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The relationship between systemic inflammation and secondary injury in traumatic brain injury (TBI) is complex. We investigated associations between inflammatory markers and clinical confirmation of TBI diagnosis and prognosis. The prospective TRACK-TBI Pilot (Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot) study enrolled TBI patients triaged to head computed tomography (CT) and received blood draw within 24 h of injury. Healthy controls (HCs) and orthopedic controls (OCs) were included. Thirty-one inflammatory markers were analyzed from plasma. Area under the receiver operating characteristic curve (AUC) was used to evaluate discriminatory ability. AUC >0.7 was considered acceptable. Criteria included: TBI diagnosis (vs. OC/HC); moderate/severe vs. mild TBI (Glasgow Coma Scale; GCS); radiographic TBI (CT positive vs. CT negative); 3- and 6-month Glasgow Outcome Scale-Extended (GOSE) dichotomized to death/greater relative disability versus less relative disability (GOSE 1-4/5-8); and incomplete versus full recovery (GOSE

IntroductionNeuroworsening may be a sign of progressive brain injury and is a factor for treatment of traumatic brain injury (TBI) in intensive care settings. The implications of neuroworsening for clinical management and long-term sequelae of TBI in the emergency department (ED) require characterization.MethodsAdult TBI subjects from the prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot Study with ED admission and disposition Glasgow Coma Scale (GCS) scores were extracted. All patients received head computed tomography (CT) scan

Many patients with mild traumatic brain injury (mTBI) are at risk for mental health problems such as posttraumatic stress disorder (PTSD). The objective of this study was to determine whether the polygenic risk for PTSD (or for related mental health disorders or traits including major depressive disorder [MDD] and neuroticism [NEU]) was associated with an increased likelihood of PTSD in the aftermath of mTBI. We used data from individuals of European ancestry with mTBI enrolled in TRACK-TBI (n = 714), a prospective longitudinal study of level 1 trauma center patients. One hundred and sixteen mTBI patients (16.3%) had probable PTSD (PCL-5 score ≥33) at 6 months post-injury. We used summary statistics from recent GWAS studies of PTSD, MDD, and NEU to generate polygenic risk scores (PRS) for individuals in our sample. A multivariable model that included age, sex, pre-injury history of mental disorder, and cause of injury explained 7% of the variance in the PTSD outcome; the addition of the PTSD-PRS (and five ancestral principal components) significantly increased the variance explained to 11%. The adjusted odds of PTSD in the uppermost PTSD-PRS quintile was nearly four times higher (aOR = 3.71, 95% CI 1.80-7.65) than in the lowest PTSD-PRS quintile. There was no evidence of a statistically significant interaction between PTSD-PRS and prior history of mental disorder, indicating that PTSD-PRS had similar predictive utility among those with and without pre-injury psychiatric illness. When added to the model, neither MDD-PRS nor NEU-PRS were significantly associated with the PTSD outcome. These findings show that the risk for PTSD in the context of mTBI is, in part, genetically influenced. They also raise the possibility that an individual's PRS could be clinically actionable if used-possibly with other non-genetic predictors-to signal the need for enhanced follow-up and early intervention; this precision medicine approach needs to be prospectively studied.

ObjectivesSpine surgery is associated with early impairment of gastrointestinal motility, with postoperative ileus rates of 5-12%. A standardized postoperative medication regimen aimed at early restoration of bowel function can reduce morbidity and cost, and its study should be prioritized.MethodsA standardized postoperative bowel medication protocol was implemented for all elective spine surgeries performed by a single neurosurgeon from March 1, 2022 to June 30, 2022 at a metropolitan Veterans Affairs medical center. Daily bowel function was tracked and medications were advanced using the protocol. Clinical, surgical, and length of stay data are reported.ResultsAcross 20 consecutive surgeries in 19 patients, mean age was 68.9 years [standard deviation (SD) = 10; range 40-84]. Seventy-four percent reported preoperative constipation. Surgeries consisted of 45% fusion and 55% decompression; lumbar retroperitoneal approaches constituted 30% (10% anterior, 20% lateral). Two patients were discharged in good condition prior to bowel movement after meeting institutional discharge criteria; the other 18 cases all had return of bowel function by postoperative day (POD) 3 (mean = 1.8-days, SD = 0.7). There were no inpatient or 30-day complications. Mean discharge occurred 3.3-days post-surgery (SD = 1.5; range 1-6; home 95%, skilled nursing facility 5%). Estimated cumulative cost of the bowel regimen was $17 on POD 3.ConclusionsCareful monitoring of return of bowel function after elective spine surgery is important for preventing ileus, reducing healthcare cost, and ensuring quality. Our standardized postoperative bowel regimen was associated with return of bowel function within 3 days and low costs. These findings can be utilized in quality-of-care pathways.

