Addressing the needs of traumatic brain injury with clinical proteomics.

BackgroundNeurotrauma or injuries to the central nervous system (CNS) are a serious public health problem worldwide. Approximately 75% of all traumatic brain injuries (TBIs) are concussions or other mild TBI (mTBI) forms. Evaluation of concussion injury today is limited to an assessment of behavioral symptoms, often with delay and subject to motivation. Hence, there is an urgent need for an accurate chemical measure in biofluids to serve as a diagnostic tool for invisible brain wounds, to monitor severe patient trajectories, and to predict survival chances. Although a number of neurotrauma marker candidates have been reported, the broad spectrum of TBI limits the significance of small cohort studies. Specificity and sensitivity issues compound the development of a conclusive diagnostic assay, especially for concussion patients. Thus, the neurotrauma field currently has no diagnostic biofluid test in clinical use.ContentWe discuss the challenges of discovering new and validating identified neurotrauma marker candidates using proteomics-based strategies, including targeting, selection strategies and the application of mass spectrometry (MS) technologies and their potential impact to the neurotrauma field.SummaryMany studies use TBI marker candidates based on literature reports, yet progress in genomics and proteomics have started to provide neurotrauma protein profiles. Choosing meaningful marker candidates from such 'long lists' is still pending, as only few can be taken through the process of preclinical verification and large scale translational validation. Quantitative mass spectrometry targeting specific molecules rather than random sampling of the whole proteome, e.g., multiple reaction monitoring (MRM), offers an efficient and effective means to multiplex the measurement of several candidates in patient samples, thereby omitting the need for antibodies prior to clinical assay design. Sample preparation challenges specific to TBI are addressed. A tailored selection strategy combined with a multiplex screening approach is helping to arrive at diagnostically suitable candidates for clinical assay development. A surrogate marker test will be instrumental for critical decisions of TBI patient care and protection of concussion victims from repeated exposures that could result in lasting neurological deficits

Machine learning for the prediction of psychosocial outcomes in acquired brain injury

Acquired brain injury (ABI) can be a life changing condition, affecting housing, independence, and employment. Machine learning (ML) is increasingly used as a method to predict ABI outcomes, however improper model evaluation poses a potential bias to initially promising findings (Chapter One). This study aimed to evaluate, with transparent reporting, three common ML classification methods. Regularised logistic regression with elastic net, random forest and linear kernel support vector machine were compared with unregularised logistic regression to predict good psychosocial outcomes after discharge from ABI inpatient neurorehabilitation using routine cognitive, psychometric and clinical admission assessments. Outcomes were selected on the basis of decision making for care packages: accommodation status, functional participation, supervision needs, occupation and quality of life. The primary outcome was accommodation (n = 164), with models internally validated using repeated nested cross-validation. Random forest was statistically superior to logistic regression for every outcome with areas under the receiver operating characteristic curve (AUC) ranging from 0.81 (95% confidence interval 0.77-0.85) for the primary outcome of accommodation, to its lowest performance for predicting occupation status with an AUC of 0.72 (0.69-0.76). The worst performing ML algorithm was support vector machine, only having statistically superior performance to logistic regression for one outcome, supervision needs, with an AUC of 0.75 (0.71-0.80). Unregularised logistic regression models were poorly calibrated compared to ML indicating severe overfitting, unlikely to perform well in new samples. Overall, ML can predict psychosocial outcomes using routine psychosocial admission data better than other statistical methods typically used by psychologists

Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics

Disorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 90% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics. To our knowledge, this is the first implementation reported of a multidomain prognostic model based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness. We therefore suggest that this novel prognostic model is accurate, robust, and interpretable.Comment: Although some prognostic indicators and models have been proposed for disorders of consciousness, each single method when used alone carries risks of false prediction. Song et al. report that a model combining resting state functional MRI with clinical characteristics provided accurate, robust, and interpretable prognostications. 52 pages, 1 table, 7 figure

