Ethical aspects of facilitating the recruitment of people with dementia for clinical trials: A call for further debate

An innovative approach to recruiting participants for dementia research: primary care and researcher perspectives

Aim: To describe clinician and researcher perceptions of a new, patient preference focused approach to recruiting patients for research from primary care-based memory clinics. Methods: Memory clinic clinicians completed a survey and key informants completed an individual interview to gather their perceptions of this new program. Results: The majority of clinicians were 'satisfied' or 'very satisfied' with this recruitment approach and indicated that this approach would have minimal negative impact on patient care or create conflict of interest. Key informants valued the program for its patient-centred approach, the integration of research into care and potential for increased recruitment. Discussion: These findings are suggestive of support for this recruitment approach. Pilot testing will inform feasibility, effectiveness and process improvements.

Artificial intelligence for dementia prevention

INTRODUCTION

A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.

METHODS

ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.

RESULTS

Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.

DISCUSSION

ML is not yet widely used but has considerable potential to enhance precision in dementia prevention.

HIGHLIGHTS

Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

Protecting the Vulnerable and Including the Under-Represented: IRB Practices and Attitudes

Since their inception, Institutional Review Boards (IRBs) have been charged with protecting the vulnerable in research. More recently, attention has turned to whether IRBs also have a role to play in ensuring representative study samples and promoting the inclusion of historically under-represented groups. These two aims-protecting the vulnerable and including the under-represented-can pull in different directions, given the potential for overlap between the vulnerable and the under-represented. We conducted a pilot, online national survey of IRB Chairs to gauge attitudes and practices with regard to protecting the vulnerable and including the under-represented in research. We found that IRBs extend the concept of vulnerability to different groups across various contexts, are confident that they effectively protect vulnerable individuals in research, and believe that IRBs have a role to play in ensuring representative samples and the inclusion of under-represented groups.