Metabolomics Profiling As a Diagnostic Tool in Severe Traumatic Brain Injury

Traumatic brain injury (TBI) is a complex disease with a multifaceted pathophysiology. Impairment of energy metabolism is a key component of secondary insults. This phenomenon is a consequence of multiple potential mechanisms including diffusion hypoxia, mitochondrial failure, and increased energy needs due to systemic trauma responses, seizures, or spreading depolarization. The degree of disturbance in brain metabolism is affected by treatment interventions and reflected in clinical patient outcome. Hence, monitoring of these secondary events in peripheral blood will provide a window into the pathophysiological course of severe TBI. New methods for assessing perturbation of brain metabolism are needed in order to monitor on-going pathophysiological processes and thus facilitate targeted interventions and predict outcome. Circulating metabolites in peripheral blood may serve as sensitive markers of pathological processes in TBI. The levels of these small molecules in blood are less dependent on the integrity of the blood–brain barrier as compared to protein biomarkers. We have recently characterized a specific metabolic profile in serum that is associated with both initial severity and patient outcome of TBI. We found that two medium-chain fatty acids, octanoic and decanoic acids, as well as several sugar derivatives are significantly associated with the severity of TBI. The top ranking peripheral blood metabolites were also highly correlated with their levels in cerebral microdialyzates. Based on the metabolite profile upon admission, we have been able to develop a model that accurately predicts patient outcome. Moreover, metabolomics profiling improved the performance of the well-established clinical prognostication model. In this review, we discuss metabolomics profiling in patients with severe TBI. We present arguments in support of the need for further development and validation of circulating biomarkers of cerebral metabolism and for their use in assessing patients with severe TBI

"Omics" in traumatic brain injury: novel approaches to a complex disease

Background: To date, there is neither any pharmacological treatment with efficacy in traumatic brain injury (TBI) nor any method to halt the disease progress. This is due to an incomplete understanding of the vast complexity of the biological cascades and failure to appreciate the diversity of secondary injury mechanisms in TBI. In recent years, techniques for high-throughput characterization and quantification of biological molecules that include genomics, proteomics, and metabolomics have evolved and referred to as omics.Methods: In this narrative review, we highlight how omics technology can be applied to potentiate diagnostics and prognostication as well as to advance our understanding of injury mechanisms in TBI.Results: The omics platforms provide possibilities to study function, dynamics, and alterations of molecular pathways of normal and TBI disease states. Through advanced bioinformatics, large datasets of molecular information from small biological samples can be analyzed in detail and provide valuable knowledge of pathophysiological mechanisms, to include in prognostic modeling when connected to clinically relevant data. In such a complex disease as TBI, omics enables broad categories of studies from gene compositions associated with susceptibility to secondary injury or poor outcome, to potential alterations in metabolites following TBI.Conclusion: The field of omics in TBI research is rapidly evolving. The recent data and novel methods reviewed herein may form the basis for improved precision medicine approaches, development of pharmacological approaches, and individualization of therapeutic efforts by implementing mathematical "big data" predictive modeling in the near future.</p

Comparing disability between traumatic brain injury and spinal cord injury using the 12-item WHODAS 2.0 and the WHO minimal generic data set covering functioning and health

Objective: To compare disability between two patient groups using short validated tools based on International Classification of Functioning, Disability and Health (ICF).Design: Cross-sectional study.Setting: University hospital specialist outpatient clinic.Subjects: A total of 94 patients with traumatic brain injury and 59 with spinal cord injury.Main measures: Disability evaluated using self-reported and proxy 12-item WHODAS 2.0 (World Health Organization Disability Assessment Schedule), and physician-rated WHO minimal generic data set covering functioning and health.Results: The two measures used showed severe but very different disabilities in these patient groups. Disability was assessed worse by physicians in the spinal cord injury population (sum 15.8 vs. 12.7, P = 0.0001), whereas disability assessed by the patients did not differ significantly between the two groups (sum 18.4 vs. 21.2). Further analysis revealed that in patients with “high disability” (the minimal generic data set score ⩾15), self-reported functioning was more severely impaired in the traumatic brain injury group compared to the spinal cord injury group (29.7 vs. 21.4, P Conclusion: Both generic measures were able to detect severe disability but also to detect differences between two patient populations with different underlying diagnoses.</p

Cognitive functions and symptoms predicting later use of psychiatric services following mild traumatic brain injury in school-age

Objective To investigate whether neuropsychological test performance or presence of some specific injury symptoms at 1-3 months following pediatric mild traumatic brain injury (mTBI) can help to identify the children at risk for developing post-traumatic psychiatric symptoms. Methods Data from 120 children and adolescents aged 7-15 years, treated at Turku University Hospital between 2010 and 2016 due to mTBI, and who had undergone neuropsychological evaluation at 1-3 months following injury, were enrolled from the hospital records. Neuropsychological test performancesand injury symptom reports were retrospectively retrieved from the patient files. Results Slow information processing speed (p = 0.044), emotion regulation deficit (p = 0.014), impulsivity (p = 0.013), verbal processing difficulties (p = 0.042) and headache (p = 0.026) were independent predictors for having later contact in psychiatric care. Conclusions Neuropsychological examination containing measure of information processing speed, injury symptom interview, and parental questionnaires on behavioural issues of the child at 1-3 months following mTBI seems to be useful in detecting children with risk for post traumatic psychiatric symptoms. Targeted support and guidance for this group of children and adolescents and their families are recommended to prevent the development of an unfavorable psychosocial outcome.Peer reviewe

