Pre-operative optimisation employing dopexamine or adrenaline for patients undergoing major elective surgery: a cost-effectiveness analysis

<b>Objective</b>: To compare the cost and cost-effectiveness of a policy of pre-operative optimisation of oxygen delivery (using either adrenaline or dopexamine) to reduce the risk associated with major elective surgery, in high-risk patients. <b>Methods</b>: A cost-effectiveness analysis using data from a randomised controlled trial (RCT). In the RCT 138 patients undergoing major elective surgery were allocated to receive pre-operative optimisation employing either adrenaline or dopexamine (assigned randomly), or to receive routine peri-operative care. Differential health service costs were based on trial data on the number and cause of hospital in-patient days and the utilisation of health care resources. These were costed using unit costs from a UK hospital. The cost-effectiveness analysis related differential costs to differential life-years during a 2 year trial follow-up. <b>Results</b>: The mean number of in-patient days was 16 in the pre-optimised groups (19 adrenaline; 13 dopexamine) and 22 in the standard care group. The number (%) of deaths, over a 2 year follow-up, was 24 (26%) in the pre-optimised groups and 15 (33%) in the standard care group. The mean total costs were EUR 11,310 in the pre-optimised groups and EUR 16,965 in the standard care group. Life-years were 1.68 in the pre-optimised groups and 1.46 in the standard care group. The probability that pre-operative optimisation is less costly than standard care is 98%. The probability that it dominates standard care is 93%. Conclusions: Based on resource use and effectiveness data collected in the trial, pre-operative optimisation of high-risk surgical patients undergoing major elective surgery is cost-effective compared with standard treatment

Simulation sample sizes for Monte Carlo partial EVPI calculations

Partial expected value of perfect information (EVPI) quantifies the value of removing uncertainty about unknown parameters in a decision model. EVPIs can be computed via Monte Carlo methods. An outer loop samples values of the parameters of interest, and an inner loop samples the remaining parameters from their conditional distribution. This nested Monte Carlo approach can result in biased estimates if small numbers of inner samples are used and can require a large number of model runs for accurate partial EVPI estimates. We present a simple algorithm to estimate the EVPI bias and confidence interval width for a specified number of inner and outer samples. The algorithm uses a relatively small number of model runs (we suggest approximately 600), is quick to compute, and can help determine how many outer and inner iterations are needed for a desired level of accuracy. We test our algorithm using three case studies. (C) 2010 Elsevier B.V. All rights reserved

An experimental investigation of the benefit derived from the employment of specially designed equipment for instruction in fundamental electrical and radio theory

This investigation was effected by sub-dividing a twenty weeks course in Advanced Wireless Training into fifteen Study Sections. After securing an initial measure of ability, using the Jenkins' Test, the pupils (204) were random assigned to either experimental groups, when they had the advantage of the special training equipment, or to control groups, when they received lectures only. At the conclusion of each Study Section attainment was estimated by application of a suitable objective progress test. In order to increase the precision of the investigation, the experimental groups for the first seven Study Sections became the control groups for the second seven Study Sections, the treatment of Study Section 11 was the same for both groups and the instructors taught classes using both instructional methods. The scores obtained in the experiments were analysed by the method of co-variance when the influences of pupil and instructor variables were eliminated and the hypothesis that the difference between the two methods-groups was solely attributable to chance was examined. In thirteen cases the results of the experimental groups were significantly superior at the 5% level to those of the control groups and in four instances these differences were outstanding at the 1% level. The Study Section, in which both groups received the same treatment, produced an insignificant value of 'F' (1.3) thereby strongly supporting the previous findings. The low value of 'F' (6.0) (F 5% - 10.14) obtained for the remaining Study Section was accepted on the basis of poorer ratio of support furnished by the practical work and it was not deemed to have established conflicting evidence. These analyses showed that the employment of the training aids, the large majority of which were original in both design and construction, had considerably improved the standard of the training investigated

The Knights of the Front: Medieval History’s Influence on Great War Propaganda

Spanning a number of academic areas, “Knights of the Front: Medieval History’s Influence on Great War Propaganda” focuses on the emergence of medieval imagery in the First World War propaganda. Examining several specific uses of medieval symbolism in propaganda posters from both Central and Allied powers, the article provides insight into the narrative of war, both politically and culturally constructed. The paper begins with an overview of the psychology behind visual persuasion and the history behind Europe’s cultural affinity for “chivalry,” then continues into specific case studies of period propaganda posters that hold not only themes of military glory and prowess, but also themes of race, gender, and religion as well. Finally, the article makes the argument that the realities of the First World War shattered the chivalrous and romantic ideals of war so completely that the concepts and images were no longer appropriate for use as propaganda

