Martin James

BACKGROUND: After acute ischemic stroke, it is uncertain whether the time of atrial fibrillation (AF) diagnosis (before or after stroke) or AF subtype (paroxysmal or permanent) modifies the treatment effect of early versus delayed direct oral anticoagulant (DOAC) initiation. METHODS: OPTIMAS (Optimal Timing of Anticoagulation After Acute Ischemic Stroke) was a randomized, parallel-group, open-label trial with blinded outcome assessment. Participants with acute ischemic stroke and AF were randomized 1:1 to early (within 4 days) or delayed (day 7–14) DOAC initiation. The primary outcome was a composite of recurrent ischemic stroke, symptomatic intracranial hemorrhage, and systemic arterial embolism. In this trial subgroup analysis, the prespecified subgroup of interest was time of AF diagnosis, classified as before or after the qualifying stroke. We also investigated AF subtype classified as persistent or paroxysmal. We fitted mixed effects logistic regression models with interaction terms between each subgroup and treatment allocation. We also investigated associations between AF time of diagnosis or subtype and outcomes using multivariable logistic regression. RESULTS: We included 3619 participants (mean age 78.0±9.9 years; 45.3% women). For AF diagnosed before stroke, 37 of 918 (4.0%) participants allocated to early DOAC had a primary outcome event versus 32 of 920 (3.5%) allocated to delayed DOAC, odds ratio, 1.17 (95% CI, 0.72–1.89), while for AF diagnosed after stroke the respective primary outcome rates were 22 of 895 (2.5%) and 27 of 886 (3.0%; odds ratio, 0.79 [95% CI, 0.45–1.40], P _interaction =0.312). AF subtype did not modify the treatment effect, with odds ratios (95% CIs) for early versus late DOAC for persistent and paroxysmal AF of 1.06 (0.71–1.58) and 0.66 (0.25–1.72), respectively, P _interaction =0.377. AF time of diagnosis was not associated with outcome events. Compared with paroxysmal AF, persistent AF was independently associated with an increased risk of the primary outcome (adjusted odds ratio, 2.10 [95% CI, 1.19–3.68]). CONCLUSIONS: We found no evidence that AF time of diagnosis or subtype modify the effects of early DOAC treatment. Persistent AF was independently associated with approximately double the risk of the primary outcome compared with paroxysmal AF. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03759938.

Background: There are a lot of advances that may affect the way treatment is delivered prehospital, including mobile stroke units and point-of-care diagnostics. These have the potential to affect populations differently and therefore affect the distribution of health outcomes. Objectives: We aimed to address the following research questions: (1) Which geographic and socioeconomic inequalities have been included when evaluating access to acute stroke treatment (including reperfusion therapies)? (2) How have the identified measures been considered/assessed/calculated? (3) We also report any methodological approaches that have been proposed that might further improve the way in which acute stroke care interventions are analysed, specified relating to inequalities. Methods: PubMed and Scopus electronic databases were searched for studies that included participants who underwent acute stroke treatment and included quantitative measures of geographic and/or socioeconomic inequalities or inequities in accessing/receiving treatment. Results: Overall, sixty-six studies were included in the review. Fifty-nine included at least one measure of geographic inequalities or inequities while thirty-six included at least one measure of socioeconomic inequalities or inequities. Twenty-eight of these studies included both a geographic and socioeconomic measure of inequalities or inequities. There were no commonalities in the methods of defining, categorising and measuring the inequalities or inequities. No study provided their definition of inequality or inequity or stated any normative judgements they had made. Conclusions: It is vital that the evaluation of programmes like acute stroke care consider impacts on inequality and inequity. Researchers and policy makers should work together to determine relevant measures of inequality/inequity and the most appropriate methods of measuring and categorising them. In addition, researchers should make it clear within their work how they are defining inequality and inequity and what (if any) normative judgements have been made.

Objectives: We aimed to address the following research questions: (1) Which geographic and socio-economic inequalities have been included when evaluating access to acute stroke treatment (including reperfusion therapies)? (2) How have the identified measures been considered/assessed/calculated? (3) Report any methodological approaches that have been proposed that might further improve the way in which acute stroke care interventions are analysed, specified relating to inequalities. Methods: PubMed and Scopus electronic databases were searched for studies that included participants who underwent acute stroke treatment and included quantitative measures of geographic and/or socioeconomic inequalities or inequities in accessing/receiving treatment. Results: Overall, 66 studies were included in the review. Fifty-nine included at least one measure of geographic inequalities or inequities while thirty-six included at least one measure of socioeconomic inequalities or inequities. Twenty-eight of these studies included both a geographic and socioeconomic measure of inequalities or inequities. There were no commonalities in the methods of defining, categorising and measuring the inequalities or inequities. No study provided their definition of inequality or inequity or stated any normative judgements they have made. Conclusions: It is vital that the evaluation of programmes like acute stroke care consider impacts on inequality and inequity. Researchers and policy makers should work together to determine relevant measures of inequality/inequity and the most appropriate methods of measuring and categorising them. In addition, researchers should make it clear within their work how they are defining inequality and inequity and what (if any) normative judgements have been made.

