Christopher F. Bladin

Background: The STANDFIRM (Shared Team Approach Between Nurses and Doctors for Improved Risk Factor Management; ANZCTR registration ACTRN12608000166370) trial was designed to test the effectiveness of chronic disease care management for modifying the Framingham risk score (FRS) among patients with stroke or transient ischemic attack. The primary outcome of change in FRS was not met. We determine baseline characteristics that predict reduction in FRS at 12 months and whether future FRS is predetermined at baseline. Methods and Results: We used machine learning regression methods to evaluate 35 variables encompassing demographics, risk factors, psychological, social and education status, and laboratory tests. We determine the optimal machine learning and associated tuning parameters from the following: random forest, extreme gradient boosting, category boosting, support vector regression, multilayer perceptron neural network, and K‐nearest neighbor. Training (n=404) and test (n=103) data were evenly matched for age, sex, baseline, and 12‐month FRS. The optimal model for predicting FRS at 12 months was category boosting ( R ² =0.712; root mean square error, 7.32). The 5 variables with highest Shapley values for category boosting were baseline FRS (Shapley additive explanation [SHAP], 8.42 of total of 12.12), age (SHAP, 1.58), systolic blood pressure (SHAP, 0.23), male sex (SHAP, 1.05), and London Handicap (SHAP, 0.18). Machine learning methods were poor at determining change in FRS at 12 months ( R ² <0.22). Conclusions: Our findings suggest that change in FRS as an end point in secondary stroke trials may have limited value as it is largely determined at baseline. In this cohort, category boosting was the optimal method to predict future FRS but not change in FRS.

BACKGROUND: Hyperglycemia in acute ischemic stroke reduces the efficacy of stroke thrombolysis and thrombectomy, with worse clinical outcomes. Insulin-based therapies are difficult to implement and may cause hypoglycemia. We investigated whether exenatide, a GLP-1 (glucagon-like peptide-1) receptor agonist, would improve stroke outcomes, and control poststroke hyperglycemia with minimal hypoglycemia. METHODS: The TEXAIS trial (Treatment With Exenatide in Acute Ischemic Stroke) was an international, multicenter, phase 2 prospective randomized clinical trial (PROBE [Prospective Randomized Open Blinded End-Point] design) enrolling adult patients with acute ischemic stroke ≤9 hours of stroke onset to receive exenatide (5 µg BID subcutaneous injection) or standard care for 5 days, or until hospital discharge (whichever sooner). The primary outcome (intention to treat) was the proportion of patients with ≥8-point improvement in National Institutes of Health Stroke Scale score (or National Institutes of Health Stroke Scale scores 0–1) at 7 days poststroke. Safety outcomes included death, episodes of hyperglycemia, hypoglycemia, and adverse event. RESULTS: From April 2016 to June 2021, 350 patients were randomized (exenatide, n=177, standard care, n=173). Median age, 71 years (interquartile range, 62–79), median National Institutes of Health Stroke Scale score, 4 (interquartile range, 2–8). Planned recruitment (n=528) was stopped early due to COVID-19 disruptions and funding constraints. The primary outcome was achieved in 97 of 171 (56.7%) in the standard care group versus 104 of 170 (61.2%) in the exenatide group (adjusted odds ratio, 1.22 [95% CI, 0.79–1.88]; P=0.38). No differences in secondary outcomes were observed. The per-patient mean daily frequency of hyperglycemia was significantly less in the exenatide group across all quartiles. No episodes of hypoglycemia were recorded over the treatment period. Adverse events of mild nausea and vomiting occurred in 6 (3.5%) exenatide patients versus 0 (0%) standard care with no withdrawal. CONCLUSIONS: Treatment with exenatide did not reduce neurological impairment at 7 days in patients with acute ischemic stroke. Exenatide did significantly reduce the frequency of hyperglycemic events, without hypoglycemia, and was safe to use. Larger acute stroke trials using GLP-1 agonists such as exenatide should be considered. REGISTRATION: URL: www.australianclinicaltrials.gov.au; Unique identifier: ACTRN12617000409370. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03287076.

Background: The Shared Team Approach between Nurses and Doctors For Improved Risk factor Management (STANDFIRM, ACTRN12608000166370) trial was designed to test the effectiveness of chronic disease care management for modifying the Framingham risk score (FRS) among patients with stroke or transient ischemic attack. The primary outcome of change in FRS between baseline and 12 months was not met. We aimed to determine characteristics of participants at baseline that predict reduction in FRS at 12 months and whether future FRS is predetermined at the time of randomization Method: Data included 35 variables encompassing demographics, risk factors, psychological, social and education status, and laboratory tests. Five supervised machine learning (ML) methods were used: random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), multilayer perceptron artificial neural network (MLP) and K-nearest neighbor (KNN). We split data for training (80%, n=406) and testing (20%, n=102). Results: Training and test data were evenly matched for age, sex, baseline and 12-month FRS. Following tuning of the five ML methods, the optimal model for predicting FRS at 12 months was SVR (R ² =0.763, root mean squared error or RMSE=8.52). The five most important variables for SVR were: baseline FRS, age, male sex, sodium/potassium excretion and proteinuria. All ML methods were poor at determining change in FRS at 12 months (R ² <0.161). Conclusion: Our findings suggest that change in FRS as an endpoint in trials may have limited value as it is largely determined at baseline. In this cohort, Support Vector Regression was the optimal method to predict future but not change in FRS.

Background: Endovascular thrombectomy (EVT) access in remote areas is limited. Preliminary data suggest that long distance transfers for EVT may be beneficial; however, the magnitude and best imaging strategy at the referring center remains uncertain. We hypothesized that patients transferred >300 miles would benefit from EVT, achieving rates of functional independence (modified Rankin Scale [mRS] score of 0–2) at 3 months similar to those patients treated at the comprehensive stroke center in the randomized EVT extended window trials and that the selection of patients with computed tomography perfusion (CTP) at the referring site would be associated with ordinal shift toward better outcomes on the mRS. Methods: This is a retrospective analysis of patients transferred from 31 referring hospitals >300 miles (measured by the most direct road distance) to 9 comprehensive stroke centers in Australia and New Zealand for EVT consideration (April 2016 through May 2021). Results: There were 131 patients; the median age was 64 [53–74] years and the median baseline National Institutes of Health Stroke Scale score was 16 [12–22]. At baseline, 79 patients (60.3%) had noncontrast CT+CT angiography, 52 (39.7%) also had CTP. At the comprehensive stroke center, 114 (87%) patients underwent cerebral angiography, and 96 (73.3%) proceeded to EVT. At 3 months, 62 patients (48.4%) had an mRS score of 0 to 2 and 81 (63.3%) mRS score of 0 to 3. CTP selection at the referring site was not associated with better ordinal scores on the mRS at 3 months (mRS median of 2 [1–3] versus 3 [1–6] in the patients selected with noncontrast CT+CT angiography, P =0.1). Nevertheless, patients selected with CTP were less likely to have an mRS score of 5 to 6 (odds ratio 0.03 [0.01–0.19]; P 300 miles, there was a benefit for EVT, with outcomes similar to those treated in the comprehensive stroke center in the EVT extended window trials. Remote hospital CTP selection was not associated with ordinal mRS improvement, but was associated with fewer very poor 3-month outcomes.

Background: We sought to determine recurrent stroke predictors among patients with embolic strokes of undetermined source (ESUS). Methods and Results: We applied Cox proportional hazards models to identify clinical features associated with recurrent stroke among participants enrolled in RE‐SPECT ESUS (Randomized, Double‐Blind, Evaluation in Secondary Stroke Prevention Comparing the Efficacy and Safety of the Oral Thrombin Inhibitor Dabigatran Etexilate Versus Acetylsalicylic Acid in Patients With Embolic Stroke of Undetermined Source) trial, an international clinical trial evaluating dabigatran versus aspirin for patients with ESUS. During a median follow‐up of 19 months, 384 of 5390 participants had recurrent stroke (annual rate, 4.5%). Multivariable models revealed that stroke or transient ischemic attack before the index event (hazard ratio [HR], 2.27 [95% CI, 1.83–2.82]), creatinine clearance 2 DS ₂ ‐VASc ≥4 (HR, 1.55 [95% CI, 1.15–2.08] and HR, 1.66 [95% CI, 1.21–2.26] for scores of 4 and ≥5, respectively) versus CHA ₂ DS ₂ ‐VASc of 2 to 3, were independent predictors for recurrent stroke. Conclusions: In RE‐SPECT ESUS trial, expected risk factors previously linked to other common stroke causes were associated with stroke recurrence. These data help define high‐risk groups for subsequent stroke that may be useful for clinicians and for researchers designing trials among patients with ESUS. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02239120.

Stroke is the second most common cause of death and remains a persistent health challenge globally. Due to its highly time-sensitive nature, earlier stroke treatments should be enforced for improved patient outcome. The mobile stroke unit (MSU) was conceptualized and implemented to deliver the diagnosis and treatment to a stroke patient in the ultra-early time window (<1 h) in the pre-hospital setting and has shown to be clinically effective. However, due to geographical challenges, most rural communities are still unable to receive timely stroke intervention, as access to specialized stroke facilities for optimal stroke treatment poses a challenge. Therefore, the aircraft counterpart (Air-MSU) of the conventional road MSU offers a plausible solution to this shortcoming by expanding the catchment area for regional locations in Australia. The implementation of Air-MSU is currently hindered by several technical limitations, where current commercially available CT scanners are still oversized and too heavy to be integrated into a conventional helicopter emergency medical service (HEMS). In collaboration with the Australian Stroke Alliance and Melbourne Brain Centre, this article aims to explore the possibilities and methodologies in reducing the weight and, effectively, the size of an existing CT scanner, such that it can be retrofitted into the proposed search and rescue helicopter—Agusta Westland AW189. The result will be Australia’s first-ever customized CT scanner structure designed to fit in a search-and-rescue helicopter used for Air-MSU.

Purpose: Technology‐based systems like telemedicine are frequently being implemented into healthcare settings, impacting clinician practices. Little is known about factors influencing acute telemedicine uptake, if factors differ across time, or between nurses and non‐nurses.Design: A mixed‐methods, pre‐post design with implementation of a new acute stroke telemedicine service.Methods: A survey based on an extended Technology Acceptance Model (TAM) was administered to clinicians involved in acute stroke care at 16 regional hospitals (2014–2017). Open‐ended questions postimplementation (at 6 months) included strengths of the program and areas to improve. Subsequently, a secondary analysis of nurses’ semistructured interviews at the first telemedicine site (2010–2011) was completed to provide greater explanatory detail.Findings: Surveys were completed by nurses (preimplementation n = 77, postimplementation n = 92) and non‐nurses (pre n = 90, post n = 44). Preimplementation, perceived usefulness was the only significant predictor of intending to use telemedicine for nurses, while perceived ease of use and social influence were significant for non‐nurses. Postimplementation, perceived usefulness was significant for both groups, as was facilitating conditions for nurses. Specific examples aligned to TAM categories from our detailed interviews (n = 11 nurses) included perceived usefulness (improved clinical support and patient care), perceived ease of use (technical, clinical aspects), facilitating conditions (setting, education, confidence), and social influence (working relationships).Conclusions: Important factors for acute stroke telemedicine varied between nurses and non‐nurses, and changed after implementation. The benefits of telemedicine should be emphasized to nurses. Preimplementation, more non‐nurses wanted systems to be easy. Support in clinical, technical, and relationship aspects of telemedicine consultations is required.Clinical Relevance: Nurses are influential in implementing acute telemedicine, which is complex, with clinical and technical aspects entwined. Evidence‐based implementation strategies must be tailored over time, and between nurses and non‐nurses, to ensure initial uptake and ongoing use.

Hospital costs for stroke are increasing and variability in care quality creates inefficiencies. In 2007, the Victorian Government (Australia) employed clinical facilitators for three years in eight public hospitals to improve stroke care. Literature on the cost implications of such roles is rare. We report changes in the costs of acute stroke care following implementation of this program. Observational controlled before-and-after cohort design. Standardised hospital costing data were compared pre-program (financial year 2006–07) and post-program (2010–11) for all admitted episodes of stroke or transient ischaemic attack (TIA) using ICD-10 discharge codes. Costs in Australian dollars (AUD) were adjusted to a common year 2010. Generalised linear regression models were used for adjusted comparisons. A 20% increase in stroke and TIA episodes was observed: 2624 pre-program (age > 75 years: 53%) and 3142 post-program (age > 75 years: 51%); largely explained by more TIA admissions (up from 785 to 1072). Average length of stay reduced by 22% (pre-program 7.3 days to post-program 5.7 days, p < 0.001). Six hospitals provided cost data. Average per-episode costs decreased by 10% (pre-program AUD7888 to post-program AUD7115). After adjusting for age, sex, stroke type, and hospital, average per-episode costs decreased by 6.1% from pre to post program (p = 0.025). When length of stay was additionally adjusted for, these costs increased by 10.8%, indicating a greater mean cost per day (p < 0.001). Cost containment of acute inpatient episodes was observed after the implementation of stroke clinical facilitators, likely associated with the shorter lengths of stay.