Carolyn W Zhu1,2, Seonjoo Lee3,4, Katherine A Ornstein1, Stephanie Cosentino3, Yian Gu3, Howard Andrews4, Yaakov Stern3. 1. Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai. 2. James J Peters VA Medical Center, Bronx, NY. 3. Cognitive Neuroscience Division of the Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center. 4. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York.
Abstract
INTRODUCTION: Dependence in Alzheimer disease has been proposed as a holistic, transparent, and meaningful representation of disease severity. Modeling clusters in dependence trajectories can help understand changes in disease course and care cost over time. METHODS: Sample consisted of 199 initially community-living patients with probable Alzheimer disease recruited from 3 academic medical centers in the United States followed for up to 10 years and had ≥2 Dependence Scale recorded. Nonparametric K-means cluster analysis for longitudinal data (KmL) was used to identify dependence clusters. Medicare expenditures data (1999-2010) were compared between clusters. RESULTS: KmL identified 2 distinct Dependence Scale clusters: (A) high initial dependence, faster decline, and (B) low initial dependence, slower decline. Adjusting for patient characteristics, 6-month Medicare expenditures increased over time with widening between-cluster differences. DISCUSSION: Dependence captures dementia care costs over time. Better characterization of dependence clusters has significant implications for understanding disease progression, trial design and care planning.
INTRODUCTION: Dependence in Alzheimer disease has been proposed as a holistic, transparent, and meaningful representation of disease severity. Modeling clusters in dependence trajectories can help understand changes in disease course and care cost over time. METHODS: Sample consisted of 199 initially community-living patients with probable Alzheimer disease recruited from 3 academic medical centers in the United States followed for up to 10 years and had ≥2 Dependence Scale recorded. Nonparametric K-means cluster analysis for longitudinal data (KmL) was used to identify dependence clusters. Medicare expenditures data (1999-2010) were compared between clusters. RESULTS: KmL identified 2 distinct Dependence Scale clusters: (A) high initial dependence, faster decline, and (B) low initial dependence, slower decline. Adjusting for patient characteristics, 6-month Medicare expenditures increased over time with widening between-cluster differences. DISCUSSION: Dependence captures dementia care costs over time. Better characterization of dependence clusters has significant implications for understanding disease progression, trial design and care planning.
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