Older adults have the highest incidence of traumatic brain injury globally. Accurate blood-based biomarkers are needed to assist with diagnosis of patients across the spectrum of age and time post-injury. Several reports have suggested lower accuracy for blood-based biomarkers in older adults, and there is a paucity of data beyond day-1 post-injury. Our aims were to investigate age-related differences in diagnostic accuracy and 2-week evolution of four leading candidate blood-based traumatic brain injury biomarkers-plasma glial fibrillary acidic protein, ubiquitin carboxy-terminal hydrolase L1, S100 calcium binding protein B and neuron-specific enolase-among participants in the 18-site prospective cohort study Transforming Research And Clinical Knowledge in Traumatic Brain Injury. Day-1 biomarker data were available for 2602 participants including 2151 patients with traumatic brain injury, 242 orthopedic trauma controls and 209 healthy controls. Participants were stratified into 3 age categories (young: 17-39 years, middle-aged: 40-64 years, older: 65-90 years). We investigated age-stratified biomarker levels and biomarker discriminative abilities across three diagnostic groups: head CT-positive/negative; traumatic brain injury/orthopedic controls; and traumatic brain injury/healthy controls. The difference in day-1 glial fibrillary acidic protein, ubiquitin carboxy-terminal hydrolase L1 and neuron-specific enolase levels across most diagnostic groups was significantly smaller for older versus younger adults, resulting in a narrower range within which a traumatic brain injury diagnosis may be discriminated in older adults. Despite this, day-1 glial fibrillary acidic protein had good to excellent performance across all age-categories for discriminating all three diagnostic groups (area under the curve 0.84-0.96; lower limit of 95% confidence intervals all >0.78). Day-1 S100 calcium-binding protein B and ubiquitin carboxy-terminal hydrolase L1 showed good discrimination of CT-positive versus negative only among adults under age 40 years within 6 hours of injury. Longitudinal blood-based biomarker data were available for 522 hospitalized patients with traumatic brain injury and 24 hospitalized orthopaedic controls. Glial fibrillary acidic protein levels maintained good to excellent discrimination across diagnostic groups until day 3 post-injury irrespective of age, until day 5 post-injury among middle-aged or younger patients and until week 2 post-injury among young patients only. In conclusion, the blood-based glial fibrillary acidic protein assay tested here has good to excellent performance across all age-categories for discriminating key traumatic brain injury diagnostic groups to at least 3 days post-injury in this trauma centre cohort. The addition of a blood-based diagnostic to the evaluation of traumatic brain injury, including geriatric traumatic brain injury, has potential to streamline diagnosis.

Several proteins have proven useful as blood-based biomarkers to assist in evaluation and management of traumatic brain injury (TBI). The objective of this study was to determine whether two day-of-injury blood-based biomarkers are predictive of posttraumatic stress disorder (PTSD). We used data from 1143 individuals with mild TBI (mTBI; defined as admission Glasgow Coma Scale [GCS] score 13-15) enrolled in TRACK-TBI, a prospective longitudinal study of level 1 trauma center patients. Plasma glial fibrillary acidic protein (GFAP) and serum high sensitivity C-reactive protein (hsCRP) were measured from blood collected within 24 h of injury. Two hundred and twenty-seven (19.9% of) patients had probable PTSD (PCL-5 score ≥ 33) at 6 months post-injury. GFAP levels were positively associated (Spearman's rho = 0.35, p

Diffusion tensor imaging (DTI) literature on single-center studies contains conflicting results regarding acute effects of mild traumatic brain injury (mTBI) on white matter (WM) microstructure and the prognostic significance. This larger-scale multi-center DTI study aimed to determine how acute mTBI affects WM microstructure over time and how early WM changes affect long-term outcome. From Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI), a cohort study at 11 United States level 1 trauma centers, a total of 391 patients with acute mTBI ages 17 to 60 years were included and studied at two weeks and six months post-injury. Demographically matched friends or family of the participants were the control group (n = 148). Axial diffusivity (AD), fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) were the measures of WM microstructure. The primary outcome was the Glasgow Outcome Scale Extended (GOSE) score of injury-related functional limitations across broad life domains at six months post-injury. The AD, MD, and RD were higher and FA was lower in mTBI versus friend control (FC) at both two weeks and six months post-injury throughout most major WM tracts of the cerebral hemispheres. In the mTBI group, AD and, to a lesser extent, MD decreased in WM from two weeks to six months post-injury. At two weeks post-injury, global WM AD and MD were both independently associated with six-month incomplete recovery (GOSE

BackgroundTraumatic subdural hematomas (SDH) can have devastating neurologic consequences. Acute-on-chronic SDHs are more frequent in the elderly, who have increased comorbidities and perioperative risks. The subdural evacuation port system (SEPS) procedure consists of a twist drill hole connected to a single drain on suction, which can be performed at bedside to evacuate SDHs without requiring general anesthesia. However, a single SEPS can be limited due to inability to evacuate across septations between SDHs of different ages.PurposeWe present to our knowledge the first case of using tandem SEPS to evacuate a multi-loculated SDH. We discuss the technical nuances of the procedure as a treatment option for complex SDHs.FindingsAn 86-year-old man with cognitive impairment and recurrent falls presented acutely after ground-level fall with worsening dysarthria and right hemiparesis. Computed tomography scan showed a 11 mm left holohemispheric mixed-density SDH with loculated acute and subacute/chronic components with 2 mm midline shift. Following two interval stability scans, the patient underwent drainage of a superficial chronic component, and a posterolateral acute/subacute component using two sequential SEPS drains at bedside in the intensive care unit. The patient's symptoms markedly improved, drains were removed, and the patient was discharged home with home health on post-procedure day 6.ConclusionsJudicious patient selection and pre-procedural planning can enable the use of tandem SEPS to evacuate multi-loculated SDHs under moderate sedation. Using multiple subdural ports to evacuate complex SDHs should be an option for proceduralists in settings where general anesthesia is not feasible.

BackgroundThe Centers for Medicare and Medicaid Services (CMS) hierarchical condition category (HCC) coding is a risk adjustment model that allows for the estimation of risk-and cost-associated with health care provision. Current models may not include key factors that fully delineate the risk associated with spine surgery.ObjectiveTo augment CMS HCC risk adjustment methodology with socioeconomic data to improve its predictive capabilities for spine surgery.MethodsThe National Inpatient Sample was queried for spinal fusion, and the data was merged with county-level coverage and socioeconomic status variables obtained from the Brookings Institute. We predicted outcomes (death, nonroutine discharge, length of stay [LOS], total charges, and perioperative complication) with pairs of hierarchical, mixed effects logistic regression models-one using CMS HCC score alone and another augmenting CMS HCC scores with demographic and socioeconomic status variables. Models were compared using receiver operating characteristic curves. Variable importance was assessed in conjunction with Wald testing for model optimization.ResultsWe analyzed 653 815 patients. Expanded models outperformed models using CMS HCC score alone for mortality, nonroutine discharge, LOS, total charges, and complications. For expanded models, variable importance analyses demonstrated that CMS HCC score was of chief importance for models of mortality, LOS, total charges, and complications. For the model of nonroutine discharge, age was the most important variable. For the model of total charges, unemployment rate was nearly as important as CMS HCC score.ConclusionThe addition of key demographic and socioeconomic characteristics substantially improves the CMS HCC risk-adjustment models when modeling spinal fusion outcomes. This finding may have important implications for payers, hospitals, and policymakers.

The Glasgow Outcome Scale-Extended (GOSE) is a functional outcome measure intended to place individuals with traumatic brain injury (TBI) into one of eight broad levels of injury-related disability. This simplicity is not always optimal, particularly when more granular assessment of individuals' injury recovery is desired. The GOSE, however, is customarily assessed using a multi-question interview that contains richer information than is reflected in the GOSE score. Using data from the multi-center Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study (N = 1544), we used item response theory (IRT) to evaluate whether rescoring the GOSE using IRT, which posits that a continuous latent variable (disability) underlies responses, can yield a more precise index of injury-related functional limitations. We fit IRT models to GOSE interview responses collected at three months post-injury. Each participant's level of functional limitation was estimated from the model (GOSE-IRT) and comparisons were made between IRT-based and standard (GOSE-Ordinal) scores. The IRT scoring resulted in 141 possible scores (vs. 7 GOSE-Ordinal scores in this sample of individuals with GOSE scores ranging between 2 and 8). Moreover, GOSE-IRT scores were significantly more strongly associated with measures of TBI-related symptoms, psychological symptoms, and quality of life. Our findings demonstrate that rescoring the GOSE interview using IRT yields more granular, meaningful measurement of injury-related functional limitations, while adding no additional respondent or examiner burden. This technique may have utility for many applications, such as clinical trials aiming to detect small treatment effects, and small-scale studies that need to maximize statistical efficiency.

BackgroundTraumatic brain injury (TBI) is a major global health problem. Little research has addressed extracranial organ dysfunction following TBI, particularly myocardial injury. Using a sensitive marker of myocardial injury-high sensitivity troponin (hsTn)-we examined the incidence of early myocardial injury following TBI and explored its association with neurological outcomes following moderate-severe TBI.MethodsWe conducted a pilot cohort study of 133 adult (age above 17 y) subjects enrolled in the TRACK-TBI 18-center prospective cohort study. Descriptive statistics were used to examine the incidence of myocardial injury (defined as hsTn >99th percentile for a standardized reference population) across TBI severities, and to explore the association of myocardial injury with a 6-month extended Glasgow Outcome Score among patients with moderate-severe TBI.ResultsThe mean (SD) age of the participants was 44 (17) years, and 87 (65%) were male. Twenty-six patients (20%) developed myocardial injury following TBI; myocardial injury was present in 15% of mild TBI patients and 29% of moderate-severe TBI patients (P=0.13). Median (interquartile range) hsTn values were 3.8 ng/L (2.1, 9.0), 5.8 ng/L (4.5, 34.6), and 10.2 ng/L (3.0, 34.0) in mild, moderate, and severe TBI participants, respectively (P=0.04). Overall, 11% of participants with moderate-severe TBI and myocardial injury experienced a good outcome (6-mo extended Glasgow Outcome Score≥5) at 6 months, compared with 65% in the group that did not experience myocardial injury (P=0.01).ConclusionsMyocardial injury is common following TBI, with a likely dose-response relationship with TBI severity. Early myocardial injury was associated with poor 6-month clinical outcomes following moderate-severe TBI.

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients' recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

Background and objectivesThe objectives of this study were to develop and establish concurrent validity of a clinically relevant definition of poor cognitive outcome 1 year after mild traumatic brain injury (mTBI), to compare baseline characteristics across cognitive outcome groups, and to determine whether poor 1-year cognitive outcome can be predicted by routinely available baseline clinical variables.MethodsProspective cohort study included 656 participants ≥17 years of age presenting to level 1 trauma centers within 24 hours of mTBI (Glasgow Coma Scale score 13-15) and 156 demographically similar healthy controls enrolled in the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study. Poor 1-year cognitive outcome was defined as cognitive impairment (below the ninth percentile of normative data on ≥2 cognitive tests), cognitive decline (change score [1-year score minus best 2-week or 6-month score] exceeding the 90% reliable change index on ≥2 cognitive tests), or both. Associations of poor 1-year cognitive outcome with 1-year neurobehavioral outcomes were performed to establish concurrent validity. Baseline characteristics were compared across cognitive outcome groups, and backward elimination logistic regression was used to build a prediction model.ResultsMean age of participants with mTBI was 40.2 years; 36.6% were female; 76.6% were White. Poor 1-year cognitive outcome was associated with worse 1-year functional outcome, more neurobehavioral symptoms, greater psychological distress, and lower satisfaction with life (all p < 0.05), establishing concurrent validity. At 1 year, 13.5% of participants with mTBI had a poor cognitive outcome vs 4.5% of controls (p = 0.003). In univariable analyses, poor 1-year cognitive outcome was associated with non-White race, lower education, lower income, lack of health insurance, hyperglycemia, preinjury depression, and greater injury severity (all p < 0.05). The final multivariable prediction model included education, health insurance, preinjury depression, hyperglycemia, and Rotterdam CT score ≥3 and achieved an area under the curve of 0.69 (95% CI 0.62-0.75) for the prediction of a poor 1-year cognitive outcome, with each variable associated with >2-fold increased odds of poor 1-year cognitive outcome.DiscussionPoor 1-year cognitive outcome is common, affecting 13.5% of patients with mTBI vs 4.5% of controls. These results highlight the need for better understanding of mechanisms underlying poor cognitive outcome after mTBI to inform interventions to optimize cognitive recovery.

