Neurocritical care
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Due to increasing prevalence of intracranial device use and multidrug-resistant and nosocomial organisms, central nervous system (CNS) infections requiring treatment with intraventricular (IVT) aminoglycosides are becoming increasingly common. This article systematically reviews IVT aminoglycoside literature in adults and integrates available evidence to serve as a practical reference for clinicians. Medline (1946 to December 2015), Embase (1974 to December 2015), PubMed (1966 to December 2015), Google, and Google Scholar were searched using the term aminoglycoside combined individually with the terms IVT, meningitis, shunt infection, ventriculitis, and cerebral spinal fluid. ⋯ TDM should not be routinely utilized but reserved for more complicated patients. Further pharmacokinetic and clinical trials of IVT aminoglycosides are necessary to fill current therapeutic gaps. Due to the relatively limited cases of IVT aminoglycoside utilization, prospective, randomized, controlled trials are likely not feasible, and clinicians will have to rely on data from non-randomized and/or retrospective studies.
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Multicenter Study
Severe Cerebral Venous and Sinus Thrombosis: Clinical Course, Imaging Correlates, and Prognosis.
Severe cerebral venous-sinus thrombosis (CVT) is a rare disease, and its clinical course, imaging correlates, as well as long-term prognosis have not yet been investigated systematically. ⋯ The outcome of severe CVT is almost equally divided between severe impairment or death and survival with no or only mild handicap. Specifically, space-occupying mass effect and associated neurologic deterioration seem to determine a poor outcome. Therefore, early detection and treatment of mass effect should be the focus of critical care.
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Although intracranial pressure (ICP) is essential to guide management of patients suffering from acute brain diseases, this signal is often neglected outside the neurocritical care environment. This is mainly attributed to the intrinsic risks of the available invasive techniques, which have prevented ICP monitoring in many conditions affecting the intracranial homeostasis, from mild traumatic brain injury to liver encephalopathy. In such scenario, methods for non-invasive monitoring of ICP (nICP) could improve clinical management of these conditions. ⋯ This discrepancy could result from inconsistent assessment measures and application in different conditions, from traumatic brain injury to hydrocephalus and stroke. Most of the reports stress a potential advantage of TCD as it provides the possibility to monitor changes of ICP in time. Overall accuracy for TCD-based methods ranges around ±12 mmHg, with a great potential of tracing dynamical changes of ICP in time, particularly those of vasogenic nature.
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External ventricular drains (EVD) are widely used to manage intracranial pressure (ICP) and hydrocephalus for aneurysmal subarachnoid hemorrhage (aSAH) patients. After days of use, a decision is made to remove the EVD or replace it with a shunt, involving EVD weaning and CT imaging to observe ventricular size and clinical status. This practice may lead to prolonged hospital stay, extra radiation exposure, and neurological insult due to ICP elevation. This study aims to apply a validated morphological clustering analysis of ICP pulse (MOCAIP) algorithm to detect signatures from the pulse waveform to differentiate an intact CSF circulatory system from an abnormal one during EVD weaning. ⋯ Patients with an impaired CSF system showed a larger mean and variability of inter-pulse distances, indicating frequent changes on the morphology of pulses. This technique may provide a method to rapidly determine if patients will need placement of a shunt or can simply have the EVD removed.
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Existing studies of quantitative electroencephalography (qEEG) as a prognostic tool after cardiac arrest (CA) use methods that ignore the longitudinal pattern of qEEG data, resulting in significant information loss and precluding analysis of clinically important temporal trends. We tested the utility of group-based trajectory modeling (GBTM) for qEEG classification, focusing on the specific example of suppression ratio (SR). ⋯ Longitudinal analysis of continuous qEEG data using GBTM provides more predictive information than analysis of qEEG at single time-points after CA.