Table_1_Network analysis and relationship of symptom factors to functional outcomes and quality of life following mild traumatic brain injury: a TRACK-TBI study.docx

IntroductionMild traumatic brain injury (mTBI) is a heterogenous injury which can be difficult to characterize and manage. Using cross-sectional network analysis (NA) to conceptualize mTBI symptoms offers an innovative solution to identify how mTBI symptoms relate to each other. The centrality hypothesis of network theory posits that certain symptoms in a network are more relevant (central) or have above average influence over the rest of the network. However, no studies have used NA to characterize the interrelationships between symptoms in a cohort of patients who presented with mTBI to a U.S. Level 1 trauma center emergency department and how subacute central symptoms relate to long-term outcomes.MethodsPatients with mTBI (Glasgow Coma Scale = 13–15) evaluated across 18 U.S. Level 1 trauma centers from 2013 to 2019 completed the Rivermead Post-Concussion Symptoms Questionnaire (RPQ) at 2 weeks (W2) post-injury (n = 1,593) and at 3 months (M3), 6 months (M6), and 12 months (M12) post-injury. Network maps were developed from RPQ subscale scores at each timepoint. RPQ scores at W2 were associated with M6 and M12 functional and quality of life outcomes.ResultsNetwork structure did not differ across timepoints, indicating no difference in symptoms/factors influence on the overall symptom network across time. The cognitive factor had the highest expected influence at W2 (1.761), M3 (1.245), and M6 (1.349). Fatigue had the highest expected influence at M12 (1.275). The emotional factor was the only other node with expected influence >1 at any timepoint, indicating disproportionate influence of emotional symptoms on overall symptom burden (M3 = 1.011; M6 = 1.076).DiscussionSeveral symptom factors at 2-weeks post-injury were more strongly associated with incomplete recovery and/or poorer injury-related quality of life at 6 and 12 months post-injury than previously validated demographic and clinical covariates. The network analysis suggests that emotional, cognitive, and fatigue symptoms may be useful treatment targets in this population due to high centrality and activating potential of the overall symptom network.</p

Sub-classifying patients with mild traumatic brain injury: A clustering approach based on baseline clinical characteristics and 90-day and 180-day outcomes - Fig 3

<p>Distributions of clinical variables in clusters A-E and in entire TRACK-TBI Pilot population. Red represents proportions of males, unemployment, unmarried, injury due to falls, abnormal CT, abnormal diastolic blood pressure at ED arrival, abnormal systolic blood pressure at ED arrival, use of fluid at ED, use of alcohol, use of tobacco, history of neurologic diseases, and history of psychiatric diseases. SesEmpl = employment; CT.Scan = CT result; A.BldPressrDiast = diastolic blood pressure at ED arrival; A.BldPressrSyst = systolic blood pressure at ED arrival; ED.Fluids = IV fluid; alcohol = alcohol use; tobacco = tobacco use; HistNeurologic = prior neurological disease; HistPsychiatric = prior psychiatric disease; TTP = TRACK-TBI Pilot.</p

Based on the gap statistic it was determined that 12 clinical variables provided optimal clustering of patients with TBI.

<p>Based on the gap statistic it was determined that 12 clinical variables provided optimal clustering of patients with TBI.</p

Percentage of patients with an abnormal brain CT in each cluster.

<p>Percentage of patients with an abnormal brain CT in each cluster.</p

Outcome variables and rules for binarization into “good” vs “bad” recovery.

<p>Outcome variables and rules for binarization into “good” vs “bad” recovery.</p

Sub-classifying patients with mild traumatic brain injury: A clustering approach based on baseline clinical characteristics and 90-day and 180-day outcomes - Fig 2

<p>Dendrogram built by sparse hierarchical clustering showing the five clusters (A-E) of patients with TBI used in subsequent analyses. The percentages refer to the proportion of TRACK-TBI Pilot patients assigned to each cluster.</p

Proportion of patients with poor recovery in the entire TRACK-TBI Pilot population.

<p>Proportion of patients with poor recovery in the entire TRACK-TBI Pilot population.</p

Clinical variables included in analyses.

<p>Clinical variables included in analyses.</p

Distributions of “good” (green) and “bad” (red) outcomes in the entire TRACK-TBI Pilot population and in each cluster.

<p>(No patient in cluster B had FIM Motor and Cognition measurements, so they are left blank). GOSE 90 = GOSE at 90 days; GOSE 180 = GOSE at 180 days; BSI = BSI at 180 days; FIM Motor = FIM–Motor at 180 days; RPQ Cognition = RPQ–Cognition at 180 days; RPQ Emotion = RPQ–Emotion at 180 days; RPQ Somatic = RPQ–Somatic at 180 days; WAIS = WASI at 180 day; TMT at 180 days.</p

Clinical characteristics of TDA-identified patient subgroup with PARP1 SNP (N = 37) alongside to all patients with PARP1 SNP (N = 298).

<p>Clinical characteristics of TDA-identified patient subgroup with PARP1 SNP (N = 37) alongside to all patients with PARP1 SNP (N = 298).</p