This study was designed to determine how raw scores correspond between two alternative measures of functional recovery from traumatic brain injury (TBI), the Functional Status Examination (FSE) and the Glasgow Outcome Scale-Extended (GOSE). Using data from 357 persons with moderate-severe TBI who participated in a large clinical trial, we performed item response theory analysis to characterize the relationship between functional ability measured by the FSE and GOSE at 6 months post-injury. Results revealed that raw scores for the FSE and GOSE can be linked, and a table is provided to translate scores from one instrument to the other. For example, a FSE score of 7 (on its 0-21 scale, where higher scores reflect more impairment) is equivalent to a GOSE score of 6 (where GOSE is scaled on an 8-point scale, with higher scores reflecting less impairment). These results allow clinicians or researchers who have a score for a person on one instrument to cross-reference it to a score on the other instrument. Importantly, this enables researchers to combine data sets where some persons only completed the GOSE and some only the FSE. In addition, an investigator could save participant time by eliminating one instrument from a battery of tests, yet still retain a score on that instrument for each participant. More broadly, the findings help anchor scores from these two instruments to the broader continuum of injury-related functional limitations.

The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population.

Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.

Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences. This is especially challenging for clinical studies on rare disorders where the smaller patient cohorts and corresponding sample size is an obstacle for reproducible modeling results. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics to assess model reproducibility. The framework enables clinicians to interpret AutoML-generated models for clinical and biological verifiability and consequently integrate domain expertise during model development. We applied the framework towards spinal cord injury prognostication to optimize the intraoperative hemodynamic range during injury-related surgery and additionally identified a strong detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured how evolving clinical practices such as faster time-to-surgery and blood pressure management affect clinical model development. Altogether, we illustrate how expert-augmented AutoML improves inferential reproducibility for biomedical discovery and can ultimately build trust in AI processes towards effective clinical integration.

Aim: There is a critical need to validate biofluid-based biomarkers as diagnostic and drug development tools for traumatic brain injury (TBI). As part of the TBI Endpoints Development Initiative, we identified four potentially predictive and pharmacodynamic biomarkers for TBI: astroglial markers GFAP and S100B and the neuronal markers UCH-L1 and Tau. Materials & methods: Several commonly used platforms for these four biomarkers were identified and compared on analytic performance and ability to detect gold standard recombinant protein antigens and to pool control versus TBI cerebrospinal fluid (CSF). Results: For each marker, only some assay formats could differentiate TBI CSF from the control CSF. Also, different assays for the same biomarker reported divergent biomarker values for the same biosamples. Conclusion: Due to the variability of TBI marker assay in performance and reported values, standardization strategies are recommended when comparing reported biomarker levels across assay platforms.

It is important to measure quality of life (QoL) after traumatic brain injury (TBI), yet limited studies have compared QoL inventories. In 2579 TBI patients, orthopedic trauma controls, and healthy friend control participants, we compared the Quality of Life After Brain Injury-Overall Scale (QOLIBRI-OS), developed for TBI patients, to the Satisfaction with Life Scale (SWLS), an index of generic life satisfaction. We tested the hypothesis that group differences (TBI and orthopedic trauma vs. healthy friend controls) would be larger for the QOLIBRI-OS than the SWLS and that the QOLIBRI-OS would manifest more substantial changes over time in the injured groups, demonstrating more relevance of the QOLIBRI-OS to traumatic injury recovery. (1) We compared the group differences (TBI vs. orthopedic trauma control vs. friend control) in QoL as indexed by the SWLS versus the QOLIBRI-OS and (2) characterized changes across time in these two inventories across 1 year in these three groups. Our secondary objective was to characterize the relationship between TBI severity and QoL. As compared with healthy friend controls, the QOLIBRI reflected greater reductions in QoL than the SWLS for both the TBI group (all time points) and the orthopedic trauma control group (2 weeks and 3 months). The QOLIBRI-OS better captured expected improvements in QoL during the injury recovery course in injured groups than the SWLS, which demonstrated smaller changes over time. TBI severity was not consistently or robustly associated with self-reported QoL. The findings imply that, as compared with the SWLS, the QOLIBRI-OS appears to identify QoL issues more specifically relevant to traumatically injured patients and may be a more appropriate primary QoL outcome measure for research focused on the sequelae of traumatic injuries.

In nearly all clinical and research contexts, the initial severity of a traumatic brain injury (TBI) is measured using the Glasgow Coma Scale (GCS) total score. The GCS total score however, may not accurately reflect level of consciousness, a critical indicator of injury severity. We investigated the relationship between GCS total scores and level of consciousness in a consecutive sample of 2455 adult subjects assessed with the GCS 69,487 times as part of the multi-center Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study. We assigned each GCS subscale score combination a level of consciousness rating based on published criteria for the following disorders of consciousness (DoC) diagnoses: coma, vegetative state/unresponsive wakefulness syndrome, minimally conscious state, and post-traumatic confusional state, and present our findings using summary statistics and four illustrative cases. Participants had the following characteristics: mean (standard deviation) age 41.9 (17.6) years, 69% male, initial GCS 3-8 = 13%; 9-12 = 5%; 13-15 = 82%. All GCS total scores between 4-14 were associated with more than one DoC diagnosis; the greatest variability was observed for scores of 7-11. Further, a wide range of total scores was associated with identical DoC diagnoses. Importantly, a diagnosis of coma was only possible with GCS total scores of 3-6. The GCS total score does not accurately reflect level of consciousness based on published DoC diagnostic criteria. To improve the classification of patients with TBI and to inform the design of future clinical trials, clinicians and investigators should consider individual subscale behaviors and more comprehensive assessments when evaluating TBI severity.

