Methods to Discover and Validate Biofluid-Based Biomarkers in Neurodegenerative Dementias.

Neurodegenerative dementias are progressive diseases that cause neuronal network breakdown in different brain regions often because of accumulation of misfolded proteins in the brain extracellular matrix, such as amyloids or inside neurons or other cell types of the brain. Several diagnostic protein biomarkers in body fluids are being used and implemented, such as for Alzheimer's disease. However, there is still a lack of biomarkers for co-pathologies and other causes of dementia. Such biofluid-based biomarkers enable precision medicine approaches for diagnosis and treatment, allow to learn more about underlying disease processes, and facilitate the development of patient inclusion and evaluation tools in clinical trials. When designing studies to discover novel biofluid-based biomarkers, choice of technology is an important starting point. But there are so many technologies to choose among. To address this, we here review the technologies that are currently available in research settings and, in some cases, in clinical laboratory practice. This presents a form of lexicon on each technology addressing its use in research and clinics, its strengths and limitations, and a future perspective.

Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE).

Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field. First, there is the technology-translation gap of different technologies for the discovery (e.g., mass spectrometry) and the large-scale validation (e.g., immunoassays) of biomarkers. In addition, there is a limited understanding of conformational states of biomarker proteins in different matrices, which affect the selection of reagents for assay development. In this review, we aim to understand the decisions taken in the initial steps of biomarker development, which is done via an interim narrative update of the work of each ESR subproject. The results describe the decision process to shortlist biomarkers from a proteomics to develop immunoassays or mass spectrometry assays for Alzheimer's disease, Lewy body dementia, and frontotemporal dementia. In addition, we explain the approach to prepare the market implementation of novel biomarkers and assays. Moreover, we describe the development of computational protein state and interaction prediction models to support biomarker development, such as the prediction of epitopes. Lastly, we reflect upon activities involved in the biomarker development process to deduce a best-practice roadmap for biomarker development.

A framework for learning about improvement: embedded implementation and evaluation design to optimize learning.

Improving health care involves many actors, often working in complex adaptive systems. Interventions tend to be multi-factorial, implementation activities diverse, and contexts dynamic and complicated. This makes improvement initiatives challenging to describe and evaluate as matching evaluation and program designs can be difficult, requiring collaboration, trust and transparency. Collaboration is required to address important epidemiological principles of bias and confounding. If this does not take place, results may lack credibility because the association between interventions implemented and outcomes achieved is obscure and attribution uncertain. Moreover, lack of clarity about what was implemented, how it was implemented, and the context in which it was implemented often lead to disappointment or outright failure of spread and scale-up efforts. The input of skilled evaluators into the design and conduct of improvement initiatives can be helpful in mitigating these potential problems. While evaluation must be rigorous, if it is too rigid necessary adaptation and learning may be compromised. This article provides a framework and guidance on how improvers and evaluators can work together to design, implement and learn about improvement interventions more effectively.

Unpacking the black box of improvement.

During the Salzburg Global Seminar Session 565-'Better Health Care: How do we learn about improvement?', participants discussed the need to unpack the 'black box' of improvement. The 'black box' refers to the fact that when quality improvement interventions are described or evaluated, there is a tendency to assume a simple, linear path between the intervention and the outcomes it yields. It is also assumed that it is enough to evaluate the results without understanding the process of by which the improvement took place. However, quality improvement interventions are complex, nonlinear and evolve in response to local settings. To accurately assess the effectiveness of quality improvement and disseminate the learning, there must be a greater understanding of the complexity of quality improvement work. To remain consistent with the language used in Salzburg, we refer to this as 'unpacking the black box' of improvement. To illustrate the complexity of improvement, this article introduces four quality improvement case studies. In unpacking the black box, we present and demonstrate how Cynefin framework from complexity theory can be used to categorize and evaluate quality improvement interventions. Many quality improvement projects are implemented in complex contexts, necessitating an approach defined as 'probe-sense-respond'. In this approach, teams experiment, learn and adapt their changes to their local setting. Quality improvement professionals intuitively use the probe-sense-respond approach in their work but document and evaluate their projects using language for 'simple' or 'complicated' contexts, rather than the 'complex' contexts in which they work. As a result, evaluations tend to ask 'How can we attribute outcomes to the intervention?', rather than 'What were the adaptations that took place?'. By unpacking the black box of improvement, improvers can more accurately document and describe their interventions, allowing evaluators to ask the right questions and more adequately evaluate quality improvement interventions.

Salzburg Global Seminar Session 565-'Better Health Care: how do we learn about improvement?'

A fundamental question for the field of healthcare improvement is the extent to which the results achieved can be attributed to the changes that were implemented and whether or not these changes are generalizable. Answering these questions is particularly challenging because the healthcare context is complex, and the interventions themselves tend to be complex and multi-dimensional. The Salzburg Global Seminar Session 565-'Better Health Care: How do we learn about improvement?' was convened to address questions of attribution, generalizability and rigor, and to think through how to approach these concerns in the field of quality improvement. The Salzburg Global Seminar Session 565 brought together 61 leaders in improvement from 22 countries, including researchers, evaluators and improvers. The primary conclusion that resulted from the session was the need for evaluation to be embedded as an integral part of the improvement. We have invited participants of the seminar to contribute to writing this supplement, which consists of eight articles reflecting insights and learning from the Salzburg Global Seminar. This editorial serves as an introduction to the supplement. The supplement explains results and insights from Salzburg Global Seminar Session 565.

Managing hundreds of improvement teams.

Recognizing the notable scale of USAID Applying Science to Strengthen and Improve Systems (ASSIST) Project activities and sizable number of improvement teams, which in some cases is close to 1,000 improvement teams managed in one country at a point in time, we sought to answer the questions: How do we manage hundreds of improvement teams in one country alone? How do we manage more than 4,000 improvement teams globally? The leaders of our improvement programs manage such efforts as though they are second-nature, without pointing to the specific skills and strategies needed to manage thousands of teams. This paper was developed to capture the lessons, considerations, and insights shared in discussions with leaders on the USAID ASSIST Project, including country Chiefs of Party and Regional Directors. More specifically, this paper seeks to describe what is involved in scaling up and managing large numbers of improvement teams. Through focus group discussions and individual interviews, participants discussed the key skills, strategies, and lessons needed to successfully manage large numbers of teams on the USAID ASSIST Project. We concluded that the six key components in managing large numbers of teams are 1) leadership; 2) management structures and capacities; 3) clear and open communication; 4) shared learning, collaboration, and support; 5) ownership, engagement, and empowerment; and 6) partnerships. We further analyzed these six components as being interrelated to one another based on the relationship between culture, strategy, and technique in implementing quality improvement activities.