Neurocritical care
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Big data (BD) and artificial intelligence (AI) have increasingly been used in neurocritical care. "BD" can be operationally defined as extremely large datasets that are so large and complex that they cannot be analyzed by using traditional statistical modeling. "AI" means the ability of machines to perform tasks similar to those performed by human intelligence. We present a brief overview of the most commonly applied AI techniques to perform BD analytics and discuss some of the recent promising examples in the field of neurocritical care. The latter include the following: cognitive motor dissociation in disorders of consciousness, hypoxic-ischemic injury following cardiac arrest, delayed cerebral ischemia and vasospasm after subarachnoid hemorrhage, and monitoring of intracranial pressure. ⋯ These collaborations will allow us to share data, combine predictive algorithms, and analyze multiple and cumulative sources of data retrospectively and prospectively. Once AI algorithms are validated at multiple centers, they should be tested in randomized controlled trials investigating their impact on clinical outcome. The neurocritical care community must work to ensure that AI incorporates standards to ensure fairness and health equity rather than reflect our biases present in our collective conscience.
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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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Despite application of the multimodal European Resuscitation Council and European Society of Intensive Care Medicine algorithm, neurological prognosis of patients who remain comatose after cardiac arrest remains uncertain in a large group of patients. In this study, we investigate the additional predictive value of visual and quantitative brain magnetic resonance imaging (MRI) to electroencephalography (EEG) for outcome estimation of comatose patients after cardiac arrest. ⋯ Magnetic resonance imaging is complementary with EEG for the prediction of poor and good outcome of patients after cardiac arrest who are comatose at admission.
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Multicenter Study
Neurological Prognostication After Hypoglycemic Coma: Role of Clinical and EEG Findings.
Hypoglycemic coma (HC) is an uncommon but severe clinical condition associated with poor neurological outcome. There is a dearth of robust neurological prognostic factors after HC. On the other hand, there is an increasing body of literature on reliable prognostic markers in the postanoxic coma, a similar-albeit not identical-situation. The objective of this study was thus to investigate the use and predictive value of these markers in HC. ⋯ This preliminary study suggests that highly malignant EEG patterns might be reliable prognostic markers of unfavorable outcome after HC. Other EEG findings, including lack of EEG reactivity and seizures and clinical findings appear less accurate. These findings should be replicated in a larger multicenter prospective study.
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Multicenter Study
Modeling Brain-Heart Crosstalk Information in Patients with Traumatic Brain Injury.
Traumatic brain injury (TBI) is an extremely heterogeneous and complex pathology that requires the integration of different physiological measurements for the optimal understanding and clinical management of patients. Information derived from intracranial pressure (ICP) monitoring can be coupled with information obtained from heart rate (HR) monitoring to assess the interplay between brain and heart. The goal of our study is to investigate events of simultaneous increases in HR and ICP and their relationship with patient mortality.. ⋯ The presence of a negative relationship between mortality and brain-heart crosstalks indicators suggests that a healthy brain-cardiovascular interaction plays a role in TBI.