The National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH-NINDS) Traumatic Brain Injury (TBI) Imaging Common Data Elements (CDEs) are standardized definitions for pathological intracranial lesions based on their appearance on neuroimaging studies. The NIH-NINDS TBI Imaging CDEs were designed to be as consistent as possible with the U.S. Food and Drug Administration (FDA) definition of biomarkers as "an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention." However, the FDA qualification process for biomarkers requires proof of reliable biomarker test measurements. We determined the interrater reliability of TBI Imaging CDEs on subacute brain magnetic resonance imaging (MRI) performed on 517 mild TBI patients presenting to 11 U.S. level 1 trauma centers. Three U.S. board-certified neuroradiologists independently evaluated brain MRI performed 2 weeks post-injury for the following CDEs: traumatic axonal injury (TAI), diffuse axonal injury (DAI), and brain contusion. We found very high interrater agreement for brain contusion, with prevalence- and bias-adjusted kappa (PABAK) values for pairs of readers from 0.92 [95% confidence interval, 0.88-0.95] to 0.94 [0.90-0.96]. We found intermediate agreement for TAI and DAI, with PABAK values of 0.74-0.78 [0.70-0.82]. The near-perfect agreement for subacute brain contusion is likely attributable to the high conspicuity and distinctive appearance of these lesions on T1-weighted images. Interrater agreement for TAI and DAI was lower, because signal void in small vascular structures, and artifactual foci of signal void, can be difficult to distinguish from the punctate round or linear areas of slight hemorrhage that are a common hallmark of TAI/DAI on MRI.

BackgroundTraumatic brainstem injury has yet to be incorporated into widely used imaging classification systems for traumatic brain injury (TBI), and questions remain regarding prognostic implications for this TBI subgroup. To address this, retrospective data on patients from the multicenter prospective Transforming Research and Clinical Knowledge in TBI study were studied.MethodsPatients with brainstem and cerebrum injury (BSI+) were matched by age, sex, and admission Glasgow Coma Scale (GCS) score to patients with cerebrum injuries only. All patients had an interpretable head computed tomography (CT) scan from the first 48 hours after injury and a 6-month Glasgow Outcome Scale Extended (GOSE) score. CT scans were reviewed for brainstem lesions and, when present, characterized by location, size, and type (traumatic axonal injury, contusion, or Duret hemorrhage). Clinical, demographic, and outcome data were then compared between the two groups.ResultsMann-Whitney U-tests showed no significant difference in 6-month GOSE scores in patients with BSI+ (mean 2.7) compared with patients with similar but only cerebrum injuries (mean 3.9), although there is a trend (p = 0.10). However, subclassification by brainstem lesion type, traumatic axonal injury (mean 4.0) versus Duret hemorrhage or contusion (mean 1.4), did identify a proportion of BSI+ with significantly less favorable outcome (p = 0.002). The incorporation of brainstem lesion type (traumatic axonal injury vs. contusion/Duret), along with GCS into a multivariate logistic regression model of favorable outcome (GOSE score 4-8) did show a significant contribution to the prognostication of this brainstem injury subgroup (odds ratio 0.08, 95% confidence interval 0.00-0.67, p = 0.01).ConclusionsThese findings suggest two groups of patients with brainstem injuries may exist with divergent recovery potential after TBI. These data support the notion that newer CT imaging classification systems may augment traditional clinical measures, such as GCS in identifying those patients with TBI and brainstem injuries that stand a higher chance of favorable outcome.

The Glasgow Outcome Scale-Extended (GOSE) has become one of the most widely used outcome instruments to assess global disability and recovery after traumatic brain injury. Achieving consistency in the application of the assessment remains a challenge, particularly in multi-center studies involving many assessors. We present a manual for the GOSE interview that is designed to support both single- and multi-center studies and promote inter-rater agreement. Many patients fall clearly into a particular category; however, patients may have outcomes that are on the borderline between adjacent categories, and cases can present other challenges for assessment. The Manual includes the general principles of assessment, advice on administering each section of the GOSE interview, and guidance on "borderline" and "difficult" cases. Finally, we discuss the properties of the GOSE, including strengths and limitations, and outline recommendations for assessor training, accreditation, and monitoring.

Missing data is a persistent and unavoidable problem in even the most carefully designed traumatic brain injury (TBI) clinical research. Missing data patterns may result from participant dropout, non-compliance, technical issues, or even death. This review describes the types of missing data that are common in TBI research, and assesses the strengths and weaknesses of the statistical approaches used to draw conclusions and make clinical decisions from these data. We review recent innovations in missing values analysis (MVA), a relatively new branch of statistics, as applied to clinical TBI data. Our discussion focuses on studies from the International Traumatic Brain Injury Research (InTBIR) initiative project: Transforming Research and Clinical Knowledge in TBI (TRACK-TBI), Collaborative Research on Acute TBI in Intensive Care Medicine in Europe (CREACTIVE), and Approaches and Decisions in Acute Pediatric TBI Trial (ADAPT). In addition, using data from the TRACK-TBI pilot study (n = 586) and the completed clinical trial assessing valproate (VPA) for the treatment of post-traumatic epilepsy (n = 379) we present real-world examples of typical missing data patterns and the application of statistical techniques to mitigate the impact of missing data in order to draw sound conclusions from ongoing clinical studies.

Recent biomarker innovations hold potential for transforming diagnosis, prognostic modeling, and precision therapeutic targeting of traumatic brain injury (TBI). However, many biomarkers, including brain imaging, genomics, and proteomics, involve vast quantities of high-throughput and high-content data. Management, curation, analysis, and evidence synthesis of these data are not trivial tasks. In this review, we discuss data management concepts and statistical and data sharing strategies when dealing with biomarker data in the context of TBI research. We propose that application of biomarkers involves three distinct steps-discovery, evaluation, and evidence synthesis. First, complex/big data has to be reduced to useful data elements at the stage of biomarker discovery. Second, inferential statistical approaches must be applied to these biomarker data elements for assessment of biomarker clinical utility and validity. Last, synthesis of relevant research is required to support practice guidelines and enable health decisions informed by the highest quality, up-to-date evidence available. We focus our discussion around recent experiences from the International Traumatic Brain Injury Research (InTBIR) initiative, with a specific focus on four major clinical projects (Transforming Research and Clinical Knowledge in TBI, Collaborative European NeuroTrauma Effectiveness Research in TBI, Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe, and Approaches and Decisions in Acute Pediatric TBI Trial), which are currently enrolling subjects in North America and Europe. We discuss common data elements, data collection efforts, data-sharing opportunities, and challenges, as well as examine the statistical techniques required to realize successful adoption and use of biomarkers in the clinic as a foundation for precision medicine in TBI.

