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      Selection of patients for intra-arterial treatment for acute ischaemic stroke: development and validation of a clinical decision tool in two randomised trials

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          Abstract

          Objective To improve the selection of patients with acute ischaemic stroke for intra-arterial treatment using a clinical decision tool to predict individual treatment benefit.

          Design Multivariable regression modelling with data from two randomised controlled clinical trials.

          Setting 16 hospitals in the Netherlands (derivation cohort) and 58 hospitals in the United States, Canada, Australia, and Europe (validation cohort).

          Participants 500 patients from the Multicenter Randomised Clinical Trial of Endovascular Treatment for Acute Ischaemic Stroke in the Netherlands trial (derivation cohort) and 260 patients with intracranial occlusion from the Interventional Management of Stroke III trial (validation cohort).

          Main outcome measures The primary outcome was the modified Rankin Scale (mRS) score at 90 days after stroke. We constructed an ordinal logistic regression model to predict outcome and treatment benefit, defined as the difference between the predicted probability of good functional outcome (mRS score 0-2) with and without intra-arterial treatment.

          Results 11 baseline clinical and radiological characteristics were included in the model. The externally validated C statistic was 0.69 (95% confidence interval 0.64 to 0.73) for the ordinal model and 0.73 (0.67 to 0.79) for the prediction of good functional outcome, indicating moderate discriminative ability. The mean predicted treatment benefit varied between patients in the combined derivation and validation cohort from −2.3% to 24.3%. There was benefit of intra-arterial treatment predicted for some individual patients from groups in which no treatment effect was found in previous subgroup analyses, such as those with no or poor collaterals.

          Conclusion The proposed clinical decision tool combines multiple baseline clinical and radiological characteristics and shows large variations in treatment benefit between patients. The tool is clinically useful as it aids in distinguishing between individual patients who may experience benefit from intra-arterial treatment for acute ischaemic stroke and those who will not.

          Trial registration  clinicaltrials.gov NCT00359424 (IMS III) and isrctn.com ISRCTN10888758 (MR CLEAN).

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          Most cited references16

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          Treating individuals 2. Subgroup analysis in randomised controlled trials: importance, indications, and interpretation.

          Large pragmatic trials provide the most reliable data about the effects of treatments, but should be designed, analysed, and reported to enable the most effective use of treatments in routine practice. Subgroup analyses are important if there are potentially large differences between groups in the risk of a poor outcome with or without treatment, if there is potential heterogeneity of treatment effect in relation to pathophysiology, if there are practical questions about when to treat, or if there are doubts about benefit in specific groups, such as elderly people, which are leading to potentially inappropriate undertreatment. Analyses must be predefined, carefully justified, and limited to a few clinically important questions, and post-hoc observations should be treated with scepticism irrespective of their statistical significance. If important subgroup effects are anticipated, trials should either be powered to detect them reliably or pooled analyses of several trials should be undertaken. Formal rules for the planning, analysis, and reporting of subgroup analyses are proposed.
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            Significance of large vessel intracranial occlusion causing acute ischemic stroke and TIA.

            Acute ischemic stroke due to large vessel occlusion (LVO)-vertebral, basilar, carotid terminus, middle and anterior cerebral arteries-likely portends a worse prognosis than stroke unassociated with LVO. Because little prospective angiographic data have been reported on a cohort of unselected patients with stroke and with transient ischemic attack, the clinical impact of LVO has been difficult to quantify. The Screening Technology and Outcome Project in Stroke Study is a prospective imaging-based study of stroke outcomes performed at 2 academic medical centers. Patients with suspected acute stroke who presented within 24 hours of symptom onset and who underwent multimodality CT/CT angiography were approached for consent for collection of clinical data and 6-month assessment of outcome. Demographic and clinical variables and 6-month modified Rankin Scale scores were collected and combined with blinded interpretation of the CT angiography data. The OR of each variable, including occlusion of intracranial vascular segment in predicting good outcome and 6-month mortality, was calculated using univariate and multivariate logistic regression. Over a 33-month period, 735 patients with suspected stroke were enrolled. Of these, 578 were adjudicated as stroke and 97 as transient ischemic attack. Among patients with stroke, 267 (46%) had LVO accounting for the stroke and 13 (13%) of patients with transient ischemic attack had LVO accounting for transient ischemic attack symptoms. LVO predicted 6-month mortality (OR, 4.5; 95% CI, 2.7 to 7.3; P<0.001). Six-month good outcome (modified Rankin Scale score
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              Early prognosis in traumatic brain injury: from prophecies to predictions.

              Traumatic brain injury (TBI) is a heterogeneous condition that encompasses a broad spectrum of disorders. Outcome can be highly variable, particularly in more severely injured patients. Despite the association of many variables with outcome, prognostic predictions are notoriously difficult to make. Multivariable analysis has identified age, clinical severity, CT abnormalities, systemic insults (hypoxia and hypotension), and laboratory variables as relevant factors to include in models to predict outcome in individual patients. Advances in statistical modelling and the availability of large datasets have facilitated the development of prognostic models that have greater performance and generalisability. Two prediction models are currently available, both of which have been developed on large datasets with state-of-the-art methods, and offer new opportunities. We see great potential for their use in clinical practice, research, and policy making, as well as for assessment of the quality of health-care delivery. Continued development, refinement, and validation is advocated, together with assessment of the clinical impact of prediction models, including treatment response. Copyright 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: PhD candidate
                Role: PhD candidate
                Role: physician and researcher
                Role: professor
                Role: associate professor
                Role: professor
                Role: physician and researcher
                Role: physician and researcher
                Role: professor
                Role: professor
                Role: professor
                Role: professor
                Role: professor
                Role: professor
                Role: professor
                Role: assistant professor
                Journal
                BMJ
                BMJ
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2017
                02 May 2017
                : 357
                : j1710
                Affiliations
                [1 ]Department of Public Health, Erasmus MC University Medical Centre Rotterdam, PO Box 2040, 3000 CA Rotterdam, Netherlands
                [2 ]Department of Neurology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
                [3 ]Department of Radiology, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
                [4 ]Department of Neurology and Rehabilitation Medicine, University of Cincinnati Gardner Neuroscience Institute, Cincinnati, OH, USA
                [5 ]Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
                [6 ]Department of Radiology, Academic Medical Centre, Amsterdam, Netherlands
                [7 ]Department of Radiology, Maastricht University Medical Centre, Maastricht, Netherlands
                [8 ]Department of Neurology, Academic Medical Centre, Amsterdam, Netherlands
                [9 ]Department of Neurology, Maastricht University Medical Centre, Maastricht, Netherlands
                [10 ]Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, Netherlands
                Author notes
                Correspondence to: E Venema e.venema@ 123456erasmusmc.nl
                Article
                vene036408
                10.1136/bmj.j1710
                5418887
                28468840
                93a3fd37-e448-4956-bc31-b8bdb8827889
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 27 March 2017
                Categories
                Research

                Medicine
                Medicine

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