Tissue Plasminogen Activator Stroke Trial Research Articles

The iScore Predicts Efficacy and Risk of Bleeding in the National Institute of Neurological Disorders and Stroke Tissue Plasminogen Activator Stroke Trial

The iScore is a validated tool to estimate outcomes after an acute ischemic stroke. A previous study showed the iScore can predict clinical response and risk of intracerebral hemorrhage (ICH) after administration of tissue plasminogen activator (tPA). We applied the iScore (www.sorcan.ca/iscore) to participants in the National Institute of Neurological Disorders and Stroke tPA stroke trials to evaluate its ability to estimate clinical response and risk of ICH after thrombolysis. Based on results from our previous study, patients were stratified a priori into iScore <200 and iScore ≥ 200. The main outcome measure was ICH. Secondary outcomes included favorable composite outcome (defined as a modified Rankin Scale score of 0 or 1, National Institutes of Health Stroke Scale score ≤ 1, Barthel Index ≥ 95, or Glasgow Outcome Scale <1 at 3 months) and functional outcomes. The iScore was calculated in all 624 patients enrolled in the trial. The cohort comprised 507 patients (81%) with an iScore <200 and 117 (19%) with an iScore ≥ 200. An iScore ≥ 200 was associated with greater risk of symptomatic ICH in the tPA group compared with the placebo group (15.4% v 3.9%; P = .04). Similar findings were found for ICH of any type (30.8% v 11.5%; P = .014), with higher ICH mortality (69.2% v 23.8%; P < .001). Despite the higher favorable composite outcome of tPA therapy in patients with an iScore <200 (58.7% v 41.9%; P < .001), this therapy had no benefit in patients with an iScore ≥ 200 (15.4% v 13.4%; P = .77). In patients receiving tPA in the National Institute of Neurological Disorders and Stroke trial, the iScore estimated the clinical response and risk of hemorrhagic complications. Further prospective studies are needed before a change in practice can be recommended.

Minimal sufficient balance-a new strategy to balance baseline covariates and preserve randomness of treatment allocation.

In many clinical trials, baseline covariates could affect the primary outcome. Commonly used strategies to balance baseline covariates include stratified constrained randomization and minimization. Stratification is limited to few categorical covariates. Minimization lacks the randomness of treatment allocation. Both apply only to categorical covariates. As a result, serious imbalances could occur in important baseline covariates not included in the randomization algorithm. Furthermore, randomness of treatment allocation could be significantly compromised because of the high proportion of deterministic assignments associated with stratified block randomization and minimization, potentially resulting in selection bias. Serious baseline covariate imbalances and selection biases often contribute to controversial interpretation of the trial results. The National Institute of Neurological Disorders and Stroke recombinant tissue plasminogen activator Stroke Trial and the Captopril Prevention Project are two examples. In this article, we propose a new randomization strategy, termed the minimal sufficient balance randomization, which will dually prevent serious imbalances in all important baseline covariates, including both categorical and continuous types, and preserve the randomness of treatment allocation. Computer simulations are conducted using the data from the National Institute of Neurological Disorders and Stroke recombinant tissue plasminogen activator Stroke Trial. Serious imbalances in four continuous and one categorical covariate are prevented with a small cost in treatment allocation randomness. A scenario of simultaneously balancing 11 baseline covariates is explored with similar promising results. The proposed minimal sufficient balance randomization algorithm can be easily implemented in computerized central randomization systems for large multicenter trials.

Bayesian Hierarchical Modeling and the Integration of Heterogeneous Information on the Effectiveness of Cardiovascular Therapies

When making therapeutic decisions for an individual patient or formulating treatment guidelines on a population level, it is often necessary to utilize information arising from different study designs, settings, or treatments. In clinical practice, heterogeneous information is frequently synthesized qualitatively, whereas in comparative effectiveness research and guideline development, it is imperative that heterogeneous data are integrated quantitatively and in a manner that accurately captures the true uncertainty in the results. Bayesian hierarchical modeling is a technique that utilizes all available information from multiple sources and naturally yields a revised estimate of the treatment effect associated with each source. A hierarchical model consists of multiple levels (ie, a hierarchy) of probability distributions that represent relationships between information arising within single populations or trials, as well as relationships between information arising from different populations or trials. We describe the structure of Bayesian hierarchical models and discuss their advantages over simpler models when multiple information sources are relevant. Two examples are presented that illustrate this technique: a meta-analysis of immunosuppressive therapy in idiopathic dilated cardiomyopathy and a subgroup analysis of the National Institute of Neurological Disorders and Stroke Intravenous Tissue Plasminogen Activator Stroke Trial.

Time of Day, Outcome, and Response to Thrombolytic Therapy: The National Institute of Neurological Disorders and Stroke Recombinant Tissue Plasminogen Activator Stroke Trial Experience

Circadian pattern for the onset of acute ischemic stroke has been described; however, data assessing an association between thrombolytic therapy efficacy and circadian rhythm are limited. We assessed the relationship between the time of stroke onset and neurologic outcomes after thrombolytic therapy for acute ischemic stroke in the National Institute of Neurological Disorders and Stroke (NINDS) Recombinant Tissue Plasminogen Activator (rt-PA) Stroke Trial. We conducted exploratory, post hoc analysis of 624 patients in the NINDS rt-PA Stroke Trial. Variables assessed included presenting time of day (4- and 6-hour time blocks), outcome variables, stroke subtypes, treatment assignment, and biological markers. Outcome variables included 3-month mortality, clinical outcome at 3 months, intracranial hemorrhage (ICH), computed tomography lesion volume at 3 months, and deterioration at 24 hours. The distribution of patients in the time blocks was balanced between the rt-PA and placebo groups. There was not a clear circadian variation in the stroke onset time. There were no associations detected between stroke onset time and clinical outcome, computed tomography lesion volume, and asymptomatic hemorrhage. Patients treated with rt-PA whose stroke onset was between 0401 and 0800 hours had less symptomatic ICH, whereas those who received rt-PA between 0000 and 0400 hours had a 43% incidence of symptomatic ICH. Patients in the placebo group who had stroke onset between 1801 and 2400 hours had lower chances for neurologic deterioration. Patients who had a stroke between 0001 and 0400 hours had the highest fibrinogen concentrations. We did not find a circadian pattern to time of day of stroke onset in the patients included in the NINDS rt-PA Stroke Trial. The effect of rt-PA treatment on favorable outcome was independent of time of day of stroke onset. Patients who received rt-PA between 4 and 8 am were less likely to develop symptomatic ICH.