Abstract
Background. Patients with diabetes are known to have worse outcomes after an acute ischemic stroke (AIS) relative to those without diabetes. However, the impact of diabetes on the outcomes after the reperfusion therapy is poorly understood.
Objectives. This study investigated prognostic accuracy of diabetes and its association with clinical and safety outcomes in AIS patients receiving intravenous thrombolysis (IVT), endovascular thrombectomy (EVT), or both.
Materials and methods. Studies were identified from PubMed, Embase and Cochrane databases, using the following inclusion criteria: (a) AIS patients receiving reperfusion therapy, (b) age ≥ 18 years, (c) hemispheric stroke, and (d) the availability of comparative data between diabetic and nondiabetic groups and relevant poststroke outcomes. Random effects modelling was used to study the association of diabetes with functional outcome at discharge and at 90 days, mortality at 90 days, recanalization status, and postreperfusion safety outcomes, including rates of symptomatic intracerebral hemorrhage (sICH) and hemorrhagic transformation (HT). Forest plots of odds ratios (ORs) were generated.
Results. Of a total cohort of 82,764 patients who received reperfusion therapy, 16,877 had diabetes. Diabetes significantly increased the odds of poor functional outcome at discharge (OR 1.310; 95% confidence interval (95% CI): [1.091; 1.574]; p = 0.0037) and at 90 days (OR 1.487; 95% CI: [1.335; 1.656]; p < 0.00010), mortality at 90 days (OR 1.709; 95% CI: [1.633; 1.788]; p < 0.0001), sICH (OR 1.595; 95% CI: [1.301; 1.956]; p < 0.0001), and HT (OR 1.276; 95% CI: [1.055; 1.543]; p = 0.0118).
Conclusions. Our meta-analysis demonstrates that diabetes is significantly associated with poor functional outcome, increased mortality and poor postprocedural safety outcomes, including sICH and HT.
Key words: diabetes, stroke, meta-analysis, cerebrovascular disease, reperfusion therapy
Background
Diabetes is known to be an important risk factor for stroke.1 The advent of reperfusion treatment, intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT) offers the opportunity to significantly improve the outcomes after acute ischemic stroke (AIS).2 Moreover, since 2015, in the era of EVT, an increasing attention has been paid to identifying patients or stratifying them based on the clinical profiles or imaging factors,3, 4 who are more likely to benefit from time-critical therapies.5, 6, 7, 8, 9 As such, it is of clinical relevance to fully delineate the role of diabetes in AIS, in the setting of reperfusion therapy.10 Diabetes is known to be associated with worse functional outcomes and mortality after AIS,11, 12 largely due to its effect on endothelial dysfunction, fibrosis and vascular remodeling.13 Furthermore, diabetes may also influence recanalization efficacy following IVT or EVT.14
Objectives
This study sought to estimate the prognostic accuracy of diabetes and investigate its association with clinical outcomes in AIS patients receiving IVT, EVT and/or both, by performing a meta-analysis. Our underlying questions concerning AIS patients receiving reperfusion therapy are as follows:
1. What is the prognostic accuracy of diabetes?
2. Is diabetes associated with functional outcomes at 90 days?
3. Is diabetes associated with functional outcomes at discharge?
4. Is diabetes associated with increased mortality at 90 days?
5. Is diabetes associated with safety profile (defined in terms of symptomatic intracerebral hemorrhage (sICH)15 or any hemorrhagic transformation (HT))?
6. Is diabetes associated with recanalization status?
Materials and methods
Literature search: Identification
and selection of studies
Studies were identified from PubMed, Embase and Cochrane Central Register of Controlled Trials (CENTRAL) databases for the period between January 1, 2005, and September 2021. The search terms included: “stroke”, or “ischemic stroke”, or “cerebrovascular accident”, or “brain ischemia”, or “brain infarction”, or “anterior circulation”, or “middle cerebral artery (MCA) stroke”, or “internal carotid artery (ICA) stroke”, or “MCA occlusion”, or “large vessel occlusion” and “reperfusion”, or “endovascular thrombectomy”, or “thrombolysis”, or “thrombolytic therapy”, or “tissue plasminogen activator”, or “clot retrieval” and “diabetes”, or “diabetes mellitus” and” clinical outcome”, or “tissue outcome”, or “mortality”, or “morbidity”, or “death”, or “adverse outcome”, or “NIHSS (National Institute of Health Stroke Scale/Score)”, or “clinical severity”, or “discharge outcome”, or “infarct volume”, or “disability score”, or “modified Rankin Score”, or “prognosis”. The full search term/strategy is provided in the Online Supplementary Information (Search Strategy). Studies written in a language other than English and not including human subjects were excluded by applying additional limits. Moreover, reference lists of relevant articles, systematic reviews and meta-analyses were also searched manually in order to retrieve additional articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart shows the search strategy, included studies and various subgroup analyses performed in the meta-analysis (Figure 1). The following reporting frameworks adhered to and were reported: the Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist (Supplementary Table 5), PRISMA 2020 checklist (Supplementary Table 6), and Standards for Reporting of Diagnostic Accuracy Studies (STARD) 2015 checklist (Supplementary Table 7); all available in Online Supplementary Information.
