Report from the third international consensus meeting to harmonise core outcome measures for atopic eczema/dermatitis clinical trials (HOME).

This report provides a summary of the third meeting of the Harmonising Outcome Measures for Eczema (HOME) initiative held in San Diego, CA, U.S.A., 6-7 April 2013 (HOME III). The meeting addressed the four domains that had previously been agreed should be measured in every eczema clinical trial: clinical signs, patient-reported symptoms, long-term control and quality of life. Formal presentations and nominal group techniques were used at this working meeting, attended by 56 voting participants (31 of whom were dermatologists). Significant progress was made on the domain of clinical signs. Without reference to any named scales, it was agreed that the intensity and extent of erythema, excoriation, oedema/papulation and lichenification should be included in the core outcome measure for the scale to have content validity. The group then discussed a systematic review of all scales measuring the clinical signs of eczema and their measurement properties, followed by a consensus vote on which scale to recommend for inclusion in the core outcome set. Research into the remaining three domains was presented, followed by discussions. The symptoms group and quality of life groups need to systematically identify all available tools and rate the quality of the tools. A definition of long-term control is needed before progress can be made towards recommending a core outcome measure

Italy vs. the United States: The rise and fall of socialist parties

Thesis (B.A.) in Liberal Arts and Sciences -- University of Illinois at Urbana-Champaign, 1987.Bibliography: leaves 44-46.Microfiche of typescript. [Urbana, Ill.]: Photographic Services, University of Illinois, U of I Library, [1987]. 2 microfiches (52 frames): negative

The Use of Decision–Analytic Models in Atopic Eczema: A Systematic Review and Critical Appraisal

Objective: The objective of this systematic review was to identify and assess the quality of published economic decision–analytic models within atopic eczema against best practice guidelines, with the intention of informing future decision–analytic models within this condition. Methods: A systematic search of the following online databases was performed: MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects, Cochrane Database of Systematic Reviews, NHS Economic Evaluation Database, EconLit, Scopus, Health Technology Assessment, Cost-Effectiveness Analysis Registry and Web of Science. Papers were eligible for inclusion if they described a decision–analytic model evaluating both the costs and benefits associated with an intervention or prevention for atopic eczema. Data were extracted using a standardised form by two independent reviewers, whilst quality was assessed using the model-specific Philips criteria. Results: Twenty-four models were identified, evaluating either preventions (n = 12) or interventions (n = 12): 14 reported using a Markov modelling approach, four utilised decision trees and one a discrete event simulation, whilst five did not specify the approach. The majority, 22 studies, reported that the intervention was dominant or cost effective, given the assumptions and analytical perspective taken. Notably, the models tended to be short-term (16 used a time horizon of ≤1 year), often providing little justification for the limited time horizon chosen. The methodological and reporting quality of the studies was generally weak, with only seven studies fulfilling more than 50% of their applicable Philips criteria. Conclusions: This is the first systematic review of decision models in eczema. Whilst the majority of models reported favourable outcomes in terms of the cost effectiveness of the new intervention, the usefulness of these findings for decision-making is questionable. In particular, there is considerable scope for increasing the range of interventions evaluated, for improving modelling structures and reporting quality

Validity of algorithms for identifying five chronic conditions in MedicineInsight, an Australian national general practice database.

Background MedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. It is one of the largest and most widely used primary health care EHR databases in Australia. This study examined the validity of algorithms that use information from various fields in the MedicineInsight data to indicate whether patients have specific health conditions. This study examined the validity of MedicineInsight algorithms for five common chronic conditions: anxiety, asthma, depression, osteoporosis and type 2 diabetes. Methods Patients’ disease status according to MedicineInsight algorithms was benchmarked against the recording of diagnoses in the original EHRs. Fifty general practices contributing data to MedicineInsight met the eligibility criteria regarding patient load and location. Five were randomly selected and four agreed to participate. Within each practice, 250 patients aged ≥ 40 years were randomly selected from the MedicineInsight database. Trained staff reviewed the original EHR for as many of the selected patients as possible within the time available for data collection in each practice. Results A total of 475 patients were included in the analysis. All the evaluated MedicineInsight algorithms had excellent specificity, positive predictive value, and negative predictive value (above 0.9) when benchmarked against the recording of diagnoses in the original EHR. The asthma and osteoporosis algorithms also had excellent sensitivity, while the algorithms for anxiety, depression and type 2 diabetes yielded sensitivities of 0.85, 0.89 and 0.89 respectively. Conclusions The MedicineInsight algorithms for asthma and osteoporosis have excellent accuracy and the algorithms for anxiety, depression and type 2 diabetes have good accuracy. This study provides support for the use of these algorithms when using MedicineInsight data for primary health care quality improvement activities, research and health system policymaking and planning

Maternal Preeclampsia and Neonatal Outcomes

Preeclampsia is a multiorgan, heterogeneous disorder of pregnancy associated with significant maternal and neonatal morbidity and mortality. Optimal strategies in the care of the women with preeclampsia have not been fully elucidated, leaving physicians with incomplete data to guide their clinical decision making. Because preeclampsia is a progressive disorder, in some circumstances, delivery is needed to halt the progression to the benefit of the mother and fetus. However, the need for premature delivery has adverse effects on important neonatal outcomes not limited to the most premature infants. Late-preterm infants account for approximately two thirds of all preterm deliveries and are at significant risk for morbidity and mortality. Reviewed is the current literature in the diagnosis and obstetrical management of preeclampsia, the outcomes of late-preterm infants, and potential strategies to optimize fetal outcomes in pregnancies complicated by preeclampsia