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

ABSTRACT

INTRODUCTION: This study aimed to predict brain amyloid beta (Aß) status in older adults using collected information from an online registry focused on cognitive aging. METHODS: Aß positron emission tomography (PET) was obtained from multiple in-clinic studies. Using logistic regression, we predicted Aß 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 Aß PET scans in CU and MCI populations. DISUCSSION The findings suggest that a novel, online approach could aid in Aß prediction.