Inclusion and exclusion criteria
Studies were eligible if they met the following criteria: (a) AIS patients receiving reperfusion therapy (IVT or EVT); (b) age ≥18 years; (c) hemispheric stroke; (d) availability of comparative data between diabetic and nondiabetic groups and relevant poststroke outcome data; and (e) studies with correct methodological design (studies with sufficient sample size, determined to be ≥20 patients in each group). The exclusion criteria were as follows: 1) patients with posterior circulation stroke; 2) animal studies; 3) duplicated publications; 4) full-text of the article not available; 5) thrombolytic agent other than tissue plasminogen activator (tPA) used; 6) intra-arterial thrombolysis used; 7) systematic reviews, meta-analyses, letters, and case reports or case series; and 8) studies presented in the abstract form, with relevant data on diabetes not available or no relevant postreperfusion clinical outcome measured.
Data extraction
First, the titles and abstracts were reviewed in Endnote (Clarivate Analytics, London, UK) to rule out the articles mismatched to the eligibility criteria. The remaining articles were examined thoroughly to determine whether they should be included in the systematic review or meta-analysis, according to the eligibility criteria. The screening was conducted independently by 2 authors. The disagreements were discussed and final decisions were reached by consensus. The data from each study/trial were extracted independently using a data extraction sheet to obtain the following information: 1) baseline demographics: author, country and year of publication; 2) study population: age of patients, sample size, characteristics of acute stroke patients, presence or absence of diabetes; 3) type and time window of the treatments; and 4) outcome measures: the functional outcome at 90 days, the functional outcome at discharge, mortality at 90 days, sICH, HT, and the recanalization status. The functional outcome was measured using the modified Rankin Scale (mRS) score. The sICH was determined using Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST), the Second European-Australasian Acute Stroke Study (ECASS-II) or the Third European Cooperative Acute Stroke Study (ECASS-III) criteria, and HT was defined as any hemorrhage observed on follow-up imaging. If a study reported more than one type of sICH, SITS-MOST results were used.
Methodological quality assessment of included studies
The methodological quality assessment using the modified Jadad analysis (MJA) scale of all studies included in the meta-analysis was performed independently by 2 researchers.16 Moreover, the risk of bias in the results owing to funding was also evaluated, based on the declaration of funding sources and conflicts of interest obtained from individual studies.17
Statistical analyses
All statistical analyses were performed using STATA v. 13.0 (StataCorp, College Station, USA). The baseline characteristics of the overall cohort included in the meta-analysis were derived from all included studies. Means and standard deviations (SDs) were calculated from the medians and interquartile ranges (IQRs) using the method of Wan et al., where appropriate.18
The prognostic utility of diabetes was evaluated by estimating the pooled sensitivity (SENS) and specificity (SPEC), positive and negative predictive values, positive and negative likelihood ratios, and area under the curve (AUC) (a global measure of prognostic accuracy obtained from summary receiver operating characteristic (SROC) curves), by performing a meta-analysis for each prognostic outcome.19 Moreover, the prognostic model was characterized using the goodness-of-fit test. The Deeks’ funnel plot asymmetry test was used to assess the publication bias.
