Endovascular treatment for acute ischemic stroke patients: implications and interpretation of IMS III, MR RESCUE, and SYNTHESIS EXPANSION trials: A report from the Working Group of International Congress of Interventional Neurology

OBJECTIVE The results of Interventional Management of Stroke (IMS) III, Magnetic Resonance and REcanalization of Stroke Clots Using Embolectomy (MR RESCUE), and SYNTHESIS EXPANSION trials are expected to affect the practice of endovascular treatment for acute ischemic stroke. The purpose of this report is to review the components of the designs and methods of these trials and to describe the influence of those components on the interpretation of trial results. METHODS A critical review of trial design and conduct of IMS III, MR RESCUE, and SYNTHESIS EXPANSION is performed with emphasis on patient selection, shortcomings in procedural aspects, and methodology of data ascertainment and analysis. The influence of each component is estimated based on published literature including multicenter clinical trials reporting on endovascular treatment for acute ischemic stroke and myocardial infarction. RESULTS We critically examined the time interval between symptom onset and treatment and rates of angiographic recanalization to differentiate between "endovascular treatment" and "parameter optimized endovascular treatment" as it relates to the IMS III, MR RESCUE, and SYNTHESIS EXPANSION trials. All the three trials failed to effectively test "parameter optimized endovascular treatment" due to the delay between symptom onset and treatment and less than optimal rates of recanalization. In all the three trials, the magnitude of benefit with endovascular treatment required to reject the null hypothesis was larger than could be expected based on previous studies. The IMS III and SYNTHESIS EXPANSION trials demonstrated that rates of symptomatic intracerebral hemorrhages subsequent to treatment are similar between IV thrombolytics and endovascular treatment in matched acute ischemic stroke patients. The trials also indirectly validated the superiority/equivalence of IV thrombolytics (compared with endovascular treatment) in patients with minor neurological deficits and those without large vessel occlusion on computed tomographic/magnetic resonance angiography. CONCLUSIONS The results do not support a large magnitude benefit of endovascular treatment in subjects randomized in all the three trials. The possibility that benefits of a smaller magnitude exist in certain patient populations cannot be excluded. Large magnitude benefits can be expected with implementation of "parameter optimized endovascular treatment" in patients with ischemic stroke who are candidates for IV thrombolytics.

Differentiating the Multipoint Expected Improvement for Optimal Batch Design

This work deals with parallel optimization of expensive objective functions which are modelled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis’ formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batchsequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.