Glycaemic control for patients with severe acute brain injury: Protocol for a systematic review

Background

Hyperglycaemia is common in patients with acute brain injury admitted to an intensive care unit (ICU). Many studies have found associations between development of hyperglycaemia and increased mortality in hospitalised patients. However, the optimal target for blood glucose control is unknown. We want to conduct a systematic review with meta-analysis and trial sequential analysis to explore the beneficial and harmful effects of restrictive versus liberal glucose control on patient outcomes in adults with severe acute brain injury.

Methods

We will systematically search medical databases including CENTRAL, Embase, MEDLINE and trial registries. We will search the following websites for ongoing or unpublished trials: http://www.controlled-trials.com/ , http://www.clinicaltrials.gov/ , www.eudraCT.com , http://centerwatch.com/ , The Cochrane Library's CENTRAL, PubMed, EMBASE, Science Citation Index Expanded and CINAHL. Two authors will independently review and select trials and extract data. We will include randomised trials comparing levels of glucose control in our analyses and observational studies will be included to address potential harms. The primary outcomes are defined as all-cause mortality, functional outcome and health-related quality of life. Secondary outcomes include serious adverse events including hypoglycaemia, length of ICU stay and duration of mechanical ventilation, and explorative outcomes including intracranial pressure and infection. Trial Sequential Analysis will be used to investigate the risk of type I error due to repetitive testing and to further explore imprecision. Quality of trials will be evaluated using the Cochrane Risk of Bias tool, and quality of evidence will be assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach.

Discussion

The results of the systematic review will be disseminated through peer-reviewed publication. With the review, we hope to inform future randomised clinical trials and improve clinical practice.     

Sex Disparities in Risk of Mortality Among Children With ESRD

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Tc-99m HMPAO SPECT imaging of the central nervous system in tuberous sclerosis

Tc-99m HMPAO was used to evaluate cerebral perfusion in a patient with tuberous sclerosis. The SPECT images demonstrated reduced HMPAO uptake in regions corresponding with MRI-confirmed locations of cortical tubers. These results indicate that the lesions are characterized by vascular perfusion deficits and support the hypothesis that cortical tubers result from developmental abnormalities of the embryonic central nervous system.     

Comparing histological data from different brains: Sources of error and strategies for minimizing them

The recent development of brain atlases with computer graphics templates, and of huge databases of neurohistochemical data on the internet, has forced a systematic re-examination of errors associated with comparing histological features between adjacent sections of the same brain, between brains treated in the same way, and between brains from groups treated in different ways. The long-term goal is to compare as accurately as possible a broad array of data from experimental brains within the framework of reference atlases. Main sources of error, each of which ideally should be measured and minimized, include intrinsic biological variation, linear and nonlinear distortion of histological sections, plane of section differences between each brain, section alignment problems, and sampling errors. These variables are discussed, along with approaches to error estimation and minimization in terms of a specific example—the distribution of neuroendocrine neurons in the rat paraventricular nucleus. Based on the strategy developed here, the main conclusion is that the best long-term solution is a high-resolution 3D computer graphics model of the brain that can be sliced in any plane and used as the framework for quantitative neuroanatomy, databases, knowledge management systems, and structure–function modeling. However, any approach to the automatic annotation of neuroanatomical data—relating its spatial distribution to a reference atlas—should deal systematically with these sources of error, which reduce localization reliability.