Browsing by Subject "Traumatic brain injury"

Introduction: Traumatic brain injury (TBI) accounts for the majority of Uganda’s neurosurgical disease burden, but invasive intracranial pressure (ICP) monitoring is infrequently used. Noninvasive monitoring through tools like pupillometry could change the care of TBI patients in such a setting. Given the novelty of noninvasive monitoring in Uganda, this study sought to assess the feasibility of pupillometry for noninvasive ICP monitoring for TBI patients. Methods: Healthcare workers in Kampala, Uganda received education on pupillometry, practiced using the device on healthy volunteers, and completed interviews focused on pupillometry and its potential implementation. Qualitative analysis of the interviews assessed pupillometry acceptability and feasibility. Quantitative analysis assessed learning time, time to obtain a measurement, and accuracy of measurements during training. Results: Twenty-two providers completed the study. Participants described how pupillometry would add value to the care of patients with TBI during examination, delivering interventions, and monitoring. Reported concerns included the cost, understanding, and maintenance needs of the pupillometer. Participants also discussed potential challenges with using pupillometry, including limited accessibility and availability as well as challenges with documentation. They suggested offering continued education and providing technical support as strategies to support successful implementation. During training, average time to learn was 13.6 minutes (IQR 3.8) and average time to obtain a measurement was 51.1 seconds (IQR 14.2). Paired t tests to evaluate accuracy after training showed no statistically significant difference in the comparison measurements. Conclusion: Pupillometry would be feasible to use for noninvasive ICP monitoring for TBI patients in Uganda, as long as concerns about the device could be addressed and implementation barriers overcome.

Background. The confluence of a capacity-exceeding disease burden and persistent resource shortages have resulted in traumatic brain injury’s (TBI) devastating impact in low and middle income countries (LMIC). Lifesaving care for TBI depends on accurate and timely decision making within the hospital. As result of technology and highly skilled provider shortages, treatment delays are common in low resource settings. This reality demands a low cost, scalable and accurate alternative to support decision making. Decision support tools leveraging the accuracy of modern prognostic modeling techniques represents one possible solution. This thesis is a collation of research dedicated to the advancement of TBI decision support technology in low resource settings. Methods. The study location included three national and referral hospitals in Uganda and Tanzania. We performed a survival analysis, externally validated existing TBI prognostic models, developed our own prognostic model, and performed a feasibility study for TBI decision support tools in an LMIC. Results. The survival analysis revealed a greater surgical benefit for mild and moderate head injuries compared to severe injuries. However, severe injury patients experienced a higher surgery rate than mild and moderate injuries. We developed a prognostic model using machine learning with a good level of accuracy. This model outperformed existing TBI models in regards to discrimination but not calibration. Our feasibility study captured the need for improved prognostication of TBI patients in the hospital. Conclusions. This pioneering work has provided a foundation for further investigation and implementation of TBI decision support technologies in low resource settings.

