We demonstrated rapid transferability of an optimized tPA protocol to a different health care setting. With the cooperation of ambulance, emergency, and stroke teams, we succeeded in the absence of a dedicated neurologic emergency department or electronic patient records, which are features of the Finnish system. The next challenge is providing the same service out-of-hours.

SummaryBackgroundStents are an alternative treatment to carotid endarterectomy for symptomatic carotid stenosis, but previous trials have not established equivalent safety and efficacy. We compared the safety of carotid artery stenting with that of carotid endarterectomy.MethodsThe International Carotid Stenting Study (ICSS) is a multicentre, international, randomised controlled trial with blinded adjudication of outcomes. Patients with recently symptomatic carotid artery stenosis were randomly assigned in a 1:1 ratio to receive carotid artery stenting or carotid endarterectomy. Randomisation was by telephone call or fax to a central computerised service and was stratified by centre with minimisation for sex, age, contralateral occlusion, and side of the randomised artery. Patients and investigators were not masked to treatment assignment. Patients were followed up by independent clinicians not directly involved in delivering the randomised treatment. The primary outcome measure of the trial is the 3-year rate of fatal or disabling stroke in any territory, which has not been analysed yet. The main outcome measure for the interim safety analysis was the 120-day rate of stroke, death, or procedural myocardial infarction. Analysis was by intention to treat (ITT). This study is registered, number ISRCTN25337470.FindingsThe trial enrolled 1713 patients (stenting group, n=855; endarterectomy group, n=858). Two patients in the stenting group and one in the endarterectomy group withdrew immediately after randomisation, and were not included in the ITT analysis. Between randomisation and 120 days, there were 34 (Kaplan-Meier estimate 4·0%) events of disabling stroke or death in the stenting group compared with 27 (3·2%) events in the endarterectomy group (hazard ratio [HR] 1·28, 95% CI 0·77–2·11). The incidence of stroke, death, or procedural myocardial infarction was 8·5% in the stenting group compared with 5·2% in the endarterectomy group (72 vs 44 events; HR 1·69, 1·16–2·45, p=0·006). Risks of any stroke (65 vs 35 events; HR 1·92, 1·27–2·89) and all-cause death (19 vs seven events; HR 2·76, 1·16–6·56) were higher in the stenting group than in the endarterectomy group. Three procedural myocardial infarctions were recorded in the stenting group, all of which were fatal, compared with four, all non-fatal, in the endarterectomy group. There was one event of cranial nerve palsy in the stenting group compared with 45 in the endarterectomy group. There were also fewer haematomas of any severity in the stenting group than in the endarterectomy group (31 vs 50 events; p=0·0197).InterpretationCompletion of long-term follow-up is needed to establish the efficacy of carotid artery stenting compared with endarterectomy. In the meantime, carotid endarterectomy should remain the treatment of choice for patients suitable for surgery.FundingMedical Research Council, the Stroke Association, Sanofi-Synthélabo, European Union.

IntroductionStroke is a major cause of death and disability. Accurately predicting stroke outcome from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity. Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute anterior circulation ischaemic stroke.MethodWe conducted a retrospective study of a prospectively collected database of acute ischaemic stroke treated by endovascular intervention. Using SPSS®, MATLAB®, and Rapidminer®, classical statistics as well as artificial neural network and support vector algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes. These algorithms were trained, validated and tested using randomly divided data.ResultsWe included 107 consecutive acute anterior circulation ischaemic stroke patients treated by endovascular technique. Sixty-six were male and the mean age of 65.3. All the available demographic, procedural and clinical factors were included into the models. The final confusion matrix of the neural network, demonstrated an overall congruency of ∼80% between the target and output classes, with favourable receiving operative characteristics. However, after optimisation, the support vector machine had a relatively better performance, with a root mean squared error of 2.064 (SD: ±0.408).DiscussionWe showed promising accuracy of outcome prediction, using supervised machine learning algorithms, with potential for incorporation of larger multicenter datasets, likely further improving prediction. Finally, we propose that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke.