Occurrence and impact of delayed cerebral ischemia after coiling and after clipping in the International Subarachnoid Aneurysm Trial (ISAT)

Delayed cerebral ischemia (DCI) is an important cause of poor outcome after aneurysmal subarachnoid hemorrhage (SAH). We studied differences in incidence and impact of DCI as defined clinically after coiling and after clipping in the International Subarachnoid Aneurysm Trial. We calculated odds ratios (OR) for DCI for clipping versus coiling with logistic regression analysis. With coiled patients without DCI as the reference group, we calculated ORs for poor outcome at 2 months and 1 year for coiled patients with DCI and for clipped patients without, and with DCI. With these ORs, we calculated relative excess risk due to Interaction (RERI). Clipping increased the risk of DCI compared to coiling in the 2,143 patients OR 1.24, 95% confidence interval (95% CI 1.01–1.51). Coiled patients with DCI, clipped patients without DCI, and clipped patients with DCI all had higher risks of poor outcome than coiled patients without DCI. Clipping and DCI showed no interaction for poor outcome at 2 months: RERI 0.12 (95% CI −1.16 to 1.40) or 1 year: RERI −0.48 (95% CI −1.69 to 0.74). Only for patients treated within 4 days, coiling and DCI was associated with a poorer outcome at 1 year than clipping and DCI (RERI −2.02, 95% CI −3.97 to −0.08). DCI was more common after clipping than after coiling in SAH patients in ISAT. Impact of DCI on poor outcome did not differ between clipped and coiled patients, except for patients treated within 4 days, in whom DCI resulted more often in poor outcome after coiling than after clipping

Hemicraniectomy after middle cerebral artery infarction with life-threatening Edema trial (HAMLET). Protocol for a randomised controlled trial of decompressive surgery in space-occupying hemispheric infarction

<p>Abstract</p> <p>Background</p> <p>Patients with a hemispheric infarct and massive space-occupying brain oedema have a poor prognosis. Despite maximal conservative treatment, the case fatality rate may be as high as 80%, and most survivors are left severely disabled. Non-randomised studies suggest that decompressive surgery reduces mortality substantially and improves functional outcome of survivors. This study is designed to compare the efficacy of decompressive surgery to improve functional outcome with that of conservative treatment in patients with space-occupying supratentorial infarction</p> <p>Methods</p> <p>The study design is that of a multi-centre, randomised clinical trial, which will include 112 patients aged between 18 and 60 years with a large hemispheric infarct with space-occupying oedema that leads to a decrease in consciousness. Patients will be randomised to receive either decompressive surgery in combination with medical treatment or best medical treatment alone. Randomisation will be stratified for the intended mode of conservative treatment (intensive care or stroke unit care). The primary outcome measure will be functional outcome, as determined by the score on the modified Rankin Scale, at one year.</p

Big Data and Causality

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory

Optimising exposure for children and adolescents with anxiety, OCD and PTSD: a systematic review

Cognitive-behavioural therapy is an effective treatment for anxiety disorders in children and young people, however, many do not benefit. Behavioural exposure appears to be the critical ingredient in the treatment of anxiety disorders. Research with adults has identified innovative strategies to optimise exposure-based treatments, yet it is not clear how to optimise the effects of exposure for children and young people. This review was a preliminary exploration of the association between potential optimisation strategies and treatment procedures and outcomes for the treatment of child anxiety symptoms/disorders. We searched PsychInfo and Medline databases using a systematic search strategy and identified 29 articles. We found preliminary evidence that some specific strategies may enhance the effects of exposure, such as dropping safety behaviours, parents and therapists discouraging avoidance, and the use of homework. However, not one significant finding was replicated by another study for the same time point using the same methodology. To a large degree, this lack of replication reflects a limited number of studies combined with a lack of consistency across studies around conceptualisations, methodological approaches, and outcome measures making it difficult to make meaningful comparisons between studies and draw firm conclusions. Examination is needed of a wide range of theoretically-driven potential optimisation strategies using methodologically robust, preclinical studies with children and young people. Furthermore, the methods used in future research must enable comparisons across studies and explore developmental differences in the effects of particular optimisation strategies

Interobserver agreement and predictive value for outcome of two rating scales for the amount of extravasated blood after aneurysmal subarachnoid haemorrhage.

Item does not contain fulltextBACKGROUND: In patients with SAH the amount of extravasated blood on the initial CT scan is related with delayed cerebral ischemia and clinical outcome. We investigated the interobserver variation of the Hijdra and Fisher scales for the amount of extravasated blood and the predictive values of these scales for delayed cerebral ischemia and outcome. METHODS: For 132 patients admitted within 48 hours after SAH three observers assessed the amount of blood on the initial CT scan by means of the Hijdra and Fisher scale. We analyzed interobserver agreement with kappa statistics and used multivariate logistic regression for the association with delayed cerebral ischemia and clinical outcome. RESULTS: The interobserver agreement of all three pairs of observers was good for the Hijdra scale (kappas for total sum scores ranging from 0.67 to 0.75) and mild to moderate for the Fisher scale (kappas ranging from 0.37 to 0.55). For the Hijdra scale the risk of DCI was higher for intermediate (OR 4.2; 95% CI 1.1-16.3) and large (OR 3.6; 95% CI 0.8-16.4) amounts of blood with small amount as reference. Fisher grade III (OR 1.0; 95% CI 0.2-5.2) and IV (OR 0.3; 95% CI 0.02-4.0) were not related with DCI. For the Hijdra scale and clinical outcome we found an increasing risk for poor outcome with intermediate (OR 3.9; 95% CI 1.0-15.9) and large (OR 10.7; 95% CI 2.3-50.1) amounts of blood. Such a relation was not found for Fisher grade III (OR 1.2; 95% CI 0.2-7.0) and IV (OR 0.2; 95% CI 0.01-3.4). CONCLUSIONS: For the Hijdra scale we found a distinct better interobserver agreement than for the Fisher score. Moreover, the Hijdra scale was an independent prognosticator for DCI and clinical outcome, which was not the case for the Fisher score