Neurocritical care
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Observational Study
Cerebral Blood Flow and Oxygen Delivery in Aneurysmal Subarachnoid Hemorrhage: Relation to Neurointensive Care Targets.
The primary aim was to determine to what extent continuously monitored neurointensive care unit (neuro-ICU) targets predict cerebral blood flow (CBF) and delivery of oxygen (CDO2) after aneurysmal subarachnoid hemorrhage. The secondary aim was to determine whether CBF and CDO2 were associated with clinical outcome. ⋯ Systemic and cerebral physiological variables exhibited a modest association with CBF and CDO2. Still, cerebral hypoperfusion and low CDO2 were common and low CDO2 was associated with poor outcome. Xe-CT imaging could be useful to help detect secondary brain injury not evident by high ICP and low CPP.
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Spontaneous intracerebral hemorrhage is a potentially devastating cause of brain injury, often occurring secondary to hypertension. Contrast extravasation on computed tomography angiography (CTA), known as the spot sign, has been shown to predict hematoma expansion and worse outcomes. Although hypertension has been associated with an increased rate of the spot sign being present, the relationship between spot sign and blood pressure has not been fully explored. ⋯ The presence of spot sign correlates with larger hematomas, worse outcomes, and increased surgical intervention. There is a significant association between spot sign and systolic blood pressure at the time of CTA, with the highest systolic blood pressure being recorded prior to CTA. Although the role of intensive blood pressure management in spontaneous intracerebral hemorrhage remains a subject of debate, patients with a spot sign may be a subgroup that could benefit from this.
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Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously been developed. More recently, techniques from the field of machine learning have been applied to neurocritical care patient data to develop models with high levels of predictive accuracy. However, although these recent models appear clinically promising, their interpretability has often not been considered and they tend to be black box models, making it extremely difficult to understand how the model came to its conclusion. ⋯ Further, the use of interpretable machine learning will be explored, in particular the potential benefits and drawbacks that the techniques may have when applied to neurocritical care data. Finding a solution to the lack of model explanation, transparency, and accountability is important because these issues have the potential to contribute to model trust and clinical acceptance, and, increasingly, regulation is stipulating a right to explanation for decisions made by models and algorithms. To ensure that the prospective gains from sophisticated predictive models to neurocritical care provision can be realized, it is imperative that interpretability of these models is fully considered.
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Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the "readiness" of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success.