To examine the impact of diabetes on postreperfusion sICH and HT, functional outcomes at discharge, and functional outcomes and mortality at 90 days, a random effects meta-analysis designed by DerSimonian and Laird (DL) was used. Summary effects and heterogeneity measures obtained for each prognostic outcome from the meta-analysis were tabulated. For the odds ratios (ORs), 95% confidence intervals (95% CIs), percentage weights, and the heterogeneity across studies included in the meta-analysis, forest plots were created (Figure 2). The heterogeneity between the studies was assessed using the I2 statistics and p-value (<40% = low, 30–60% = moderate, 50–90% = substantial, 75–100% = considerable).20 The random effects model was used across all subgroup analyses. The subgroup analyses for IVT or EVT studies were also performed. The presence of publication bias was visually detected using Begg’s funnel plot. In the funnel plot, any asymmetry on either side indicated the presence of publication bias. We have also computed meta-analysis estimates when a specific study was excluded, to account for the influence of the individual study on the overall meta-analysis (Supplementary Fig. 5 in Online Supplementary Information). The value of p < 0.05 was considered statistically significant.
Results
Description of included studies
A total of 24 studies, comprising 82,764 patients, were included in this meta-analysis. Eighteen studies included patients who primarily received IVT, with or without EVT; 6 studies included patients who primarily received EVT, with or without IVT. Nine studies were excluded from this meta-analysis because they had cohort sizes that were too small, 5 were excluded for having the same cohort (or part of the same cohort) as later studies and 1 was excluded because of the use of intra-arterial thrombolysis as the treatment.
Of all patients included in this meta-analysis, 16,877 had diabetes (20.4%). The mean age ± standard deviation (SD) of all included studies was 69.5 ±33.4 years (n = 77,319). With regard to clinical history, 22.8% of patients had atrial fibrillation (n = 78,804), 37.5% had dyslipidemia (n = 74,551), 67.6% had hypertension (n = 64,064), 18.4% had prior stroke and/or transient ischemic attack (TIA) (n = 75,242), and 18.6% were prior or current smokers (n = 62,208). The description of the clinical characteristics and outcomes of the studies included in the meta-analysis can be found in Table 1 and Table 2, respectively.
Summary effects and heterogeneity from the meta-analysis on the association of diabetes are provided in Table 3. Supplementary Table 1 also provides the summary of the level of significance of the association of diabetes with various clinical and/or safety outcomes. There were variations in definitions of sICH and recanalization status across studies. The findings of the assessment of methodological quality and funding bias of the included studies are given in Supplementary Table 3. Effect size analyses for the functional outcome, sICH, HT, mortality, and recanalization status are also presented (Supplementary Fig. 6). Two studies demonstrated a moderate potential for funding bias and 1 study demonstrated a significant potential (Supplementary Table 3). The publication bias assessment, using the Egger’s test, of the included studies is summarized in Supplementary Table 4. The subgroup analysis was conducted to address the prognostic (n = 24 studies) capability of diabetes in AIS.
Prognostic capability of diabetes
in acute ischemic stroke
The SROC curves for diabetes to predict the outcomes are shown in Figure 3. A summary of prognostic summary estimates is provided in Supplementary Table 2 and Supplementary Fig. 3. Supplementary Fig. 4 illustrates the likelihood ratio scatter matrix (all supplementary materials are available in Online Supplementary Information). Twenty-four studies investigated the prognostic capability of diabetes in AIS. The meta-analysis demonstrated that the prognostic accuracy of diabetes for poor functional outcome at 90 days was 56% (AUC: 0.56; 95% CI: [0.03; 0.98]). The pooled prognostic sensitivity of diabetes for poor functional outcome at 90 days was 59% (SENS: 0.59; 95% CI: [0.54; 0.64]; p < 0.0001). The test of heterogeneity revealed a considerable heterogeneity for diagnostic sensitivity (I2 = 99.87) and specificity (I2 = 99.93). The prognostic accuracy of diabetes for poor functional outcome at discharge was 56% (AUC: 0.56; 95% CI: [0.04; 0.98]). The pooled prognostic sensitivity of diabetes for poor functional outcome at discharge was 61% (SENS: 0.61; 95% CI: [0.47; 0.73]; p < 0.0001). The test of heterogeneity revealed a considerable heterogeneity for the diagnostic sensitivity (I2 = 99.81) and specificity (I2 = 99.45). The prognostic accuracy of diabetes for mortality at 90 days was 54% (AUC: 0.54; 95% CI [0.01; 0.99]). The pooled prognostic sensitivity of diabetes for mortality at 90 days was 23% (SENS: 0.23; 95% CI: [0.20; 0.26]; p < 0.0001). The test of heterogeneity revealed a considerable heterogeneity for the diagnostic sensitivity (I2 = 94.13) and specificity (I2 = 98.85). The prognostic accuracy of diabetes for poor recanalization was 61% (AUC: 0.61; 95% CI: [0.06; 0.98]). The pooled prognostic sensitivity of diabetes for poor recanalization was 70% (SENS: 0.7; 95% CI: [0.45; 0.86]; p < 0.0001). The test of heterogeneity revealed a considerable heterogeneity for diagnostic sensitivity (I2 = 98.16) and specificity (I2 = 96.72). The prognostic accuracy of diabetes for sICH was 42% (AUC: 0.42; 95% CI: [0.00; 1.00]). The pooled prognostic sensitivity of diabetes for sICH was 4% (SENS: 0.04; 95% CI: [0.03; 0.06]; p < 0.0001). The test of heterogeneity revealed considerable heterogeneity for diagnostic sensitivity (I2 = 99.84) and specificity (I2 = 99.99). The prognostic accuracy of diabetes for HT could not be determined.