The Glasgow Outcome Scale (GOS) in its original or extended (GOSE) form is the most widely used assessment of global disability in traumatic brain injury (TBI) research. Several publications have reported concerns about assessor scoring inconsistencies, but without documentation of contributing factors. We reviewed 6801 GOSE assessments collected longitudinally, across 18 sites in the 5-year, observational Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study. We recorded error rates (i.e., corrections to a section or an overall rating) based on site assessor documentation and categorized scoring issues, which then informed further training. In cohort 1 (n = 1261; February 2014 to May 2016), 24% of GOSEs had errors identified by central review. In cohort 2 (n = 1130; June 2016 to July 2018), acquired after curation of cohort 1 data, feedback, and further training of site assessors, the error rate was reduced to 10%. GOSE sections associated with the most frequent interpretation and scoring difficulties included whether current functioning represented a change from pre-injury (466 corrected ratings in cohort 1; 62 in cohort 2), defining dependency in the home and community (163 corrections in cohort 1; three in cohort 2) and return to work/school (72 corrections in cohort 1; 35 in cohort 2). These results highlight the importance of central review in improving consistency across sites and over time. Establishing clear scoring criteria, coupled with ongoing guidance and feedback to data collectors, is essential to avoid scoring errors and resultant misclassification, which carry potential to result in "failure" of clinical trials that rely on the GOSE as their primary outcome measure.

This study aimed to elucidate the structure of the Rivermead Postconcussion Symptoms Questionnaire (RPQ) and evaluate its longitudinal and group variance. Factor structures were developed and compared in 1,011 patients with mild traumatic brain injury (mTBI; i.e., Glasgow Coma Scale score 13-15) from the Transforming Research and Clinical Knowledge in TBI study, using RPQ data collected at 2 weeks, and 3, 6, and 12 months postinjury. A bifactor model specifying a general factor and emotional, cognitive, and visual symptom factors best represented the latent structure of the RPQ. The model evinced strict measurement invariance over time and across sex, age, race, psychiatric history, and mTBI severity groups, indicating that differences in symptom endorsement were completely accounted for by these latent dimensions. While highly unidimensional, the RPQ has multidimensional features observable through a bifactor model, which may help differentiate symptom expression patterns in the future.

ImportanceA head computed tomography (CT) with positive results for acute intracranial hemorrhage is the gold-standard diagnostic biomarker for acute traumatic brain injury (TBI). In moderate to severe TBI (Glasgow Coma Scale [GCS] scores 3-12), some CT features have been shown to be associated with outcomes. In mild TBI (mTBI; GCS scores 13-15), distribution and co-occurrence of pathological CT features and their prognostic importance are not well understood.ObjectiveTo identify pathological CT features associated with adverse outcomes after mTBI.Design, setting, and participantsThe longitudinal, observational Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study enrolled patients with TBI, including those 17 years and older with GCS scores of 13 to 15 who presented to emergency departments at 18 US level 1 trauma centers between February 26, 2014, and August 8, 2018, and underwent head CT imaging within 24 hours of TBI. Evaluations of CT imaging used TBI Common Data Elements. Glasgow Outcome Scale-Extended (GOSE) scores were assessed at 2 weeks and 3, 6, and 12 months postinjury. External validation of results was performed via the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Data analyses were completed from February 2020 to February 2021.ExposuresAcute nonpenetrating head trauma.Main outcomes and measuresFrequency, co-occurrence, and clustering of CT features; incomplete recovery (GOSE scores

Neuropsychiatric symptoms are common, comorbid, and often disabling for patients with traumatic brain injury (TBI). Identifying transdiagnostic symptom dimensions post-TBI may help overcome limitations of traditional psychiatric diagnoses and advance treatment development. We characterized the dimensional structure of neuropsychiatric symptoms at 2-weeks postinjury in n = 1,732 TBI patients and n = 238 orthopedic-injured trauma controls (OTC) from the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study. Symptoms were reported on the Brief Symptom Inventory-18, Patient Health Questionnaire-9 Depression checklist, PTSD Checklist for DSM-5, PROMIS Pain Intensity scale, and Insomnia Severity Index. We established a novel factor model of neuropsychiatric symptoms and evaluated how 3 TBI severity strata and OTC patients differed in symptom severity. The final factor model had 6 first-order factors subsumed by 2 second-order factors: Internalizing (encompassing Depression, Anxiety, and Fear) and Somatic symptoms (Sleep, Physical, Pain). Somatic symptoms fit better as a correlated factor of (vs. a lower-order factor within) Internalizing. All symptom dimensions except for Pain were more severe in 1 or more TBI subgroups, as compared to the OTC group. Milder brain injury was generally associated with more severe symptoms, whereas more general injury severity (higher level of care, e.g., emergency department, intensive care unit) was associated with more pain. The findings indicate a broad factor resembling the internalizing factor of general psychopathology in traumatically injured patients, alongside a distinct somatic symptom factor. Brain injury, especially milder brain injury, may exacerbate liabilities toward these symptoms. These neuropsychiatric dimensions may help advance more precision medicine research for TBI. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Diffusion tractography magnetic resonance imaging (MRI) can infer changes in network connectivity in patients with traumatic brain injury (TBI), but the pathological substrates of disconnected tracts have not been well defined because of a lack of high-resolution imaging with histopathological validation. We developed an ex vivo MRI protocol to analyze tract terminations at 750-μm isotropic resolution, followed by histopathological evaluation of white matter pathology, and applied these methods to a 60-year-old man who died 26 days after TBI. Analysis of 74 cerebral hemispheric white matter regions revealed a heterogeneous distribution of tract disruptions. Associated histopathology identified variable white matter injury with patchy deposition of amyloid precursor protein (APP), loss of neurofilament-positive axonal processes, myelin dissolution, astrogliosis, microgliosis, and perivascular hemosiderin-laden macrophages. Multiple linear regression revealed that tract disruption strongly correlated with the density of APP-positive axonal swellings and neurofilament loss. Ex vivo diffusion MRI can detect tract disruptions in the human brain that reflect axonal injury.

