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

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 t...

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Autores principales: Stallard, Eric, Kinosian, Bruce, Stern, Yaakov
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5607490/
https://www.ncbi.nlm.nih.gov/pubmed/28931428
http://dx.doi.org/10.1186/s13195-017-0302-6
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author Stallard, Eric
Kinosian, Bruce
Stern, Yaakov
author_facet Stallard, Eric
Kinosian, Bruce
Stern, Yaakov
author_sort Stallard, Eric
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13195-017-0302-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-56074902017-09-24 Personalized predictive modeling for patients with Alzheimer’s disease using an extension of Sullivan’s life table model Stallard, Eric Kinosian, Bruce Stern, Yaakov Alzheimers Res Ther Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13195-017-0302-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-09-20 /pmc/articles/PMC5607490/ /pubmed/28931428 http://dx.doi.org/10.1186/s13195-017-0302-6 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Stallard, Eric
Kinosian, Bruce
Stern, Yaakov
Personalized predictive modeling for patients with Alzheimer’s disease using an extension of Sullivan’s life table model
title Personalized predictive modeling for patients with Alzheimer’s disease using an extension of Sullivan’s life table model
title_full Personalized predictive modeling for patients with Alzheimer’s disease using an extension of Sullivan’s life table model
title_fullStr Personalized predictive modeling for patients with Alzheimer’s disease using an extension of Sullivan’s life table model
title_full_unstemmed Personalized predictive modeling for patients with Alzheimer’s disease using an extension of Sullivan’s life table model
title_short Personalized predictive modeling for patients with Alzheimer’s disease using an extension of Sullivan’s life table model
title_sort personalized predictive modeling for patients with alzheimer’s disease using an extension of sullivan’s life table model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5607490/
https://www.ncbi.nlm.nih.gov/pubmed/28931428
http://dx.doi.org/10.1186/s13195-017-0302-6
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