Articles: traumatic-brain-injuries.
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Traumatic brain injury (TBI) is a common reason for presenting to emergency departments (EDs). The assessment of these patients is frequently hampered by various confounders, and diagnostics is still often based on nonspecific clinical signs. Throughout Europe, there is wide variation in clinical practices, including the follow-up of those discharged from the ED. ⋯ The main results of this paper contain practical, clinically usable recommendations for acute clinical assessment, decision-making on acute head computerized tomography (CT), use of biomarkers, discharge options, and needs for follow-up, as well as a discussion of the main features and risk factors for prolonged recovery. In conclusion, this consensus paper provides a practical stepwise approach for the clinical assessment of patients with an acute TBI at the ED. Recommendations are given for the performance of acute head CT, use of brain biomarkers and disposition after ED care including careful patient information and organization of follow-up for those discharged.
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The neurological examination has remained key for the detection of worsening in neurocritical care patients, particularly after traumatic brain injury (TBI). New-onset, unreactive anisocoria frequently occurs in such situations, triggering aggressive diagnostic and therapeutic measures to address life-threatening elevations in intracranial pressure (ICP). As such, the field needs objective, unbiased, portable, and reliable methods for quickly assessing such pupillary changes. ⋯ Among QP variables, serial rather than isolated measurements of neurologic pupillary index, constriction velocity, and maximal constriction velocity demonstrated the best correlation with invasive ICP measurement values, particularly in predicting refractory intracranial hypertension. Neurologic pupillary index and ICP also showed an inverse relationship when trends were simultaneously compared. As such, QP, when used repetitively, seems to be a promising tool for noninvasive ICP monitoring in patients with TBI, especially when used in conjunction with other clinical and neuromonitoring data.
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Journal of neurotrauma · Aug 2024
ReviewVisual Impairment in Preclinical Models of Mild Traumatic Brain Injury.
Impairment in visual function is common after traumatic brain injury (TBI) in the clinical setting, a phenomenon that translates to pre-clinical animal models as well. In Morris et al. (2021), we reported histological changes following weight-drop-induced TBI in a rodent model including retinal ganglion cell (RGC) loss, decreased electroretinogram (ERG) evoked potential, optic nerve diameter reduction, induced inflammation and gliosis, and loss of myelin accompanied by markedly impaired visual acuity. ⋯ This underscores the importance of understanding the role of the visual system and the potential detrimental sequelae to this sensory modality post-TBI. Given that most commonly employed behavioral tests such as the Elevated Plus Maze and Morris Water Maze rely on an intact visual system, interpretation of functional deficits in diffuse models may be confounded by off- target effects on the visual system.
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Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. ⋯ A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.
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Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use artificial intelligence (AI) to optimize the interpretation of imaging studies, prognosis estimation, and critical care issues. In this study we present a bibliometric analysis and mini-review on the main uses that have been developed for TBI in AI. ⋯ AI has become a relevant research field in TBI during the last 20 years, demonstrating great potential in imaging, but a more modest performance for prognostic estimation and neuromonitoring.