Nicklas Linz, Raphael Ullmann, Johannes Tröger, Alexandra König, Ruth Croney, Tobias Bittner & Thanneer M. Perumal
* Poster presented at the 17th Clinical Trials on Alzheimer’s Disease (CTAD), Madrid (Spain)
Abstract
Background: Recruitment for Alzheimer’s disease (AD) clinical trials has historically been challenging and costly. The average recruitment time for phase II studies is 2.5 years for pre-symptomatic AD and 2.9 years for mild cognitive impairment (MCI), largely due to high screen failure rates exceeding 95% [1]. Additionally, there is generally a lack of diversity among trial participants in AD studies. Advances in blood-based biomarkers (BBBM) and remote digital cognitive assessments (DCA) offer promising strategies to enhance recruitment efficiency. This work models the potential impact on AD trial recruitment costs using different pre-screening strategies with combinations of DCA and BBBM.
Method: We modeled recruitment costs for AD trials with and without the integration of DCAs and BBBM. A staged funnel approach was employed, incorporating assumed conversion rates and per-participant costs at each stage. The analysis considered two distinct AD study populations and three different recruitment strategies (community-based, primary care, and memory clinic). Prevalence rates for cognitive impairment and PET positivity were derived from existing literature [2]. To estimate pre-screening effectiveness, performance data from current DCAs and BBBM were utilized[3,4].
Results: All pre-screening strategies reduced recruitment costs or were nearly cost-neutral. The combination of DCA and BBBM in a staged approach yielded the greatest cost reduction for the recruitment of participants with MCI (12 to 40%). Using BBBM or DCA alone resulted in varying cost reductions (-3 to 36%), with BBBMs alone being more effective for pre-symptomatic studies, and DCAs for studies focused on MCI. Community recruitment strategies saw the largest cost reductions. Furthermore, although pre-screening strategies necessitated reaching out to more individuals initially to achieve the same recruitment target, they reduced the number of participants needing formal on-site screening by 11 to 68%. This reduction has the potential to decrease timelines and reduce patient burden. In pre-symptomatic studies, the benefit of pre-screening was maintained, with DCAs tending to be cost-neutral overall, resulting in 11 to 33% fewer participants needing on-site screening.
Conclusion: Pre-screening could be an economically beneficial strategy for AD trials. Since PET scans are typically performed at the end of screening protocols, reducing screen failure rates for PET may have the most significant impact on overall cost reduction. Beyond direct cost savings, pre-screening might expedite overall trial timelines and improve site resource utilization. It may be worthwhile for pre-symptomatic trials and those aiming for diverse participant recruitment from the community to consider implementing these pre-screening methods to potentially enhance efficiency and cost-effectiveness.
References:
1. Malzbender, K., Lavin-Mena, L., Hughes, L., Bose, N., Goldman, D., & Patel, D. (2020). Key Barriers for Clinical Trials for Alzheimer’s Disease. White Papers – USC Schaeffer Center.
2. Manly, J. J., Jones, R. N., Langa, K. M., Ryan, L. H., Levine, D. A., McCammon, R., Heeringa, S. G., & Weir, D. (2022). Estimating the Prevalence of Dementia and Mild Cognitive Impairment in the US: The 2016 Health and Retirement Study Harmonized Cognitive Assessment Protocol Project. JAMA Neurology, 79(12), 1242–1249. https://doi.org/10.1001/jamaneurol.2022.3543
3. Palmqvist, S., Janelidze, S., Quiroz, Y. T., Zetterberg, H., Lopera, F., Stomrud, E., Su, Y., Chen, Y., Serrano, G. E., Leuzy, A., Mattsson-Carlgren, N., Strandberg, O., Smith, R., Villegas, A., Sepulveda-Falla, D., Chai, X., Proctor, N. K., Beach, T. G., Blennow, K., … Hansson, O. (2020). Discriminative Accuracy of Plasma Phospho-tau217 for Alzheimer Disease vs Other Neurodegenerative Disorders. JAMA, 324(8), 772–781. https://doi.org/10.1001/jama.2020.12134
4. Schäfer, S., Mallick, E., Schwed, L., König, A., Zhao, J., Linz, N., Bodin, T. H., Skoog, J., Possemis, N., ter Huurne, D., Zettergren, A., Kern, S., Sacuiu, S., Ramakers, I., Skoog, I., & Tröger, J. (2023). Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts. Journal of Alzheimer’s Disease, 91(3), 1165–1171. https://doi.org/10.3233/JAD-220762
