Neurocritical care
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Multicenter Study Observational Study
Prolonged Automated Robotic TCD Monitoring in Acute Severe TBI: Study Design and Rationale.
Transcranial Doppler ultrasonography (TCD) is a portable, bedside, noninvasive diagnostic tool used for the real-time assessment of cerebral hemodynamics. Despite the evident utility of TCD and the ability of this technique to function as a stethoscope to the brain, its use has been limited to specialized centers because of the dearth of technical and clinical expertise required to acquire and interpret the cerebrovascular parameters. Additionally, the conventional pragmatic episodic TCD monitoring protocols lack dynamic real-time feedback to guide time-critical clinical interventions. Fortunately, with the recent advent of automated robotic TCD technology in conjunction with the automated software for TCD data processing, we now have the technology to automatically acquire TCD data and obtain clinically relevant information in real-time. By obviating the need for highly trained clinical personnel, this technology shows great promise toward a future of widespread noninvasive monitoring to guide clinical care in patients with acute brain injury. ⋯ The overarching goal of this study is to establish safety and feasibility of prolonged automated TCD monitoring for patients with TBI in the intensive care unit and identify clinically meaningful and pragmatic noninvasive targets for future interventions.
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Twitter journal clubs are a modern way of highlighting articles published in a scientific journal. The Neurocritical Care journal (NCC) initiated a bimonthly, Twitter-based, online journal club in 2015 to increase the outreach of its published articles. We hypothesize that articles included in the Neurocritical Care Society Twitter Journal Club (NCSTJC) had greater engagement than other articles published during the same time period. We also investigated the relationship between number of citations and Altmetric score to assess whether the enhanced online activity resulted in higher citations. ⋯ Scientific journals are evolving their social media strategies in attempt to increase the outreach of their articles to the medical community. Platforms such as Twitter journal clubs can enhance such engagement. The long-term influence of such strategies on the impact factor of a medical journal and traditional engagement metrics, such as citations, calls for further research.
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Strong evidence in support of guidelines for traumatic brain injury (TBI) is lacking. Large-scale observational studies may offer a complementary source of evidence to clinical trials to improve the care and outcome for patients with TBI. They are, however, challenging to execute. ⋯ We see potential for individual patient data meta-analyses in connection to other large-scale projects. Our collaboration with Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) has taught us that although standardized data collection and coding according to common data elements can facilitate such meta-analyses, further data harmonization is required for meaningful results. Both CENTER-TBI and TRACK-TBI have demonstrated the complexity of the conduct of large-scale collaborative studies that produce high-quality science and new insights.
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Traumatic brain injury (TBI) and obstructive sleep apnea (OSA) are common in the general population and are associated with significant morbidity and mortality. The objective of this study was to assess hospital outcomes of patients with TBI with and without a pre-existing OSA diagnosis. ⋯ Patients with TBI with underlying OSA diagnosis were older and had higher comorbidity burden; however, hospital mortality was lower. Pre-existing OSA may result in protective physiologic changes such as hypoxic-ischemic preconditioning especially to cardiac and neural tissues, which can provide protection following neurological trauma, which may lead to a reduction in mortality.
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Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously been developed. More recently, techniques from the field of machine learning have been applied to neurocritical care patient data to develop models with high levels of predictive accuracy. However, although these recent models appear clinically promising, their interpretability has often not been considered and they tend to be black box models, making it extremely difficult to understand how the model came to its conclusion. ⋯ Further, the use of interpretable machine learning will be explored, in particular the potential benefits and drawbacks that the techniques may have when applied to neurocritical care data. Finding a solution to the lack of model explanation, transparency, and accountability is important because these issues have the potential to contribute to model trust and clinical acceptance, and, increasingly, regulation is stipulating a right to explanation for decisions made by models and algorithms. To ensure that the prospective gains from sophisticated predictive models to neurocritical care provision can be realized, it is imperative that interpretability of these models is fully considered.