Development of Simple Risk Scores for Prediction of Brain β-Amyloid and Tau Status in Older Adults With Mild Cognitive Impairment: A Machine Learning Approach

Abstract Objectives The aim of this work is to use a machine learning framework to develop simple risk scores for predicting β-amyloid (Aβ) and tau positivity among individuals with mild cognitive impairment (MCI). Methods Data for 657 individuals with MCI from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset were used. A modified version of AutoScore, a machine learning-based software tool, was used to develop risk scores based on hierarchical combinations of predictor categories, including demographics, neuropsychological assessments, APOE4 status, and imaging biomarkers. Results The highest area under the receiver operating characteristic curve (AUC) for predicting Aβ positivity was 0.79, which was achieved by two separate models with predictors of age, Alzheimer's Disease Assessment Scale–Cognitive Subscale (ADAS-cog), APOE4 status, and either Trail Making Test Part B (TMT-B) or white matter hyperintensity. The best performing model for tau positivity had an AUC of 0.91 using age, ADAS-13 and TMT-B scores, APOE4 information, abnormal hippocampal volume, and amyloid status as predictors. Discussion Simple integer-based risk scores using available data could be used for predicting Aβ and tau positivity in individuals with MCI. Models have the potential to improve clinical trials through improved screening of individuals.