Ten-year trends in traumatic brain injury: a retrospective cohort study of California emergency department and hospital revisits and readmissions.

OBJECTIVE:To describe visits and visit rates of adults presenting to emergency departments (EDs) with a diagnosis of traumatic brain injury (TBI). TBI is a major cause of death and disability in the USA; yet, current literature is limited because few studies examine longer-term ED revisits and hospital readmission patterns of TBI patients across a broad spectrum of injury severity, which can help inform potential unmet healthcare needs. DESIGN:We performed a retrospective cohort study. SETTING:We analysed non-public patient-level data from California's Office of Statewide Health Planning and Development for years 2005 to 2014. PARTICIPANTS:We identified 1.2 million adult patients aged ≥18 years presenting to California EDs and hospitals with an index diagnosis of TBI. PRIMARY AND SECONDARY OUTCOME MEASURES:Our main outcomes included revisits, readmissions and mortality over time. We also examined demographics, mechanism and severity of injury and disposition at discharge. RESULTS:We found a 57.7% increase in the number of TBI ED visits, representing a 40.5% increase in TBI visit rates over the 10-year period (346-487 per 100 000 residents). During this time, there was also a 33.8% decrease in the proportion of patients admitted to the hospital. Older, publicly insured and black populations had the highest visit rates, and falls were the most common mechanism of injury (45.5% of visits). Of all patients with an index TBI visit, 40.5% of them had a revisit during the first year, with 46.7% of them seeking care at a different hospital from their initial hospital or ED visit. Additionally, of revisits within the first year, 13.4% of them resulted in hospital readmission. CONCLUSIONS:The large proportion of patients with TBI who are discharged directly from the ED, along with the high rates of revisits and readmissions, suggest a role for an established system for follow-up, treatment and care of TBI

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis

IntroductionAdvances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome.MethodsMultivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality.ResultsWe identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters.ConclusionsHere we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients

Common Data Elements for Traumatic Brain Injury: Recommendations From the Biospecimens and Biomarkers Working Group

Recent advances in genomics, proteomics, and biotechnology have provided unprecedented opportunities for translational research and personalized medicine. Human biospecimens and biofluids represent an important resource from which molecular data can be generated to detect and classify injury and to identify molecular mechanisms and therapeutic targets. To date, there has been considerable variability in biospecimen and biofluid collection, storage, and processing in traumatic brain injury (TBI) studies. To realize the full potential of this important resource, standardization and adoption of best practice guidelines are required to insure the quality and consistency of these specimens. The aim of the Biospecimens and Biomarkers Working Group was to provide recommendations for core data elements for TBI research and develop best practice guidelines to standardize the quality and accessibility of these specimens. Consensus recommendations were developed through interactions with focus groups and input from stakeholders participating in the interagency workshop on Standardization of Data Collection in TBI and Psychological Health held in Washington, DC, in March 2009. With the adoption of these standards and best practices, future investigators will be able to obtain data across multiple studies with reduced costs and effort and accelerate the progress of genomic, proteomic, and metabolomic research in TBI

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data.

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community

A novel inhibitor of p75-neurotrophin receptor improves functional outcomes in two models of traumatic brain injury.

The p75 neurotrophin receptor is important in multiple physiological actions including neuronal survival and neurite outgrowth during development, and after central nervous system injury. We have discovered a novel piperazine-derived compound, EVT901, which interferes with p75 neurotrophin receptor oligomerization through direct interaction with the first cysteine-rich domain of the extracellular region. Using ligand binding assays with cysteine-rich domains-fused p75 neurotrophin receptor, we confirmed that EVT901 interferes with oligomerization of full-length p75 neurotrophin receptor in a dose-dependent manner. Here we report that EVT901 reduces binding of pro-nerve growth factor to p75 neurotrophin receptor, blocks pro-nerve growth factor induced apoptosis in cells expressing p75 neurotrophin receptor, and enhances neurite outgrowth in vitro Furthermore, we demonstrate that EVT901 abrogates p75 neurotrophin receptor signalling by other ligands, such as prion peptide and amyloid-β. To test the efficacy of EVT901 in vivo, we evaluated the outcome in two models of traumatic brain injury. We generated controlled cortical impacts in adult rats. Using unbiased stereological analysis, we found that EVT901 delivered intravenously daily for 1 week after injury, reduced lesion size, protected cortical neurons and oligodendrocytes, and had a positive effect on neurological function. After lateral fluid percussion injury in adult rats, oral treatment with EVT901 reduced neuronal death in the hippocampus and thalamus, reduced long-term cognitive deficits, and reduced the occurrence of post-traumatic seizure activity. Together, these studies provide a new reagent for altering p75 neurotrophin receptor actions after injury and suggest that EVT901 may be useful in treatment of central nervous system trauma and other neurological disorders where p75 neurotrophin receptor signalling is affected