阿尔茨海默病中的依赖簇与医疗保险支出：来自预测因子研究的纵向分析。
Dependence Clusters in Alzheimer Disease and Medicare Expenditures: A Longitudinal Analysis From the Predictors Study.
机构信息
Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai.
James J Peters VA Medical Center, Bronx, NY.
出版信息
Alzheimer Dis Assoc Disord. 2020 Oct-Dec;34(4):293-298. doi: 10.1097/WAD.0000000000000402.
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.
简介
在阿尔茨海默病中，依赖被认为是一种全面、透明且有意义的疾病严重程度的表示方法。对依赖轨迹中的聚类进行建模可以帮助了解疾病过程和护理费用随时间的变化。
方法
样本包括 199 名最初居住在社区的可能患有阿尔茨海默病的患者，他们从美国的 3 个学术医疗中心招募，随访时间长达 10 年，并且至少有 2 次记录了依赖量表。用于纵向数据的非参数 K-均值聚类分析（KmL）用于识别依赖聚类。对聚类之间的医疗保险支出数据（1999-2010 年）进行了比较。
结果
KmL 确定了 2 个不同的依赖量表聚类：（A）初始依赖性高，下降速度快，（B）初始依赖性低，下降速度慢。在调整了患者特征后，随着时间的推移，6 个月的医疗保险支出增加，且聚类间的差异也在扩大。
讨论
依赖可以捕捉到痴呆症护理费用随时间的变化。更好地描述依赖聚类对理解疾病进展、试验设计和护理计划具有重要意义。
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