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Traumatic Brain Injury (TBI) has become a marquee injury of this generation, prevalent in both military and civilian populations (Meaney 2014). Blunt impacts to the head are the known cause of approximately 1.7 million of TBI hospitalizations per year (Meaney 2014), and while mild TBI has the highest incidence (approximately 75% of TBIs) the injuries range from mild concussions to life threating severe bleeding within the brain (Meaney 2014).Due to wide spread prominence, blunt impact TBI has garnered a wealth of academic research interest focusing on the full spectrum of the biological scale, from subcellular and cellular response, to global human body modeling. The foundational theory of current blunt impact TBI research is neurological tissue damage by simple shear strain caused by motion of the skull (Cullen 2016, Alshareef 2020). While this likely contributes to tissue damage, its global perspective does not provide a satisfactory solution to the focal symptomology of TBI etiology. This is most likely because there are less appreciated mechanisms of injury contributing to TBI such as shear shock formation or cerebrospinal fluid (CSF) cavitation. The focus of this dissertation is to unpack the role of CSF cavitation in blunt impact TBI and contribute an important piece missing from the mechanistic understanding of TBI. This work develops an acoustic biomarker that indicates transient cavitation collapse, uses this biomarker to investigate cavitation mechanisms, observes cavitation in fresh, non-frozen, full body pig cadaver blunt impact testing, and provides clinical implications for transient cavitation through a reanalysis of live subhuman primate seminal data. It takes advantage of the large magnitude wideband acoustic emission of transient cavitation collapse, advanced acoustic sensor technology, and novel acoustic analysis methods to uncover a piece of the mechanistic mystery surrounding blunt impact TBI. There are five major conclusions reached in this dissertation. 1: The blunt impact head kinematics that induce cavitation are not significantly influenced by neck strength or cervical muscle activation. 2: Broadband acoustic emissions can be used as an acoustic biomarker to detect the incidence of transient cavitation collapse through the skull. 3: Compliance of the vessel containing a cavitating medium significantly influences the levels at which cavitation occurs during a blunt impact. 4: Blunt impact CSF cavitation occurs in a fresh, non-frozen, uncompromised pig cadaver head at impact levels below catastrophic injury thresholds. 5: Brain contusions are a potential clinical implication of transient cavitation collapse. Due to a lack of tools and technology, previous work on blunt impact cavitation was restricted to experimentation with limitations prohibiting the direct study of intracranial transient CSF cavitation. This innovative work provides direct observation of blunt impact CSF cavitation that benefits tools, injury risk functions, safety device design, and detection methodologies.

Background: Traumatic brain injury (TBI) has a disproportionate burden on low- and middle-income countries (LMICs) and cost-effective and culturally relevant measures are necessary to improve TBI care. This study aims to characterize emergency healthcare providers’ decision making when treating TBI patients, develop a machine learning-based model to predict TBI patient outcome, and conduct a decision curve analysis (DCA) to evaluate model clinical applicability. Methods: This study is twofold: 1) a secondary analysis of a TBI data registry with 4142 patients and 2) a survey examining physicians decision-making in treating 50 TBI patients in real time. Results: Five machine learning models were developed with AUCs ranging from 70.86% (Single C5.0 Ruleset) to 85.67% (Ensemble Model). DCA showed that all models exhibited a greater net benefit over ranges of clinical thresholds. The survey collected information on 50 patients providing insight on tools used by physicians in real-time when treating TBI patients as well as the unmet need patients at KCMC faced. Conclusions: This study is the first to use machine learning modeling and DCA in the context of TBI prognosis in Sub-Saharan Africa. Prognostic models have great potential within the decision-making process for treating TBI patients in LMIC health systems and such utility can be expanded through determining different threshold probabilities for various interventions.

Appropriately modulating inflammation after traumatic brain injury (TBI) may prevent disabilities in the millions that suffer TBI every year. Important mediators of inflammation include macrophages and microglia and these cell types can possess a range of phenotypes. An anti-inflammatory, “M2-like” macrophage phenotype after TBI is associated with neurogenesis, axonal regeneration, and improved white matter integrity. To boost these subpopulations, a promising approach is the enrichment of two cytokines: Fractalkine (FKN, CX3CL1) or Interleukin-4 (IL-4). FKN is a chemokine and thus recruits non-classical monocytes which are precursors to M2-like macrophages. IL-4 polarizes and proliferates M2-like macrophages. However, delivering recombinant or purified cytokines is not ideal due to their short half-lives, suboptimal efficacy, immunogenic potential, batch variabilities, and cost. Here we explore two strategies to enrich endogenous FKN or IL-4, obviating the need for delivery of exogenous proteins.

In the first study, we synthesize a biomaterial to elevate endogenous FKN at an injury site. Modified FKN-binding-aptamers are integrated with poly(ethylene glycol) diacrylate to form aptamer-functionalized hydrogels (“aptagels”) that dramatically enrich and passively release FKN in vitro for at least one week. Implantation in a mouse model of excisional skin injury demonstrates that aptagels enrich endogenous FKN and stimulate local increases in non-classical monocytes and M2-like macrophages.

