Dataset Information

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

ABSTRACT:

Introduction

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 mild to moderate 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.

SUBMITTER: Green C

PROVIDER: S-EPMC5104191 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Publications

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

Green Colin C Zhang Shenqiu S

Alzheimer's & dementia : the journal of the Alzheimer's Association 20160324 7

<h4>Introduction</h4>We develop a multidomain model to predict progression of Alzheimer's disease dementia (AD).<h4>Methods</h4>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 mild to moderate AD progres ...[more]

PMID: 27016691

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Project description:BackgroundAlzheimer's disease and related dementia (ADRD) are characterized by multiple and progressive anatomo-clinical changes including accumulation of abnormal proteins in the brain, brain atrophy and severe cognitive impairment. Understanding the sequence and timing of these changes is of primary importance to gain insight into the disease natural history and ultimately allow earlier diagnosis. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales (time since inclusion, chronological age) are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable.MethodsWe developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. In contrast with the existing literature, our methodology exploits the clinical diagnosis information as a partially observed and approximate reference to guide the estimation of the latent disease time. The model estimation was carried out in the Bayesian Framework using Stan. We applied the methodology to the MEMENTO study, a French multicentric clinic-based cohort of 2186 participants with 5-year intensive follow-up. Repeated measures of 12 ADRD markers stemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests were analyzed.ResultsThe estimated latent disease time spanned over twenty years before the clinical diagnosis. Considering the profile of a woman aged 70 with a high level of education and APOE4 carrier (the main genetic risk factor for ADRD), CSF markers of tau proteins accumulation preceded markers of brain atrophy by 5 years and cognitive decline by 10 years. However we observed that individual characteristics could substantially modify the sequence and timing of these changes, in particular for CSF level of A[Formula: see text].ConclusionBy leveraging the available clinical diagnosis timing information, our disease progression model does not only realign trajectories into the most homogeneous way. It accounts for the inherent residual inter-individual variability in dementia progression to describe the long-term anatomo-clinical degradations according to the years preceding clinical diagnosis, and to provide clinically meaningful information on the sequence of events.Trial registrationclinicaltrials.gov, NCT01926249. Registered on 16 August 2013.

Dataset Information

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

Introduction

Methods

Results

Discussion

Publications

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

Similar Datasets

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