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利用沙利文寿命表模型的扩展,对阿尔茨海默病患者进行个性化预测建模。

Personalized predictive modeling for patients with Alzheimer's disease using an extension of Sullivan's life table model.

机构信息

Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, 2024 West Main Street, Room A102H, Durham, NC, 27708-0408, USA.

Geriatrics and Extended Care Data Analysis Center, Philadelphia VA Medical Center, 3900 Woodland Avenue, Philadelphia, PA, 19104, USA.

出版信息

Alzheimers Res Ther. 2017 Sep 20;9(1):75. doi: 10.1186/s13195-017-0302-6.

DOI:10.1186/s13195-017-0302-6
PMID:28931428
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5607490/
Abstract

BACKGROUND

Alzheimer's disease (AD) progression varies substantially among patients, hindering calculation of residual total life expectancy (TLE) and its decomposition into disability-free life expectancy (DFLE) and disabled life expectancy (DLE) for individual patients with AD. The objective of the present study was to assess the accuracy of a new synthesis of Sullivan's life table (SLT) and longitudinal Grade of Membership (L-GoM) models that estimates individualized TLEs, DFLEs, and DLEs for patients with AD. If sufficiently accurate, such information could enhance the quality of important decisions in AD treatment and patient care.

METHODS

We estimated a new SLT/L-GoM model of the natural history of AD over 10 years in the Predictors 2 Study cohort: N = 229 with 6 fixed and 73 time-varying covariates over 21 examinations covering 11 measurement domains including cognitive, functional, behavioral, psychiatric, and other symptoms/signs. Total remaining life expectancy was censored at 10 years. Disability was defined as need for full-time care (FTC), the outcome most strongly associated with AD progression. All parameters were estimated via weighted maximum likelihood using data-dependent weights designed to ensure that the estimates of the prognostic subtypes were of high quality. Goodness of fit was tested/confirmed for survival and FTC disability for five relatively homogeneous subgroups defined to cover the range of patient outcomes over the 21 examinations.

RESULTS

The substantial heterogeneity in initial patient presentation and AD progression was captured using three clinically meaningful prognostic subtypes and one terminal subtype exhibiting highly differentiated symptom severity on 7 of the 11 measurement domains. Comparisons of the observed and estimated survival and FTC disability probabilities demonstrated that the estimates were accurate for all five subgroups, supporting their use in AD life expectancy calculations. Mean 10-year TLE differed widely across subgroups: range 3.6-8.0 years, average 6.1 years. Mean 10-year DFLE differed relatively even more widely across subgroups: range 1.2-6.5 years, average 4.0 years. Mean 10-year DLE was relatively much closer: range 1.5-2.3 years, average 2.1 years.

CONCLUSIONS

The SLT/L-GoM model yields accurate maximum likelihood estimates of TLE, DFLE, and DLE for patients with AD; it provides a realistic, comprehensive modeling framework for endpoint and resource use/cost calculations.

摘要

背景

阿尔茨海默病(AD)的进展在患者之间存在很大差异,这阻碍了对剩余总预期寿命(TLE)的计算,并将其分解为 AD 患者的无残疾预期寿命(DFLE)和残疾预期寿命(DLE)。本研究的目的是评估 Sullivan 生命表(SLT)和纵向成员等级(L-GoM)模型的新综合方法的准确性,该方法用于估计 AD 患者的个体化 TLE、DFLE 和 DLE。如果足够准确,此类信息可以提高 AD 治疗和患者护理中重要决策的质量。

方法

我们在 Predictors 2 研究队列中估计了 AD 自然史的新 SLT/L-GoM 模型,该模型覆盖了 11 个测量领域,包括认知、功能、行为、精神和其他症状/体征,共进行了 21 次检查,其中 6 次为固定检查,73 次为时间变化检查,N=229。总剩余预期寿命在 10 年内被截尾。残疾定义为需要全职护理(FTC),这是与 AD 进展最密切相关的结果。所有参数均通过加权最大似然法进行估计,使用数据依赖权重设计来确保预后亚型的估计具有高质量。通过五个相对同质的亚组测试/确认了生存和 FTC 残疾的拟合优度,这些亚组的定义涵盖了 21 次检查中患者结局的范围。

结果

使用三种具有临床意义的预后亚型和一种在 7 个 11 个测量领域中表现出高度分化症状严重程度的终末期亚型,捕获了患者初始表现和 AD 进展的高度异质性。观察到的和估计的生存和 FTC 残疾概率的比较表明,这些估计在所有五个亚组中都是准确的,支持它们在 AD 预期寿命计算中的使用。亚组间平均 10 年 TLE 差异很大:范围 3.6-8.0 年,平均 6.1 年。亚组间平均 10 年 DFLE 差异更大:范围 1.2-6.5 年,平均 4.0 年。平均 10 年 DLE 则相对更接近:范围 1.5-2.3 年,平均 2.1 年。

结论

SLT/L-GoM 模型为 AD 患者提供了 TLE、DFLE 和 DLE 的准确最大似然估计值;它为终点和资源使用/成本计算提供了现实、全面的建模框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47df/5607490/36d3145dc7d2/13195_2017_302_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47df/5607490/6ac4d62daa50/13195_2017_302_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47df/5607490/0f0f2e3648c2/13195_2017_302_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47df/5607490/c2cab8918979/13195_2017_302_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47df/5607490/36d3145dc7d2/13195_2017_302_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47df/5607490/6ac4d62daa50/13195_2017_302_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47df/5607490/0f0f2e3648c2/13195_2017_302_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47df/5607490/c2cab8918979/13195_2017_302_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47df/5607490/36d3145dc7d2/13195_2017_302_Fig4_HTML.jpg

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