Training in the Conduct of Population-Based Multi-Site and Multi-Disciplinary Studies: the Cancer Research Network’s Scholars Program

Expanding research capacity of large research networks within health care delivery systems requires strategically training both embedded and external investigators in necessary skills for this purpose. Researchers new to these settings frequently lack the skills and specialized knowledge conducive to multi-site and multi-disciplinary research set in delivery systems. This report describes the goals and components of the Cancer Research Network (CRN) Scholars Program, a 26-month training program developed to increase the capacity for cancer research conducted within the network’s participating sites, its progression from training embedded investigators to a mix of internal and external investigators, and the content evolution of the training program. The CRN Scholars program was launched in 2007 to assist junior investigators from member sites develop independent and sustainable research programs within the CRN. Resulting from CRN’s increased emphasis on promoting external collaborations, the 2013 Scholars program began recruiting junior investigators from external institutions committed to conducting delivery system science. Based on involvement of this broader population and feedback from prior Scholar cohorts, the program has honed its focus on specific opportunities and issues encountered in conducting cancer research within health care delivery systems. Efficiency and effectiveness of working within networks is accelerated by strategic and mentored navigation of these networks. Investing in training programs specific to these settings provides the opportunity to improve multi-disciplinary and multi-institutional collaboration, particularly for early-stage investigators. Aspects of the CRN Scholars Program may help inform others considering developing similar programs to expand delivery system research or within large, multi-disciplinary research networks

Correction: Pulsed moxifloxacin for the prevention of exacerbations of chronic obstructive pulmonary disease: a randomized controlled trial

BACKGROUND: Acute exacerbations contribute to the morbidity and mortality associated with chronic obstructive pulmonary disease (COPD). This proof-of-concept study evaluates whether intermittent pulsed moxifloxacin treatment could reduce the frequency of these exacerbations. METHODS: Stable patients with COPD were randomized in a double-blind, placebo-controlled trial to receive moxifloxacin 400 mg PO once daily (N = 573) or placebo (N = 584) once a day for 5 days. Treatment was repeated every 8 weeks for a total of six courses. Patients were repeatedly assessed clinically and microbiologically during the 48-week treatment period, and for a further 24 weeks' follow-up. RESULTS: At 48 weeks the odds ratio (OR) for suffering an exacerbation favoured moxifloxacin: per-protocol (PP) population (N = 738, OR 0.75, 95% confidence interval (CI) 0.565-0.994, p = 0.046), intent-to-treat (ITT) population (N = 1149, OR 0.81, 95% CI 0.645-1.008, p = 0.059), and a post-hoc analysis of per-protocol (PP) patients with purulent/mucopurulent sputum production at baseline (N = 323, OR 0.55, 95% CI 0.36-0.84, p = 0.006).There were no significant differences between moxifloxacin and placebo in any pre-specified efficacy subgroup analyses or in hospitalization rates, mortality rates, lung function or changes in St George's Respiratory Questionnaire (SGRQ) total scores. There was, however, a significant difference in favour of moxifloxacin in the SGRQ symptom domain (ITT: -8.2 vs -3.8, p = 0.009; PP: -8.8 vs -4.4, p = 0.006). Moxifloxacin treatment was not associated with consistent changes in moxifloxacin susceptibility. There were more treatment-emergent, drug related adverse events with moxifloxacin vs placebo (p < 0.001) largely due to gastrointestinal events (4.7% vs 0.7%). CONCLUSIONS: Intermittent pulsed therapy with moxifloxacin reduced the odds of exacerbation by 20% in the ITT population, by 25% among the PP population and by 45% in PP patients with purulent/mucopurulent sputum at baseline. There were no unexpected adverse events and there was no evidence of resistance development. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT00473460 (ClincalTrials.gov)

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A reusable scientific workflow for conservation planning

In order to perform complex scientific data analysis, multiple software and skillsets are generally required. These analyses can involve collaborations between scientific and technical communities, with expertise in problem formulation and the use of tools and programming languages. While such collaborations are useful for solving a given problem, transferability and productivity of the approach is low and requires considerable assistance from the original tool developers.Any complex scientific data analysis involves accessing and refining large volumes of data, running simulations and algorithms, and visualising results. These steps can incorporate a variety of tools and programming languages, and can be constructed as a series of activities to achieve a desired outcome. This is where scientific workflows are very useful. Scientific workflows abstract complex analyses into a series of inter-dependent computational steps that lead to a solution for a scientific problem. Once constructed, the workflow can be executed repeatedly and the results reproduced with minimal assistance from the original tool developers. This improves transferability, repeatability and productivity, and reduces costs by reusing workflow components for similar problems but using different datasets. Kepler is a popular open-source scientific workflow tool for designing, executing, archiving and sharing workflows. It has the ability to couple disparate execution environments on a single platform. For example, users can run analysis steps written in Python, R and Matlab on a single platform as part of a single analysis and synthesis experiment. Kepler provides a wide variety of reusable components that perform various tasks, including data access from databases, remote system, file system and web services, and data servers, and executes these processes in a local or distributed environment. Together these functionalities provide greater flexibility for researchers to undertake complex scientific analyses compared with traditional homogeneous environments.In this paper, we will describe a new scientific workflow based on Kepler that automates data analysis tasks for Marxan, a widely used conservation planning software. Marxan is used by over 4,200 active users in more than 180 countries to identify gaps in biodiversity protection, identify cost effective areas for conservation investment and inform multiple-use zoning. Its use is expanding rapidly and this new functionality will improve the application of Marxan to various conservation planning problems. A Kepler workbench has been extended to provide functionality to invoke Marxan and execute it within a distributed environment using Nimrod/K. Our aim was to develop a reproducible, reusable workflow to generate conservation planning scenarios on the Kepler platform. The workflow components include data acquisition and pre-processing, construction of planning scenarios, generation of efficient solutions to the complex problem formulations and visualization of outputs. The workflow components are shared for reuse and reconfigured to design and simulate other conservation planning applications. We also present a use case to demonstrate a Kepler Marxan workflow to design and implement conservation planning computational simulation experiments