Periodic Reporting for period 2 - COMPAR-EU (Comparing effectiveness of self-management interventions in 4 high priority chronic diseases in Europe)
Reporting period: 2019-07-01 to 2020-12-31
We are applying network meta-analysis, an extension of meta-analysis methodology that allows multiple (rather than pairwise) comparisons of intervention effectiveness, to randomised controlled trials (RCTs). This centralised analysis of several thousand RCTs will help overcoming current problems associated with the dispersion and duplication of evidence.
The objectives of COMPAR-EU are:
To identify, compare, and rank the most effective and cost-effective self-management interventions (including preventive and management domains) in Europe for adults living with four chronic diseases.
• To validate a taxonomy of SMIs
• To identify and prioritise SMI outcomes from patients´ perspectives
• To synthesise existing evidence on SMIs from RCTs
• To compare the relative effectiveness of SMIs through systematic review and NMA methodology
• To model the impact of SMIs from the perspective of cost-effectiveness
• To analyse contextual and implementation factors to improve the implementation of SMIs
• To develop, including piloting, decision-making tools to facilitate and disseminate the use of the most effective SMIs to key end users through the COMPAR-EU technology information platform
• To conduct a comprehensive dissemination, communication, and exploitation plan to maximise the impact of the project
COMPAR-EU aims to facilitate informed decision-making and to support implementation of best practices in different healthcare contexts through an interactive platform featuring decision-making tools and other end products tailored to the needs of end users (policymakers, guideline developers and researchers, healthcare professionals, patients, and industry).
• Externally validated taxonomy: we carried out a review of the literature on taxonomies related to self-management, which were the base to develop the COMPAR-EU taxonomy on SMIs. The taxonomy was validated in a two-round Delphi in which 26 international experts participated. The taxonomy is composed of 132 components, classified in four domains (intervention characteristics, expected patient (or carer) self-management behaviours, type of outcomes to measure self-management interventions and target population characteristics). The taxonomy is guiding part of the review of existing RCTs.
• Core Outcome Sets (COS) for each disease: we prepared an initial catalogue of outcomes that was prioritized by patients and patient representatives in two rounds of an online Delphi survey. The results of the Delphi were combined with those of the overview on patients’ preferences. A consensus meeting was held with 19 patients and patient representatives, 20 healthcare professionals and researchers, each representing one of the four disease-areas. The final COS for each condition includes 16 outcomes for COPD, 16 for Heart Failure, 13 for T2DM and 15 for Obesity. The COS are guiding the selection of outcomes to be analysed in our project.
• Completion of the extraction and descriptive results for each disease: We searched Pubmed, Embase, Cinahl, PsycINFO and Cochrane and used earlier findings from PROSTEP to find SMIs for diabetes type II, COPD, Heart Failure and Obesity. Included were RCTs on the effectiveness of SMIs for people of 18 years and older with these diseases and their caregivers that included one or more of the outcomes from the COS. Finally, 698 studies for Diabetes, 252 studies for COPD, 288 studies for Heart Failure and 517 studies for Obesity were included. Descriptive analysis of the studies found per disease included patient characteristics, disease and comorbidities, intervention characteristics (guided by the taxonomy), outcomes (guided by the four COSs), results, study design and risk of bias. Specific attention was paid to subgroups of patients according to comorbidity, gender and socioeconomic variables (e.g. health literacy).
• The project has also advanced in the NMA analysis, completing the NMA geometries for all four diseases and started work in contextual and cost-effectiveness analysis and the development of the decision aids (summary of findings, evidence to decision and patient decision aids) and the COMPAR-EU platform.
• We have developed COS with a strong involvement of patients, clinicians and decision makers, so selected outcomes are relevant to those living with the targeted condition.
• We will integrate existing disperse evidence on SMIs in the four chronic conditions based on a comprehensive taxonomy and rank them based on effectiveness and cost-effectiveness, helping stakeholders to make informed decisions.
• We will address differences in the effectiveness of SMIs for groups that frequently face health inequities, including analysis of subgroups of patients by socioeconomic dimensions or other relevant factors (e.g. gender, age, health literacy), complemented with a contextual analysis to detect barriers to the implementation.
The planned COMPAR-EU end-products, that will be integrated in the online COMPAR-EU platform include but are not limited to: taxonomy of SMIs, COS for each disease, ranking of effective SMIs, list of most cost-effective SMIs, report on contextual factors, Interactive Summary of Findings, Evidence to decision frameworks and Patient-Professional Decision Aids.
For more detail on the end-products including the relation between end-products and end-users see figure 1.
With this end-products we plan to reach all the expected impacts that were set up in the call, providing the required evidence base:
• … for more effective and safer SMIs interventions at an individual and population level, for patients living with T2DM, obesity, COPD, and/or heart failure, including effectiveness, cost-effectiveness and contextual analysis.
• … for enhanced adherence to SMIs, by developing interactive decision-making tools tailored to the health system, organisations, and patients-prescriber interaction.
• … to introduce novel methodologies for the synthesis of evidence (network meta-analysis [NMA] and cost-effectiveness modelling) in health technology assessment methodology.
• … for the improvement of individual patient outcomes and health outcome predictability through tailoring of interventions by NMA and cost-effectiveness modelling, providing results per individual outcome and patient subgroups.
• … for the improvement of guideline development for prevention or treatment of diseases and the management of comorbidities, enhancing self-management as a possible treatment and the inclusion of patient-preferred outcomes in COS in future guidelines.
• … for the provision of more accurate information to patients, caregivers, and prescribers, giving them access to evidence tailored to their needs, though online decision-making tools and other end-product.