Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model

Abstract

Aims: Both amnestic mild cognitive impairment (aMCI) and remitted late-onset depression (rLOD) confer a high risk of developing Alzheimer's disease (AD). This study aims to determine whether the Characterizing AD Risk Events (CARE) index model can effectively predict conversion in individuals at high risk for AD development either in an independent aMCI population or in an rLOD population. Methods: The CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow-up period. Results: AD diagnoses were predicted in the aMCI population with a balanced accuracy of 80.6%, a sensitivity of 83.3%, and a specificity of 77.8%. They were also predicted in the rLOD population with a balanced accuracy of 74.5%, a sensitivity of 80.0%, and a specificity of 69.0%. In addition, the CARE index scores were observed to be negatively correlated with the composite Z scores for episodic memory (R2 =.17, P