  54. Tang, W.K.; Chen, Y.K.; Lu, J.; Ahuja, A.T.; Chu, W.C.; Mok, V.C.; Ungvari, G.S.; Xiang, Y.T.; Wong, K.S. Cerebral microbleeds and quality of life in acute ischemic stroke. Neurol. Sci. 2011, 32, 449–454. [Google Scholar] [CrossRef] [PubMed]
  55. Aoki, J.; Iguchi, Y.; Urabe, T.; Yamagami, H.; Todo, K.; Fujimoto, S.; Idomari, K.; Kaneko, N.; Iwanaga, T.; Terasaki, T.; et al. Microbleeds and clinical outcome in acute mild stroke patients treated with antiplatelet therapy: ADS post-hoc analysis. J. Clin. Neurosci. 2021, 89, 216–222. [Google Scholar] [CrossRef]
  56. Fan, Y.H.; Zhang, L.; Lam, W.W.; Mok, V.C.; Wong, K.S. Cerebral microbleeds as a risk factor for subsequent intracerebral hemorrhages among patients with acute ischemic stroke. Stroke 2003, 34, 2459–2462. [Google Scholar] [CrossRef] [PubMed]
  57. Farag, S.; Kenawy, F.F.; Shokri, H.M.; Zakaria, M.; Aref, H.; Fahmi, N.; Khayat, N.; Shalash, A.; Nahas, N.E. The Clinical Characteristics of Patients with Pre-Existing Leukoaraiosis Compared to Those Without Leukoaraiosis in Acute Ischemic Stroke. J. Stroke Cerebrovasc. Dis. 2021, 30, 105956. [Google Scholar] [CrossRef]
  58. Gao, Z.; Zhai, Y.; Zhao, X.; Wang, W.; Wu, W.; Wang, Z.; Liu, R.; Wang, X. Deep cerebral microbleeds are associated with the severity of lacunar infarcts and hypertension: A retrospective analysis. Medicine 2018, 97, e11031. [Google Scholar] [CrossRef]
  59. Ge, L.; Ouyang, X.; Ban, C.; Yu, H.; Wu, Q.; Wu, H.; Liang, J. Cerebral microbleeds in patients with ischemic cerebrovascular disease taking aspirin or clopidogrel. Medicine 2019, 98, e14685. [Google Scholar] [CrossRef]
  60. Kato, H.; Izumiyama, M.; Izumiyama, K.; Takahashi, A.; Itoyama, Y. Silent cerebral microbleeds on T2*-weighted MRI: Correlation with stroke subtype, stroke recurrence, and leukoaraiosis. Stroke 2002, 33, 1536–1540. [Google Scholar] [CrossRef]
  61. Kim, T.W.; Lee, S.J.; Koo, J.; Choi, H.S.; Park, J.W.; Lee, K.S.; Kim, J.S. Cerebral microbleeds and functional outcomes after ischemic stroke. Can. J. Neurol. Sci. 2014, 41, 577–582. [Google Scholar] [CrossRef] [PubMed]
  62. Lau, K.K.; Wong, Y.K.; Teo, K.C.; Chang, R.S.K.; Tse, M.Y.; Hoi, C.P.; Chan, C.Y.; Chan, O.L.; Cheung, R.H.K.; Wong, E.K.M.; et al. Long-Term Prognostic Implications of Cerebral Microbleeds in Chinese Patients with Ischemic Stroke. J. Am. Heart Assoc. 2017, 6, 577–582. [Google Scholar] [CrossRef] [PubMed]
  63. Liu, W.; Liu, R.; Sun, W.; Peng, Q.; Zhang, W.; Xu, E.; Cheng, Y.; Ding, M.; Li, Y.; Hong, Z.; et al. Different impacts of blood pressure variability on the progression of cerebral microbleeds and white matter lesions. Stroke 2012, 43, 2916–2922. [Google Scholar] [CrossRef]
  64. Malhotra, P.; Prasad, H.; Jain, A.; Gupta, V.; Varma, N. Variables affecting the presence of occult cerebral microbleeds and subsequent spontaneous intracranial haemorrhage in adult patients with severe thrombocytopenia. Br. J. Haematol. 2021, 194, e67–e70. [Google Scholar] [CrossRef]
  65. Meng, N.; Zhang, W.; Su, Y.; Ye, Z.; Qin, C. Antiplatelet therapy may be safe in ischemic stroke patients with cerebral microbleed. J. Int. Med. Res. 2020, 48, E67–E70. [Google Scholar] [CrossRef] [PubMed]
  66. Mok, V.; Wong, K.K.; Xiong, Y.; Wong, A.; Schmidt, R.; Chu, W.; Hu, X.; Leung, E.Y.; Chen, S.; Chen, Y.; et al. Cortical and frontal atrophy are associated with cognitive impairment in age-related confluent white-matter lesion. J. Neurol. Neurosurg. Psychiatry 2011, 82, 52–57. [Google Scholar] [CrossRef] [PubMed]
  67. Mok, V.C.; Lau, A.Y.; Wong, A.; Lam, W.W.; Chan, A.; Leung, H.; Wong, E.; Soo, Y.; Leung, T.; Wong, L.K. Long-term prognosis of Chinese patients with a lacunar infarct associated with small vessel disease: A five-year longitudinal study. Int. J. Stroke 2009, 4, 81–88. [Google Scholar] [CrossRef]
  68. Nagaraja, N.; Farooqui, A.; Bin Zahid, A.; Kaur, S. Factors associated with the presence of cerebral microbleeds and its influence on outcomes of stroke not treated with alteplase. Clin. Neurol. Neurosurg. 2021, 207, 106798. [Google Scholar] [CrossRef]
  69. Nahas, N.E.; Shokri, H.; Abdulghani, O.; Zakaria, M.; Kamel, T.; Fahmi, N.; Khayat, N.; Shalash, A.; Basiony, A.E.; Reda, R.; et al. Clinical Characteristics of Borderzone Infarction in Egyptian Population. J. Stroke Cerebrovasc. Dis. 2019, 28, 1178–1184. [Google Scholar] [CrossRef]
  70. Naka, H.; Nomura, E.; Takahashi, T.; Wakabayashi, S.; Kajikawa, H.; Kohriyama, T.; Mimori, Y.; Matsumoto, M. Plasma total homocysteine levels are associated with advanced leukoaraiosis but not with asymptomatic microbleeds on T2*-weighted MRI in patients with stroke. Eur. J. Neurol. 2006, 13, 261–265. [Google Scholar] [CrossRef]
  71. Nam, K.W.; Kwon, H.M.; Lee, Y.S.; Kim, J.M.; Ko, S.B. Effects of cerebral small vessel disease on the outcomes in cryptogenic stroke with active cancer. Sci. Rep. 2021, 11, 17510. [Google Scholar] [CrossRef] [PubMed]
  72. Orken, D.N.; Kenangil, G.; Uysal, E.; Forta, H. Cerebral microbleeds in ischemic stroke patients on warfarin treatment. Stroke 2009, 40, 3638–3640. [Google Scholar] [CrossRef] [PubMed]
