Improving Patient Classification and Outcome Measurement in TBI

Aim 1. Develop and validate the optimal clinical phenotypic model of TBI. This work will expand upon our ongoing R03 study that is developing phenotypic models for mild TBI using symptom data to develop a phenotypic model of all-severity TBI using a large range of measures from the TBI Common Data Elements (CDE). H1.1. A model that classifies patients into distinct groups will fit better than one that lumps TBI patients into a single diagnostic group. H1.2. Patients with distinct patterns of acute clinical presentation will vary in their levels of acute TBI biomarkers.

Aim 2. Test the hypothesis that global outcome can be measured with more precision using IRT-informed approaches as compared to the GOSE. H2.1. The GOSE can be supplemented with other measures in the existing CDE/TRACK-TBI dataset to more precisely measure global outcome across the spectrum of TBI-related disability. H2.2. The Functional Status Examination (FSE) yields more precise measure of global outcome across the spectrum of TBI-related disability than the GOSE. Read more here.


Nancy R. Temkin, PhD (Site PI)

Award Info

Sponsor: NIH/NINDS