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Validating Older Adult Morbidity Trajectories Using Multiple Comorbidity Indices

Many older adults lead healthy lives while aging, with little or no morbidity. This group has been identified as “Escapers”, for escaping the 10 most common lethal diseases in older adults. “Morbidity Trajectories” (MOTRs) are a metric based on the temporal patterning of comorbidity, which is used t...

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Autores principales: Newman, Michael, Hanson, Heidi, Schliep, Karen, Abdelrahman, Samir, VanDerslice, Jim, Smith, Ken, Porucznik, Christy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7741134/
http://dx.doi.org/10.1093/geroni/igaa057.564
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author Newman, Michael
Hanson, Heidi
Schliep, Karen
Abdelrahman, Samir
VanDerslice, Jim
Smith, Ken
Porucznik, Christy
author_facet Newman, Michael
Hanson, Heidi
Schliep, Karen
Abdelrahman, Samir
VanDerslice, Jim
Smith, Ken
Porucznik, Christy
author_sort Newman, Michael
collection PubMed
description Many older adults lead healthy lives while aging, with little or no morbidity. This group has been identified as “Escapers”, for escaping the 10 most common lethal diseases in older adults. “Morbidity Trajectories” (MOTRs) are a metric based on the temporal patterning of comorbidity, which is used to characterize changes in disease status as a person ages. While these trajectories have been used to identify Escapers in various populations, they are sensitive to the choice of the disease metric. This study seeks to describe the differences in MOTR scale by alternative comorbidity indices. Understanding these differences is important because of the need to validate the potential end-point in health trajectory risk scores that may be used in a clinical setting. We found that 15-19 percent of a Medicare utilizing population (n=321722) aged >= 65 between 1992 and 2012 fall into the Escaper category, where there is a consistent Quan modification Charlson Comorbidity Index (CCI) score of 0 during the entire study period. Using the vanWalraven (vW) Elixhauser Comorbidity Index modification, we found that about a third (35.2%) of the study population have a vW Elixhauser score of 0 over the span, a significantly higher portion than the CCI estimate. We will discuss this difference and the resulting varying trajectories from each of these indices. Future work includes further validation of the MOTR scale using unsupervised machine learning clustering methods, and using supervised machine learning models to identify clinical factors and early life conditions that may influence MOTR membership.
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spelling pubmed-77411342020-12-21 Validating Older Adult Morbidity Trajectories Using Multiple Comorbidity Indices Newman, Michael Hanson, Heidi Schliep, Karen Abdelrahman, Samir VanDerslice, Jim Smith, Ken Porucznik, Christy Innov Aging Abstracts Many older adults lead healthy lives while aging, with little or no morbidity. This group has been identified as “Escapers”, for escaping the 10 most common lethal diseases in older adults. “Morbidity Trajectories” (MOTRs) are a metric based on the temporal patterning of comorbidity, which is used to characterize changes in disease status as a person ages. While these trajectories have been used to identify Escapers in various populations, they are sensitive to the choice of the disease metric. This study seeks to describe the differences in MOTR scale by alternative comorbidity indices. Understanding these differences is important because of the need to validate the potential end-point in health trajectory risk scores that may be used in a clinical setting. We found that 15-19 percent of a Medicare utilizing population (n=321722) aged >= 65 between 1992 and 2012 fall into the Escaper category, where there is a consistent Quan modification Charlson Comorbidity Index (CCI) score of 0 during the entire study period. Using the vanWalraven (vW) Elixhauser Comorbidity Index modification, we found that about a third (35.2%) of the study population have a vW Elixhauser score of 0 over the span, a significantly higher portion than the CCI estimate. We will discuss this difference and the resulting varying trajectories from each of these indices. Future work includes further validation of the MOTR scale using unsupervised machine learning clustering methods, and using supervised machine learning models to identify clinical factors and early life conditions that may influence MOTR membership. Oxford University Press 2020-12-16 /pmc/articles/PMC7741134/ http://dx.doi.org/10.1093/geroni/igaa057.564 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Abstracts
Newman, Michael
Hanson, Heidi
Schliep, Karen
Abdelrahman, Samir
VanDerslice, Jim
Smith, Ken
Porucznik, Christy
Validating Older Adult Morbidity Trajectories Using Multiple Comorbidity Indices
title Validating Older Adult Morbidity Trajectories Using Multiple Comorbidity Indices
title_full Validating Older Adult Morbidity Trajectories Using Multiple Comorbidity Indices
title_fullStr Validating Older Adult Morbidity Trajectories Using Multiple Comorbidity Indices
title_full_unstemmed Validating Older Adult Morbidity Trajectories Using Multiple Comorbidity Indices
title_short Validating Older Adult Morbidity Trajectories Using Multiple Comorbidity Indices
title_sort validating older adult morbidity trajectories using multiple comorbidity indices
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7741134/
http://dx.doi.org/10.1093/geroni/igaa057.564
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