  73. Park, H.K.; Lee, J.S.; Kim, B.J.; Park, J.H.; Kim, Y.J.; Yu, S.; Hwang, Y.H.; Rha, J.H.; Heo, S.H.; Ahn, S.H.; et al. Cilostazol versus aspirin in ischemic stroke with cerebral microbleeds versus prior intracerebral hemorrhage. Int. J. Stroke 2021, 16, 1019–1030. [Google Scholar] [CrossRef] [PubMed]
  74. Purrucker, J.C.; Wolf, M.; Haas, K.; Siedler, T.; Rizos, T.; Khan, S.; Heuschmann, P.U.; Veltkamp, R.; The RASUNOA Investigators. Microbleeds in ischemic vs. hemorrhagic strokes on novel oral anticoagulants. Acta Neurol. Scand. 2018, 138, 163–169. [Google Scholar] [CrossRef]
  75. Ryu, W.S.; Jeong, S.W.; Kim, D.E. Total small vessel disease burden and functional outcome in patients with ischemic stroke. PLoS ONE 2020, 15, e0242319. [Google Scholar] [CrossRef] [PubMed]
  76. Sakuta, K.; Yaguchi, H.; Nakada, R.; Sato, T.; Kitagawa, T.; Takatsu, H.; Miyagawa, S.; Komatsu, T.; Sakai, K.; Mitsumura, H.; et al. Cerebral Microbleeds Load and Long-Term Outcomes in Minor Ischemic Stroke. J. Stroke Cerebrovasc. Dis. 2021, 30, 105973. [Google Scholar] [CrossRef]
  77. Sharma, M.; Smith, E.E.; Pearce, L.A.; Perera, K.S.; Kasner, S.E.; Yoon, B.W.; Ameriso, S.F.; Puig, J.; Damgaard, D.; Fiebach, J.B.; et al. Rivaroxaban versus aspirin for prevention of covert brain infarcts in patients with embolic stroke of undetermined source: NAVIGATE ESUS MRI substudy. Int. J. Stroke 2022, 17, 799–805. [Google Scholar] [CrossRef]
  78. Shoamanesh, A.; Hart, R.G.; Kasner, S.E.; Smith, E.E.; Marti-Fabregas, J.; Liu, Y.Y.; Uchiyama, S.; Mikulik, R.; Veltkamp, R.; Ntaios, G.; et al. Abstract 41: Cerebral Microbleeds and the Effect of Anticoagulation on Outcomes in 3699 Patients With Embolic Strokes of Undetermined Source: An Exploratory Analysis of the NAVIGATE ESUS Trial. Stroke 2019, 50, A41. [Google Scholar] [CrossRef]
  79. Song, T.J.; Kim, J.; Song, D.; Nam, H.S.; Kim, Y.D.; Lee, H.S.; Heo, J.H. Association of cerebral microbleeds with mortality in stroke patients having atrial fibrillation. Neurology 2014, 83, 1308–1315. [Google Scholar] [CrossRef]
  80. Soo, Y.O.; Yang, S.R.; Lam, W.W.; Wong, A.; Fan, Y.H.; Leung, H.H.; Chan, A.Y.; Leung, C.; Leung, T.W.; Wong, L.K. Risk vs. benefit of anti-thrombotic therapy in ischaemic stroke patients with cerebral microbleeds. J. Neurol. 2008, 255, 1679–1686. [Google Scholar] [CrossRef]
  81. Takahashi, W.; Moriya, Y.; Mizuma, A.; Uesugi, T.; Ohnuki, Y.; Takizawa, S. Cerebral microbleeds on T2*-weighted images and hemorrhagic transformation after antithrombotic therapies for ischemic stroke. J. Stroke Cerebrovasc. Dis. 2013, 22, e528–e532. [Google Scholar] [CrossRef] [PubMed]
  82. Ueno, H.; Naka, H.; Ohshita, T.; Kondo, K.; Nomura, E.; Ohtsuki, T.; Kohriyama, T.; Wakabayashi, S.; Matsumoto, M. Association between cerebral microbleeds on T2*-weighted MR images and recurrent hemorrhagic stroke in patients treated with warfarin following ischemic stroke. AJNR Am. J. Neuroradiol. 2008, 29, 1483–1486. [Google Scholar] [CrossRef] [PubMed]
  83. van Overbeek, E.C.; Staals, J.; van Oostenbrugge, R.J. Decreased kidney function relates to progression of cerebral microbleeds in lacunar stroke patients. Int. J. Stroke 2016, 11, 695–700. [Google Scholar] [CrossRef] [PubMed]
  84. Wang, Z.; Xu, C.; Wang, P.; Wang, Y.; Xin, H. Combined clopidogrel-aspirin treatment for high risk TIA or minor stroke does not increase cerebral microbleeds. Neurol. Res. 2015, 37, 993–997. [Google Scholar] [CrossRef]
  85. Tsivgoulis, G.; Zand, R.; Katsanos, A.H.; Turc, G.; Nolte, C.H.; Jung, S.; Cordonnier, C.; Fiebach, J.B.; Scheitz, J.F.; Klinger-Gratz, P.P.; et al. Risk of Symptomatic Intracerebral Hemorrhage After Intravenous Thrombolysis in Patients with Acute Ischemic Stroke and High Cerebral Microbleed Burden: A Meta-analysis. JAMA Neurol. 2016, 73, 675–683. [Google Scholar] [CrossRef]
  86. Lam, B.Y.K.; Cai, Y.; Akinyemi, R.; Biessels, G.J.; van den Brink, H.; Chen, C.; Cheung, C.W.; Chow, K.N.; Chung, H.K.H.; Duering, M.; et al. The global burden of cerebral small vessel disease in low- and middle-income countries: A systematic review and meta-analysis. Int. J. Stroke 2023, 18, 15–27. [Google Scholar] [CrossRef]
  87. Sousanidou, A.; Tsiptsios, D.; Christidi, F.; Karatzetzou, S.; Kokkotis, C.; Gkantzios, A.; Bairaktaris, C.; Karapepera, V.; Bebeletsi, P.; Karagiannakidou, I.; et al. Exploring the Impact of Cerebral Microbleeds on Stroke Management. Neurol. Int. 2023, 15, 188–224. [Google Scholar] [CrossRef]
  88. Sueda, Y.; Naka, H.; Ohtsuki, T.; Kono, T.; Aoki, S.; Ohshita, T.; Nomura, E.; Wakabayashi, S.; Kohriyama, T.; Matsumoto, M. Positional relationship between recurrent intracerebral hemorrhage/lacunar infarction and previously detected microbleeds. AJNR Am. J. Neuroradiol. 2010, 31, 1498–1503. [Google Scholar] [CrossRef]
  89. Charidimou, A.; Shoamanesh, A.; Wilson, D.; Gang, Q.; Fox, Z.; Jager, H.R.; Benavente, O.R.; Werring, D.J. Cerebral microbleeds and postthrombolysis intracerebral hemorrhage risk Updated meta-analysis. Neurology 2015, 85, 927–934. [Google Scholar] [CrossRef]
  90. Arboix, A.; Miguel, M.; Císcar, E.; García-Eroles, L.; Massons, J.; Balcells, M. Cardiovascular risk factors in patients aged 85 or older with ischemic stroke. Clin. Neurol. Neurosurg. 2006, 108, 638–643. [Google Scholar] [CrossRef]