First-Principles Calculation of Electric Field Gradients and Hyperfine Couplings in YBa2Cu3O7

The local electronic structure of YBa2Cu3O7 has been calculated using first-principles cluster methods. Several clusters embedded in an appropriate background potential have been investigated. The electric field gradients at the copper and oxygen sites are determined and compared to previous theoretical calculations and experiments. Spin polarized calculations with different spin multiplicities have enabled a detailed study of the spin density distribution to be made and a simultaneous determination of magnetic hyperfine coupling parameters. The contributions from on-site and transferred hyperfine fields have been disentangled with the conclusion that the transferred spin densities essentially are due to nearest neighbour copper ions only with marginal influence of ions further away. This implies that the variant temperature dependencies of the planar copper and oxygen NMR spin-lattice relaxation rates are only compatible with commensurate antiferromagnetic correlations. The theoretical hyperfine parameters are compared with those derived from experimental data.Comment: 14 pages, 12 figures, accepted to appear in EPJ

Codesigned Shared Decision-Making Diabetes Management Plan Tool for Adolescents With Type 1 Diabetes Mellitus and Their Parents: Prototype Development and Pilot Test

Background: Adolescents with type 1 diabetes mellitus have difficulty achieving optimal glycemic control, partly due to competing priorities that interfere with diabetes self-care. Often, significant diabetes-related family conflict occurs, and adolescents’ thoughts and feelings about diabetes management may be disregarded. Patient-centered diabetes outcomes may be better when adolescents feel engaged in the decision-making process. Objective: The objective of our study was to codesign a clinic intervention using shared decision making for addressing diabetes self-care with an adolescent patient and parent advisory board. Methods: The patient and parent advisory board consisted of 6 adolescents (teens) between the ages 12 and 18 years with type 1 diabetes mellitus and their parents recruited through our institution’s Pediatric Diabetes Program. Teens and parents provided informed consent and participated in 1 or both of 2 patient and parent advisory board sessions, lasting 3 to 4 hours each. Session 1 topics were (1) patient-centered outcomes related to quality of life, parent-teen shared diabetes management, and shared family experiences; and (2) implementation and acceptability of a patient-centered diabetes care plan intervention where shared decision making was used. We analyzed audio recordings, notes, and other materials to identify and extract ideas relevant to the development of a patient-centered diabetes management plan. These data were visually coded into similar themes. We used the information to develop a prototype for a diabetes management plan tool that we pilot tested during session 2. Results: Session 1 identified 6 principal patient-centered quality-of-life measurement domains: stress, fear and worry, mealtime struggles, assumptions and judgments, feeling abnormal, and conflict. We determined 2 objectives to be principally important for a diabetes management plan intervention: (1) focusing the intervention on diabetes distress and conflict resolution strategies, and (2) working toward a verbalized common goal. In session 2, we created the diabetes management plan tool according to these findings and will use it in a clinical trial with the aim of assisting with patient-centered goal setting. Conclusions: Patients with type 1 diabetes mellitus can be effectively engaged and involved in patient-centered research design. Teens with type 1 diabetes mellitus prioritize reducing family conflict and fitting into their social milieu over health outcomes at this time in their lives. It is important to acknowledge this when designing interventions to improve health outcomes in teens with type 1 diabetes mellitus

Calculating partial expected value of perfect information via Monte Carlo sampling algorithms

Partial expected value of perfect information (EVPI) calculations can quantify the value of learning about particular subsets of uncertain parameters in decision models. Published case studies have used different computational approaches. This article examines the computation of partial EVPI estimates via Monte Carlo sampling algorithms. The mathematical definition shows 2 nested expectations, which must be evaluated separately because of the need to compute a maximum between them. A generalized Monte Carlo sampling algorithm uses nested simulation with an outer loop to sample parameters of interest and, conditional upon these, an inner loop to sample remaining uncertain parameters. Alternative computation methods and shortcut algorithms are discussed and mathematical conditions for their use considered. Maxima of Monte Carlo estimates of expectations are biased upward, and the authors show that the use of small samples results in biased EVPI estimates. Three case studies illustrate 1) the bias due to maximization and also the inaccuracy of shortcut algorithms 2) when correlated variables are present and 3) when there is nonlinearity in net benefit functions. If relatively small correlation or nonlinearity is present, then the shortcut algorithm can be substantially inaccurate. Empirical investigation of the numbers of Monte Carlo samples suggests that fewer samples on the outer level and more on the inner level could be efficient and that relatively small numbers of samples can sometimes be used. Several remaining areas for methodological development are set out. A wider application of partial EVPI is recommended both for greater understanding of decision uncertainty and for analyzing research priorities