Predicting the progression of Alzheimer's disease dementia: A multidomain health policy model

Abstract

INTRODUCTION: Here, we develop a multidomain model to predict progression of Alzheimer's disease dementia (AD). METHODS: Data from the US National Alzheimer's Coordinating Center (n = 3009) are used to examine change in symptom status and to estimate transition probabilities between health states described using cognitive function, functional ability, and behavior. A model is used to predict progression and to assess a hypothetical treatment scenario that slows AD progression. RESULTS: More than 70% of participants moved state over 12 months. The majority moved in domains other than cognitive function. Over 5 years, of those alive more than half are in severe AD health states. Assessing an intervention scenario, we see fewer years in more severe health states and a potential impact (life years saved) due to mortality improvements. DISCUSSION: The model developed is exploratory and has limitations but illustrates the importance of using a multidomain approach when assessing impacts of AD and interventions.