In our second approach, we augment mesenchymal stem/stromal cells (MSCs), to transiently express IL-4. As MSCs do not endogenously synthesize IL-4, we transfect them with synthetic IL-4 mRNA. We suggest that mRNA transfection is a better strategy than DNA transfection, viral transduction, and recombinant IL-4 delivery for TBI. Our studies first characterize the IL-4 expression. Then, in a TBI model of closed head injury, we observe that IL-4 expressing MSCs successfully induce a robust M2-like macrophage phenotype and promote anti-inflammatory gene expression. Curiously, this does not translate to improvements in function, histology, or white matter integrity.

The results demonstrate that orchestrators of inflammation can be manipulated without delivery of foreign proteins. Both FKN-aptamer functionalized biomaterials and IL-4 expressing MSCs may be promising approaches to boost anti-inflammatory subpopulations at sites of injury. However, our studies also begin to question whether M2-like macrophages alone orchestrate the neurogenesis, axonal regeneration, and improved white matter integrity that has previously been observed.

Finally, both strategies could have important immunomodulatory roles outside of TBI. Aptagels are readily synthesized, highly customizable and could combine different aptamers to treat complex diseases in which regulation or enrichment of multiple proteins may be therapeutic. IL-4 expressing MSCs could assist tissue regeneration in cavitary diseases or improve biomaterial integration into tissues.

Background:

Traumatic Brain Injury (TBI) is a leading cause of death and disability. TBI patients in low and middle- income countries (LMIC) have twice the odds of death than in high-income countries. There is limited data describing the epidemiology and mortality predictors for TBI in LMIC.

Objective:

Determine epidemiology and predictors of mortality in TBI patients at Kigali University Teaching Hospital Accident and Emergency Department (KUTH A&E).

Methods:

Consecutive, injured KUTH A&E patients were prospectively screened for inclusion by reported head trauma, alteration in consciousness, headache, or visible head trauma. Exclusion criteria were <10 years old, presenting >48 hours after injury, or repeat visits. Data were assessed for association with death using logistic regression. Significant variables were included in an adjusted multivariable logistic regression model then refined via backwards elimination until all variables were significant at P <0.05.

Results:

684 patients enrolled between October 7, 2013 and April 6, 2014. 12 (2%) were excluded due to incomplete data. 81% were male with mean age of 31.5 years (range 10 - 89). Most patients (75%) had mild TBI (Glasgow Coma Score (GCS) 14-15), while 15% had moderate (GCS 9-13), and 10% had severe TBI (GCS 3-8). Multivariable logistic regression and refinement by backwards elimination determined that GCS <14, hypoxia, tachycardia and age >50 years predicted mortality.

Conclusion:

GCS <14, hypoxia, tachycardia and age >50 years were associated with mortality among TBI patients at KUTH A&E. These findings can guide clinicians in prioritizing care for patients at highest risk of mortality.

BACKGROUND: This study describes the prehospital transport of traumatic brain injury (TBI) patients and its impact on TBI outcome to inform quality improvement for the existing trauma system. Data was collected over 4 months at a major referral hospital in Moshi,Tanzania.

METHODS: Patient demographics, mechanism of injury, injury severity (Glasgow Coma Score), and vitals were recorded on presentation to the Casualty Department. Prehospital factors recorded include time, distance and cost. Multivariable regression analyses evaluated the effect of prehospital factors on unfavourable patient TBI outcome, in-hospital factors and demographics were controlled for. Unfavorable outcome was defined as Glasgow Outcome Score<5 on discharge or death.

RESULTS: Road traffic injuries were the most common mechanism of injury (67.1%). The majority of patients were referred from other facilities in and around the region (62.3%), with 23% from the local public hospital There was no evidence of prehospital care available in this region. Average prehospital duration was more than 1 hour, a third of this was spent in prehospital transit for a majority of the patients. A minority used Ambulances. Predictors of unfavourable outcome (GOS<5) were: prehospital time greater than 60 minutes, multiple physical transfers during the prehospital course and being referred from another hospital.

