干旱区生态环境知识资源中心(Arid): Prediction of Cognitive Decline for Enrichment of Alzheimer's Disease Clinical Trials

Arid

DOI	10.14283/jpad.2022.49
	Prediction of Cognitive Decline for Enrichment of Alzheimer's Disease Clinical Trials
	Tam, A.; Laurent, C.; Gauthier, S.; Dansereau, C.
通讯作者	Dansereau, C
来源期刊	JPAD-JOURNAL OF PREVENTION OF ALZHEIMERS DISEASE
ISSN	2274-5807
EISSN	2426-0266
出版年	2022
卷号	9 期号:3 页码:400-409
英文摘要	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.
英文关键词	Alzheimer's disease clinical trials cognitive decline machine learning trial enrichment
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000790645000001
WOS关键词	RANDOMIZED CONTROLLED-TRIALS ; DEMENTIA ; ONSET ; VERUBECESTAT ; PROGRESSION ; SUBTYPES ; DESIGN ; MCI
WOS类目	Clinical Neurology
WOS研究方向	Neurosciences & Neurology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393647
推荐引用方式 GB/T 7714	Tam, A.,Laurent, C.,Gauthier, S.,et al. Prediction of Cognitive Decline for Enrichment of Alzheimer's Disease Clinical Trials[J],2022,9(3):400-409.
APA	Tam, A.,Laurent, C.,Gauthier, S.,&Dansereau, C..(2022).Prediction of Cognitive Decline for Enrichment of Alzheimer's Disease Clinical Trials.JPAD-JOURNAL OF PREVENTION OF ALZHEIMERS DISEASE,9(3),400-409.
MLA	Tam, A.,et al."Prediction of Cognitive Decline for Enrichment of Alzheimer's Disease Clinical Trials".JPAD-JOURNAL OF PREVENTION OF ALZHEIMERS DISEASE 9.3(2022):400-409.