Sociodemographic and Injury Severity Characteristics as Predictors of Functional Independence in Older Adults with TBI up to 10 Years Post Injury

Traumatic brain injury (TBI) incidence rates have been increasing in recent years, with the greatest number of TBIs and the highest morbidity and mortality rates in individuals aged 80 and over. As average life expectancy continues to increase, the older adult population is expected to comprise nearly one-quarter of the U.S. populace by 2060. With the increased risk to a larger proportion of the U.S. population posed by TBI, the aim of the current study was to examine the roles of sociodemographic and injury severity characteristics as predictors of functional independence trajectories across 1, 2, 5, and 10 years after TBI in older adults. The main hierarchical linear modeling (HLM) analyses showed that functional independence trajectories generally decreased over the 10 years after TBI. Individuals who were older, male, underrepresented minorities, had lower education, were unemployed at time of injury, had no history of substance use disorder, or had difficulties with learning, dressing, and going out of the home prior to the TBI, or longer time in posttraumatic amnesia had lower functional independence trajectories across at least one of the functional independence outcomes. Functional independence trajectories were also significantly predicted by interactions between time terms and several of these variables. Attention to the characteristics identified in this study as predictors of functional independence in older adults with TBI may serve patients and care providers when planning treatments, monitoring health over the long term, and ultimately as a way to decrease morbidity and mortality in this older adult population

An investigation into the effects of commencing haemodialysis in the critically ill

<b>Introduction:</b> We have aimed to describe haemodynamic changes when haemodialysis is instituted in the critically ill. 3 hypotheses are tested: 1)The initial session is associated with cardiovascular instability, 2)The initial session is associated with more cardiovascular instability compared to subsequent sessions, and 3)Looking at unstable sessions alone, there will be a greater proportion of potentially harmful changes in the initial sessions compared to subsequent ones. <b>Methods:</b> Data was collected for 209 patients, identifying 1605 dialysis sessions. Analysis was performed on hourly records, classifying sessions as stable/unstable by a cutoff of >+/-20% change in baseline physiology (HR/MAP). Data from 3 hours prior, and 4 hours after dialysis was included, and average and minimum values derived. 3 time comparisons were made (pre-HD:during, during HD:post, pre-HD:post). Initial sessions were analysed separately from subsequent sessions to derive 2 groups. If a session was identified as being unstable, then the nature of instability was examined by recording whether changes crossed defined physiological ranges. The changes seen in unstable sessions could be described as to their effects: being harmful/potentially harmful, or beneficial/potentially beneficial. <b>Results:</b> Discarding incomplete data, 181 initial and 1382 subsequent sessions were analysed. A session was deemed to be stable if there was no significant change (>+/-20%) in the time-averaged or minimum MAP/HR across time comparisons. By this definition 85/181 initial sessions were unstable (47%, 95% CI SEM 39.8-54.2). Therefore Hypothesis 1 is accepted. This compares to 44% of subsequent sessions (95% CI 41.1-46.3). Comparing these proportions and their respective CI gives a 95% CI for the standard error of the difference of -4% to 10%. Therefore Hypothesis 2 is rejected. In initial sessions there were 92/1020 harmful changes. This gives a proportion of 9.0% (95% CI SEM 7.4-10.9). In the subsequent sessions there were 712/7248 harmful changes. This gives a proportion of 9.8% (95% CI SEM 9.1-10.5). Comparing the two unpaired proportions gives a difference of -0.08% with a 95% CI of the SE of the difference of -2.5 to +1.2. Hypothesis 3 is rejected. Fisher’s exact test gives a result of p=0.68, reinforcing the lack of significant variance. <b>Conclusions:</b> Our results reject the claims that using haemodialysis is an inherently unstable choice of therapy. Although proportionally more of the initial sessions are classed as unstable, the majority of MAP and HR changes are beneficial in nature