CONCLUSION: The lack of prehospital care calls for further research into prehospital interventions for this setting. Further analysis should be conducted with a larger sample size to increase accuracy of the findings.

Background: Globally, traumatic brain injury (TBI) accounts for the highest burden of deaths and disabilities globally. Studies suggest a complex relationship between injury, mental health, and alcohol. Though hazardous alcohol use and TBI exert heavy burdens in Tanzania, their interaction with mental health is largely unknown. This study aims to explore the mental health and alcohol use profiles of TBI patients in a low-income country.

Methods: Secondary data analysis of a registry of adults (≥ 18) with TBI of any severity presenting to the Kilimanjaro Christian Medical Center Emergency Department (ED) within 24 hours of injury. Patient data were collected at ED arrival and at three months follow-up. Variables included measures of functional independence, psychiatric health, quality of life, and alcohol use. Hazardous alcohol use was defined as an Alcohol Use Disorder Identification Test (AUDIT) score greater than seven. We conducted a latent profile analysis (LPA) to determine pre-injury mental health profiles of patients and logistic regression to assess association of patient profile with hazardous drinking at three months after injury.

Results: Of 190 participants, 51 (26.8%) were hazardous drinkers. The majority of the sample was male (83.7%) and the median age was 29.5 years. The LPA model with the strongest fitness revealed five profiles of mental health and drinking patterns. The “Poor Mental Health Drinkers” (9.4%) profile had worse quality of life and higher depression and hazardous drinking scores. The “Disabled Non-drinkers” (11.4%) profile had worse motor functional independence and low hazardous drinking scores. The “Non-drinkers” (53.5%) had good quality of life, little to no depression, good functional independence and low hazardous drinking scores. The “Drinkers” were similar to the “Non-drinkers” profile, except with high hazardous drinking scores. Predictors of hazardous drinking three months post-injury included disability and being a hazardous drinker before injury.

Conclusions: This study provides insight into the possible mental health and drinking pattern profiles for TBI patients. The categorization of patients may help in resource allocation of alcohol interventions for those who are at the highest risk for hazardous alcohol use. Limitations included recall bias for pre-injury information.

Explosions are associated with more than 80% of the casualties in the Iraq and Afghanistan wars. Given the widespread use of thoracic protective armor, the most prevalent injury for combat personnel is blast-related traumatic brain injury (TBI). Almost 20% of veterans returning from duty had one or more clinically confirmed cases of TBI. In the decades of research prior to 2000, neurotrauma was under-recognized as a blast injury and the etiology and pathology of these injuries remains unclear.

This dissertation used the finite element (FE) method to address many of the biomechanics-based questions related to blast brain injuries. FE modeling is a powerful tool for studying the biomechanical response of a human or animal body to blast loading, particularly because of the many challenges related to experimental work in this field. In this dissertation, novel FE models of the human and ferret head were developed for blast and blunt impact simulation, and the ensuing response of the brain was investigated. The blast conditions simulated in this research were representative of peak overpressures and durations of real-world explosives. In general, intracranial pressures were dependent on the peak pressure of the impinging blast wave, but deviatoric responses in the brain were dependent on both peak pressure and duration. The biomechanical response of the ferret brain model was correlated with in vivo injury data from shock tube experiments. This accomplishment was the first of its kind in the blast neurotrauma field.

This dissertation made major contributions to the field of blast brain injury and to the understanding of blast neurotrauma. This research determined that blast brain injuries were brain size-dependent. For example, mouse-sized brains were predicted to have approximately 7 times larger brain tissue strains than the human-sized brains for the same blast exposure. This finding has important implications for in vivo injury model design, and a scaling model was developed to relate animal experimental models to humans via scaling blast duration by the fourth-root of the ratio of brain masses.

