Prediction of Cognitive Decline for Enrichment of Alzheimer’s Disease Clinical Trials - 21/11/24
Alzheimer’s Disease Neuroimaging Initiative
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Abstract |
Background |
A key issue to Alzheimer’s disease clinical trial failures is poor participant selection. Participants have heterogeneous cognitive trajectories and many do not decline during trials, which reduces a study’s power to detect treatment effects. Trials need enrichment strategies to enroll individuals who are more likely to decline.
Objectives |
To develop machine learning models to predict cognitive trajectories in participants with early Alzheimer’s disease and presymptomatic individuals over 24 and 48 months respectively.
Design |
Prognostic machine learning models were trained from a combination of demographics, cognitive tests, APOE genotype, and brain imaging data.
Setting |
Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), National Alzheimer’s Coordinating Center (NACC), Open Access Series of Imaging Studies (OASIS-3), PharmaCog, and a Phase 3 clinical trial in early Alzheimer’s disease were used for this study.
Participants |
A total of 2098 participants who had demographics, cognitive tests, APOE genotype, and brain imaging data, as well as follow-up visits for 24–48 months were included.
Measurements |
Baseline magnetic resonance imaging, cognitive tests, demographics, and APOE genotype were used to separate decliners, defined as individuals whose CDR-Sum of Boxes scores increased during a predefined time window, from stable individuals. A prognostic model to predict decline at 24 months in early Alzheimer’s disease was trained on 1151 individuals who had baseline diagnoses of mild cognitive impairment and Alzheimer’s dementia from ADNI and NACC. This model was validated on 115 individuals from a placebo arm of a Phase 3 clinical trial and 76 individuals from the PharmaCog dataset. A second prognostic model to predict decline at 48 months in presymptomatic populations was trained on 628 individuals from ADNI and NACC who were cognitively unimpaired at baseline. This model was validated on 128 individuals from OASIS-3.
Results |
The models achieved up to 79% area under the curve (cross-validated and out-of-sample). Power analyses showed that using prognostic models to recruit enriched cohorts of predicted decliners can reduce clinical trial sample sizes by as much as 51% while maintaining the same detection power.
Conclusions |
Prognostic tools for predicting cognitive decline and enriching clinical trials with participants at the highest risk of decline can improve trial quality, derisk endpoint failures, and accelerate therapeutic development in Alzheimer’s disease.
Le texte complet de cet article est disponible en PDF.Key words : Alzheimer’s disease, clinical trials, cognitive decline, machine learning, trial enrichment
Plan
| How to cite this article: A. Tam, C. Laurent, S. Gauthier, et al. Prediction of Cognitive Decline for Enrichment of Alzheimer’s Disease Clinical Trials. J Prev Alz Dis 2022;3(9):400-409; jpad.2022.49 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: ADNI_Acknowledgement_List.pdf Declarations: Dr Tam, Dr Laurent, and Dr Dansereau are employees of Perceiv Research Inc and hold stocks/stock options in Perceiv Research Inc. Dr Gauthier has nothing to disclose. |
Vol 9 - N° 3
P. 400-409 - juillet 2022 Retour au numéro
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