Towards the Development of an Integrative, Evidence-based Suite of Indicators for the Prediction of Outcome Following Mild Traumatic Brain Injury

This thesis focuses on identifying factors that could be used to predict recovery following concussion. The first study is a pilot assessment of blood-based biomarkers, neuropsychological tests and MRI outcomes, followed by a protocol paper for a large scale clinical study designed to identify predictive indicators. The thesis features three journal publications, one of which is a seminal review article on a novel neuroimaging analysis technique called Quantitative Susceptibility Mapping

Development and validation of a hospital indicator of resource use intensity for injury admissions

Introduction : Les blessures représentent la 5ème cause d’hospitalisation au Canada. En 2010, leur soins ont couté 16 milliards de dollars. Selon des études Américaines, l’utilisation des ressources en traumatologie n’est pas strictement dictée par l’état des patients. Toutefois, le manque d’outil de mesure et de surveillance de l’intensité d’utilisation des ressources a jusque là empêché le développement d’interventions visant à améliorer l’efficience des soins en traumatologie. Objectifs : Notre objectif général était de développer et valider un indicateur de l’intensité d’utilisation des ressources pour les soins aigus en traumatologie. Nos objectifs spécifiques étaient de (1) faire une synthèse des méthodes d’évaluation des coûts des soins aigus en traumatologie ; (2) estimer l’utilisation des ressources pour les soins aigus en traumatologie, identifier les déterminants de cette utilisation et en évaluer la variation inter-hospitalière et (3) développer un indicateur de l’intensité d’utilisation des ressources pour les soins aigus en traumatologie et en évaluer les validités interne et temporelle. Méthodes : Pour le premier objectif, nous avons effectué une revue systématique de la littérature. Pour les second et troisième objectifs, nous avons mené des études de cohortes ur les personnes de ≥ 16 ans hospitalisées dans les centres de traumatologie pour adultes au Québec, de 2014 à 2016. Nous avons extrait les données du registre des traumatismes et des rapports financiers des hôpitaux et estimé l’utilisation des ressources avec des coûts par centre d’activité hospitalière. Pour le second objectif, nous avons identifié les déterminants avec un modèle linéaire multi-niveau, déterminé leur importance relative avec le coefficient f² de Cohen et évalué la variation avec le coefficient de corrélation intra-classe (CCI) et son intervalle de confiance à 95%. Pour le troisième objectif, nous avons effectué les analyses par niveau de désignation des centres de traumatologie (I/II et III/IV). Nous avons développé des modèles d’ajustement pour tous les patients et pour des groupes diagnostics spécifiques puis évalué les validités interne et temporelle avec respectivement le coefficient de détermination (r²) et le r²) annuel. Résultats : Pour la revue systématique, 10 études étaient éligibles. L’évaluation des hôpitaux était ajustée selon l’état des patients à l’arrivée dans seulement cinq études (50%). Dans la seconde étude (n = 32,411), les plus importantes composantes de l’utilisation des ressources étaient les soins réguliers (57%), le bloc opératoire (23%) et les soins intensifs(13%). Le plus important déterminant était la destination à la sortie de l’hôpital (f² = 7%). La plus grande utilisation des ressources était observée pour les blessures médullaires :11193$(7115-17606) par admission. Alors que l’utilisation des ressources augmentait avec l’âge pour les soins réguliers, elle diminuait avec l’âge pour le bloc opératoire. L’utilisation des ressources était 19$ (Quartiles 1 et 3 : 3045-8762). Nous avons identifié des centres où l’utilisation des ressources était plus grande ou plus petite que la moyenne géométrique provinciale, globalement et pour les blessures cranio-cérébrales, orthopédiques isolées et thoraco-abdominales isolées. Conclusions : Nos données suggèrent que 70% à 90% de l’utilisation des ressources en traumatologie au Québec est dictée par des facteurs autres que le statut clinique des patients. Nous avons développé un indicateur pour identifier les variations de l’utilisation des ressources dans un même centre/système de traumatologie, au fil du temps, ou entre centres/systèmes de traumatologie dans un(e) même province/pays. Cet indicateur ainsi que les déterminants de l’utilisation des ressources que nous avons identifiés peuvent servir de données probantes pour