Neurocritical care
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Patients with critical neurological illness are diverse. As a result of the heterogeneity of this patient population, standardized approaches to patient management might not confer benefit. A precision medicine approach to neurocritical care is therefore urgently needed to improve our understanding of neurocritical illness and the care provided to this vulnerable cohort. ⋯ This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care. We review the basic principles underlying Bayes' theorem, compare the use of Bayesian versus frequentist statistics in medicine, and discuss the relevance of Bayesian statistics to the field of neuroscience and to clinical research. Finally, we explore the potential benefits of employing Bayesian methods within the field of neurocritical care as a steppingstone toward implementing precision medicine approaches to improve patient outcomes for complex, heterogeneous disorders.
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Randomized Controlled Trial
Cytotoxic Edema and Adverse Clinical Outcomes in Patients with Intracerebral Hemorrhage.
Cytotoxic edema (CE) is an important form of perihematomal edema (PHE), which is a surrogate marker of secondary injury after intracerebral hemorrhage (ICH). However, knowledge about CE after ICH is insufficient. Whether CE has adverse effects on clinical outcomes of patients with ICH remains unknown. Therefore, we aimed to investigate the temporal pattern of CE and its association with clinical outcomes in patients with ICH. ⋯ CE appears in the acute phase of ICH and might be reversible. CE within 7-12 days post ICH was related to poor outcomes, which provides a novel therapeutic target for ICH intervention.
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The objective of this study was to validate the value of the Status Epilepticus Severity Score (STESS) in the prediction of the risk of in-hospital mortality in patients with nonhypoxic status epilepticus (SE) using a machine learning analysis. ⋯ This validation study using a machine learning system showed that STESS is a valuable prognostic tool. The score appears particularly accurate and effective in identifying patients who are alive at discharge (high negative predictive value), whereas it has a lower predictive value for in-hospital mortality.
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Disorders of consciousness due to severe hypoglycemia are rare but challenging to treat. The aim of this retrospective cohort study was to describe our multimodal neurological assessment of patients with hypoglycemic encephalopathy hospitalized in the intensive care unit and their neurological outcomes. ⋯ The overall prognosis of patients with severe hypoglycemic encephalopathy was poor, with only a small fraction of patients who slowly improved after intensive care unit discharge. Of note, patients who did not improve during the first 6 months did not recover consciousness. This study suggests that a multimodal approach capitalizing on advanced brain imaging and bedside electrophysiology techniques could improve diagnostic and prognostic performance in severe hypoglycemic encephalopathy.
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Acute kidney injury (AKI), a prevalent non-neurological complication following traumatic brain injury (TBI), is a major clinical issue with an unfavorable prognosis. This study aimed to develop and validate machine learning models to predict severe AKI (stage 3 or greater) incidence in patients with TBI. ⋯ In this study, the RF model demonstrated better discrimination in predicting severe AKI than other models. An online calculator could facilitate its application, potentially improving the early detection of severe AKI and subsequently improving the clinical outcomes among patients with msTBI.