Predicting amyloid status using self‐report information from an online research and recruitment registry: The Brain Health Registry

• Main Authors: Miriam T. Ashford, John Neuhaus, Chengshi Jin, Monica R. Camacho, Juliet Fockler, Diana Truran, R. Scott Mackin, Gil D. Rabinovici, Michael W. Weiner, Rachel L. Nosheny
• Format: Article
• Language: English
• Published: Wiley 2020-01-01
• Series: Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
• Subjects: amyloid; Brain Health Registry; cognitively unimpaired; dementia; IDEAS study; Internet
• Online Access: https://doi.org/10.1002/dad2.12102

Abstract Introduction 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.