Targeting Neuroprotection Clinical Trials to Ischemic Stroke Patients With Potential to Benefit From Therapy

Abstract

Background and Purpose-Clinical trials of neuroprotective drugs have had limited success. We investigated whether selecting patients according to prognostic features would improve the statistical power of a trial to identify an efficacious treatment. Methods-Using placebo data from the Glycine Antagonist in Neuroprotection (GAIN) International and National Institute of Neurological Disorders and Stroke (NINDS) recombinant tissue plasminogen activator (rtPA) clinical trials, we developed and validated simple prognostic models for stroke trial end points: Barthel Index Ն95, modified Rankin Scale Յ1, National Institutes of Health Stroke Scale Յ1, and Glasgow Outcome Scaleϭ1. Using these models, we simulated 1000 clinical trials and estimated, under several hypothetical treatment effect patterns of neuroprotection, the effect on statistical power of including only patients with moderate prognosis. We calculated the number of patients that would have to be enrolled to maintain the statistical power achieved in selecting the whole trial population. Reanalysis of actual data from the NINDS rtPA trials confirmed the results independently. Results-Selecting patients with moderate prognosis (predicted probability of favorable outcome 0.2 to 0.8) enabled a sample size reduction, without loss of statistical power, of between 54.6% (51.3% to 57.6%) and 68.6% (66.0% to 71.1%), depending on the treatment effect pattern and outcome measure. These benefits were largely due to the exclusion of patients with poor prognosis. Conclusions-Targeting patients with potential to benefit enables a substantial sample size reduction without compromising statistical power or duration of recruitment. As part of a broader trial design strategy, informed use of prognostic data available acutely would help in identifying effective neuroprotective treatments.

Improving Patient Selection for Clinical Acute Stroke Trials

“…This strategy has already been employed in the Acute Stroke Therapy by Inhibition of Neutrophils (ASTIN) Study [8], which investigated changes from baseline NIH-SS, and in the Stroke Treatment with Ancrod Trial (STAT) [9], which assessed a return to a BI score at least equal to the individual prestroke value. Based on data from the GAIN trial [10] and the NINDS thrombolysis trials [11], a recent publication suggested sample size reductions of 55–69% depending on the assumed therapeutic effect by eliminating mainly patients with very unfavorable prognosis [1]. However, the prognostic models for these analyses are not described in detail and were developed and validated in more selected study populations.…”

“…Exclusion of those patients with little chance of showing a drug effect can thus optimize statistical power to detect a potential treatment effect. Two recent articles have suggested new strategies for both patient and endpoint selection [1, 2]. However, these studies were based on clinical study populations with preselected patient cohorts, which may already have excluded potential treatment responders.…”

Predicting Long-Term Outcome After Acute Ischemic Stroke

“…1,2 In addition to clinical reasons, such as information for patients and family as well as adapting treatment and rehabilitation options, inclusion of prognostic information in controlled clinical trials helps to define individual clinical end points, to select suitable patients, and to reduce required sample sizes. [3][4][5] To be useful and applicable to clinical practice, a prognostic model needs to be validated and easy to implement; ie, it should contain only a few variables that are readily available for all patients. 6 A systematic review that included studies until 1997 showed that the methodology of most reported prognostic models for stroke recovery was poor, and none of the models was recommended for clinical practice or research.…”

Improving Trial Power Through Use of Prognosis-Adjusted End Points

“…14,15 Elsewhere, we have also recently proposed that age and baseline NIHSS should be used together when considering eligibility for acute stroke studies. 16 We have investigated a range of prognosis-adjusted end points and found that adjusting the cut points for patients depending on prognosis offers analytical power advantages over use of a single fixed end point. Our optimal method of assigning cut points used a prognostic model that considered age and baseline NIHSS, though simply subgrouping according to baseline NIHSS was more straightforward and almost as effective.…”