Group-Based Trajectory Analysis Applications for Prognostic Biomarker Model Development in Severe TBI: A Practical Example

Abstract

Over the last decade, biomarker research has identified potential biomarkers for the diagnosis, prognosis, and management of traumatic brain injury (TBI). Several cerebrospinal fluid (CSF) and serum biomarkers have shown promise in predicting long-term outcome after severe TBI. Despite this increased focus on identifying biomarkers for outcome prognostication after a severe TBI, several challenges still exist in effectively modeling the significant heterogeneity observed in TBI-related pathology, as well as the biomarker-outcome relationships. Biomarker data collected over time are usually summarized into single-point estimates (e.g., average or peak biomarker levels), which are, in turn, used to examine the relationships between biomarker levels and outcomes. Further, many biomarker studies to date have focused on the prediction power of biomarkers without controlling for potential clinical and demographic confounders that have been previously shown to affect long-term outcome. In this article, we demonstrate the application of a practical approach to delineate and describe distinct subpopulations having similar longitudinal biomarker profiles and to model the relationships between these biomarker profiles and outcomes while taking into account potential confounding factors. As an example, we demonstrate a group-based modeling technique to identify temporal S100 calcium-binding protein B (S100b) profiles, measured from CSF over the first week post-injury, in a sample of adult subjects with TBI, and we use multivariate logistic regression to show that the prediction power of S100b biomarker profiles can be superior to the prediction power of single-point estimates.

Cerebrospinal Fluid Cortisol and Progesterone Profiles and Outcomes Prognostication after Severe Traumatic Brain Injury

“…Mean hormone values for the entire time course were explored in multivariate analysis (data not shown). Similar to other CSF biomarkers, however, 44 TRAJ based multivariate models were superior in predicting outcome, and thus are reported in the results.…”

“…33,43,44 TRAJ is a specialized application of finite mixture modeling that assesses patterns of change over time, 45 and models were estimated using the PROC TRAJ Macro 46 for SAS software. The TRAJ procedure uses a probability function to discern a set of trajectories that closely resemble one another.…”

Acute CSF interleukin-6 trajectories after TBI: Associations with neuroinflammation, polytrauma, and outcome

“…TRAJ groups were generated using the SAS Macro, PROC TRAJ (Jones et al, 2001). TRAJ identifies distinct subpopulations over time for a marker using methods previously described (Wagner et al, 2011a,b;Niyonkuru et al, 2013). This technique offers advantages over traditional mean or peak biomarker analyses by assessing temporal patterns of change (Nagin and Odgers, 2010).…”

“…CSF IL-6 concentrations in clinical TBI populations are highly variable, ranging from 50-124,000 pg/mL in one study (Hillman et al, 2007), suggesting that robust statistical methods may be required to accurately characterize the inflammatory response. Previously, our group has applied group based trajectory modeling (TRAJ) analysis in order to identify temporal biomarker profiles for distinct TBI populations with similar physiological responses post-injury (Wagner et al, 2011a,b;Goyal et al, 2013;Niyonkuru et al, 2013;Santarsieri et al, 2014). Applying TRAJ to inflammatory biomarker studies may capture and characterize longitudinal inflammatory responses after TBI and identify patient and clinical characteristics and prognoses associated with these profiles consistent with a Rehabilomics approach (Wagner, 2010;Wagner and Zitelli, 2013;Wagner, 2014).…”

S100b as a Prognostic Biomarker in Outcome Prediction for Patients with Severe Traumatic Brain Injury

“…CSF and serum S100b: Bivariate associations with TRAJ group membership and outcome In our companion article, three statistically distinct TRAJ profiles were derived using samples from the CSF S100b cohort, 46 and daily mean S100b concentrations over time were plotted for each TRAJ group (Fig. 3A).…”