Open AccessPredicting amyloid status using self‐report information from an online research and recruitment registry: The Brain Health Registry
Author(s) -
Ashford Miriam T.,
Neuhaus John,
Jin Chengshi,
Camacho Monica R.,
Fockler Juliet,
Truran Diana,
Mackin R. Scott,
Rabinovici Gil D.,
Weiner Michael W.,
Nosheny Rachel L.
Publication year - 2020
Publication title -
alzheimer's and dementia: diagnosis, assessment and disease monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.497
H-Index - 37
ISSN - 2352-8729
DOI - 10.1002/dad2.12102
Subject(s) - logistic regression , depression (economics) , cognition , cognitive impairment , medicine , geriatric depression scale , clinical psychology , gerontology , disease , psychology , psychiatry , depressive symptoms , economics , macroeconomics
This study aimed to predict brain amyloid beta (Aβ) status in older adults using collected information from an online registry focused on cognitive aging. Methods Aβ positron emission tomography (PET) was obtained from multiple in‐clinic studies. Using logistic regression, we predicted Aβ using self‐report variables collected in the Brain Health Registry in 634 participants, as well as a subsample (N = 533) identified as either cognitively unimpaired (CU) or mild cognitive impairment (MCI). Cross‐validated area under the curve (cAUC) evaluated the predictive performance. Results The best prediction model included age, sex, education, subjective memory concern, family history of Alzheimer's disease, Geriatric Depression Scale Short‐Form, self‐reported Everyday Cognition, and self‐reported cognitive impairment. The cross‐validated AUCs ranged from 0.62 to 0.66. This online model could help reduce between 15.2% and 23.7% of unnecessary Aβ PET scans in CU and MCI populations. Disucssion The findings suggest that a novel, online approach could aid in Aβ prediction.