Targeted Intervention Improves Symptoms and Impairments in Patients With Mild Traumatic Brain Injury With Chronic Symptom: A Prospective, Multiple Interventional Research Trial

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

Introduction

Mild 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.

Methods

Patients 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.

Results

Network 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).

Discussion

Several 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.

Association of obesity with mild traumatic brain injury symptoms, inflammatory profile, quality of life and functional outcomes: a TRACK-TBI Study

OBJECTIVES

Obesity is associated with chronic inflammation, which may impact recovery from mild traumatic brain injury (mTBI). The objective was to assess the role of obesity in recovery of symptoms, functional outcome and inflammatory blood biomarkers after mTBI.

METHODS

TRACK-TBI is a prospective study of patients with acute mTBI (Glasgow Coma Scale=13-15) who were enrolled ≤24 hours of injury at an emergency department of level 1 trauma centres and followed for 12 months. A total of 770 hospitalised patients who were either obese (body mass index (BMI) >30.0) or healthy mass (BMI=18.5-24.9) were enrolled. Blood concentrations of high-sensitivity C reactive protein (hsCRP), interleukin (IL) 6, IL-10, tumour necrosis factor alpha; Rivermead Post-Concussion Symptoms Questionnaire (RPQ), Quality of Life After Brain Injury and Glasgow Outcome Score-Extended reflecting injury-related functional limitations at 6 and 12 months were collected.

RESULTS

After adjusting for age and gender, obese participants had higher concentrations of hsCRP 1 day after injury (mean difference (MD)=0.65; 95% CI: 0.44 to 0.87, p<0.001), at 2 weeks (MD=0.99; 95% CI: 0.74 to 1.25, p<0.001) and at 6 months (MD=1.08; 95% CI: 0.79 to 1.37, p<0.001) compared with healthy mass participants. Obese participants had higher concentrations of IL-6 at 2 weeks (MD=0.37; 95% CI: 0.11 to 0.64, p=0.006) and 6 months (MD=0.42; 95% CI: 0.12 to 0.72, p=0.006). Obese participants had higher RPQ total score at 6 months (MD=2.79; p=0.02) and 12 months (MD=2.37; p=0.049).

CONCLUSIONS

Obesity is associated with higher symptomatology at 6 and 12 months and higher concentrations of blood inflammatory markers throughout recovery following mTBI.

Evaluating Targeted Therapeutic Response With Predictive Blood-Based Biomarkers in Patients With Chronic Mild Traumatic Brain Injury

Chronic consequences of mild traumatic brain injury (mTBI) are heterogeneous, but may be treatable with targeted medical and rehabilitation interventions. A biological signature for the likelihood of response to therapy (i.e., "predictive" biomarkers) would empower personalized medicine post-mTBI. The purpose of this study was to correlate pre-intervention blood biomarker levels and the likelihood of response to targeted interventions for patients with chronic issues attributable to mTBI. Patients with chronic symptoms and/or disorders secondary to mTBI >3 months previous (104 days to 15 years; n = 74) were enrolled. Participants completed pre-intervention assessments of symptom burden, comprehensive clinical evaluation, and blood-based biomarker measurements. Multi-domain targeted interventions for specific symptoms and impairments across a 6-month treatment period were prescribed. Participants completed a follow-up testing after the treatment period. An all-possible model's backward logistic regression was built to identify predictors of improvement in relation to blood biomarker levels before intervention. The minimum clinically important difference (MCID) of the change score (post-intervention subtracted from pre-intervention) for the Post-Concussion Symptom Scale (PCSS) to identify treatment responders from non-responders was the primary outcome. The MCID for total PCSS score was 10. The model to predict change in PCSS score over the 6-month intervention was significant (R² = 0.09; p = 0.01) and identified ubiquitin C-terminal hydrolase L1 (odds ratio [OR] = 2.53; 95% confidence interval [CI], 1.18-5.46; p = 0.02) and hyperphosphorylated tau (p-tau; OR = 0.70; 95% CI, 0.51-0.96; p = 0.03) as significant predictors of symptom improvement beyond the PCSS MCID. In this cohort of chronic TBI subjects, blood biomarkers before rehabilitation intervention predicted the likelihood of response to targeted therapy for chronic disorders post-TBI.

Association of Plasma Biomarkers with Sleep Outcomes and Treatment Response After Mild Traumatic Brain Injury

Sleep disturbances occur in up to 70% of patients with mild traumatic brain injury (mTBI). Modern mTBI management recommends targeted treatment for the patient's unique clinical manifestations (i.e., obstructive sleep apnea, insomnia). The purpose of this study was to evaluate the association of plasma biomarkers with symptom reports, overnight sleep evaluations, and response to treatment for sleep disturbances secondary to mTBI. This study is a secondary analysis of a prospective multiple interventional trial of patients with chronic issues related to mTBI. Pre- and post-intervention assessments were conducted, including overnight sleep apnea evaluation, the Pittsburgh Sleep Quality Index (PSQI), and blinded analysis of blood biomarkers. Bivariate Spearman correlations were conducted for pre-intervention plasma biomarker concentrations and 1) PSQI change scores and 2) pre-intervention sleep apnea outcomes (i.e., oxygen saturation measures). A backward logistic regression model was built to evaluate the association of pre-intervention plasma biomarkers with improvement in PSQI over the treatment period (p < 0.05). Participants were 36.3 ± 8.6 years old and 6.1 ± 3.8 years from their index mTBI. Participants reported subjective improvements (PSQI = -3.7 ± 3.8), whereas 39.3% (n = 11) had improved PSQI scores beyond the minimum clinically important difference (MCID). PSQI change scores correlated with von Willebrand factor (vWF; ρ = -0.50; p = 0.02) and tau (ρ = -0.53; p = 0.01). Hyperphosphorylated tau correlated with average saturation (ρ = -0.29; p = 0.03), lowest desaturation (ρ = -0.27; p = 0.048), and baseline saturation (ρ = -0.31; p = 0.02). The multi-variate model (R ² = 0.33; p = 0.001) retained only pre-intervention vWF as a predictor (odds ratio = 3.41; 95% confidence interval, 1.44-8.08; p = 0.005) of improving PSQI scores beyond the MCID. vWF had good discrimination (area under the curve = 0.83; p = 0.01), with an overall accuracy of 77%, sensitivity of 46.2%, and specificity of 90.0%. Validation of vWF as a potential predictive biomarker of sleep improvement post-mTBI could optimize personalized management and healthcare utilization.