Neurocritical care
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Review Meta Analysis
Perihematomal Edema and Clinical Outcome After Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.
Perihematomal edema (PHE) has been proposed as a radiological marker of secondary injury and therapeutic target in intracerebral hemorrhage (ICH). We conducted a systematic review and meta-analysis to assess the prognostic impact of PHE on functional outcome and mortality in patients with ICH. ⋯ This meta-analysis demonstrates that PHE volume within the first 72 h after ictus has a weak effect on functional outcome and mortality after ICH, whereas PHE growth might have a slightly larger impact during this time frame. Definitive conclusions are limited by the large variability of PHE measures, heterogeneity, and different evaluation time points between studies.
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Beta-lactam neurotoxicity is a relatively uncommon yet clinically significant adverse effect in critically ill patients. This study sought to define the incidence of neurotoxicity, derive a prediction model for beta-lactam neurotoxicity, and then validate the model in an independent cohort of critically ill adults. ⋯ In this single center cohort of critically ill patients, beta-lactam neurotoxicity was demonstrated less frequently than previously reported. We identified obesity as a novel risk factor for the development of neurotoxicity. The prediction model needs to be further refined before it can be used in clinical practice as a tool to avoid drug-related harm.
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Subtle and profound changes in autonomic nervous system (ANS) function affecting sympathetic and parasympathetic homeostasis occur as a result of critical illness. Changes in ANS function are particularly salient in neurocritical illness, when direct structural and functional perturbations to autonomic network pathways occur and may herald impending clinical deterioration or intervenable evolving mechanisms of secondary injury. Sympathetic and parasympathetic balance can be measured quantitatively at the bedside using multiple methods, most readily by extracting data from electrocardiographic or photoplethysmography waveforms. ⋯ Here, we review data-analytic approaches to measuring ANS dysfunction from routine bedside physiologic data streams and integrating this data into multimodal machine learning-based model development to better understand phenotypical expression of pathophysiologic mechanisms and perhaps even serve as early detection signals. Attention will be given to examples from our work in acute traumatic brain injury on detection and monitoring of paroxysmal sympathetic hyperactivity and prediction of neurologic deterioration, and in large hemispheric infarction on prediction of malignant cerebral edema. We also discuss future clinical applications and data-analytic challenges and future directions.
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Randomized Controlled Trial
Timely and Appropriate Administration of Inhaled Argon Provides Better Outcomes for tMCAO Mice: A Controlled, Randomized, and Double-Blind Animal Study.
Inhaled argon (iAr) has shown promising therapeutic efficacy for acute ischemic stroke and has exhibited impressive advantages over other inert gases as a neuroprotective agent. However, the optimal dose, duration, and time point of iAr for acute ischemic stroke are unknown. Here, we explored variable iAr schedules and evaluated the neuroprotective effects of acute iAr administration on lesion volume, brain edema, and neurological function in a mouse model of cerebral ischemic/reperfusion injury. ⋯ Timely iAr administration during ischemia showed optimal neurological outcomes and minimal infarct volumes. Moreover, an appropriate duration of argon administration was important for better neuroprotective efficacy. These findings may provide vital guidance for using argon as a neuroprotective agent and moving to clinical trials in acute ischemic stroke.
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Multicenter Study
Hospital Length of Stay and 30-Day Mortality Prediction in Stroke: A Machine Learning Analysis of 17,000 ICU Admissions in Brazil.
Hospital length of stay and mortality are associated with resource use and clinical severity, respectively, in patients admitted to the intensive care unit (ICU) with acute stroke. We proposed a structured data-driven methodology to develop length of stay and 30-day mortality prediction models in a large multicenter Brazilian ICU cohort. ⋯ Hospital length of stay and 30-day mortality of patients admitted to the ICU with stroke were accurately predicted through machine learning methods, even in the absence of stroke-specific data, such as the National Institutes of Health Stroke Scale score or neuroimaging findings. The proposed methods using general intensive care databases may be used for resource use allocation planning and performance assessment of ICUs treating stroke. More detailed acute neurological and management data, as well as long-term functional outcomes, may improve the accuracy and applicability of future machine-learning-based prediction algorithms.