Association of diabetes
with the functional outcome at 90 days
Overall, 12 studies were included in the final meta-analysis of the association of diabetes with the poor functional outcome at 90 days, comprising a total of 72,874 patients. Poor functional outcome at 90 days was defined as mRS score of 3–6 in all studies. Diabetes was associated with significantly increased odds of poor functional outcome at 90 days (OR 1.487; 95% CI: [1.335; 1.656]; p < 0.0001) (Figure 2F). Moderate to substantial heterogeneity was found between the studies (I2 = 50.1%, p = 0.024). There was the evidence of publication bias, observed by visual inspection of the funnel plot (Supplementary Fig. 1), revealed by Egger’s test (Supplementary Fig. 2; all supplementary materials are available in Online Supplementary Information). There was a significant association of diabetes with mRS at 90 days, observed in patients receiving IVT (OR 1.430; 95% CI: [1.270; 1.611]; p < 0.0001) and in patients receiving EVT (OR 1.941; 95% CI: [1.424; 2.646]; p < 0.0001).
Association of diabetes
with mortality at 90 days
Seven studies were included in the final meta-analysis of the association of diabetes with mortality at 90 days, comprising a total of 71,312 patients. Diabetes was significantly associated with mortality at 90 days (OR 1.709; 95% CI: [1.633; 1.788]; p < 0.0001) (Figure 2B). A low heterogeneity was found between the studies (I2 = 0.0%, p = 0.644). There was the evidence of publication bias, observed by visual inspection of the funnel plot (Supplementary Fig. 1) and revealed by Egger’s test (Supplementary Fig. 2; all supplementary materials are available in Online Supplementary Information). A significant association of diabetes with mortality at 90 days was observed in patients receiving IVT (OR 1.713; 95% CI: [1.629; 1.801]; p < 0.0001). No significant association of diabetes with mortality at 90 days was observed in patients receiving EVT (OR 1.573; 95% CI: [0.994; 2.489]; p = 0.0532) (Figure 2B).
Association of diabetes with sICH
Overall, 11 studies were included in the final meta-analysis of the association of diabetes with sICH, comprising a total of 70,677 patients. The sICH was defined by SITS-MOST21 criteria in 6 studies, ECASS-II22 criteria in 1 study, National Institute of Neurological Disorders and Stroke (NINDS) criteria in 1 study, ECASS-III23 criteria in 1 study, as ≥2 point change in National Institutes of Health Stroke Scale (NIHSS) score associated with any degree of hemorrhage on computed tomography (CT) or magnetic resonance (MR) in 1 study, and not defined in 2 studies. Overall, diabetes was significantly associated with an increased sICH rate (OR 1.595; 95% CI: [1.301; 1.956]; p < 0.0001) (Figure 2E). A low to moderate heterogeneity was found between the studies (I2 = 33.7%, p = 0.129). Some evidence of publication bias was observed by the visual inspection of the funnel plot (Supplementary Fig. 1) and by Egger’s test (Supplementary Fig. 2; all supplementary materials are available in Online Supplementary Information). A significant association between diabetes and the odds of sICH was observed in patients receiving IVT (OR 1.524; 95% CI: [1.245; 1.866]; p < 0.0001) and those receiving EVT (OR 2.917; 95% CI: [1.421; 5.99]; p = 0.0035) (Figure 2E).