  91. Heidbuchel, H.; Verhamme, P.; Alings, M.; Antz, M.; Hacke, W.; Oldgren, J.; Sinnaeve, P.; Camm, A.J.; Kirchhof, P. EHRA practical guide on the use of new oral anticoagulants in patients with non-valvular atrial fibrillation: Executive summary. Eur. Heart J. 2013, 34, 2094–2106. [Google Scholar] [CrossRef] [PubMed]
  92. Hindsholm, M.F.; Damgaard, D.; Gurol, M.E.; Gaist, D.; Simonsen, C.Z. Management and Prognosis of Acute Stroke in Atrial Fibrillation. J. Clin. Med. 2023, 12, 5752. [Google Scholar] [CrossRef] [PubMed]
  93. Haller, S.; Haacke, E.M.; Thurnher, M.M.; Barkhof, F. Susceptibility-weighted Imaging: Technical Essentials and Clinical Neurologic Applications. Radiology 2021, 299, 3–26. [Google Scholar] [CrossRef] [PubMed]
  94. Cheng, A.L.; Batool, S.; McCreary, C.R.; Lauzon, M.L.; Frayne, R.; Goyal, M.; Smith, E.E. Susceptibility-weighted imaging is more reliable than T2*-weighted gradient-recalled echo MRI for detecting microbleeds. Stroke 2013, 44, 2782–2786. [Google Scholar] [CrossRef] [PubMed]
  95. Juneja, A.; Azad, R.; Malhotra, A. Magnetic Resonance Imaging Evaluation of Cerebral Microbleeds: A Comparative Analysis of Susceptibility Weighted Imaging and T2* Gradient Recalled Echo Sequences. J. Clin. Diagn. Res. 2020, 14, TC06–TC12. [Google Scholar] [CrossRef]
  96. Arboix, A.; Cartanyà, A.; Lowak, M.; García-Eroles, L.; Parra, O.; Oliveres, M.; Massons, J. Gender differences and woman-specific trends in acute stroke: Results from a hospital-based registry (1986–2009). Clin. Neurol. Neurosurg. 2014, 127, 19–24. [Google Scholar] [CrossRef]
Biomedicines 11 02865 g001
Figure 1. The Preferred Reporting System for Systematic Reviews and Meta-Analyses (PRISMA) flowchart showing the studies included in this meta-analysis. Abbreviations: n = number of studies, n = total number of patients, CMBs = cerebral microbleeds, IVT = intravenous thrombolysis EVT = endovascular thrombectomy, BT = bridging therapy, sICH = symptomatic intracerebral haemorrhage, HT = haemorrhagic transformation.
Figure 1. The Preferred Reporting System for Systematic Reviews and Meta-Analyses (PRISMA) flowchart showing the studies included in this meta-analysis. Abbreviations: n = number of studies, n = total number of patients, CMBs = cerebral microbleeds, IVT = intravenous thrombolysis EVT = endovascular thrombectomy, BT = bridging therapy, sICH = symptomatic intracerebral haemorrhage, HT = haemorrhagic transformation.
Biomedicines 11 02865 g001
Biomedicines 11 02865 g002
Figure 2. Prevalence of cerebral microbleeds in acute ischemic stroke patients undergoing reperfusion therapy [11,15,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]. Note: a, b and c correspond to cohorts receiving IVT, EVT and bridging, respectively, in the said study. Choi et al. [21] and Choi et al. (2) [22] refer to studies from 2019 and 2020, respectively. Abbreviations: CMB = cerebral microbleeds, AIS = acute ischaemic stroke, n = number of patients with CMBs, C = total cohort number, p = prevalence, IVT = intravenous thrombolysis, EVT = endovascular thrombectomy, ES = effect size, I2 = heterogeneity value, p = p-value.
Figure 2. Prevalence of cerebral microbleeds in acute ischemic stroke patients undergoing reperfusion therapy [11,15,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]. Note: a, b and c correspond to cohorts receiving IVT, EVT and bridging, respectively, in the said study. Choi et al. [21] and Choi et al. (2) [22] refer to studies from 2019 and 2020, respectively. Abbreviations: CMB = cerebral microbleeds, AIS = acute ischaemic stroke, n = number of patients with CMBs, C = total cohort number, p = prevalence, IVT = intravenous thrombolysis, EVT = endovascular thrombectomy, ES = effect size, I2 = heterogeneity value, p = p-value.
Biomedicines 11 02865 g002
Biomedicines 11 02865 g003
Figure 3. Forest plots of association of cerebral microbleeds with clinical outcomes: (A) symptomatic intracerebral haemorrhage (sICH) [11,15,18,19,21,23,25,26,27,28,29,36,40] and (B) haemorrhagic transformation (HT) [19,20,21,24,26,27,28,29,31,38,39] in acute ischemic stroke patients undergoing reperfusion therapy. Note: Capuana et al. a, b, c corresponds to cohorts assessed for sICH using the SITS-MOST, ECASS-II and NINDS criteria, respectively, in the said study. Schlemm et al. a, b, c, d correspond to cohorts assessed for sICH using the SITS-MOST, ECASS-II, ECASS-III, and NINDS, respectively. Gratz et al. [11] a, b, c and overall represent cohorts receiving IVT, EVT, Bridging and IVT and/or EVT, respectively, within this study. Abbreviations: CMBs = cerebral microbleeds, sICH = symptomatic intracerebral haemorrhage, HT = haemorrhagic transformation, IVT = intravenous thrombolysis, EVT = endovascular thrombectomy, OR = odds ratio, CI = confidence interval, p = p-value, DL = DerSimmonian and Laird, I2 = heterogeneity, ECASS = European Cooperative Acute Stroke Study, ECASS-II = second European Cooperative Acute Stroke Study, ECASS-III = third European Cooperative Acute Stroke Study, NINDS = National Institute of Neurological Disorders and Stroke, SITS-MOST = Safe Implementation of Thrombolysis in Stroke-Monitoring Study, PROACT-II = Prolyse in Acute Cerebral Thromboembolism trial 2.
