Predicting Amyloid Burden to Accelerate Recruitment of Secondary Prevention Clinical Trials - 21/11/24
TRC-PAD Investigators
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Abstract |
Background |
Screening to identify individuals with elevated brain amyloid (Aβ+) for clinical trials in Preclinical Alzheimer’s Disease (PAD), such as the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s disease (A4) trial, is slow and costly. The Trial-Ready Cohort in Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) aims to accelerate and reduce costs of AD trial recruitment by maintaining a web-based registry of potential trial participants, and using predictive algorithms to assess their likelihood of suitability for PAD trials.
Objectives |
Here we describe how algorithms used to predict amyloid burden within TRC-PAD project were derived using screening data from the A4 trial.
Design |
We apply machine learning techniques to predict amyloid positivity. Demographic variables, APOE genotype, and measures of cognition and function are considered as predictors. Model data were derived from the A4 trial.
Setting |
TRC-PAD data are collected from web-based and in-person assessments and are used to predict the risk of elevated amyloid and assess eligibility for AD trials.
Participants |
Pre-randomization, cross-sectional data from the ongoing A4 trial are used to develop statistical models.
Measurements |
Models use a range of cognitive tests and subjective memory assessments, along with demographic variables. Amyloid positivity in A4 was confirmed using positron emission tomography (PET).
Results |
The A4 trial screened N=4,486 participants, of which N=1323 (29%) were classified as Aβ+ (SUVR ≥ 1.15). The Area under the Receiver Operating Characteristic curves for these models ranged from 0.60 (95% CI 0.56 to 0.64) for a web-based battery without APOE to 0.74 (95% CI 0.70 to 0.78) for an in-person battery. The number needed to screen to identify an Aβ+ individual is reduced from 3.39 in A4 to 2.62 in the remote setting without APOE, and 1.61 in the remote setting with APOE.
Conclusions |
Predictive algorithms in a web-based registry can improve the efficiency of screening in future secondary prevention trials. APOE status contributes most to predictive accuracy with cross-sectional data. Blood-based assays of amyloid will likely improve the prediction of amyloid PET positivity.
Il testo completo di questo articolo è disponibile in PDF.Key words : Trial-ready cohort, Alzheimer’s disease, machine learning
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| TRC-PAD Investigators are listed at www.trcpad.org |
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| Conflict of interest: Dr. Raman reports grants from National Institute on Aging, grants from Eli Lilly, during the conduct of the study. Dr. Sperling reports personal fees from AC Immune, personal fees from Biogen, personal fees from Janssen, personal fees from Neurocentria, personal fees from Eisai, personal fees from GE Healthcare, personal fees from Roche, personal fees from InSightec, personal fees from Cytox, personal fees from Prothena, personal fees from Acumen, personal fees from JOMDD, personal fees from Renew, personal fees from Takeda Pharmaceuticals, personal fees from Alnylam Pharmaceuticals, personal fees from Neuraly, grants from Eli Lilly, grants from Janssen, grants from Digital Cognition Technologies, grants from Eisai, grants from NIA, grants from Alzheimer’s Association, personal fees and other from Novartis, personal fees and other from AC Immune, personal fees and other from Janssen, outside the submitted work. Dr. Cummings has provided consultation to Acadia, Actinogen, AgeneBio, Alkahest, Alzheon, Annovis, Avanir, Axsome, Biogen, BioXcel, Cassava, Cerecin, Cerevel, Cortexyme, Cytox, EIP Pharma, Eisai, Foresight, GemVax, Genentech, Green Valley, Grifols, Karuna, Merck, Novo Nordisk, Otsuka, Resverlogix, Roche, Samumed, Samus, Signant Health, Suven, Third Rock, and United Neuroscience pharmaceutical and assessment companies. Dr. Cummings has stock options in ADAMAS, AnnovisBio, MedAvante, BiOasis. Dr. Cummings owns the copyright of the Neuropsychiatrie Inventory. Dr Cummings is supported by Keep Memory Alive (KMA); NIGMS grant P20GM109025; NINDS grant U01NS093334; and NIA grant R01AG053798. Mrs. Jimenez-Maggiora, Langford, and Sun report grants from National Institutes of Health (NIH) National Institute on Aging Grant number: R01AG053798, during the conduct of the study. Dr. Aisen reports grants from Janssen, grants from NIA, grants from FNIH, grants from Alzheimer’s Association, grants from Eisai, personal fees from Merck, personal fees from Biogen, personal fees from Roche, personal fees from Lundbeck, personal fees from Proclara, personal fees from Immunobrain Checkpoint, outside the submitted work. Dr. Donohue reports grants from National Institutes of Health (NIH) National Institute on Aging Grant number: R01AG053798, during the conduct of the study; personal fees from Biogen, personal fees from Roche, personal fees from Neurotrack, personal fees from Eli Lilly, other from Janssen, outside the submitted work. |
Vol 7 - N° 4
P. 213-218 - Settembre 2020 Ritorno al numero
Articolo precedente
- The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) Project: An Overview
- P.S. Aisen, R.A. Sperling, J. Cummings, M.C. Donohue, O. Langford, G.A. Jimenez-Maggiora, R.A. Rissman, M.S. Rafii, S. Walter, T. Clanton, R. Raman
- Recruitment into the Alzheimer Prevention Trials (APT) Webstudy for a Trial-Ready Cohort for Preclinical and Prodromal Alzheimer’s Disease (TRC-PAD)
- S. Walter, T.B. Clanton, O.G. Langford, M.S. Rafii, E.J. Shaffer, J.D. Grill, G.A. Jimenez-Maggiora, R.A. Sperling, J.L. Cummings, P.S. Aisen, TRC-PAD Investigators
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