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.

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.