Figure 3. Forest plots of association of cerebral microbleeds with clinical outcomes: (A) symptomatic intracerebral haemorrhage (sICH) [11,15,18,19,21,23,25,26,27,28,29,36,40] and (B) haemorrhagic transformation (HT) [19,20,21,24,26,27,28,29,31,38,39] in acute ischemic stroke patients undergoing reperfusion therapy. Note: Capuana et al. a, b, c corresponds to cohorts assessed for sICH using the SITS-MOST, ECASS-II and NINDS criteria, respectively, in the said study. Schlemm et al. a, b, c, d correspond to cohorts assessed for sICH using the SITS-MOST, ECASS-II, ECASS-III, and NINDS, respectively. Gratz et al. [11] a, b, c and overall represent cohorts receiving IVT, EVT, Bridging and IVT and/or EVT, respectively, within this study. Abbreviations: CMBs = cerebral microbleeds, sICH = symptomatic intracerebral haemorrhage, HT = haemorrhagic transformation, IVT = intravenous thrombolysis, EVT = endovascular thrombectomy, OR = odds ratio, CI = confidence interval, p = p-value, DL = DerSimmonian and Laird, I2 = heterogeneity, ECASS = European Cooperative Acute Stroke Study, ECASS-II = second European Cooperative Acute Stroke Study, ECASS-III = third European Cooperative Acute Stroke Study, NINDS = National Institute of Neurological Disorders and Stroke, SITS-MOST = Safe Implementation of Thrombolysis in Stroke-Monitoring Study, PROACT-II = Prolyse in Acute Cerebral Thromboembolism trial 2.
Biomedicines 11 02865 g003
Biomedicines 11 02865 g004
Figure 4. Forest plots of association of cerebral microbleeds with adverse outcomes: (A) poor functional outcome at 90 days [11,15,18,19,21,25,38] and (B) mortality at 90 days [11,15,18,21,25] in acute ischemic stroke patients undergoing reperfusion therapy. Note: Gratz et al. a, b and c correspond to cohorts receiving IVT, EVT and bridging therapy, respectively, in the said study. Abbreviations: IVT = intravenous thrombolysis, EVT = endovascular thrombectomy, OR = odds ratio, CI = confidence interval, p = p-value, DL = DerSimmonian and Laird, I2 = heterogeneity.
Figure 4. Forest plots of association of cerebral microbleeds with adverse outcomes: (A) poor functional outcome at 90 days [11,15,18,19,21,25,38] and (B) mortality at 90 days [11,15,18,21,25] in acute ischemic stroke patients undergoing reperfusion therapy. Note: Gratz et al. a, b and c correspond to cohorts receiving IVT, EVT and bridging therapy, respectively, in the said study. Abbreviations: IVT = intravenous thrombolysis, EVT = endovascular thrombectomy, OR = odds ratio, CI = confidence interval, p = p-value, DL = DerSimmonian and Laird, I2 = heterogeneity.
Biomedicines 11 02865 g004
Table 1. Clinical characteristics of studies selected for meta-analysis for acute ischemic stroke patients who underwent reperfusion therapy.
Table 1. Clinical characteristics of studies selected for meta-analysis for acute ischemic stroke patients who underwent reperfusion therapy.
AuthorYearContinentStudy TypeNumber of CMBsCohortMale % OverallAge Mean (SD)Baseline NIHSSReperfusionCMB Imaging MRI Follow-Up TimeHT Detection TimeframeHT Imaging ModalitysICH DefinitionsICH Detection TimeframesICH Imaging Modality
OverallCMBNo CMBOverallCMBNo CMBOverallCMBNo CMB
Bai et al. [17]2013AsiaProspective 46113NRNRNRNRNRNRNR2.32IVTSWI24 h24 hCTNRNRMRI
Brauner et al. [37]2023EuropeProspective7224647.6NRNE73.6 (13.3)NRNRNRNRNREVTT2*GRE, SWI24–36 hNRNRNRNRCT
Capuana et al. [18]2021EuropeProspective10143460.8NR7568.3 (13.5)69.0 (12.6)68.1 (13.8)16.3 (6.0)NRNRIVTT1WI, T2WI, T2*GRE, FLAIR, DWI, PWI, MRA24 hNRNRSITS-MOST, ECASS-II, NINDS22–36 hNR
Chacon-Portillo et al. [19]2018North AmericaRetrospective6229282.2NRNR63 (15)NRNR9.3 (7.4)9.7 (7.5)9 (6.7)IVTSWI72 h—6 days24 hGRE, NC CTNINDSNRNR
Chatzikonstaninou et al. [20]2011EuropeProspective 913250.810.490.074 (9.9)74.2 (9.9)72.7 (7.1)7.9 (5.5)NRNRIVTT2*GRE72 hNRNRNRNRNR
Choi et al. a [21]2019AsiaProspective 1261310NRNRNRNRNRNRNRNRNRIVT T2*GRE7 days24 hGRE, CTECASS-IWithin 7 daysNR
Choi et al. b [21]2019AsiaProspective 74837NRNRNRNRNRNRNRNR EVTT2*GRE7 days24 hGRE, CTECASS-IWithin 7 daysNR
Choi et al. overall [21]2019AsiaProspective 165153255.89.290.869.2 (NR)72 (11.2)68.9 (11.9)6.7 (7.4)12.3 (4.8)11 (5.1)IVT and/or EVTT2*GRE7 days24 hGRE, CTECASS-IWithin 7 daysNR
Choi et al. (2)a [22]
2020AsiaProspective 105422NRNRNRNRNRNRNRNRNRIVT T2*GRENRNRNRNRNRNR
Choi et al. (2)b [22]2020AsiaProspective 57281NRNRNRNRNRNRNRNRNREVTT2*GRENRNRNRNRNRNR
Choi et al. (2) Overall [22]2020AsiaProspective 393174253.120.679.472.6 (NR)73.2 (NR)72.5 (NR)NR8 (7.4)6.7 (7.4)IVT and/or EVTT2*GRENRNRNRNRNRNR
Dannenburg et al. [23]2014EuropeProspective81326NRNRNR76 (11.9)NRNR9 (6.7)NRNRIVTT2*GRE36 hNRNRECASS-IIINRT2WI
