Practical algorithms for amyloid β probability in subjective or mild cognitive impairment

Abstract

Abstract Introduction Practical algorithms predicting the probability of amyloid pathology among patients with subjective cognitive decline or mild cognitive impairment may help clinical decisions regarding confirmatory biomarker testing for Alzheimer's disease. Methods Algorithm feature selection was conducted with Alzheimer's Disease Neuroimaging Initiative and Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing data. Probability algorithms were developed in Alzheimer's Disease Neuroimaging Initiative using nested cross‐validation accompanied by stratified subsampling to obtain 1000 internally validated decision trees. Semi‐independent validation was conducted using Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing. Independent external validation was conducted in the population‐based Mayo Clinic Study of Aging. Results Two algorithms were developed using age and normalized immediate recall z‐scores, with or without apolipoprotein E ε4 carrier status. Both algorithms had robust performance across data sets and when substituting different recall memory tests. Discussion The statistical framework resulted in robust probability estimation. Application of these algorithms may assist in clinical decision‐making for further testing to diagnose amyloid pathology.

Keywords

• ADNI
• AIBL
• MCSA
• Algorithm
• Amyloid
• Alzheimer's disease