Campbell, B. C.V. et al. (2019) Penumbral imaging and functional outcome in patients with anterior circulation ischaemic stroke treated with endovascular thrombectomy versus medical therapy: a meta-analysis of individual patient-level data.ABSTRACT Background: CT-perfusion (CTP) and MRI may assist patient selection for endovascular thrombectomy. We aimed to establish whether imaging assessments of ischaemic core and penumbra volumes were associated with functional outcomes and treatment effect.

Campbell, B. C. V. et al. (2018) Effect of general anaesthesia on functional outcome in patients with anterior circulation ischaemic stroke having endovascular thrombectomy versus standard care: a meta-analysis of individual patient data. Lancet Neurology, 17(1), pp. 47-53. (doi:10.1016/S1474-4422(17)30407-6) This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/149670/ variables. An alternative approach using propensity-score stratification was also used. To account for between-trial variance we used mixed-effects modeling with a random effect for trial incorporated in all models. Bias was assessed using the Cochrane tool.Findings: Of 1764 patients in 7 trials, 871 were allocated to endovascular thrombectomy. After exclusion of 74 patients (72 who did not undergo the procedure and 2 with missing data on anaesthetic strategy), 236/797 (30%) of endovascular patients were treated under GA. At baseline, GA patients were younger and had shorter time to randomisation but similar pre-treatment clinical severity compared to non-GA. Endovascular thrombectomy improved functional outcome at 3 months versus standard care in both GA (adjusted common odds ratio (cOR) 1·52, 95%CI 1·09-2·11, p=0·014) and non-GA (adjusted cOR 2·33, 95%CI 1·75-3·10, p<0·001) patients. However, outcomes were significantly better for those treated under non-GA versus GA (covariate-adjusted cOR 1·53, 95%CI 1·14-2·04, p=0·004; propensitystratified cOR 1·44 95%CI 1·08-1·92, p=0·012). The risk of bias and variability among studies was assessed to be low.Interpretation: Worse outcomes after endovascular thrombectomy were associated with GA, after adjustment for baseline prognostic variables. These data support avoidance of GA whenever possible. The procedure did, however, remain effective versus standard care in patients treated under GA, indicating that treatment should not be withheld in those who require anaesthesia for medical reasons. Funding:The HERMES collaboration was funded by an unrestricted grant from Medtronic to the University of Calgary. Research in contextEvidence before this study between abolition of the thrombectomy treatment effect in MR CLEAN and no effect in THRACE. Three single-centre randomised trials of general anaesthesia versus conscious sedation found either no difference in functional outcome between groups or a slight benefit of general anaesthesia. Added value of this studyThese data from contemporary, high quality randomised trials form the largest study to date of the association between general anesthesia and the benefit of endovascular thrombectomy versus standard care. We used two different approaches to adjust for baseline imbalances (multivariable logistic regression and propensity-score stratification). We found that GA for endovascular thrombectomy, as practiced in contemporary clinical care across a wide range of expert centres during the rand...

No abstract

Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables.Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ≥ 2b) and functional independence (modified Rankin Scale ≤2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed.Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53–0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77–0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88–0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00–0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge).Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.

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).