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Resilience in Incident Hemodialysis: Characterization and Outcome Prediction
The Resiliency in Dialysis Initiation (ReDI) Study aims to develop physical resilience signatures in older adults initiating hemodialysis. Study design—comprising a pilot, confirmatory study, and secondary data analyses—will be presented. So also will a method for characterizing resilience phenotype...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7742740/ http://dx.doi.org/10.1093/geroni/igaa057.2713 |
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author | Bandeen-Roche, Karen Zhu, Jiafeng Crews, Deidra McAdams-DeMarco, Mara Buta, Brian Varadhan, Ravi Walston, Jeremy |
author_facet | Bandeen-Roche, Karen Zhu, Jiafeng Crews, Deidra McAdams-DeMarco, Mara Buta, Brian Varadhan, Ravi Walston, Jeremy |
author_sort | Bandeen-Roche, Karen |
collection | PubMed |
description | The Resiliency in Dialysis Initiation (ReDI) Study aims to develop physical resilience signatures in older adults initiating hemodialysis. Study design—comprising a pilot, confirmatory study, and secondary data analyses—will be presented. So also will a method for characterizing resilience phenotypes—using mixed-model analysis of SF-36 subscale trajectories—among participants of age 55 and older who had undergone hemodialysis in the Choices for Healthy Outcomes in Caring for ESRD study (n=485). Analyses revealed stable, improving, and declining phenotypes. In Cox models, both baseline phenotypic status and trajectory type predicted mortality after adjusting for age, CVD status, and CHF (global Wald test for trajectory type P-value=0.020 for vitality; 0.030 for general health). These analyses evidence usefulness of resilience phenotypes as markers of adverse outcome risk and foreshadow application to novel ReDI data. |
format | Online Article Text |
id | pubmed-7742740 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77427402020-12-21 Resilience in Incident Hemodialysis: Characterization and Outcome Prediction Bandeen-Roche, Karen Zhu, Jiafeng Crews, Deidra McAdams-DeMarco, Mara Buta, Brian Varadhan, Ravi Walston, Jeremy Innov Aging Abstracts The Resiliency in Dialysis Initiation (ReDI) Study aims to develop physical resilience signatures in older adults initiating hemodialysis. Study design—comprising a pilot, confirmatory study, and secondary data analyses—will be presented. So also will a method for characterizing resilience phenotypes—using mixed-model analysis of SF-36 subscale trajectories—among participants of age 55 and older who had undergone hemodialysis in the Choices for Healthy Outcomes in Caring for ESRD study (n=485). Analyses revealed stable, improving, and declining phenotypes. In Cox models, both baseline phenotypic status and trajectory type predicted mortality after adjusting for age, CVD status, and CHF (global Wald test for trajectory type P-value=0.020 for vitality; 0.030 for general health). These analyses evidence usefulness of resilience phenotypes as markers of adverse outcome risk and foreshadow application to novel ReDI data. Oxford University Press 2020-12-16 /pmc/articles/PMC7742740/ http://dx.doi.org/10.1093/geroni/igaa057.2713 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 Bandeen-Roche, Karen Zhu, Jiafeng Crews, Deidra McAdams-DeMarco, Mara Buta, Brian Varadhan, Ravi Walston, Jeremy Resilience in Incident Hemodialysis: Characterization and Outcome Prediction |
title | Resilience in Incident Hemodialysis: Characterization and Outcome Prediction |
title_full | Resilience in Incident Hemodialysis: Characterization and Outcome Prediction |
title_fullStr | Resilience in Incident Hemodialysis: Characterization and Outcome Prediction |
title_full_unstemmed | Resilience in Incident Hemodialysis: Characterization and Outcome Prediction |
title_short | Resilience in Incident Hemodialysis: Characterization and Outcome Prediction |
title_sort | resilience in incident hemodialysis: characterization and outcome prediction |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7742740/ http://dx.doi.org/10.1093/geroni/igaa057.2713 |
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