Derex et al. [24]2004EuropeProspective 844NRNRNR63.2 (14.1)NRNR14 (5.8)NRNRIVTT2*GRE24 h and 7th dayNRNRPROACT-II⍬NRCT
Derraz et al. [25]2021EuropeProspective 8951347.416.583.569.4 (25.9)80.8 (15.7)67.3 (25.4)16 (7.4)17.7 (6.8)15.7 (8.2)EVTT2*GRENRNRNRECASS-II24 hNR
Fiehler et al. [26]2007MultiRetrospective8657059.8NRNR68.3 (13.3)NRNRNRNRNRIVTT2*GRE6 hClinical deterioration—10 daysNRECASS-I1–10 daysT2WI
Gratz et al. a [11] *2014EuropeProspective 38174NRNRNRNRNRNR8 (4.5)NRNRIVT T2*GRE72 h72 hMRI, NCCT with CTAPROACT-II ⍬72 hMRI, NCCT with CTA
Gratz et al. b [11] *2014EuropeProspective 35150NRNRNRNRNRNR12.7 (6.7)NRNREVTSWI72 h72 hMRI, NCCT with CTAPROACT-II ⍬72 hMRI, NCCT with CTA
Gratz et al. c [11] *2014EuropeProspective 668NRNRNRNRNRNRNRNRNRBridging SWI72 h72 hMRI, NCCT with CTAPROACT-II ⍬72 hMRI, NCCT with CTA
Gratz et al. overall [11] *2014EuropeProspective 66856.9NRNR1 (13.7)NRNR15.3 (26.0)NRNRIVT and/or EVTSWI72 h72 hMRI, NCCT with CTAPROACT-II ⍬72 hMRI, NCCT with CTA
Kakuda et al. [27] ⍦2005MultiProspective117044.319.4NRNR70 (32)71 (29) NRNRIVTT2*GRE, DWI, MRA, T1WI, FLAIR30th dayNRCT, MRIECASS-II36 hCT, MRI
Kidwell et al. [28]2002North AmericaProspective54192.0NRNRNRNRNRNRNRNRIVTT2WI, T2*GRE, EPI-SWINRImmediately after tPA and 24 hCTNRNRNR
Kim et al. [29]2006AsiaRetrospective256556.9NRNRNR67 (NR)NRNRNRNRIVTT2*GRE, DWI, FLAIR, MRA24–72 hNRT2WINR48 hT2WI
Kimura et al. [30]2013AsiaProspective7422454.0NRNR76.2 (10.6)NRNRNR11 (9.2)13 (9.8)IVTT1WI, T2WI, T2*GRE, DWI, FLAIR, MRA24 hNRT2WISITS-MOST24 hT2WI, CT
Lee et al. [38]2022AsiaProspective9157755.814.985.167 (13)NRNRNRNRNREVTDWI, T2*GRENR5–7 daysCTNRNRNR
Moriya et al. [31]2012AsiaRetrospective147170.4NRNRNRNRNRNRNRNRIVTT2*GRE, T1WI, T2WI, DWI, MRANR24 h, 4–7 daysCTNRNRNR
Nighoghossian et al. [32]2002EuropeProspective 2010058.0NRNR60 (13)71.2 (8.6)56.7 (13)NRNRNRIVTDWI, T2*GRE, T2WI, MRA10 ± 7 h7th dayNRNRNRNR
Pratz-Sanchez et al. [33]2016EuropeProspective 640852.2NRNRNRNRNR5.2 (5.5)5.9 (6.1)4.8 (5.1)IVTNR1–13 days36 hCTNRNRNR
Schlemm et al. [15] ⍧2022EuropeProspective 9845962.819.480.6NR71.7 (NR)67 (NR)NRNRNRIVTSWI, T2*GRE22–36 h22–36 hNRSITS-MOST, ECASS-II, ECASS-III, NINDS22–36 hNR
Shi et al. [39]2016AsiaProspective 3720642.214.985.1NR77 (14)66.7 (9.8)NRNRNREVTT2*GRE, FLAIRNR18–36 hCT, GRENRNRNR
Soo et al. [40]2012AsiaProspective 23133NRNRNRNR67 (17)67.4 (14)NRNR16 (5.9)EVTT2*GRE, T1WI, T2WINRNRNRNRNRCT
Turc et al. [34]2015EuropeProspective 15071749.0NRNR74 (NR)NRNRNRNRNRIVTT2*GRENRNRNRNINDS, ECASS-II, ECASS-III, SITS-MOST24 hNR
Yan et al. [35] ⍭2015AsiaProspective57121NRNRNRNRNRNRNRNRNRIVTDWI, T2*GRE, FLAIR24 hNRGREECASS-II24 hGRE
Zand et al. [36]2017MultiProspective 10367251.9NRNR62 (14)NRNRNRNRNRIVTT2*GRE24 hNRCTECASS-II24 hMRI
Note: ⍦ Kakuda et al. [27]: symptomatic and asymptomatic haemorrhages were categorised based on CT or MRI. Data on sICH or HT imaging was not specified. * Gratz et al. [11]: follow up imaging was performed within 72 h, it did not specify whether that was for sICH or HT. ⍬ PROACT-II in Derex et al. [24] Gratz et al. [11] had bleeding complications defined by the PROACT-II trial. ⍧ Schlemm et al. [15]: follow up imaging was performed from 22–36 h after treatment, it did not specify whether that was for sICH or HT. ⍭ Yan et al. [35]: follow up imaging was performed from 24 h after treatment, it did not specify whether that was for sICH or HT. Gratz [11]/Choi [22] et al. a, b and c correspond to cohorts receiving IVT, EVT and bridging, respectively, in the said study. Choi et al. [21] and Choi et al. (2) [22] refer to studies from 2019 and 2020, respectively. Abbreviations: CMB = cerebral microbleed, Multi = multiple continents, IVT = intravenous thrombolysis, EVT = endovascular thrombectomy, NR = not reported, T2*GRE = T2 Gradient Echo Imaging, T2WI = T2 Weighted Imaging, T1WI = T1 Weighted Imaging, DWI = Diffusion Weighted Imaging, SWI = Susceptibility Weighted Imaging, FLAIR = Fluid Attenuated Inversion Recovery, MRA = Magnetic Resonance Angiography, EPI-SWI = Echo Planar Imaging Susceptibility Weighted Imaging, PWI = Perfusion Weighted Imaging, CT = Computed Tomography, NCCT = Non-Contrast Computed Tomography, CTA = CT Angiography, GRE = Gradient Echo Sequences, NIHSS = National Institutes of Health Stroke Scale, ECASS = European Cooperative Acute Stroke Study, ECASS-II = second European Cooperative Acute Stroke Study, ECASS-III = third European Cooperative Acute Stroke Study, NINDS = National Institute of Neurological Disorders and Stroke, SITS-MOST = Safe Implementation of Thrombolysis in Stroke-Monitoring Study, PROACT-II = Prolyse in Acute Cerebral Thromboembolism trial 2.
