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      Predictors of futile recanalization after endovascular treatment in acute ischemic stroke: a meta-analysis

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          Abstract

          Background

          Despite successful recanalization after endovascular treatment, many patients with acute ischemic stroke due to large vessel occlusion still show functional dependence, namely futile recanalization.

          Methods

          PubMed and Embase were searched up to April 30, 2021. Studies that reported risk factors for futile recanalization following endovascular treatment of acute ischemic stroke were included. The mean difference (MD) or odds ratio (OR) and 95% confidence interval (95% CI) of each study were pooled for a meta-analysis.

          Results

          Twelve studies enrolling 2138 patients were included. The pooled analysis showed that age (MD 5.81, 95% CI 4.16 to 7.46), female sex (OR 1.40, 95% CI 1.16 to 1.68), National Institutes of Health Stroke Scale (NIHSS) score (MD 4.22, 95% CI 3.38 to 5.07), Alberta Stroke Program Early CT Score (ASPECTS) (MD −0.71, 95% CI −1.23 to –0.19), hypertension (OR 1.73, 95% CI 1.43 to 2.09), diabetes (OR 1.78, 95% CI 1.41 to 2.24), atrial fibrillation (OR 1.24, 95% CI 1.01 to 1.51), admission systolic blood pressure (MD 4.98, 95% CI 1.87 to 8.09), serum glucose (MD 0.59, 95% CI 0.37 to 0.81), internal carotid artery occlusion (OR 1.85, 95% CI 1.17 to 2.95), pre-treatment intravenous thrombolysis (OR 0.67, 95% CI 0.55 to 0.83), onset-to-puncture time (MD 16.92, 95% CI 6.52 to 27.31), puncture-to-recanalization time (MD 12.37, 95% CI 7.96 to 16.79), and post-treatment symptomatic intracerebral hemorrhage (OR 6.09, 95% CI 3.18 to 11.68) were significantly associated with futile recanalization.

          Conclusion

          This study identified female sex, comorbidities, admission systolic blood pressure, serum glucose, occlusion site, non-bridging therapy, and post-procedural complication as predictors of futile recanalization, and also confirmed previously reported factors. Further large-scale prospective studies are needed.

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          Most cited references45

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          Is Open Access

          Estimating the mean and variance from the median, range, and the size of a sample

          Background Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. Methods In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. Results We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n ≤ 15). For moderately sized samples (15 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. Conclusion Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported.
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            Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials.

            In 2015, five randomised trials showed efficacy of endovascular thrombectomy over standard medical care in patients with acute ischaemic stroke caused by occlusion of arteries of the proximal anterior circulation. In this meta-analysis we, the trial investigators, aimed to pool individual patient data from these trials to address remaining questions about whether the therapy is efficacious across the diverse populations included.
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              Baseline NIH Stroke Scale score strongly predicts outcome after stroke: A report of the Trial of Org 10172 in Acute Stroke Treatment (TOAST).

              To compare the baseline National Institutes of Health Stroke Scale (NIHSS) score and the Trial of Org 10172 in Acute Stroke Treatment (TOAST) stroke subtype as predictors of outcomes at 7 days and 3 months after ischemic stroke. Using data collected from 1,281 patients enrolled in a clinical trial, subtype of stroke was categorized using the TOAST classification, and neurologic impairment at baseline was quantified using the NIHSS. Outcomes were assessed at 7 days and 3 months using the Barthel Index (BI) and the Glasgow Outcome Scale (GOS). An outcome was rated as excellent if the GOS score was 1 and the BI was 19 or 20 (scale of 0 to 20). Analyses were adjusted for age, sex, race, and history of previous stroke. The baseline NIHSS score strongly predicted outcome, with one additional point on the NIHSS decreasing the likelihood of excellent outcomes at 7 days by 24% and at 3 months by 17%. At 3 months, excellent outcomes were noted in 46% of patients with NIHSS scores of 7 to 10 and in 23% of patients with scores of 11 to 15. After multivariate adjustment, lacunar stroke had an odds ratio of 3.1 (95% CI, 1.5 to 6.4) for an excellent outcome at 3 months. The NIHSS score strongly predicts the likelihood of a patient's recovery after stroke. A score of > or =16 forecasts a high probability of death or severe disability whereas a score of < or =6 forecasts a good recovery. Only the TOAST subtype of lacunar stroke predicts outcomes independent of the NIHSS score.
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                Author and article information

                Contributors
                Journal
                Journal of NeuroInterventional Surgery
                J NeuroIntervent Surg
                BMJ
                1759-8478
                1759-8486
                September 20 2021
                : neurintsurg-2021-017963
                Article
                10.1136/neurintsurg-2021-017963
                34544824
                2e5f495b-0fc0-46d1-bf84-445d47e1fbef
                © 2021
                History

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