Identification of surgeon burnout via a single-item measure

Background Burnout is endemic in surgeons in the UK and linked with poor patient safety and quality of care, mental health problems, and workforce sustainability. Mechanisms are required to facilitate the efficient identification of burnout in this population. Multi-item measures of burnout may be unsuitable for this purpose owing to assessment burden, expertise required for analysis, and cost. Aims To determine whether surgeons in the UK reporting burnout on the 22-item Maslach Burnout Inventory (MBI) can be reliably identified by a single-item measure of burnout. Methods Consultant (n = 333) and trainee (n = 217) surgeons completed the MBI and a single-item measure of burnout. We applied tests of discriminatory power to assess whether a report of high burnout on the single-item measure correctly classified MBI cases and non-cases. Results The single-item measure demonstrated high discriminatory power on the emotional exhaustion burnout domain: the area under the curve was excellent for consultants and trainees (0.86 and 0.80), indicating high sensitivity and specificity. On the depersonalisation domain, discrimination was acceptable for consultants (0.76) and poor for trainees (0.69). In contrast, discrimination was acceptable for trainees (0.71) and poor for consultants (0.62) on the personal accomplishment domain. Conclusions A single-item measure of burnout is suitable for the efficient assessment of emotional exhaustion in consultant and trainee surgeons in the UK. Administered regularly, such a measure would facilitate the early identification of at-risk surgeons and swift intervention, as well as the monitoring of group-level temporal trends to inform resource allocation to coincide with peak periods

Novel methodology to discern predictors of remission and patterns of disease activity over time using rheumatoid arthritis clinical trials data

Objectives To identify predictors of remission and disease activity patterns in patients with rheumatoid arthritis (RA) using individual participant data (IPD) from clinical trials. Methods Phases II and III clinical trials completed between 2002 and 2012 were identified by systematic literature review and contact with UK market authorisation holders. Anonymised baseline and follow-up IPD from non-biological arms were amalgamated. Multiple imputation was used to handle missing outcome and covariate information. Random effects logistic regression was used to identify predictors of remission, measured by the DAS28 score at 6 months. Novel latent class mixed models characterised DAS28 over time.Results IPD of 3290 participants from 18 trials were included. Of these participants, 92% received methotrexate (MTX). Remission rates were estimated at 8.4% (95%CI: 7.4%-9.5%) overall, 17% (95%CI: 14.8%-19.4%) for MTX-naïve early RA patients, and 3.2% (95%CI: 2.4%-4.3%) for those with prior MTX exposure at entry. In prior MTX-exposed patients, lower baseline DAS28 and MTX-re-initiation were associated with remission. In MTX-naïve patients, being young, white, male, with better functional and mental health, lower baseline DAS28 and receiving concomitant glucocorticoids were associated with remission. Three DAS28 trajectory sub-populations were identified in MTX-naïve and MTX-exposed patients. A number of variables were associated with sub-population membership and DAS28 levels within sub-populations. Conclusions Predictors of remission differed between MTX-naïve and prior MTX-exposed patients at entry. Latent class mixed models supported differential non-biologic therapy response, with three distinct trajectories observed in both MTX-naïve and MTX-exposed patients. Findings should be useful when designing future RA trials and interpreting results of biomarker studies. <br/

Batteries in the energy transition: A cross-border use case from the Rhine-Waal region

The built environment accounts for approximately 30-40% of the total energy consumption in the Netherlands and Germany (RVO, 2015). The INTERREG project Cleantech Energy Crossing therefore aims to facilitate the energy transition in this area by promoting cross-border collaboration. Through the realization of various innovations and new technological products, this project wants to make an important contribution in achieving the climate targets of the Netherlands (-20% CO2 emissions to 2020) and North Rhine-Westphalia (-25% CO2 emissions to 2020)