Table 2. Rates of comorbidities cohorts of acute ischemic stroke patients with cerebral microbleeds included in the meta-analysis.
Table 2. Rates of comorbidities cohorts of acute ischemic stroke patients with cerebral microbleeds included in the meta-analysis.
AuthorYearClinical Risk Factors, n (%)
AFHLHTNCADPS/TIASmokingDM
Bai et al. [17]2013NRNRNRNRNRNRNR
Brauner et al. [37]2023NRNRNRNRNRNRNR
Capuana et al. [18]202117 (17%)30 (30%)77 (76%)NR10 (10%) a34 (34%) b15 (15%)
Chacon-Portillo et al. [19]2018NRNRNRNRNRNRNR
Chatzikonstaninou et al. [20]2011NRNRNRNRNRNRNR
Choi et al. a [21]2019NRNRNRNRNRNRNR
Choi et al. b [21]2019NRNRNRNRNRNRNR
Choi et al. overall [21]201994 (57%)13 (8%)123 (75%)NR40 (24%)44 (27%) d56 (34%)
Choi et al. (2)a [22]2020NRNRNRNRNRNRNR
Choi et al. (2)b [22]2020NRNRNRNRNRNRNR
Choi et al. (2) overall [22]2020393 (100%)50 (13%),245 (62%)NR106 (27%)65 (17%) c85 (22%)
Dannenburg et al. [23]2014NRNRNRNRNRNRNR
Derex et al. [24]2004NR2 (25%)4 (50%)NRNRNR1 (13%)
Derraz et al. [25]202138 (43%)36 (40%)65 (73%)21 (24%)19 (21%)28 (31%) c14 (16%)
Fiehler et al. [26].2007NRNRNRNRNRNRNR
Gratz et al. a [11]2014NRNRNRNRNRNRNR
Gratz et al. b [11]2014NRNRNRNRNRNRNR
Gratz et al. c [11]2014NRNRNRNRNRNRNR
Gratz et al. overall [11]2014NRNRNRNRNRNRNR
Kakuda et al. [27]2005NR2 (18%)8 (73%)NRNR6 (55%) b4 (36%)
Kidwell et al. [28]2002NRNRNRNRNRNRNR
Kim et al. [29]2006NRNRNRNRNRNRNR
Kimura et al. [30]2013NRNRNRNRNRNRNR
Lee et al. [38]202242 (46%)27 (30%)71 (78%)NR24 (26%) a19 (21%) d27 (30%)
Moriya et al. [31]2012NRNRNRNRNRNRNR
Nighoghossian et al. [32]2002NR5 (25%)16 (80%)NR7 (35%) a5 (25%) d6 (30%)
Pratz-Sanchez et al. [33]2016NRNRNRNRNRNRNR
Schlemm et al. [15]202216 (16%)38 (39%)64 (65%)NR14 (14%) aNR22 (22%)
Shi et al. [39]201616 (43%)10 (27%)26 (70%)11 (30%)29 (78%)NR13 (35%)
Soo et al. [40]2012NR20 (87%)20 (87%)6 (26%)12 (52%)12 (52%) d8 (35%)
Turc et al. [34]2015NRNRNRNRNRNRNR
Yan et al. [35]2015NRNRNRNRNRNRNR
Zand et al. [36]201711 (11%)41 (40%)91 (88%)NR36 (35%) a36 (35%) c39 (38%)
Note: a, b and c correspond to cohorts receiving IVT, EVT and bridging, respectively, in the said study. Choi et al. [21] and Choi et al. (2) [22] refer to studies from 2019 and 2020, respectively. Abbreviations: CMB = cerebral microbleed, NR = not reported, n = number, AF = atrial fibrillation, HL = hyperlipidaemia, HTN = hypertension, CAD = coronary artery disease, PS = prior stroke, TIA = transient ischaemic attack, DM = diabetes mellitus, a: Prior stroke only, b: current and previous smoking, c: current smoking, d: previous/current smoking not specified.
Table 3. Clinical outcomes of studies selected for meta-analysis.
Table 3. Clinical outcomes of studies selected for meta-analysis.