Association of diabetes with HT
Nine studies were included in the final meta-analysis of the association of diabetes with HT, comprising a total of 4530 patients. Overall, diabetes was associated with increased odds of HT (OR 1.276; 95% CI: [1.055; 1.543]; p = 0.0118) (Figure 2A). A low heterogeneity was found between the studies (I2 = 0.0%, p = 0.564). There was the evidence of publication bias, observed by a visual inspection of the funnel plot (Supplementary Fig. 1), and revealed by Egger’s test (Supplementary Fig. 2; all supplementary materials are available in Online Supplementary Information). There was also a significant association of diabetes with HT in patients specifically receiving IVT (OR 1.267; 95% CI: [1.040; 1.543]; p = 0.0188) (Figure 2A).
Association of diabetes
with recanalization status
The meta-analysis of the association of diabetes with recanalization status included 4 studies encompassing 601 patients. A poor recanalization outcome was defined as a Thrombolysis in Myocardial Infarction (TIMI) score <3 in 1 study, a modified Thrombolysis in Cerebral Infarction (mTICI) score <2b in 1 study and incomplete recanalization on transcranial Doppler ultrasound in 1 study, while it was not defined in 1 study. Although diabetes was associated with increased odds of incomplete recanalization status, the association failed to reach statistical significance (OR 2.059; 95% CI: [0.963; 4.400]; p = 0.0624) (Figure 2C). A moderate to substantial heterogeneity was reported (I2 = 67.1%, p = 0.028). There was no evidence of publication bias, observed by a visual inspection of the funnel plot (Supplementary Fig. 1) and revealed by Egger’s test (Supplementary Fig. 2; all supplementary materials are available in Online Supplementary Information). There was, however, a significant association of diabetes with incomplete recanalization status in IVT patients (OR 2.693; 95% CI: [1.204; 6.027]; p = 0.0159).
Association of diabetes
with the functional outcome at discharge
Four studies were included in the meta-analysis of the association of diabetes with poor functional outcome at discharge, defined as mRS score of 3–6 at discharge. A total of 4600 patients were included, and patients in all included studies received IVT. Diabetes was associated with a significantly increased odds of poor functional outcome at discharge (OR 1.310; 95% CI: [1.091; 1.574]; p = 0.0037) (Figure 2D). Low to moderate heterogeneity was found (I2 = 32.3%, p = 0.219). There was the evidence of publication bias, observed by visual inspection of the funnel plot (Supplementary Fig. 1) and revealed by Egger’s test (Supplementary Fig. 2; all supplementary materials are available in Online Supplementary Information).
Discussion
The results of this meta-analysis demonstrate that diabetes is associated with increased mortality and poor clinical and safety outcomes in AIS patients receiving reperfusion therapy. Specifically, diabetes was associated with poor functional outcome at discharge and at 90 days, as well as mortality at 90 days. The IVT and EVT subgroup analysis revealed similar outcomes; however, the association of diabetes with mortality at 90 days in EVT patients was not significant. With regard to postprocedural outcomes, AIS patients with pre-existing diabetes were associated with significantly increased odds of any HT or sICH after the reperfusion therapy. In particular, there was a strong association between diabetes and sICH in IVT patients. Although patients with diabetes were at increased odds of incomplete recanalization after the reperfusion therapy, the association failed to reach statistical significance overall. However, the association of diabetes with incomplete recanalization was significant for the IVT subgroup.
