Neurocritical care
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Delayed cerebral ischemia increases mortality and morbidity after aneurysmal subarachnoid hemorrhage (aSAH). Various techniques are applied to detect cerebral vasospasm and hypoperfusion. Contrast-enhanced ultrasound perfusion imaging (UPI) is able to detect cerebral hypoperfusion in acute ischemic stroke. This prospective study aimed to evaluate the use of UPI to enable detection of cerebral hypoperfusion after aSAH. ⋯ UPI is feasible to enable detection of cerebral tissue hypoperfusion after aSAH, and the left-right difference of TTP values is the most indicative result of this finding.
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The Curing Coma Campaign (CCC) is a multidisciplinary global initiative focused on evaluation, diagnosis, treatment, research, and prognostication for patients who are comatose due to any etiology. To support this mission, the CCC Ethics Working Group conducted a survey of CCC collaborators to identify the ethics priorities of the CCC and the variability in priorities based on country of practice. ⋯ Collaborators of the CCC considered clinical care, diagnostic definitions, and clinical research the top ethics priorities of the CCC. These priorities should be considered as the CCC explores ways to improve evaluation, diagnosis, treatment, research, and prognostication of patients with coma and associated disorders of consciousness. There is some variability in ethics priorities based on country of practice.
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Blood pressure variability (BPV) is associated with outcome after endovascular thrombectomy in acute large vessel occlusion stroke. We aimed to provide the optimal sampling frequency and BPV index for outcome prediction by using high-resolution blood pressure (BP) data. ⋯ Using high-resolution BP data of 1 Hz, downsampling by averaging all BP values within 5-min intervals is essential to find relevant differences in systolic BPV, as noise can be avoided (confirmed by the significance of the power of midrange frequencies). These results demonstrate how high-resolution BP data can be processed for effective outcome prediction.
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Abstraction of critical data from unstructured radiologic reports using natural language processing (NLP) is a powerful tool to automate the detection of important clinical features and enhance research efforts. We present a set of NLP approaches to identify critical findings in patients with acute ischemic stroke from radiology reports of computed tomography (CT) and magnetic resonance imaging (MRI). ⋯ Our study demonstrates robust performance and external validity of a core NLP tool kit for identifying both categorical and continuous outcomes of ischemic stroke from unstructured radiographic text data. Medically tailored NLP methods have multiple important big data applications, including scalable electronic phenotyping, augmentation of clinical risk prediction models, and facilitation of automatic alert systems in the hospital setting.
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Most trials in critical care have been neutral, in part because between-patient heterogeneity means not all patients respond identically to the same treatment. The Precision Care in Cardiac Arrest: Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (PRECICECAP) study will apply machine learning to high-resolution, multimodality data collected from patients resuscitated from out-of-hospital cardiac arrest. We aim to discover novel biomarker signatures to predict the optimal duration of therapeutic hypothermia and 90-day functional outcomes. In parallel, we are developing a freely available software platform for standardized curation of intensive care unit-acquired data for machine learning applications. ⋯ Cardiac arrest is a heterogeneous disease that causes substantial morbidity and mortality. PRECICECAP will advance the overarching goal of titrating personalized neurocritical care on the basis of robust measures of individual need and treatment responsiveness. The software platform we develop will be broadly applicable to hospital-based research after acute illness or injury.