Long-term outcomes and cost effectiveness of high-dose dexamethasone for cardiac surgery: a randomised trial.

Prophylactic intra-operative administration of dexamethasone may improve short-term clinical outcomes in cardiac surgical patients. The purpose of this study was to evaluate long-term clinical outcomes and cost effectiveness of dexamethasone versus placebo. Patients included in the multicentre, randomised, double-blind, placebo-controlled DExamethasone for Cardiac Surgery (DECS) trial were followed up for 12 months after their cardiac surgical procedure. In the DECS trial, patients received a single intra-operative dose of dexamethasone 1 mg.kg-1 (n = 2239) or placebo (n = 2255). The effects on the incidence of major postoperative events were evaluated. Also, overall costs for the 12-month postoperative period, and cost effectiveness, were compared between groups. Of 4494 randomised patients, 4457 patients (99%) were followed up until 12 months after surgery. There was no difference in the incidence of major postoperative events, the relative risk (95%CI) being 0.86 (0.72-1.03); p = 0.1. Treatment with dexamethasone reduced costs per patient by £921 [€1084] (95%CI £-1672 to -137; p = 0.02), mainly through reduction of postoperative respiratory failure and duration of postoperative hospital stay. The probability of dexamethasone being cost effective compared with placebo was 97% at a threshold value of £17,000 [€20,000] per quality-adjusted life year. We conclude that intra-operative high-dose dexamethasone did not have an effect on major adverse events at 12 months after cardiac surgery, but was associated with a reduction in costs. Routine dexamethasone administration is expected to be cost effective at commonly accepted threshold levels for cost effectiveness.

Insight into "Calculated Risk": An Application to the Prioritization of Emerging Infectious Diseases for Blood Transfusion Safety.

Increasing identification of transmissions of emerging infectious diseases (EIDs) by blood transfusion raised the question which of these EIDs poses the highest risk to blood safety. For a number of the EIDs that are perceived to be a threat to blood safety, evidence on actual disease or transmission characteristics is lacking, which might render measures against such EIDs disputable. On the other hand, the fact that we call them "emerging" implies almost by definition that we are uncertain about at least some of their characteristics. So what is the relative importance of various disease and transmission characteristics, and how are these influenced by the degree of uncertainty associated with their actual values? We identified the likelihood of transmission by blood transfusion, the presence of an asymptomatic phase of infection, prevalence of infection, and the disease impact as the main characteristics of the perceived risk of disease transmission by blood transfusion. A group of experts in the field of infectious diseases and blood transfusion ranked sets of (hypothetical) diseases with varying degrees of uncertainty associated with their disease characteristics, and used probabilistic inversion to obtain probability distributions for the weight of each of these risk characteristics. These distribution weights can be used to rank both existing and newly emerging infectious diseases with (partially) known characteristics. Analyses show that in case there is a lack of data concerning disease characteristics, it is the uncertainty concerning the asymptomatic phase and the disease impact that are the most important drivers of the perceived risk. On the other hand, if disease characteristics are well established, it is the prevalence of infection and the transmissibility of the disease by blood transfusion that will drive the perceived risk. The risk prioritization model derived provides an easy to obtain and rational expert assessment of the relative importance of an (emerging) infectious disease, requiring only a limited amount of information. Such a model might be used to justify a rational and proportional response to an emerging infectious disease, especially in situations where little or no specific information is available.

Identifying and measuring stakeholder preferences for disease prioritisation: A case study of the pig industry in Australia.

We describe stakeholder preference modelling using a combination of new and recently developed techniques to elicit criterion weights to incorporate into a multi-criteria decision analysis framework to prioritise exotic diseases for the pig industry in Australia. Australian pig producers were requested to rank disease scenarios comprising nine criteria in an online questionnaire. Parallel coordinate plots were used to visualise stakeholder preferences, which aided identification of two diverse groups of stakeholders - one group prioritised diseases with impacts on livestock, and the other group placed more importance on diseases with zoonotic impacts. Probabilistic inversion was used to derive weights for the criteria to reflect the values of each of these groups, modelling their choice using a weighted sum value function. Validation of weights against stakeholders' rankings for scenarios based on real diseases showed that the elicited criterion weights for the group who prioritised diseases with livestock impacts were a good reflection of their values, indicating that the producers were able to consistently infer impacts from the disease information in the scenarios presented to them. The highest weighted criteria for this group were attack rate and length of clinical disease in pigs, and market loss to the pig industry. The values of the stakeholders who prioritised zoonotic diseases were less well reflected by validation, indicating either that the criteria were inadequate to consistently describe zoonotic impacts, the weighted sum model did not describe stakeholder choice, or that preference modelling for zoonotic diseases should be undertaken separately from livestock diseases. Limitations of this study included sampling bias, as the group participating were not necessarily representative of all pig producers in Australia, and response bias within this group. The method used to elicit criterion weights in this study ensured value trade-offs between a range of potential impacts, and that the weights were implicitly related to the scale of measurement of disease criteria. Validation of the results of the criterion weights against real diseases - a step rarely used in MCDA - added scientific rigour to the process. The study demonstrated that these are useful techniques for elicitation of criterion weights for disease prioritisation by stakeholders who are not disease experts. Preference modelling for zoonotic diseases needs further characterisation in this context.