Accurate risk estimation of β-amyloid positivity to identify prodromal Alzheimer's disease: Cross-validation study of practical algorithms

Introduction: The aim was to create readily available algorithms that estimate the individual risk of beta-amyloid (A beta) positivity. Methods: The algorithms were tested in BioFINDER (n = 391, subjective cognitive decline or mild cognitive impairment) and validated in Alzheimer's Disease Neuroimaging Initiative (n = 661, subjective cognitive decline or mild cognitive impairment). The examined predictors of A beta status were demographics; cognitive tests; white matter lesions; apolipoprotein E (APOE); and plasma A beta(42)/A beta(40), tau, and neurofilament light. Results: A beta status was accurately estimated in BioFINDER using age, 10-word delayed recall or Mini-Mental State Examination, and APOE (area under the receiver operating characteristics curve 5 0.81 [0.77-0.85] to 0.83 [0.79-0.87]). When validated, the models performed almost identical in Alzheimer's Disease Neuroimaging Initiative (area under the receiver operating characteristics curve 5 0.80-0.82) and within different age, subjective cognitive decline, and mild cognitive impairment populations. Plasma A beta(42)/A beta(40) improved the models slightly. Discussion: The algorithms are implemented on http://amyloidrisk.com where the individual probability of being A beta positive can be calculated. This is useful in the workup of prodromal Alzheimer's disease and can reduce the number needed to screen in Alzheimer's disease trials. (C) 2018 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.