AuthorReperfusion
Therapy		sICH (n, %)HTmRS 3–6 at 90 Days
(n, %)		Mortality at 90 Days
(n, %)
OverallCMBNo-CMBOverallCMBNo-CMBOverallCMBNo-CMBOverallCMBNo-CMB
Bai et al. [17]IVTNRNRNR13 (11.5)NRNRNRNRNRNRNRNR
Brauner et al. [37]EVT22 (9.1)NRNRNRNRNR106 (49.5)NRNRNRNRNR
Capuana et al. a [18] (SITS-MOST)IVT2 (0.46)2 (0.46)0 (0)NRNRNR130 (33.5)NRNR33 (8.5)NRNR
Capuana et al. b [18] (ECASS)IVT28 (6.45)11 (2.53)17 (3.92)NRNRNR130 (33.5)NRNR33 (8.5)NRNR
Capuana et al. c [18] (NINDS)IVT13 (3.00)7 (1.61)6 (1.38)NRNRNR130 (33.5)NRNR33 (8.5)NRNR
Capuana et al. Overall [18] IVTNRNRNRNRNRNR130 (33.5)39 (8.99)91 (20.97)33 (8.5)11 (2.53)22 (5.07)
Chacon-Portillo et al. [19].IVT6 (2.05)3 (1.03)3 (1.03)46 (15.75)12 (4.11)34 (11.64)63 (28.8)16 (5.48)47 (21.5)NRNRNR
Chatzikonstaninou et al. [20].IVTNRNRNR18 (13.64)0 (0)18 (13.64)NRNRNRNRNRNR
Choi et al. a [21]IVTNRNRNR NRNRNRNRNRNRNRNR
Choi et al. b [21]EVTNRNRNRNRNRNRNRNRNRNRNRNR
Choi et al. Overall [21]IVT/EVT69 (5.16)17 (10.30)52 (3.80)420 (27.42)66 (4.31)354 (23.11)865 (56.46)103 (62.42)762 (55.74)187 (12.21)26 (15.76)161 (11.78)
Dannenburg et al. [23].IVT10 (3.07)7 (2.15)3 (0.92)NRNRNRNRNRNRNRNRNR
Derex et al. [24].IVT3 (6.82)1 (2.27)2 (4.55)
NR		NRNRNRNRNRNR
NR		NRNRNR
NR
Derraz et al. [25].EVT66 (12.87)15 (2.92)51 (9.94)NRNRNR281 (54.78)59
(11.50)		222
(43.27)		88 (17.15)24 (4.68)64 (12.48)
Fiehler et al. [26].IVT18 (3.16)5
(0.88)		13
(2.28)		NRNRNRNRNRNRNRNRNR
Gratz et al. a [11]IVT6 (1.53)0
(0)		6
(1.53)		NRNRNR70 (17.86)22
(5.61)		48
(12.24)		38 (9.69)10
(2.55)		28
(7.14)
Gratz et al. b [11]EVT9 (2.30)1
(0.26)		8
(2.04)		NRNRNR93 (23.72)26
(6.63)		67
(17.09)		47 (12.00)16
(4.08)		31
(7.91)
Gratz et al. c [11]Bridging6 (1.53)0
(0)		6
(1.53)		NRNRNR30 (7.65)4
(1.02)		26
(6.63)		15 (3.83)1
(0.26)		14
(3.57)
Gratz et al. Overall [11]IVT and/or EVT21 (5.36)1 (0.26)20 (5.10)NRNRNR193 (49.23)52 (13.27)141 (35.97)100 (25.51)27 (6.89)73 (18.62)
Kakuda et al. [27]IVT7 (9.72)0
(0)		7
(9.72)		20 (27.78)3
(4.29)		17
(24.29)		NRNRNRNRNRNR
Kidwell et al. [28]IVTNRNRNR15 (36.59)2 (4.88)13 (31.71)NRNRNRNRNRNR
Kim et al. [29]IVT5 (7.69)3
(4.62)		2
(3.08)		17 (26.15)8
(12.31)		9
(13.85)		NRNRNRNRNRNR
Kimura et al. [30].IVT6 (2.46)NRNR65 ()29.02NRNRNRNRNRNRNRNR
Lee et al. [38]EVTNRNRNR170 (29.46)32 (5.55)138 (23.91)288 (49.91)59
(10.23)		229
(39.69)		NRNRNR
Moriya et al. [31]IVTNRNRNR26 (36.62)6
(8.54)		20
(28.17)		NRNRNRNRNRNR
Nighoghossian et al. [32]IVTNRNRNR34 (34.00)NRNRNRNRNRNRNRNR
Pratz-Sanchez et al. [33]IVTNRNRNR78 (7.86)NRNR432 (43.55)NRNR134 (13.51)NRNR
Schlemm et al. [15] a. (SITS-MOST)IVT6 (1.31)2 (0.44)4 (0.87)102 (22.22)30 (6.54)72 (15.69)128 (28.51)37
(8.06)		91
(19.83)		10 (2.18)4
(0.87)		6 (1.31)
Schlemm et al. [15] b. (ECASS II)IVT8 (1.74)3 (0.65)5 (1.09)102 (22.22)30 (6.54)72 (15.69)128 (28.51)37
(8.06)		91
(19.83)		10 (2.18)4 (0.87)6 (1.31)
Schlemm et al. [15] c. (ECASS II!)IVT6 (1.31)2 (0.44)4 (0.87)102 (22.22)30 (6.54)72 (15.69)128 (28.51)37
(8.06)		91
(19.83)		10 (2.18)4 (0.87)6 (1.31)
Schlemm et al. [15] d. (NINDS)IVT26 (5.66)11
(2.40)		15
(3.27)		102 (22.22)30 (6.54)72 (15.69)128 (28.51)37
(8.06)		91
(19.83)		10 (2.18)4 (0.87)6 (1.31)
Shi et al. [39]. *EVTNRNRNR91 (44.14)14
(6.80)		77
(37.38)		NRNRNRNRNRNR
Soo et al. [40].EVTNRNR7
(5.26)		1
(0.75)		6
(4.51)		NRNRNRNRNRNRNR
Turc et al. [34] a. (NINDS)IVT65 (9.07)NRNRNRNRNR329 (45.89)NRNRNRNRNR
Turc et al. [34] b. (ECASS II)IVT64 (8.93)NRNRNRNRNR329 (45.89)NRNRNRNRNR
Turc et al. [34] c. (ECASS III)IVT27 (3.77)NRNRNRNRNR329 (45.89)NRNRNRNRNR
Turc et al. [34] d. (SITS-MOST)IVT27 (3.77)NRNRNRNRNR329 (45.89)NRNRNRNRNR
Yan et al. [35]IVT2 (1.65)NRNR36 (29.75)NRNRNRNRNRNRNRNR
Zand et al. [36] *IVT25 (3.72)5
(0.74)		20
(2.98)		NRNRNRNRNRNRNRNRNR
Note: * Shi et al. [39] and Zand et al. [36] reported in hospital mortality, not 90-day mortality. Capuana et al. [18] a, b, c corresponds to cohorts assessed for sICH using the SITS-MOST, ECASS-II and NINDS criteria, respectively, in the said study. Schlemm et al. [15] a, b, c, d correspond to cohorts assessed for sICH using the SITS-MOST, ECASS-II, ECASS-III, and NINDS, respectively. Choi et al. [21] a, b and overall correspond to cohorts receiving IVT, EVT and IVT and/or EVT, respectively, within this study. Gratz et al. [11] a, b, c and overall represents cohorts receiving IVT, EVT, Bridging and IVT and/or EVT, respectively, within this study. Turc et al. [34] a, b, c and d correspond to cohorts assessed for sICH using the NINDS, ECASS-II, ECASS-III and SITS-MOST criteria, respectively, in the said study. Abbreviations: CMB = cerebral microbleed, sICH = symptomatic intracerebral haemorrhage, HT = haemorrhagic transformation, IVT = intravenous thrombolysis, EVT = endovascular thrombectomy, NR = not reported, NIHSS = National Institutes of Health Stroke Scale, ECASS = European Cooperative Acute Stroke Study, ECASS-II = second European Cooperative Acute Stroke Study, ECASS-III = third European Cooperative Acute Stroke Study, NINDS = National Institute of Neurological Disorders and Stroke, SITS-MOST = Safe Implementation of Thrombolysis in Stroke-Monitoring Study.
