Predicting outcome after traumatic brain injury

Abstract

Developing insight into which factors determine prognosis after traumatic brain injury (TBI) is useful for clinical practice, research, and policy making. Several steps can be identified in prediction research: univariate analysis, multivariable analysis, and the development of prediction models. For each step, several methodological issues should be considered, such as selection/coding of predictors and dealing with missing data. "Traditional" predictors include demographic factors (age), type of injury, clinical severity, second insults, and the presence of structural abnormalities on neuroimaging. In combination, these predictors can explain approximately 35% of the variance in outcome in populations with severe and moderate TBI. Novel and emerging predictors include genetic constitution, biomarkers, and advanced magnetic resonance (MR) imaging. To estimate prognosis for individual patients reliably, multiple predictors need to be considered jointly in prognostic models. Two prognostic models for use in TBI, developed upon large patient numbers, have been extensively validated externally: the IMPACT and CRASH prediction models. Both models showed good performance in validations across a wide range of settings. Importantly, these models were developed not only for mortality but also for functional outcome. Prognostic models can be used for providing information to relatives of individual patients, for resource allocation, and to support decisions on treatment. At the group level, prognostic models aid in the characterization of patient populations, are important to clinical trial design and analysis, and importantly, can serve as benchmarks for assessing quality of care. Continued development, refinement, and validation of prognostic models for TBI is required and this should become an ongoing process.

Keywords: Traumatic brain injury; advanced MR imaging; biomarkers; discrimination; prediction model; prognosis; validation.