Advancing care for traumatic brain injury: findings from the IMPACT studies and perspectives on future research

Abstract

Research in traumatic brain injury (TBI) is challenging for several reasons; in particular, the heterogeneity between patients regarding causes, pathophysiology, treatment, and outcome. Advances in basic science have failed to translate into successful clinical treatments, and the evidence underpinning guideline recommendations is weak. Because clinical research has been hampered by non-standardised data collection, restricted multidisciplinary collaboration, and the lack of sensitivity of classification and efficacy analyses, multidisciplinary collaborations are now being fostered. Approaches to deal with heterogeneity have been developed by the IMPACT study group. These approaches can increase statistical power in clinical trials by up to 50% and are also relevant to other heterogeneous neurological diseases, such as stroke and subarachnoid haemorrhage. Rather than trying to limit heterogeneity, we might also be able to exploit it by analysing differences in treatment and outcome between countries and centres in comparative effectiveness research. This approach has great potential to advance care in patients with TBI.

Figure 1. Relationship between systolic blood pressure and outcome (n=8172)

X axis: systolic blood pressure at admission Y axis: linear predictor of unfavorable outcome (GOS 1–3). A higher linear predictor corresponds to a higher probability of unfavorable outcome.

Figure 2

Graphical illustration of three approaches to the analysis of the Glasgow Outcome Scale as the primary outcome measure of randomized controlled trials for traumatic brain injury. The traditional approach (A) to the efficacy analysis in a clinical traumatic brain injury trial is to dichotomize the Glasgow Outcome Scale (GOS) into unfavorable (Dead, Vegetative State, Severe Disability) versus favorable outcome (Moderate Disability, Good Recovery). The proportions of patients with an unfavorable outcome in the treatment and placebo groups are compared, by calculating an odds ratio with logistic regression analysis. With the sliding dichotomy approach (B), the study population is first subdivided in (for example three) equally large prognostic risk groups. For each of the risk groups, the point of the dichotomy of the GOS is based on the baseline prognostic risk (e.g., for patients in the good prognostic risk group, only Good Recovery is considered a favorable outcome). A pooled odds ratio is calculated, which can be interpreted as the summary measure for having a better outcome than expected. With the proportional odds approach (C) the population is not subdivided. The proportional odds model considers every possible way the GOS can be dichotomized, assuming that the odds ratio for a better versus a worse outcome is similar wherever the GOS is dichotomized (the proportional odds assumption). The common odds ratio can be interpreted as a summary measure for the shift in outcome across the full GOS. Abbreviations: D= Dead, VS= Vegetative State, SD= Severe Disability, MD= Moderate Disability, GR= Good Recovery, OR= odds ratio.