{Reference Type}: Journal Article
{Title}: Predicting amyloid status using self-report information from an online research and recruitment registry: The Brain Health Registry.
{Author}: Ashford MT;Neuhaus J;Jin C;Camacho MR;Fockler J;Truran D;Mackin RS;Rabinovici GD;Weiner MW;Nosheny RL;
{Journal}: Alzheimers Dement (Amst)
{Volume}: 12
{Issue}: 1
{Year}: 2020
暂无{DOI}: 10.1002/dad2.12102
{Abstract}: <B>BACKGROUND: </B>This study aimed to predict brain amyloid beta (Aβ) status in older adults using collected information from an online registry focused on cognitive aging.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>The findings suggest that a novel, online approach could aid in Aβ prediction.