Table 4. Summary effects and heterogeneity obtained from the meta-analysis of the association of cerebral microbleeds with clinical outcomes in acute ischaemic stroke patients who underwent reperfusion therapy.
Table 4. Summary effects and heterogeneity obtained from the meta-analysis of the association of cerebral microbleeds with clinical outcomes in acute ischaemic stroke patients who underwent reperfusion therapy.
OutcomeReperfusion TherapyEffect MeasureTest of ES = 0Summary EffectsHeterogeneity ⍺Heterogeneity Variance Estimates
REDLTests of Overall EffectCochran’s QChi-SquaredHI2 ≤ *p-Valueτ2 ≤ †
OR (95% CI)
sICHIVTORN/A2.57 [1.82; 3.61]p < 0.0001 z = 5.4169.37N/AN/A0.0%0.806N/A
EVTORN/A1.14 [0.40; 3.21]p = 0.805 z = 0.2461.39N/AN/A28.1%0.238N/A
IVT/EVTORN/A8.96 [4.82; 16.63]p < 0.0001 z = 6.9450.00N/AN/ANRNRN/A
BridgingORN/A0.67 [0.03; 13.28]p = 0.792 z = −0.2640.00N/AN/ANRNRN/A
OverallORN/A2.57 [1.72; 3.83]p < 0.001 z = 4.63431.33N/A1.319 [1.000; 1.782]68.5%0.0260.2762
HTIVTORN/A1.46 [1.03; 2.07]p = 0.034 z = 2.1252.17N/AN/A0.0%0.903N/A
EVTORN/A1.19 [0.81; 1.76]p = 0.373 z = 0.3731.00N/AN/A0.0%0.606N/A
IVT/EVTORN/A1.92 [1.37; 2.68]p < 0.0001 z = 3.8110.00N/AN/ANRNRN/A
BridgingORN/AN/AN/AN/AN/AN/AN/AN/AN/A
OverallORN/A1.53 [1.25; 1.88]p < 0.0001 z = 4.0976.54N/A0.828 [1.000; 1.212]0.0%0.768<0.0001
Poor Functional Outcome at 90 DaysIVTORN/A1.70 [1.28; 2.25]p < 0.0001 z = 3.7092.23N/AN/A0.0%0.526N/A
EVTORN/A1.70 [1.26; 2.29]p = 0.001 z = 3.4531.98N/AN/A0.0%0.372N/A
IVT/EVTORN/A1.32 [0.95; 1.84]p = 0.103 z = 1.6310.00N/AN/ANRNRN/A
BridgingORN/A2.77 [0.47; 16.27]p = 0.260 z = 1.1270.00N/AN/ANRNRN/A
OverallORN/A1.59 [1.34; 1.89]p < 0.001 z = 5.2576.19N/A0.880 [1.000; 1.309]41.7%0.626<0.0001
Mortality at 90 DaysIVTORN/A1.52 [0.91; 2.54]p = 0.109 z = 1.6031.11N/AN/A0.0%0.573N/A
EVTORN/A2.14 [1.37; 3.34]p = 0.001 z = 3.3640.04N/AN/A0.0%0.846N/A
IVT/EVTORN/A1.40 [0.89; 2.20]p = 0.142 z = 1.4690.00N/AN/ANRNRN/A
BridgingORN/A0.69 [0.07; 6.37]p = 0.740 z = −0.3320.00N/AN/ANRNRN/A
OverallORN/A1.65 [1.27; 2.16]p < 0.001 z = 3.6923.67N/A0.783 [1.000; 1.255]0.0%0.721<0.0001
PrevalenceIVTPrevalencep < 0.001
z = 14.62		0.20 [0.16; 0.25]N/AN/A417.94N/A95.21%<0.0010.06
EVTPrevalencep < 0.001 z = 12.980.18 [0.14; 0.23]N/AN/A75.85N/A90.77%<0.0010.03
BridgingPrevalencep < 0.001 z = 4.16N/AN/AN/AN/AN/AN/AN/AN/A
OverallPrevalencep < 0.001 z = 19.060.19 [0.16; 0.23]N/AN/A501.52N/A94.02%<0.0010.03
Note: In IVT/EVT Reperfusion Subgroup, the study patients received reperfusion therapy, however, it didn’t specify how many patients received specific reperfusion treatment: IVT or EVT. Abbreviations: sICH = symptomatic intracerebral haemorrhage, HT = haemorrhagic transformation, IVT = intravenous thrombolysis, EVT = endovascular thrombectomy, OR = odds ratio, CI = confidence interval, NR = not reported, N/A = not applicable, REDL = DerSimonian and Laird Random Effects method, Q = heterogeneity measures were calculated from data with 95% confidence intervals (95% CIs), based on non-central χ2 (common effect) distribution for Cochran’s Q test, H = relative excess in Cochran’s Q divided by degrees of freedom, I2 = proportion of total variation in effect estimate between study heterogeneity (based on Cochran’s Q test), τ2 = between-study variance to test the heterogeneity among subgroups, * = numbers in I2 ≤ are percentages; ⍺ = heterogeneity measures were calculated from the data with 95% CIs based on gamma (random effects) distribution for Q, †—heterogeneity variance estimates (tau≤) were derived from the DerSimonian and Laird method.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Tipirneni, S.; Stanwell, P.; Weissert, R.; Bhaskar, S.M.M. Prevalence and Impact of Cerebral Microbleeds on Clinical and Safety Outcomes in Acute Ischaemic Stroke Patients Receiving Reperfusion Therapy: A Systematic Review and Meta-Analysis. Biomedicines 2023, 11, 2865. https://doi.org/10.3390/biomedicines11102865

AMA Style

Tipirneni S, Stanwell P, Weissert R, Bhaskar SMM. Prevalence and Impact of Cerebral Microbleeds on Clinical and Safety Outcomes in Acute Ischaemic Stroke Patients Receiving Reperfusion Therapy: A Systematic Review and Meta-Analysis. Biomedicines. 2023; 11(10):2865. https://doi.org/10.3390/biomedicines11102865

Chicago/Turabian Style

Tipirneni, Shraddha, Peter Stanwell, Robert Weissert, and Sonu M. M. Bhaskar. 2023. "Prevalence and Impact of Cerebral Microbleeds on Clinical and Safety Outcomes in Acute Ischaemic Stroke Patients Receiving Reperfusion Therapy: A Systematic Review and Meta-Analysis" Biomedicines 11, no. 10: 2865. https://doi.org/10.3390/biomedicines11102865

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop