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Background: Benefit from blood glucose (BG) control during acute ischemic stroke may depend on glycemic parameters. We evaluated for associations between the SHINE (Stroke Hyperglycemia Insulin Network Effort) randomized treatment group and the SHINE predefined 90-day functional outcome, within-patient subgroups defined by various glycemic parameters. Methods: The SHINE Trial randomized 1151 patients within 12 hours with acute ischemic stroke and hyperglycemia to standard (target BG 80–179 mg/dL) or intensive (target BG 80–130 mg/dL) BG control for 72 hours. We predefined 6 glycemic parameters: acute BG level, absence versus presence of diagnosed and undiagnosed diabetes, hemoglobin A1c, glycemic gap (acute BG–average daily hemoglobin A1c based BG), stress hyperglycemia ratio (acute BG/average daily hemoglobin A1c based BG), and BG variability (SD). Favorable functional outcome was defined by the SHINE Trial and based on the modified Rankin Scale score at 90 days, adjusted for stroke severity. We computed relative risks adjusted for baseline stroke severity and thrombolysis use. Results: Likelihood for favorable outcome was lowest among patients with undiagnosed diabetes compared to patients with true nondiabetes (adjusted relative risk, 0.42 [99% CI, 0.19–0.94]). We did not find any relationship between the favorable outcome rate and baseline BG or any of the glycemic parameters. No differences between SHINE treatment groups were identified among any of these patient subgroups. Conclusions: In this exploratory subgroup analysis, intensive versus standard insulin treatment of hyperglycemia in acute ischemic stroke patient subgroups, did not influence the 90-day functional outcomes, nor did we identify associations between these glycemic parameters and 90-day functional outcomes.

Background: Pharmacological and clinical evidence suggest that sulfonylurea (SU) medications may improve acute ischemic stroke (AIS) outcomes. The SHINE trial studied intensive vs standard blood sugar management in patients presenting with AIS and hyperglycemia. We investigated whether pre-existing SU use impacted functional outcomes in SHINE. Methods: SHINE data collection forms were reviewed to identify AIS patients taking SUs at the time of study enrollment. Our primary outcome compared the adjusted 90-day utility weighted modified Rankin Scale (UW-mRS) score in the SU and non-SU cohorts. The UW-mRS score was adjusted for age, baseline NIHSS, baseline glucose, and reperfusion therapy. Pre-specified analyses of the 90-day UW-mRS scores assessed for heterogeneity of SU effect across the following subgroups: age, stroke severity, reperfusion therapy, type of SU, and SHINE study arm. Results: In total, 1066 SHINE subjects with a final diagnosis of AIS were included (SU=248 [23.3%], non-SU=818 [76.7%]). Baseline demographics were generally similar (Table 1). Calculated 90-day mean UW-mRS score were higher in the non-SU group compared to the SU group: mean 0.617 ± 0.012 versus mean 0.566 ± 0.022 (Diff 0.051, 95% CI 0.001-0.1) without heterogeneity of treatment effect (Figure 1). Conclusion: Contrary to our hypothesis and published literature, our retrospective analysis of the SHINE trial demonstrated better functional outcomes in the non-SU cohort. This may be due to selection bias with inadequate controlling for baseline risk factors present in the SU cohort. Definitive prospective studies are required.

Bayesian adaptive trial design has the potential to create more efficient clinical trials. However, a barrier to the uptake of bayesian adaptive designs for confirmatory trials is limited experience with how they may perform compared with a frequentist design.To compare the performance of a bayesian and a frequentist adaptive clinical trial design.This prospective cohort study compared 2 trial designs for a completed multicenter acute stroke trial conducted within a National Institutes of Health neurologic emergencies clinical trials network, with individual patient-level data, including the timing and order of enrollments and outcome ascertainment, from 1151 patients with acute stroke and hyperglycemia randomized to receive intensive or standard insulin therapy. The implemented frequentist design had group sequential boundaries for efficacy and futility interim analyses at 90 days after randomization for 500, 700, 900, and 1100 patients. The bayesian alternative used predictive probability of trial success to govern early termination for efficacy and futility with a first interim analysis at 500 randomized patients and subsequent interims after every 100 randomizations.The main outcome was the sample size at end of study, which was defined as the sample size at which each of the studies stopped accrual of patients.Data were collected from 1151 patients. As conducted, the frequentist design passed the futility boundary after 936 participants were randomized. Using the same sequence and timing of randomization and outcome data, the bayesian alternative crossed the futility boundary approximately 3 months earlier after 800 participants were randomized.Both trial designs stopped for futility before reaching the planned maximum sample size. In both cases, the clinical community and patients would benefit from learning the answer to the trial's primary question earlier. The common feature across the 2 designs was frequent interim analyses to stop early for efficacy or for futility. Differences between how these analyses were implemented between the 2 trials resulted in the differences in early stopping.