Articles: traumatic-brain-injuries.
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Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of enhancing TBI triage in LMICs, we aim to develop the first deep learning model to predict outcomes after TBI and compare its performance with that of less complex algorithms. ⋯ We present the first use of deep learning for TBI prognostication, with an emphasis on LMICs, where there is great need for decision support to allocate limited resources. Optimal algorithm selection depends on the specific clinical setting; deep learning is not a panacea, though it may have a role in these efforts.
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Subtle and profound changes in autonomic nervous system (ANS) function affecting sympathetic and parasympathetic homeostasis occur as a result of critical illness. Changes in ANS function are particularly salient in neurocritical illness, when direct structural and functional perturbations to autonomic network pathways occur and may herald impending clinical deterioration or intervenable evolving mechanisms of secondary injury. Sympathetic and parasympathetic balance can be measured quantitatively at the bedside using multiple methods, most readily by extracting data from electrocardiographic or photoplethysmography waveforms. ⋯ Here, we review data-analytic approaches to measuring ANS dysfunction from routine bedside physiologic data streams and integrating this data into multimodal machine learning-based model development to better understand phenotypical expression of pathophysiologic mechanisms and perhaps even serve as early detection signals. Attention will be given to examples from our work in acute traumatic brain injury on detection and monitoring of paroxysmal sympathetic hyperactivity and prediction of neurologic deterioration, and in large hemispheric infarction on prediction of malignant cerebral edema. We also discuss future clinical applications and data-analytic challenges and future directions.
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Cerebral edema and intracranial hypertension are major contributors to unfavorable prognosis in traumatic brain injury (TBI). Local epigenetic changes, particularly in DNA methylation, may influence gene expression and thus host response/secondary injury after TBI. It remains unknown whether DNA methylation in the central nervous system is associated with cerebral edema severity or intracranial hypertension post TBI. We sought to identify epigenome-wide DNA methylation patterns associated with these forms of secondary injury after TBI. ⋯ We report a novel potential relationship between intracranial hypertension after TBI and an acute, nonsustained reduction in DNA methylation at cg22111818 in the RGMA gene. To our knowledge, this is the largest EWAS in severe TBI. Our findings are further strengthened by previous findings that RGMA modulates axonal repair in other central nervous system disorders, but a role in intracranial hypertension or TBI has not been previously identified. Additional work is warranted to validate and extend these findings, including assessment of its possible role in risk stratification, identification of novel druggable targets, and ultimately our ability to personalize therapy in TBI.