l’allocation des ressources et l’élaboration d’interventions visant à améliorer l’efficience des soins en traumatologie. Présentement, des études examinent l’association entre l’intensité d’utilisation des ressources et les résultats cliniques des patients à partir des méthodes développées dans ce projet. Les études futures devraient identifier les déterminants des variations inter-hospitalières de l’utilisation des ressources.Background: Injuries are the 5th leading cause of hospitalization in Canada and their care cost 16 billion dollars in 2010. Studies in the United States suggest that resource use fo racute injury care may be driven by factors other than the clinical status of patients. However, the lack of tools to measure and monitor resource use intensity has hampered the development of interventions aiming to improve the efficiency of injury care. Objectives: Our goal was to develop and validate a hospital indicator of resource use intensity for injury admissions. Our objectives were to (1) review how data on costs have been used to evaluate injury care; (2) estimate patient-level resource use for injury admissions, identify determinants of resource use intensity, and evaluate inter-hospital variations in resource use; and (3) develop a hospital indicator of resource use intensity fo rinjury admissions, and evaluate its internal and temporal validity. Methods: For the first objective, we conducted a systematic review of the literature. For the second and third objectives, we conducted retrospective, multicenter cohort studies based on ≥ 16-year-olds admitted to adult trauma centers in Quebec from 2014 to 2016. We extracted data from the Quebec trauma registry and hospital financial reports and estimated resource use with activity-based costs. For the second objective, we identified determinants using a multilevel linear model and assessed their relative importance with Cohen’s f² , and evaluated variations with intraclass correlation coefficients (ICC) and 95% confidence intervals. For the third objective, we conducted analyses by trauma center designation level (I/II and III/IV). We developed risk-adjustment models using a competing risks framework for the whole sample and for specific diagnostic groups. We assessed model internal validity with the optimism-corrected coefficient of determination (r² ), and temporal validity with yearly r² . We performed benchmarking by comparing the adjusted geometric mean cost of each center, obtained using shrinkage estimates, to the provincial geometric mean. Results: In our systematic review, we identified 10 eligible studies, of which nine were conducted in the United States. Hospital comparisons were adjusted according to patient case mix in only five studies (50%). In our second study (n = 32,411), activity centers associated with the greatest resource use were the regular ward (57%), followed by the operating room (23%) and the intensive care unit (13%). The strongest determinant of resource use was discharge destination (f² = 7%). Among injury types, the highest resource use was observed for spinal cord injuries: $11,193 (7115-17,606) per admission. While resource use increased with increasing age for the regular ward, it decreased with increasing age for the operating room. Resource use was 19$5014 (Quartiles 1 and 3: 3045-8762). In the whole sample and among patients with traumatic brain, isolated orthopedic and isolated thoracoabdominal injuries, we identified centers with higher or lower than expected resource use. Conclusions: Our review highlighted the need for more data on trauma center resource use, particularly in single-payer healthcare systems. Results from our second and third studies suggest that between 70% and 90% of the variation in resource use for injury care in Quebec is dictated by factors other than the clinical status of patients on arrival. We developed an indicator to identify variations in resource use intensity within a single trauma center or system over time, or across provinces or countries. This indicator and the determinants of resource use intensity we identified can be used to establish evidence-based resource allocations and design high-impact interventions to improve the efficiency of acute injury care. Research is underway to examine the association between hospital resource use intensity and clinical outcomes for trauma patients based on the methods we developed. Future research should identify determinants of inter-hospital variations inresource use intensity and aspects of resource use that drive optimal patient outcomes