Predicting long-term outcome after acute ischemic stroke: a simple index works in patients from controlled clinical trials

Background and Purpose—An early and reliable prognosis for recovery in stroke patients is important for initiation of individual treatment and for informing patients and relatives. We recently developed and validated models for predicting survival and functional independence within 3 months after acute stroke, based on age and the National Institutes of Health Stroke Scale score assessed within 6 hours after stroke. Herein we demonstrate the applicability of our models in an independent sample of patients from controlled clinical trials. Methods—The prognostic models were used to predict survival and functional recovery in 5419 patients from the Virtual International Stroke Trials Archive (VISTA). Furthermore, we tried to improve the accuracy by adapting intercepts and estimating new model parameters. Results—The original models were able to correctly classify 70.4% (survival) and 72.9% (functional recovery) of patients. Because the prediction was slightly pessimistic for patients in the controlled trials, adapting the intercept improved the accuracy to 74.8% (survival) and 74.0% (functional recovery). Novel estimation of parameters, however, yielded no relevant further improvement. Conclusions—For acute ischemic stroke patients included in controlled trials, our easy-to-apply prognostic models based on age and National Institutes of Health Stroke Scale score correctly predicted survival and functional recovery after 3 months. Furthermore, a simple adaptation helps to adjust for a different prognosis and is recommended if a large data set is available. (Stroke. 2008;39:000-000.)

Toward blueprints for network architecture, biophysical dynamics, and signal transduction

We review mathematical aspects of biophysical dynamics, signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations, as well as coincidence detection in pairs of dendrites and the dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools that will ultimately underpin systems neuroscience.

The Virtual International Stroke Trials Archive

Background and Purpose - Stroke has global importance and it causes an increasing amount of human suffering and economic burden, but its management is far from optimal. The unsuccessful outcome of several research programs highlights the need for reliable data on which to plan future clinical trials. The Virtual International Stroke Trials Archive aims to aid the planning of clinical trials by collating and providing access to a rich resource of patient data to perform exploratory analyses. Methods - Data were contributed by the principal investigators of numerous trials from the past 16 years. These data have been centrally collated and are available for anonymized analysis and hypothesis testing. Results - ”Currently, the Virtual International Stroke Trials Archive contains 21 trials. There are data on 15 000 patients with both ischemic and hemorrhagic stroke. Ages range between 18 and 103 years, with a mean age of 6912 years. Outcome measures include the Barthel Index, Scandinavian Stroke Scale, National Institutes of Health Stroke Scale, Orgogozo Scale, and modified Rankin Scale. Medical history and onset-to-treatment time are readily available, and computed tomography lesion data are available for selected trials. Conclusions - This resource has the potential to influence clinical trial design and implementation through data analyses that inform planning. (Stroke. 2007;38:1905-1910.)

Systematic reviews as a tool for planning and interpreting trials

Background Systematic reviews followed by ameta-analysis are carried out in medical research to combine the results of two or more related studies. Stroke trials have struggled to show beneficial effects and meta-analysis should be used more widely throughout the research process to either speed up the development of useful interventions, or halt more quickly research with hazardous or ineffective interventions. Summary of review. This review summarises the clinical research process and illustrates how and when systematic reviews may be used throughout the development programme. Meta-analyses should be performed after observational studies, preclinical studies in experimental stroke, and after phase I, II, and III clinical trials and phase IV clinical surveillance studies. Although meta-analyses most commonly work with summary data, they may be performed to assess relationships between variables (meta-regression) and, ideally, should utilise individual patient data. Meta-analysis techniques may alsoworkwith ordered categorical outcome data (ordinal meta-analysis) and be used to perform indirect comparisons where original trial data do not exist. Conclusion Systematic review/meta-analyses are powerful tools in medical research and should be used throughout the development of all stroke and other interventions