This research also determined that blast neurotrauma is correlated to deviatoric metrics of the brain tissue rather than dilatational metrics. In addition, strains in the blasted brain were an order-of-magnitude lower than expected to produce injury with traditional closed-head TBI, but an order-of-magnitude higher in strain rate. The 50th percentile peak principle strain metric of values of 0.6%, 1.8%, and 1.6% corresponded to the 50% risk of mild brain bleeding, moderate brain bleeding, and apnea respectively. These findings imply that the mechanical thresholds for brain tissue are strain-based for primary blast injury, and different from the thresholds associated with blunt impact or concussive brain injury because of strain rate effects.

The conclusions in this dissertation provide an important guide to the biomechanics community for studying neurotrauma using in vivo, in vitro, and in silico models. Additionally, the injury risk curves developed in this dissertation provide an injury risk metric for improving the effectiveness of personal protective equipment or evaluating neurotrauma from blast.

Traumatic brain injury (TBI) is a leading cause of death and disability worldwide and this burden is increasing exponentially and will surpass many other diseases by 2020. The burden of TBI rests primarily in low and middle-income countries where they are woefully under resourced. Kilimanjaro Christian Medical Center (KCMC) in Moshi, Tanzania a neurosurgical referral center for 11 million people in the northwest of the country represents many other under resourced settings as they have limited diagnostic capacity (no computed tomography) and no trained neurosurgeon. In order to address understand how to address the burden of TBI at KCMC this project aims to describe the epidemiology and clinical presentation of TBI patients and determine predictors of death. This information will inform the next step of creating a KCMC specific clinical practice guideline or management plan for TBI patients in order to standardize and improve clinical care. This project utilized a retrospective review of de-identified data from a newly established Acute TBI Care Registry at KCMC that was developed for quality improvement. Three months of data was extracted yielding 190 patients who suffered TBI most of which were men (4:1 ratio) between 15 and 44 years of age and were motorcycle drivers. Alcohol use at the time of injury occurred for 28% of the patients almost exclusively among men. The mortality rates were high at 12% for all patients, 13% for admitted patients, and over 70% for those admitted to the Intensive Care Unit. Predictors of mortality were low Glasgow Coma scale on admission and hypotension. Further analysis with a large sample size is necessary to understand the impact of hypoxemia on mortality. Predictors of morbidity were low Glasgow Coma scale only. Further analysis should be planned with a larger sample size in order to improve the accuracy of these findings.

Background: Traumatic brain injury (TBI) is the leading cause of trauma related death and disability worldwide. Poor TBI outcomes disproportionately affect low- and middle-income countries (LMICs). Treatment delays may contribute to poor TBI outcomes in LMIC emergency departments (EDs). A prognostic model is a low-cost, user-friendly solution to optimizing patient care in low-resource hospitals. The aim of this study was twofold: (1) assess associations between care delays and TBI patient outcomes, and (2) build a prognostic model that uses care delays to predict TBI patient outcomes.

Methods: This study uses a 3209 de-identified TBI patient registry from Kilimanjaro Christian Medical Center (KCMC) ED in Moshi, Tanzania. We created nine variables representing delays to care and assessed their association with poor outcomes (Glasgow Coma Score (GCS) < 4) using logistic regression. We then constructed a prognostic model that predicts TBI patient outcomes dichotomized as good (GCS ≥ 4) and poor (GCS < 4). Predictors included socio-demographics, injury characteristics, vital signs, and care delays.

Results: Associations between care delays and TBI outcomes were not significant. However, care delays were top predictors of a poor outcome in our prognostic model. Our model achieved an area under the receiver operating curve of 89.5% (95% CI: 88.8, 90.3).

Conclusion: Our TBI prognostic model demonstrates the predictive value of care delay information. Time to care data is easy to collect. A prognostic model that uses time to care data allows healthcare providers to update patient prognosis as patients progress through their hospital stay.

Abstract

Background: Traumatic brain injury (TBI) is a major cause of death and disability. About 10 million people annually are affected by TBI, with a prominent burden in low- and middle-income countries (LMICs). In Brazil, TBI is responsible for 125,500 admissions and 9700 hospital deaths annually. The poor prognosis could be caused by insufficient medical professionals and diagnostic machines. This study aims to find an optimum TBI prognostic model to serve as a diagnostic tool that can be adapted from prior work in Tanzania to Brazil. We aim to develop an effective TBI prognostic model that could be generalized in LMICs.

