Neurocritical care
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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.
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Pregabalin (PGB) is an effective adjunctive treatment for focal epilepsy and acts by binding to the alpha2-delta subunit of voltage-gated calcium channels to reduce excitatory neurotransmitter release. Limited data exist on its use in the neurocritical care setting, including cyclic seizures-a pattern of recurrent seizures occurring at nearly regular intervals. Although the mechanism underpinning cyclic seizures remains elusive, spreading excitation linked to spreading depolarizations may play a role in seizure recurrence and periodicity. PGB has been shown to increase spreading depolarization threshold; hence, we hypothesized that the magnitude of antiseizure effect from PGB is more pronounced in patients with cyclic versus noncyclic seizures in a critically ill cohort with recurrent seizures. ⋯ PGB was associated with a relative reduction in seizure burden in neurocritically ill patients with recurrent seizures, especially those with cyclic seizures, and may be considered in the therapeutic arsenal for refractory seizures. Whether this effect is mediated via modulation of spreading depolarization requires further study.
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Observational Study
Prognosis Predictions by Families, Physicians, and Nurses of Patients with Severe Acute Brain Injury: Agreement and Accuracy.
Effective shared decision-making relies on some degree of alignment between families and the medical team regarding a patient's likelihood of recovery. Patients with severe acute brain injury (SABI) are often unable to participate in decisions, and therefore family members make decisions on their behalf. The goal of this study was to evaluate agreement between prognostic predictions by families, physicians, and nurses of patients with SABI regarding their likelihood of regaining independence and to measure each group's prediction accuracy. ⋯ For patients with SABI, agreement in predictions between families, physicians, and nurses regarding likelihood of recovery is poor. Accuracy appears higher for physicians and nurses compared with families, with no significant difference between physicians and nurses.