ADvanced IMage Algebra (ADIMA): a novel method for depicting multiple sclerosis lesion heterogeneity, as demonstrated by quantitative MRI.

There are modest correlations between multiple sclerosis (MS) disability and white matter lesion (WML) volumes, as measured by T2-weighted (T2w) magnetic resonance imaging (MRI) scans (T2-WML). This may partly reflect pathological heterogeneity in WMLs, which is not apparent on T2w scans

Characteristics of lesional and extra-lesional cortical grey matter in relapsing-remitting and secondary progressive multiple sclerosis: A magnetisation transfer and diffusion tensor imaging study

BACKGROUND: In multiple sclerosis (MS), diffusion tensor and magnetisation transfer imaging are both abnormal in lesional and extra-lesional cortical grey matter, but differences between clinical subtypes and associations with clinical outcomes have only been partly assessed. OBJECTIVE: To compare mean diffusivity, fractional anisotropy and magnetisation transfer ratio (MTR) in cortical grey matter lesions (detected using phase-sensitive inversion recovery (PSIR) imaging) and extra-lesional cortical grey matter, and assess associations with disability in relapse-onset MS. METHODS: Seventy-two people with MS (46 relapsing–remitting (RR), 26 secondary progressive (SP)) and 36 healthy controls were included in this study. MTR, mean diffusivity and fractional anisotropy were measured in lesional and extra-lesional cortical grey matter. RESULTS: Mean fractional anisotropy was higher and MTR lower in lesional compared with extra-lesional cortical grey matter. In extra-lesional cortical grey matter mean fractional anisotropy and MTR were lower, and mean diffusivity was higher in the MS group compared with controls. Mean MTR was lower and mean diffusivity was higher in lesional and extra-lesional cortical grey matter in SPMS when compared with RRMS. These differences were independent of disease duration. In multivariate analyses, MTR in extra-lesional more so than lesional cortical grey matter was associated with disability. CONCLUSION: Magnetic resonance abnormalities in lesional and extra-lesional cortical grey matter are greater in SPMS than RRMS. Changes in extra-lesional compared with lesional cortical grey matter are more consistently associated with disability

Serum screening with Down's syndrome markers to predict pre-eclampsia and small for gestational age: Systematic review and meta-analysis

<p>Abstract</p> <p>Background</p> <p>Reliable antenatal identification of pre-eclampsia and small for gestational age is crucial to judicious allocation of monitoring resources and use of preventative treatment with the prospect of improving maternal/perinatal outcome. The purpose of this systematic review was to determine the accuracy of five serum analytes used in Down's serum screening for prediction of pre-eclampsia and/or small for gestational age.</p> <p>Methods</p> <p>The data sources included Medline, Embase, Cochrane library, Medion (inception to February 2007), hand searching of relevant journals, reference list checking of included articles, contact with experts. Two reviewers independently selected the articles in which the accuracy of an analyte used in Downs's serum screening before the 25^thgestational week was associated with the occurrence of pre-eclampsia and/or small for gestational age without language restrictions. Two authors independently extracted data on study characteristics, quality and results.</p> <p>Results</p> <p>Five serum screening markers were evaluated. 44 studies, testing 169,637 pregnant women (4376 pre-eclampsia cases) and 86 studies, testing 382,005 women (20,339 fetal growth restriction cases) met the selection criteria. The results showed low predictive accuracy overall. For pre-eclampsia the best predictor was inhibin A>2.79MoM positive likelihood ratio 19.52 (8.33,45.79) and negative likelihood ratio 0.30 (0.13,0.68) (single study). For small for gestational age it was AFP>2.0MoM to predict birth weight < 10^thcentile with birth < 37 weeks positive likelihood ratio 27.96 (8.02,97.48) and negative likelihood ratio 0.78 (0.55,1.11) (single study). A potential clinical application using aspirin as a treatment is given as an example.</p> <p>There were methodological and reporting limitations in the included studies thus studies were heterogeneous giving pooled results with wide confidence intervals.</p> <p>Conclusion</p> <p>Down's serum screening analytes have low predictive accuracy for pre-eclampsia and small for gestational age. They may be a useful means of risk assessment or of use in prediction when combined with other tests.</p

A statistical framework for quantifying clinical equipoise for individual cases during randomized controlled surgical trials

Background Randomised controlled trials are being increasingly used to evaluate new surgical interventions. There are a number of problematic methodological issues specific to surgical trials, the most important being identifying whether patients are eligible for recruitment into the trial. This is in part due to the diversity in practice patterns across institutions and the enormous range of available interventions that often leads to a low level of agreement between clinicians about both the value and the appropriate choice of intervention. We argue that a clinician should offer patients the option of recruitment into a trial, even if the clinician is not individually in a position of equipoise, if there is collective (clinical) equipoise amongst the wider clinical community about the effectiveness of a proposed intervention (the clinical equipoise principle). We show how this process can work using data collected from an ongoing trial of a surgical intervention. Results We describe a statistical framework for the assessment of uncertainty prior to patient recruitment to a clinical trial using a panel of expert clinical assessors and techniques for eliciting, pooling and modelling of expert opinions. The methodology is illustrated using example data from the UK Heel Fracture Trial. The statistical modelling provided results that were clear and simple to present to clinicians and showed how decisions regarding recruitment were influenced by both the collective opinion of the expert panel and the type of decision rule selected. Conclusions The statistical framework presented has potential to identify eligible patients and assist in the simplification of eligibility criteria which might encourage greater participation in clinical trials evaluating surgical interventions