Objectives To understand between-hospital variation in thrombolysis use among patients in England and Wales who arrive at hospital within 4 hours of stroke onset. Design Machine learning was applied to the Sentinel Stroke National Audit Programme (SSNAP) data set, to learn which patients in each hospital would likely receive thrombolysis. Setting All hospitals (n=132) providing emergency stroke care in England and Wales. Thrombolysis use in patients arriving within 4 hours of known or estimated stroke onset ranged from 7% to 49% between hospitals. Participants 88,928 stroke patients recorded in the national stroke audit who arrived at hospital within 4 hours of stroke onset, from 2016 to 2018. Intervention Extreme Gradient Boosting (XGBoost) machine learning models, coupled with a SHAP model for explainability. Main Outcome Measures Shapley (SHAP) values, providing estimates of how patient features, and hospital identity, influence the odds of receiving thrombolysis. Results The XGBoost/SHAP model revealed that the odds of receiving thrombolysis reduced 9 fold over the first 120 minutes of arrival-to-scan time, varied 30 fold depending on stroke severity, reduced 3 fold with estimated rather than precise stroke onset time, fell 6 fold with increasing pre-stroke disability, fell 4 fold with onset during sleep, fell 5 fold with use of anticoagulants, fell 2 fold between 80 and 110 years of age, reduced 3 fold between 120 and 240 minutes of onset-to-arrival time, and varied 13 fold between hospitals. The hospital attended explained 56% of the variance in between-hospital thrombolysis use, adding in other hospital processes explained 74%, the patient population alone explained 36%, and the combined information from both patient population and hospital processes explained 95% of the variance in between-hospital thrombolysis use. Patient SHAP values expose how suitable a patient is considered for thrombolysis. Hospital SHAP values expose the threshold at which patients are likely to receive thrombolysis. Conclusions Using explainable machine learning, we have identified that the majority of the between-hospital variation in thrombolysis use in England and Wales, for patients arriving with time to thrombolyse, may be explained by differences in in-hospital processes and differences in attitudes to judging suitability for thrombolysis.

Background: Stroke is a common cause of adult disability. Expert opinion is that about 20% of patients should receive thrombolysis to break up a clot causing the stroke. Currently, 11–12% of patients in England and Wales receive this treatment, ranging between 2% and 24% between hospitals.Objectives: We sought to enhance the national stroke audit by providing further analysis of the key sources of inter-hospital variation to determine how a target of 20% of stroke patients receiving thrombolysis may be reached.Design: We modelled three aspects of the thrombolysis pathway, using machine learning and clinical pathway simulation. In addition, the project had a qualitative research arm, with the objective of understanding clinicians’ attitudes to use of modelling and machine learning applied to the national stroke audit.Participants and data source: Anonymised data were collected for 246,676 emergency stroke admissions to acute stroke teams in England and Wales between 2016 and 2018, obtained from the Sentinel Stroke National Audit Programme.Results: Use of thrombolysis could be predicted with 85% accuracy for those patients with a chance of receiving thrombolysis (i.e. those arriving within 4 hours of stroke onset). Machine learning models allowed prediction of likely treatment choice for each patient at all hospitals. A clinical pathway simulation predicted hospital thrombolysis use with an average absolute error of 0.5 percentage points. We found that about half of the inter-hospital variation in thrombolysis use came from differences in local patient populations, and half from in-hospital processes and decision-making. Three changes were applied to all hospitals in the model: (1) arrival to treatment in 30 minutes, (2) proportion of patients with determined stroke onset times set to at least the national upper quartile and (3) thrombolysis decisions made based on majority vote of a benchmark set of 30 hospitals. Any single change alone was predicted to increase national thrombolysis use from 11.6% to between 12.3% and 14.5% (with clinical decision-making having the most effect). Combined, these changes would be expected to increase thrombolysis to 18.3% (and to double the clinical benefit of thrombolysis, as speed increases also improve clinical benefit independently of the proportion of patients receiving thrombolysis); however, there would still be significant variation between hospitals depending on local patient population. For each hospital, the effect of each change could be predicted alone or in combination. Qualitative research with 19 clinicians showed that engagement with, and trust in, the model was greatest in physicians from units with higher thrombolysis rates. Physicians also wanted to see a machine learning model predicting outcome with probability of adverse effect of thrombolysis to counter a fear that driving thrombolysis use up may cause more harm than good.Limitations: Models may be built using data available in the Sentinel Stroke National Audit Programme only. Not all factors affecting use of thrombolysis are contained in Sentinel Stroke National Audit Programme data and the model, therefore, provides information on patterns of thrombolysis use in hospitals, but is not suitable for, or intended as, a decision aid to thrombolysis.Conclusions: Machine learning and clinical pathway simulation may be applied at scale to national audit data, allowing extended use and analysis of audit data. Stroke thrombolysis rates of at least 18% look achievable in England and Wales, but each hospital should have its own target.Future work: Future studies should extend machine learning modelling to predict the patient-level outcome and probability of adverse effects of thrombolysis, and apply co-production techniques, with clinicians and other stakeholders, to communicate model outputs.Funding: This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full inHealth and Social Care Delivery Research; Vol. 10, No. 31. See the NIHR Journals Library website for further project information.

A hydrodynamic model of active, low Reynolds number suspensions, shows the emergence of an asymptotic state with a universal spectral scaling and non-Gaussian (intermittent) fluctuations in the velocity field. Such states arise when these systems are pushed beyond a critical level of activity and show features akin to high Reynolds number, inertial turbulence. We provide compelling numerical and analytical evidence for the existence of such a transition at a critical value of activity and further show that the maximally chaotic states are tied to this transition.

Background: Expert opinion is that about 20% of emergency stroke patients should receive thrombolysis. Currently, 11% to 12% of patients in England and Wales receive thrombolysis, ranging from 2% to 24% between hospitals. The aim of this study was to assess how much variation is due to differences in local patient populations, and how much is due to differences in clinical decision-making and stroke pathway performance, while estimating a realistic target thrombolysis use. Methods: Anonymised data for 246 676 emergency stroke admissions to 132 acute hospitals in England and Wales between 2016 and 2018 was obtained from the Sentinel Stroke National Audit Programme data. We used machine learning to learn decisions on who to give thrombolysis to at each hospital. We used clinical pathway simulation to model effects of changing pathway performance. Qualitative research was used to assess clinician attitudes to these methods. Three changes were modeled: (1) arrival-to-treatment in 30 minutes, (2) proportion of patients with determined stroke onset times set to at least the national upper quartile, (3) thrombolysis decisions made based on majority vote of a benchmark set of hospitals. Results: Of the modeled changes, any single change was predicted to increase national thrombolysis use from 11.6% to between 12.3% to 14.5% (clinical decision-making having the most effect). Combined, these changes would be expected to increase thrombolysis to 18.3%, but there would still be significant variation between hospitals depending on local patient population. Clinicians engaged well with the modeling, but those from hospitals with lower thrombolysis use were most cautious about the methods. Conclusions: Machine learning and clinical pathway simulation may be applied at scale to national stroke audit data, allowing extended use and analysis of audit data. Stroke thrombolysis rates of at least 18% look achievable in England and Wales, but each hospital should have its own target.

Background: GLP-1 RA (glucagon-like peptide-1 receptor agonists), including semaglutide, may reduce stroke risk in people with type 2 diabetes. This post hoc analysis examined the subcutaneous and oral semaglutide effects, versus placebo, on stroke and its subtypes in people with type 2 diabetes at high cardiovascular risk. Methods: SUSTAIN 6 (Trial to Evaluate Cardiovascular and Other Long-Term Outcomes With Semaglutide in Subjects With Type 2 Diabetes) and PIONEER 6 (Peptide Innovation for Early Diabetes Treatment) were randomized cardiovascular outcome trials of subcutaneous and oral semaglutide in people with type 2 diabetes at high cardiovascular risk, respectively. Time to first stroke and stroke subtypes were analyzed using a Cox proportional hazards model stratified by trial with pooled treatment as a factor. The impact of prior stroke, prior myocardial infarction or stroke, age, sex, systolic blood pressure, estimated glomerular filtration rate, and prior atrial fibrillation on treatment effects was assessed using interaction P values. Risk of major adverse cardiovascular event was analyzed according to prior stroke. Results: A total of 106/6480 participants had a stroke (1.0 event/100 patient-years of observation [PYO]). Semaglutide reduced incidence of any stroke versus placebo (0.8 versus 1.1 events/100 PYO; hazard ratio, 0.68 [95% CI, 0.46–1.00]; P =0.048), driven by significant reductions in risk of small-vessel occlusion (0.3 versus 0.7 events/100 PYO; hazard ratio, 0.51 [95% CI, 0.29–0.89]; P =0.017). Hazard ratios for risk of any stroke with semaglutide versus placebo were 0.60 (95% CI, 0.37–0.99; 0.5 versus 0.9 events/100 PYO) and 0.89 (95% CI, 0.47–1.69; 2.7 versus 3.0 events/100 PYO) in those without and with prior stroke, respectively. Except for prior atrial fibrillation ( P _interaction =0.025), no significant interactions were observed between treatment effects on risk of any stroke and subgroups investigated, or between treatment effects on risk of major adverse cardiovascular event and prior stroke ( P _interaction >0.05 for all). Conclusions: Semaglutide reduced incidence of any first stroke during the trials versus placebo in people with type 2 diabetes at high cardiovascular risk, primarily driven by small-vessel occlusion prevention. Semaglutide treatment, versus placebo, lowered the risk of stroke irrespective of prior stroke at baseline. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01720446 and NCT02692716.