Abstract
The guiding principle for data stewardship dictates that data be FAIR: findable, accessible, interoperable, and reusable. Data reuse allows researchers to probe data that may have been originally collected for other scientific purposes in order to gain novel insights. The current study reuses the Transforming Research and Clinical Knowledge for Traumatic Brain Injury (TRACK-TBI) Pilot dataset to build upon prior findings and ask new scientific questions. Specifically, we have previously used a multivariate analytics approach to multianalyte serum protein data from the TRACK-TBI Pilot dataset to show that an inflammatory ensemble of biomarkers can predict functional outcome at 3 and 6 months post-TBI. We and others have shown that there are quantitative and qualitative changes in inflammation that come with age, but little is known about how this interaction affects recovery from TBI. Here we replicate the prior proteomics findings with improved missing value analyses and non-linear principal component analysis and then expand upon this work to determine whether age moderates the effect of inflammation on recovery. We show that increased age correlates with worse functional recovery on the Glasgow Outcome Scale-Extended (GOS-E) as well as increased inflammatory signature. We then explore the interaction between age and inflammation on recovery, which suggests that inflammation has a more detrimental effect on recovery for older TBI patients.
Highlights
As data science technologies rapidly advance, we are able to collect, store, and leverage more clinical traumatic brain injury (TBI) research data than ever before, opening new opportunities for precision medicine in neurotrauma
We found that patients who exhibited complete recovery (GOS-E = 8) at 3 and 6 months after TBI were significantly younger than those with incomplete recovery
This result was seen despite adjustments for common predictors and compared the TBI patients to non-TBI trauma controls, rather than the general population, to account for the possibility of reverse causality [15]
Summary
As data science technologies rapidly advance, we are able to collect, store, and leverage more clinical traumatic brain injury (TBI) research data than ever before, opening new opportunities for precision medicine in neurotrauma. The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study is an example of early adoption of large-scale FAIR data sharing with the goal of advancing knowledge discovery in clinical TBI [7,8,9]. The initial TRACK-TBI Pilot project generated a dataset of 586 subjects from three level 1 trauma centers that were deeply phenotyped across 900+ variables spanning clinical variables, biofluid biomarkers, imaging biomarkers, and neuropsychiatric and cognitive outcomes. The multidimensional curation across variables began to reach sufficient maturity such that later versions of the dataset were primed for advanced multidimensional analytics, including application of machine learning and other artificial intelligence tools to predict outcomes at the level of individual subjects while taking into account ever larger numbers of features [10,11,12]. The TRACK-TBI Pilot gave rise to an 18-center TRACK-TBI study that has generated 52 papers (and counting)
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