Identifying biomarkers or phenotypes associated with poor or better clinical profiles in AIS patients receiving reperfusion therapy is important in order to stratify patients for an optimal therapy.10 Furthermore, given the rising prevalence of diabetes in the increasingly developing world, the proportion of AIS patients with diabetes is also expected to increase, warranting public health and clinical attention.24 Within the AIS population with diabetes, patients with acute hyperglycemia are at an even increased risk of poor outcome profiles, as acute hyperglycemia is associated with an increased risk of infarct growth – by potentially impairing the vulnerability of penumbra.25 Therefore, patients with diabetes, especially those with acute hyperglycemia, need urgent attention and rapid reperfusion treatment. Previous studies have indicated longer times to reperfusion in diabetes patients, owing to the additional need for medical care for hyperglycemia or diabetes management, prior to the reperfusion therapy.26, 27 On a systemic level, this warrants establishing specialized pathways to identify AIS patients with a high risk of poor outcomes.9, 28
Current evidence on functional outcome at 90 days for patients with diabetes who have undergone reperfusion therapy is mixed.5, 26, 29, 30, 31 Most of the studies included in this meta-analysis did not individually find a significant relationship between diabetes and the odds of poor functional outcome. Tsivgoulis et al.,32 with the largest cohort of any study (n = 54,206), did find a significant relationship. However, the study was of retrospective design. De Silva et al.,33 Fang et al.34 and Tsivgoulis et al.32 all found a significant relationship between the glucose level at admission and a poor functional outcome, in patients with or without diabetes, indicating that a poor functional outcome may be associated more with acute hyperglycemia seen in AIS patients. Our meta-analysis demonstrated a significant association between diabetes and a poor functional outcome at discharge and at 3 months. It also found a significant association between diabetes and increased 90 days mortality after the reperfusion therapy. A further investigation is needed in order to determine whether factors such as acute hyperglycemia and prior stroke play a role in these findings. Our meta-analysis found a significant association, although in a limited sample size drawn from 4 studies, between diabetes and unsuccessful recanalization, which contrasted with the individual findings of most of the included studies.14, 31, 35, 36, 37
With regard to safety outcomes, our meta-analysis revealed significantly increased odds of sICH and HT for AIS patients with diabetes who have undergone reperfusion therapy, compared to those without diabetes. This corroborates previous meta-analyses stating that diabetes and tPA independently increase the risk of hemorrhagic events after a stroke.14, 38 This meta-analysis considers the impact of diabetes across all reperfusion therapies. From a pathophysiological perspective, rodent models have demonstrated that increased MMP-9, the receptor for advanced glycation end products (RAGE) and vascular endothelial growth factor (VEGF) in diabetic mice are associated with increased blood–brain barrier (BBB) leakage, hemorrhage and impaired functional outcome.37, 39 The IVT may further exacerbate BBB leakage through BBB disruption.40 Factors such as delayed onset-to-reperfusion time and leukoaraiosis have also been implicated.3, 41 Regardless, studies have shown that reperfusion therapy is safe for patients with diabetes and leads to better outcomes compared to patients who did not receive reperfusion therapy.30, 42
During the current coronavirus disease 2019 (COVID-19) pandemic, an increasing attention is being paid to the disproportionate burden on patients with pre-existing diabetes.43, 44, 45 Furthermore, studies also indicate that patients with diabetes may be at an increased risk of COVID-19 infection.44 This highlights the need for comprehensive and tailored management of patients with diabetes during the pandemic and beyond,43 as well as in stroke patients with pre-existing diabetes who are at risk of poor outcomes after AIS.46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60
Limitations
This study has several limitations. Most studies included in the meta-analysis were retrospective, and thus inherently limited in their design. This also resulted in most of the included studies relying on a history of diabetes diagnosis or diabetes treatment as criteria for inclusion in the treatment group. This means that there may be some patients in the control group with undiagnosed diabetes. The inclusion of case-controlled studies may cause spectrum bias or random error. However, most of the studies reported that all consecutive acute ischemic stroke patients receiving reperfusion therapy were included; this may minimize the selection bias. Furthermore, certain parameters such as diabetic severity, duration and type were either minimally reported or not reported at all. Due to its large sample size, the study by Tsivgoulis et al.32 had a disproportionate effect on the overall results. Supplementary Fig. 5 (Online Supplementary Information) displays the results with the exclusion of that study. The outcomes of poor recanalization and functional outcome at discharge had relatively small cohorts and were therefore not highly powered. Last, not all studies clearly defined the number of patients who received both thrombolysis and thrombectomy. Findings should be interpreted on methodological design and the study population. However, given the fact that we performed a random effects model, some of these variabilities and heterogeneities will be accounted for.
Conclusions
Diabetes is an important clinical consideration in AIS patients receiving reperfusion therapy. Our meta-analysis demonstrates that diabetes is associated with poor outcomes such as poor functional outcome, mortality, and poor safety outcomes, including sICH and HT. These results are mostly consistent across reperfusion treatment subgroups.
Data availability statement
The original contributions presented in the study are included in the article Online Supplementary Information; further inquiries can be directed to the corresponding author. The Online Supplementary Information is available online at https://doi.org/10.5281/zenodo.5930131.
