Journal of neurotrauma
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Journal of neurotrauma · Jul 2023
ReviewGrowth Hormone Deficiency Following Traumatic Brain Injury in Pediatric and Adolescent Patients: Presentation, Treatment, and Challenges of Transitioning From Pediatric to Adult Services.
Abstract Traumatic brain injury (TBI) is increasingly recognized, with an incidence of approximately 110 per 100,000 in pediatric populations and 618 per 100,000 in adolescent and adult populations. TBI often leads to cognitive, behavioral, and physical consequences, including endocrinopathies. Deficiencies in anterior pituitary hormones (e.g., adrenocorticotropic hormone, thyroid-stimulating hormone, gonadotropins, and growth hormone [GH]) can negatively impact health outcomes and quality of life post-TBI. ⋯ We place particular emphasis on the ways in which testing and dosage recommendations may change during the transition phase. We conclude with a review of the challenges faced by transition-age patients and how these may be addressed to improve access to adequate healthcare. Little information is currently available to help guide patients with TBI and GHD through the transition phase and there is a risk of interrupted care; therefore, a strength of this review is its emphasis on this critical period in a patient's healthcare journey.
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Journal of neurotrauma · Jul 2023
Lifetime Traumatic Brain Injury and Cognitive Domain Deficits in Late Life: The PROTECT-TBI Cohort Study.
Traumatic brain injury (TBI) causes cognitive impairment but it remains contested regarding which cognitive domains are most affected. Further, moderate-severe TBI is known to be deleterious, but studies of mild TBI (mTBI) show a greater mix of negative and positive findings. This study examines the longer-term cognitive effects of TBI severity and number of mTBIs in later life. ⋯ The most sensitive cognitive domains are attention and executive function, with approximately double the effect compared with processing speed and working memory. Post-TBI cognitive rehabilitation should be targeted appropriately to domain-specific effects. Significant long-term cognitive deficits were associated with three or more lifetime mTBIs, a critical consideration when counseling individuals post-TBI about continuing high-risk activities.
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Journal of neurotrauma · Jul 2023
Brain Targeted Xenon Protects Cerebral Vasculature After Traumatic Brain Injury.
Abstract Cerebrovascular dysfunction following traumatic brain injury (TBI) is a well-characterized phenomenon. Given the therapeutic potential of xenon, we aimed to study its effects after localized delivery to the brain using microbubbles. We designed xenon-containing microbubbles stabilized by dibehenoylphosphatidylcholine (DBPC) and polyethylene glycol (PEG) attached to saturated phospholipid (DPSE-PEG5000). ⋯ Endothelial cell culture experiments showed that glutamate reduces tight junction protein zona occludens-1 (ZO-1), but treatment with xenon microbubbles attenuates this effect. Xenon treatment protects cerebrovasculature and reduces astroglial reactivity after TBI. Further, these data support the future use of localized delivery of various therapeutic agents for brain injury using microbubbles in order to limit systemic side effects and reduce costs.
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Journal of neurotrauma · Jul 2023
Multicenter StudyPrediction of Mortality Among Patients with Isolated Traumatic Brain Injury Using Machine Learning Models in Asian Countries: An International Multicenter Cohort Study.
Abstract Traumatic brain injury (TBI) is a significant healthcare concern in several countries, accounting for a major burden of morbidity, mortality, disability, and socioeconomic losses. Although conventional prognostic models for patients with TBI have been validated, their performance has been limited. Therefore, we aimed to construct machine learning (ML) models to predict the clinical outcomes in adult patients with isolated TBI in Asian countries. ⋯ Among the tested models, the gradient-boosted decision tree showed the best performance (AUPRC, 0.746 [0.700-0.789]; AUROC, 0.940 [0.929-0.952]). The most powerful contributors to model prediction were the Glasgow Coma Scale, O2 saturation, transfusion, systolic and diastolic blood pressure, body temperature, and age. Our study suggests that ML techniques might perform better than conventional multi-variate models in predicting the outcomes among adult patients with isolated moderate and severe TBI.
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Journal of neurotrauma · Jul 2023
Mortality Prediction in Severe Traumatic Brain Injury Using Traditional and Machine Learning Algorithms.
Abstract Prognostic prediction of traumatic brain injury (TBI) in patients is crucial in clinical decision and health care policy making. This study aimed to develop and validate prediction models for in-hospital mortality after severe traumatic brain injury (sTBI). We developed and validated logistic regression (LR), LASSO regression, and machine learning (ML) algorithms including support vector machines (SVM) and XGBoost models. ⋯ All the prediction models can be accessed via a web-based calculator. Glasgow Coma Scale (GCS) score, age, pupillary light reflex, Injury Severity Score (ISS) for brain region, and the presence of acute subdural hematoma were the five strongest predictors for mortality prediction. The study showed that ML techniques such as XGBoost may capture information hidden in demographic and clinical predictors of patients with sTBI and yield more precise predictions compared with LR approaches.