Cranioplasty After Severe Traumatic Brain Injury: Effects of Trauma and Patient Recovery on Cranioplasty Outcome

Background: In patients with severe traumatic brain injury (sTBI) treated with decompressive craniectomy (DC), factors affecting the success of later cranioplasty are poorly known.Objective: We sought to investigate if injury- and treatment-related factors, and state of recovery could predict the risk of major complications in cranioplasty requiring implant removal, and how these complications affect the outcome.Methods: A retrospective cohort of 40 patients with DC following sTBI and subsequent cranioplasty was studied. Non-injury-related factors were compared with a reference population of 115 patients with DC due to other conditions.Results: Outcome assessed 1 day before cranioplasty did not predict major complications leading to implant removal. Successful cranioplasty was associated with better outcome, whereas a major complication attenuates patient recovery: in patients with favorable outcome assessed 1 year after cranioplasty, major complication rate was 7%, while in patients with unfavorable outcome the rate was 42% (p = 0.003). Of patients with traumatic subarachnoid hemorrhage (tSAH) on admission imaging 30% developed a major complication, while none of patients without tSAH had a major complication (p = 0.014). Other imaging findings, age, admission Glasgow Coma Scale, extracranial injuries, length of stay at intensive care unit, cranioplasty materials, and timing of cranioplasty were not associated with major complications.Conclusion: A successful cranioplasty after sTBI and DC predicts favorable outcome 1 year after cranioplasty, while stage of recovery before cranioplasty does not predict cranioplasty success or failure. tSAH on admission imaging is a major risk factor for a major complication leading to implant removal

Randomized Controlled Trials of Rehabilitation Services in the Post-acute Phase of Moderate and Severe Traumatic Brain Injury - A Systematic Review

Background and aims: There is a gap in knowledge regarding effective rehabilitation service delivery in the post-acute phase after traumatic brain injury (TBI). Recently, Gutenbrunner et al. proposed a classification system for health-related rehabilitation services (International Classification System for Service Organization in Health-related Rehabilitation, ICSO-R) that could be useful for contrasting and comparing rehabilitation services. The ICSO-R describes the dimensions of Provision (i.e., context of delivered services), Funding (i.e., sources of income and refunding), and Delivery (i.e., mode, structure and intensity) at the meso-level of services.We aim to:Provide an overview of randomized, controlled trials (RCTs) with rehabilitation service relevance provided to patients with moderate and severe TBI in the post-acute phase using the ICSO-R as a framework; andEvaluate the extent to which the provision, funding and delivery dimensions of rehabilitation services were addressed and differed between the intervention arms in these studies.Materials and methods: A systematic literature search was performed in OVID MEDLINE, EMBASE, CINHAL, PsychINFO, and CENTRAL, including multidisciplinary rehabilitation interventions with RCT designs and service relevance targeting moderate and severe TBI in the post-acute phase.Results: 23 studies with 4,644 TBI patients were included. More than two-thirds of the studies were conducted in a hospital-based rehabilitation setting. The contrast in Context between the intervention arms often co-varied with Resources. The funding of the services was explicitly described in only one study. Aspects of the Delivery dimension were described in all of the studies, and the Mode of Production, Intensity, Aspects of Time and Peer Support were contrasted in the intervention arms in several of the studies. A wide variety of outcome measures were applied often covering Body function, as well as the Activities and Participation domains of the International Classification of Functioning, Disability, and Health (ICF).Conclusion: Aspects of service organization and resources as well as delivery may clearly influence outcome of rehabilitation. Presently, lack of uniformity of data and collection methods, the heterogeneity of structures and processes of rehabilitation services, and a lack of common outcome measurements make comparisons between the studies difficult. Standardized descriptions of services by ICSO-R, offer the possibility to improve comparability in the future and thus enhance the relevance of rehabilitation studies.</div

A Decision Support System for Diagnostics and Treatment Planning in Traumatic Brain Injury.

Traumatic brain injury (TBI) occurs when an external force causes functional or structural alterations in the brain. Clinical characteristics of TBI vary greatly from patient to patient, and a large amount of data is gathered during various phases of clinical care in these patients. It is hard for clinicians to efficiently integrate and interpret all of these data and plan interventions in a timely manner. This paper describes the technical architecture and functionality of a web-based decision support system (DSS), which not only provides advanced support for visualizing complex TBI data but also predicts a possible outcome by using a state-of-the-art Disease State Index machine-learning algorithm. The DSS is developed by using a three-layered architecture and by employing modern programming principles, software design patterns, and using robust technologies (C#, ASP.NET MVC, HTML5, JavaScript, Entity Framework, etc.). The DSS is comprised of a patient overview module, a disease-state prediction module, and an imaging module. After deploying it on a web-server, the DSS was made available to two hospitals in U.K. and Finland. Afterwards, we conducted a validation study to evaluate its usability in clinical settings. Initial results of the study indicate that especially less experience clinicians may benefit from this type of decision support software tool