Articles: neurocritical-care.
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Frontiers in neurology · Jan 2020
ReviewMachine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?
The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. ⋯ Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too difficult for humans to comprehend. Therefore, the application of ML in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to (1) briefly summarize ML and compare the different types of MLs, (2) review recent ML applications to improve neuro ICU management and (3) describe the future implications of ML to neuro ICU management.
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Transcranial Doppler (TCD) ultrasonography is an inexpensive, noninvasive means of measuring blood flow within the arteries of the brain. In this review, the authors outline the technology underlying TCD ultrasonography and describe its uses in patients with neurosurgical diseases. One of the most common uses of TCD ultrasonography is monitoring for vasospasm following subarachnoid hemorrhage. ⋯ Finally, assessment of cerebral autoregulation can be performed using TCD ultrasonography, providing data important to the management of patients with severe traumatic brain injury. As the clinical applications of TCD ultrasonography have expanded over time, so has their importance in the management of neurosurgical patients. Familiarity with this diagnostic tool is crucial for the modern neurological surgeon.
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Curr Neurol Neurosci Rep · Nov 2019
ReviewMachine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success.
Neurocritical care combines the complexity of both medical and surgical disease states with the inherent limitations of assessing patients with neurologic injury. Artificial intelligence (AI) has garnered interest in the basic management of these complicated patients as data collection becomes increasingly automated. ⋯ In this opinion article, we highlight the potential AI has in aiding the clinician in several aspects of neurocritical care, particularly in monitoring and managing intracranial pressure, seizures, hemodynamics, and ventilation. The model-based method and data-driven method are currently the two major AI methods for analyzing critical care data. Both are able to analyze the vast quantities of patient data that are accumulated in the neurocritical care unit. AI has the potential to reduce healthcare costs, minimize delays in patient management, and reduce medical errors. However, these systems are an aid to, not a replacement for, the clinician's judgment.
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Review Historical Article
Simulation in Neurocritical Care: Past, Present, and Future.
Simulation-based medical education is a technique that leverages adult learning theory to train healthcare professionals by recreating real-world scenarios in an interactive way. It allows learners to emotionally engage in the assessment and management of critically ill patients without putting patients at risk. Learners are encouraged to work at the edge of their expertise to promote growth and are provided with feedback to nurture development. ⋯ In contrast, other critical care educators have embraced the technique and built an impressive foundation of literature supporting its use. Slowly, neurocritical care educators have started experimenting with simulation-based medical education and sharing their results. In this review, we will investigate the historical origins of simulation in the neurosciences, the conceptual framework supporting the technique, current applications, and future directions.
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J Intensive Care Med · Jun 2019
ReviewMultimodality Monitoring in Neurocritical Care: Decision-Making Utilizing Direct And Indirect Surrogate Markers.
Substantial progress has been made to create innovative technology that can monitor the different physiological characteristics that precede the onset of secondary brain injury, with the ultimate goal of intervening prior to the onset of irreversible neurological damage. One of the goals of neurocritical care is to recognize and preemptively manage secondary neurological injury by analyzing physiologic markers of ischemia and brain injury prior to the development of irreversible damage. This is helpful in a multitude of neurological conditions, whereby secondary neurological injury could present including but not limited to traumatic intracranial hemorrhage and, specifically, subarachnoid hemorrhage, which has the potential of progressing to delayed cerebral ischemia and monitoring postneurosurgical interventions. In this study, we examine the utilization of direct and indirect surrogate physiologic markers of ongoing neurologic injury, including intracranial pressure, cerebral blood flow, and brain metabolism.