Methods: The study was a secondary data analysis on clinical and sociodemographic variables of 3209 TBI patients at Kilimanjaro Christian Medical Center (KCMC) and 725 TBI patients at six Brazilian traumatic care hospitals. We trained and tested eight machine learning models using three strategies: 1) using Tanzanian dataset trained models to test Brazilian dataset, 2) using Tanzanian-Brazilian combined dataset for training and testing and 3) using Brazilian dataset for training and testing. We compared the performance of models using confusion matrix statistics: area under the ROC curve(AUC), sensitivity, specificity, positive predictive value, negative predictive value and accuracy.

Findings: Models using Tanzanian-Brazilian combined dataset for training and testing outperformed models of other two strategies. The AUC of the models varied from 80.9% (K nearest neighbor) to 91.9% (Random Forest). The optimum model, Random Forest, had a strong predictive power of classification with sensitivity of 0.927, specificity of 0.756, positive predictive value of 0.960, negative predictive value of 0.620 and accuracy of 0.903.

Interpretations: Our study shows the successful adaptation of TBI prognostic model from Tanzania to Brazil. Additionally, it indicates the possibility of generalizing a TBI prognostic model to LMICs. With larger multi-national data, we hope to develop an effective model that could accurately predict the potential outcome of TBI patients. The model could serve as a powerful auxiliary tool for diagnosis and help reduce mortality of TBI patients in LMICs.

Source of Funding: The project is conducted with the funding from Duke Global Health Institute.

Background: Uganda is experiencing a high rate of Traumatic Brain Injuries (TBI), approximately 170 per 100,000 when compared to the global rate of 106 per 100,000. This may be due to an increasing rate of road traffic incidents (RTIs) and falls. LMICs like Uganda are disproportionately burdened with a higher number of RTI and other risk factors for TBI. One of the foremost reasons for poor outcomes for moderate and severe TBI patients are the delays in seeking, reaching, and receiving care. The aim of this study is to understand the perceptions of pre-hospital and in-hospital delays in seeking, reaching, and receiving care for patients diagnosed with TBI at Mulago National Referral Hospital (MNRH), and obtain perceptions of interventions that could reduce delay for these patients.

Methods: The study is a qualitative research project and will be carried out at Mulago National Referral Hospital, Kampala district, Uganda. The study participants were healthcare professionals in the Neurological ward of this hospital. This study will utilize semi-structured in-depth qualitative interviews, outlined through “The Three Delay Framework”, to understand perceptions of the reasons behind the three delays: seeking, reaching, and receiving care. Additionally, collecting perspectives on what can be done about the delays.

Results: During the study period, fourteen healthcare professionals in the Neurological ward of MNRH were interviewed. Of the fourteen, three were senior neurosurgeons, six were neurosurgical residents, and five were nurses. Four themes were derived from the data, Transportation, Knowledge and Stigma, Surgical Intervention Preparedness, and Financial Burdens. Nineteen sub-themes or sub-codes were found during analysis and were deductively pre-coded for either delay or solution. Transportation Means, Physical Distance, Road Conditions, Injury Knowledge, Hospital Knowledge, Hospital Stigma, Communicable Disease Information, Instruments, Resources, Staff, Space, Equipment, Investigations, Cost of Obtaining care, Cost of Transport, Cost of Cheaper Care, Cost of Investigations, Cost of Surgical Equipment, and Cost of Medication are all found within the four main themes.

Conclusions: Understanding perceptions of delay and methods to reduce them from the prospective of the healthcare professional established confirmation of current issues affecting care at MNRH. The data also demonstrated the issue of understanding the delays but not methods to solve them. Interviews with patients and their families are the next step in understanding these prevalent issues and creating an appropriate intervention to reduce them.