Our objective was to examine the construct validity of the Brief Test of Adult Cognition by Telephone (BTACT) and its relationship to traumatic brain injury (TBI) of differing severities. Data were analyzed on 1422 patients with TBI and 170 orthopedic trauma controls (OTC) from the multi-center Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study. Participants were assessed at 6 months post-injury with the BTACT and an in-person neuropsychological battery. We examined the BTACT's factor structure, factorial group invariance, convergent and discriminant validity, and relationship to TBI and TBI severity. Confirmatory factor analysis supported both a 1-factor model and a 2-factor model comprising correlated Episodic Memory and Executive Function (EF) factors. Both models demonstrated strict invariance across TBI severity and OTC groups. Correlations between BTACT and criterion measures suggested that the BTACT memory indices predominantly reflect verbal episodic memory, whereas the BTACT EF factor correlated with a diverse range of cognitive tests. Although the EF factor and other BTACT indices showed significant relationships with TBI and TBI severity, some group effect sizes were larger for more comprehensive in-person cognitive tests than the BTACT. The BTACT is a promising, brief, phone-based cognitive screening tool for patients with TBI. Although the BTACT's memory items appear to index verbal Episodic Memory, items that purport to assess EFs may reflect a broader array of cognitive domains. The sensitivity of the BTACT to TBI severity is lower than domain-specific neuropsychological measures, suggesting it should not be used as a substitute for comprehensive, in-person cognitive testing at 6 months post-TBI.

Seizures are important neurological complications after traumatic brain injury (TBI) and are reported for up to 50% of patients with TBI. Despite several studies, no drug strategy has been able to alter the biological events leading to epileptogenesis. The glial water channel, aquaporin-4 (AQP4), was shown to facilitate cytotoxic cell swelling in ischemia and glial scar formation after stab wound injury. In this study, we examined post-traumatic seizure susceptibility of AQP4-deficient mice (AQP4-/-) after injection of pentylenetetrazole (PTZ) 1 month after controlled cortical impact (CCI) and compared them to wild-type sham injury controls. After PTZ injection, AQP4-/- mice demonstrated dramatically shortened seizure latency (120 ± 40 vs. 300 ± 70 sec; p  0.05) and severity of seizures evoked by PTZ (grade 4.0 ± 0.5 vs. 3.81 ± 0.30; p > 0.05) compared to wild-type counterparts. Immunohistochemical analysis demonstrated decreased immunostaining of microglia to levels comparable to wild-type (12 ± 2 vs. 11 ± 4 cells/hpf, respectively; p > 0.05). Taken together, these results suggest a protective role of AQP4 in post-traumatic seizure susceptibility by promoting astrogliosis, formation of a glial scar, and preventing microgliosis.

Systemic inflammation impacts outcome after traumatic brain injury (TBI), but most TBI biomarker studies have focused on brain-specific proteins. C-reactive protein (CRP) is a widely used biomarker of inflammation with potential as a prognostic biomarker after TBI. The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study prospectively enrolled TBI patients within 24 h of injury, as well as orthopedic injury and uninjured controls; biospecimens were collected at enrollment. A subset of hospitalized participants had blood collected on day 3, day 5, and 2 weeks. High-sensitivity CRP (hsCRP) and glial fibrillary acidic protein (GFAP) were measured. Receiver operating characteristic analysis was used to evaluate the prognostic ability of hsCRP for 6-month outcome, using the Glasgow Outcome Scale-Extended (GOSE). We included 1206 TBI subjects, 122 orthopedic trauma controls (OTCs), and 209 healthy controls (HCs). Longitudinal biomarker sampling was performed in 254 hospitalized TBI subjects and 19 OTCs. hsCRP rose between days 1 and 5 for TBI and OTC subjects, and fell by 2 weeks, but remained elevated compared with HCs (p

BackgroundBrain volumes in regions such as the hippocampus and amygdala have been associated with risk for the development of posttraumatic stress disorder (PTSD). The objective of this study was to determine whether a set of regional brain volumes, measured by magnetic resonance imaging at 2 weeks following mild traumatic brain injury, were predictive of PTSD at 3 and 6 months after injury.MethodsUsing data from TRACK-TBI (Transforming Research and Clinical Knowledge in TBI), we included patients (N = 421) with Glasgow Coma Scale scores 13-15 assessed after evaluation in the emergency department and at 2 weeks, 3 months, and 6 months after injury. Probable PTSD diagnosis (PTSD Checklist for DSM-5 score, ≥33) was the outcome. FreeSurfer 6.0 was used to perform volumetric analysis of three-dimensional T1-weighted magnetic resonance images at 3T obtained 2 weeks post injury. Brain regions selected a priori for volumetric analyses were insula, hippocampus, amygdala, superior frontal cortex, rostral and caudal anterior cingulate, and lateral and medial orbitofrontal cortices.ResultsOverall, 77 (18.3%) and 70 (16.6%) patients had probable PTSD at 3 and 6 months. A composite volume derived as the first principal component incorporating 73.8% of the variance in insula, superior frontal cortex, and rostral and caudal cingulate contributed to the prediction of 3-month (but not 6-month) PTSD in multivariable models incorporating other established risk factors.ConclusionsResults, while needing replication, provide support for a brain reserve hypothesis of PTSD and proof of principle for how prediction of at-risk individuals might be accomplished to enhance prognostic accuracy and enrich clinical prevention trials for individuals at the highest risk of PTSD following mild traumatic brain injury.

