Randomized controlled trials (RCT) are the basis for evidence-based acute stroke care. For an RCT to change practice, its results have to be statistically significant and clinically meaningful. While methods to assess statistical significance are standardized and widely agreed upon, there is no clear consensus on how to assess clinical significance. Researchers often refer to the minimal clinically important difference (MCID) when describing the smallest change in outcomes that is considered meaningful to patients and leads to a change in patient management. It is widely accepted that a treatment should only be adopted when its effect on outcome is equal to or larger than the MCID. There are however situations in which it is reasonable to decide against adopting a treatment, even when its beneficial effect matches or exceeds the MCID, for example when it is resource- intensive and associated with high costs. Furthermore, while the MCID represents an important concept in this regard, defining it for an individual trial is difficult as it is highly context specific. In the following, we use hypothetical stroke trial examples to review the challenges related to MCID, sample size and pragmatic considerations that researchers face in acute stroke trials, and propose a framework for designing meaningful stroke trials that have the potential to change clinical practice.

There are several controversies regarding the role of sex and gender in the pathophysiology and management of acute stroke. Assessing the role of sex, i.e., biological/pathophysiological factors, and gender, i.e., sociocultural factors, in isolation is often not possible since they are closely intertwined with each other. To complicate matters even more, the functional baseline status of women and men at the time of their first stroke is substantially different, whereby women have, on average, a poorer reported/ascertained baseline function compared to men. These differences in baseline variables account for a large part of the differences in post-stroke outcomes between women and men. Adjusting for these baseline differences is difficult, and in many cases, residual confounding cannot be excluded. Despite these obstacles, a better understanding of how patient sex and gender differences influence acute stroke and stroke care pathways is crucial to avoid biases and allow us to provide the best possible care for all acute stroke patients. Disregarding patient sex and gender on one hand and ignoring potential confounding factors in sex- and gender-stratified analyses on the other hand, may cause researchers to come to erroneous conclusions and physicians to provide suboptimal care. This review outlines sex- and gender-related factors in key aspects of acute stroke, including acute stroke epidemiology, diagnosis, access to care, treatment outcomes, and post-acute care. We also attempt to outline knowledge gaps, which deserve to be studied in further detail, and practical implications for physicians treating acute stroke patients in their daily practice.

The utility of intravenous tissue plasminogen activator (IV t-PA) in improving the clinical outcomes after acute ischemic stroke has been well demonstrated in past clinical trials. Though multiple initial small series of endovascular stroke therapy had shown good outcomes as compared to IV t-PA, a similar beneficial effect had not been translated in multiple randomized clinical trials of endovascular stroke therapy. Over the same time, there have been parallel advances in imaging technology and better understanding and utility of the imaging in therapy of acute stroke. In this review, we will discuss the evolution of endovascular stroke therapy followed by a discussion of the key factors that have to be considered during endovascular stroke therapy and directions for future endovascular stroke trials.
Stroke* ; Tissue Plasminogen Activator

Background: and Purpose Multiphase computed tomographic angiography (mCTA) provides time variant images of pial vasculature supplying brain in patients with acute ischemic stroke (AIS). To develop a machine learning (ML) technique to predict tissue perfusion and infarction from mCTA source images. Methods: 284 patients with AIS were included from the Precise and Rapid assessment of collaterals using multi-phase CTA in the triage of patients with acute ischemic stroke for Intra-artery Therapy (Prove-IT) study. All patients had non-contrast computed tomography, mCTA, and computed tomographic perfusion (CTP) at baseline and follow-up magnetic resonance imagingon-contrast-enhanced computed tomography. Of the 284 patient images, 140 patient images were randomly selected to train and validate three ML models to predict a pre-defined Tmax thresholded perfusion abnormality, core and penumbra on CTP. The remaining 144 patient images were used to test the ML models. The predicted perfusion, core and penumbra lesions from ML models were compared to CTP perfusion lesion and to follow-up infarct using Bland-Altman plots, concordance correlation coefficient (CCC), intra-class correlation coefficient (ICC), and Dice similarity coefficient. Results: Mean difference between the mCTA predicted perfusion volume and CTP perfusion volume was 4.6 mL (limit of agreement [LoA], –53 to 62.1 mL; P=0.56; CCC 0.63 [95% confidence interval [CI], 0.53 to 0.71; P<0.01], ICC 0.68 [95% CI, 0.58 to 0.78; P<0.001]). Mean difference between the mCTA predicted infarct and follow-up infarct in the 100 patients with acute reperfusion (modified thrombolysis in cerebral infarction [mTICI] 2b/2c/3) was 21.7 mL, while it was 3.4 mL in the 44 patients not achieving reperfusion (mTICI 0/1). Amongst reperfused subjects, CCC was 0.4 (95% CI, 0.15 to 0.55; P<0.01) and ICC was 0.42 (95% CI, 0.18 to 0.50; P<0.01); in non-reperfused subjects CCC was 0.52 (95% CI, 0.20 to 0.60; P<0.001) and ICC was 0.60 (95% CI, 0.37 to 0.76; P<0.001). No difference was observed between the mCTA and CTP predicted infarct volume in the test cohort (P=0.67). Conclusions A ML based mCTA model is able to predict brain tissue perfusion abnormality and follow-up infarction, comparable to CTP.

Background: and Purpose Multiphase computed tomographic angiography (mCTA) provides time variant images of pial vasculature supplying brain in patients with acute ischemic stroke (AIS). To develop a machine learning (ML) technique to predict tissue perfusion and infarction from mCTA source images. Methods: 284 patients with AIS were included from the Precise and Rapid assessment of collaterals using multi-phase CTA in the triage of patients with acute ischemic stroke for Intra-artery Therapy (Prove-IT) study. All patients had non-contrast computed tomography, mCTA, and computed tomographic perfusion (CTP) at baseline and follow-up magnetic resonance imagingon-contrast-enhanced computed tomography. Of the 284 patient images, 140 patient images were randomly selected to train and validate three ML models to predict a pre-defined Tmax thresholded perfusion abnormality, core and penumbra on CTP. The remaining 144 patient images were used to test the ML models. The predicted perfusion, core and penumbra lesions from ML models were compared to CTP perfusion lesion and to follow-up infarct using Bland-Altman plots, concordance correlation coefficient (CCC), intra-class correlation coefficient (ICC), and Dice similarity coefficient. Results: Mean difference between the mCTA predicted perfusion volume and CTP perfusion volume was 4.6 mL (limit of agreement [LoA], –53 to 62.1 mL; P=0.56; CCC 0.63 [95% confidence interval [CI], 0.53 to 0.71; P<0.01], ICC 0.68 [95% CI, 0.58 to 0.78; P<0.001]). Mean difference between the mCTA predicted infarct and follow-up infarct in the 100 patients with acute reperfusion (modified thrombolysis in cerebral infarction [mTICI] 2b/2c/3) was 21.7 mL, while it was 3.4 mL in the 44 patients not achieving reperfusion (mTICI 0/1). Amongst reperfused subjects, CCC was 0.4 (95% CI, 0.15 to 0.55; P<0.01) and ICC was 0.42 (95% CI, 0.18 to 0.50; P<0.01); in non-reperfused subjects CCC was 0.52 (95% CI, 0.20 to 0.60; P<0.001) and ICC was 0.60 (95% CI, 0.37 to 0.76; P<0.001). No difference was observed between the mCTA and CTP predicted infarct volume in the test cohort (P=0.67). Conclusions A ML based mCTA model is able to predict brain tissue perfusion abnormality and follow-up infarction, comparable to CTP.

Background: and Purpose Various imaging paradigms are used for endovascular treatment (EVT) decision-making and outcome estimation in acute ischemic stroke (AIS). We aim to compare how these imaging paradigms perform for EVT patient selection and outcome estimation. Methods: Prospective multi-center cohort study of patients with AIS symptoms with multi-phase computed tomography angiography (mCTA) and computed tomography perfusion (CTP) baseline imaging. mCTA-based EVT-eligibility was defined as presence of large vessel occlusion (LVO) and moderate-to-good collaterals on mCTA. CTP-based eligibility was defined as presence of LVO, ischemic core (defined on relative cerebral blood flow, absolute cerebral blood flow, and cerebral blood volume maps) <70 mL, mismatch-ratio >1.8, absolute mismatch >15 mL. EVT-eligibility and adjusted rates of good outcome (modified Rankin Scale 0–2) based on these imaging paradigms were compared. Results: Of 289/464 patients with LVO, 263 (91%) were EVT-eligible by mCTA-criteria versus 63 (22%), 19 (7%) and 103 (36%) by rCBF, aCBF, and CBV-CTP-criteria. CTP and mCTA-criteria were discordant in 40% to 53%. Estimated outcomes were best in patients who met both mCTA and CTP eligibility-criteria and were treated with EVT (62% to 87% good outcome). Patients eligible for EVT by mCTA-criteria and not by CTP-criteria receiving EVT achieved good outcome rates of 53% to 57%. Few patients met CTP-criteria and not mCTA-criteria for EVT. Conclusions Simpler imaging selection criteria that rely on little else than detection of the occluded blood vessel may be more sensitive and less specific, thus resulting in more patients being offered EVT and arguably benefiting from it.