Identifying the principal determinants of life satisfaction following mild TBI (mTBI) may inform efforts to improve subjective well-being in this population. We examined life satisfaction among participants in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study who presented with mTBI (Glasgow Coma Scale [GCS] score = 13-15; n = 1152). An L1-regularization path algorithm was used to select optimal sets of baseline and concurrent symptom measures for prediction of scores on the Satisfaction with Life Scale (SWLS) at 2 weeks and 3, 6, and 12 months post-injury. Multi-variable linear regression models (all n = 744-894) were then fit to evaluate associations between the empirically selected predictors and SWLS scores at each follow-up visit. Results indicated that emotional post-TBI symptoms (all b = -1.27 to -0.77, all p

Diagnosis of spinal cord injury (SCI) severity at the ultra-acute stage is of great importance for emergency clinical care of patients as well as for potential enrollment into clinical trials. The lack of a diagnostic biomarker for SCI has played a major role in the poor results of clinical trials. We analyzed global gene expression in peripheral white blood cells during the acute injury phase and identified 197 genes whose expression changed after SCI compared with healthy and trauma controls and in direct relation to SCI severity. Unsupervised coexpression network analysis identified several gene modules that predicted injury severity (AIS grades) with an overall accuracy of 72.7% and included signatures of immune cell subtypes. Specifically, for complete SCIs (AIS A), ROC analysis showed impressive specificity and sensitivity (AUC: 0.865). Similar precision was also shown for AIS D SCIs (AUC: 0.938). Our findings indicate that global transcriptomic changes in peripheral blood cells have diagnostic and potentially prognostic value for SCI severity.

ObjectiveTo identify risk factors for suicidal ideation (SI) following mild traumatic brain injury (mTBI).SettingEleven US level 1 trauma centers.ParticipantsA total of 1158 emergency department patients with mTBI (Glasgow Coma Scale score = 13-15) enrolled in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study.DesignProspective observational study; weights-adjusted multivariable logistic regression models (n's = 727-883) estimated associations of baseline factors and post-TBI symptoms with SI at 2 weeks and 3, 6, and 12 months postinjury.Main measuresPatient Health Questionnaire, Rivermead Post-Concussion Symptoms Questionnaire.ResultsPreinjury psychiatric history predicted SI at all follow-ups (adjusted odds ratios [AORs] = 2.26-6.33, P values

Glial fibrillary acidic protein (GFAP) and ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1) may aid in the evaluation of traumatic brain injury (TBI). The objective of this analysis was to compare GFAP and UCH-L1 values measured using a handheld device compared with a core laboratory platform. We analyzed plasma samples from patients with TBI and healthy controls enrolled in the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) cohort study. GFAP and UCH-L1 were measured twice in each subject using prototype assays, first with the Abbott i-STAT™ handheld device, and second with the Abbott ARCHITECT® platform. We then quantified the agreement in biomarker values obtained using these two methods. GFAP and UCH-L1 were measured twice in 570 and 572 samples, respectively. GFAP values measured by the ARCHITECT platform (median 143.3 [interquartile range (IQR): 19.8-925.8] pg/mL) were higher than values measured by the i-STAT (median 116.0 [IQR: 9.2-856.5] pg/mL). GFAP values from the two platforms were strongly correlated (p = 0.985). Similarly, UCH-L1 values measured by the ARCHITECT platform (median 163.9 [IQR: 82.5-412.4] pg/mL) were higher than values measured by the i-STAT (median 122.5 [IQR: 63.0-297.3] pg/mL). UCH-L1 values from the two platforms were strongly correlated (p = 0.933). Passing-Bablok regression equations were developed to estimate the relationship between the two platforms, specifically to predict i-STAT values from the ARCHITECT platform. GFAP and UCH-L1 values measured using the prototype assays on the Abbott i-STAT and ARCHITECT platforms are strongly correlated and values from either platform may be converted to the other.

Return to work (RTW) is an important milestone of mild traumatic brain injury (mTBI) recovery. The objective of this study was to evaluate whether baseline clinical variables, three-month RTW, and three-month postconcussional symptoms (PCS) were associated with six-month RTW after mTBI. Adult subjects from the prospective multicenter Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot study with mTBI (Glasgow Coma Scale 13-15) who were employed at baseline, with completed three-and six-month RTW status, and three-month Acute Concussion Evaluation (ACE), were extracted. Univariate and multivariable analyses were performed for six-month RTW, with focus on baseline employment, three-month RTW, and three-month ACE domains (physical, cognitive, sleep, and/or emotional postconcussional symptoms (PCS)). Odds ratios (OR) and 95% confidence intervals [CI] were reported. Significance was assessed at p < 0.05. In 152 patients aged 40.7 ± 15.0years, 72% were employed full-time at baseline. Three- and six-month RTW were 77.6% and 78.9%, respectively. At three months, 59.2%, 47.4%, 46.1% and 31.6% scored positive for ACE physical, cognitive, sleep, and emotional PCS domains, respectively. Three-month RTW predicted six-month RTW (OR = 19.80, 95% CI [7.61-51.52]). On univariate analysis, scoring positive in any three-month ACE domain predicted inability for six-month RTW (OR = 0.10-0.11). On multivariable analysis, emotional symptoms predicted inability to six-month RTW (OR = 0.19 [0.04-0.85]). Subjects who scored positive in all four ACE domains were more likely to be unable to RTW at six months (4 domains: 58.3%, vs. 0-to-3 domains: 9.5%; multivariable OR = 0.09 [0.02-0.33]). Three-month post-injury is an important time point at which RTW status and PCS should be assessed, as both are prognostic markers for six-month RTW. Clinicians should be particularly vigilant of patients who present with emotional symptoms, and patients with symptoms across multiple PCS categories, as these patients are at further risk of inability to RTW and may benefit from targeted evaluation and support.

BackgroundPredicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients.MethodsIntra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods.ResultsNetwork analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76-[104-117] mmHg associated with neurological recovery.ConclusionsWe show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention.FundingNIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB).