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Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach

BACKGROUND AND OBJECTIVES: Hospice programs assist people with serious illness and their caregivers with aging in place, avoiding unnecessary hospitalizations, and remaining at home through the end-of-life. While evidence is emerging of the myriad of factors influencing end-of-life care transitions...

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Autores principales: Sullivan, Suzanne S, Bo, Wei, Li, Chin-Shang, Xu, Wenyao, Chang, Yu-Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9701063/
https://www.ncbi.nlm.nih.gov/pubmed/36452051
http://dx.doi.org/10.1093/geroni/igac051
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author Sullivan, Suzanne S
Bo, Wei
Li, Chin-Shang
Xu, Wenyao
Chang, Yu-Ping
author_facet Sullivan, Suzanne S
Bo, Wei
Li, Chin-Shang
Xu, Wenyao
Chang, Yu-Ping
author_sort Sullivan, Suzanne S
collection PubMed
description BACKGROUND AND OBJECTIVES: Hospice programs assist people with serious illness and their caregivers with aging in place, avoiding unnecessary hospitalizations, and remaining at home through the end-of-life. While evidence is emerging of the myriad of factors influencing end-of-life care transitions among persons living with dementia, current research is primarily cross- sectional and does not account for the effect that changes over time have on hospice care uptake, access, and equity within dyads. RESEARCH DESIGN AND METHODS: Secondary data analysis linking the National Health and Aging Trends Study to the National Study of Caregiving investigating important social determinants of health and quality-of-life factors of persons living with dementia and their primary caregivers (n = 117) on hospice utilization over 3 years (2015–2018). We employ cutting-edge machine learning approaches (correlation matrix analysis, principal component analysis, random forest [RF], and information gain ratio [IGR]). RESULTS: IGR indicators of hospice use include persons living with dementia having diabetes, a regular physician, a good memory rating, not relying on food stamps, not having chewing or swallowing problems, and whether health prevents them from enjoying life (accuracy = 0.685; sensitivity = 0.824; specificity = 0.537; area under the curve (AUC) = 0.743). RF indicates primary caregivers’ age, and the person living with dementia’s income, census division, number of days help provided by caregiver per month, and whether health prevents them from enjoying life predicts hospice use (accuracy = 0.624; sensitivity = 0.713; specificity = 0.557; AUC = 0.703). DISCUSSION AND IMPLICATIONS: Our exploratory models create a starting point for the future development of precision health approaches that may be integrated into learning health systems that prompt providers with actionable information about who may benefit from discussions around serious illness goals-for-care. Future work is necessary to investigate those not considered in this study—that is, persons living with dementia who do not use hospice care so additional insights can be gathered around barriers to care.
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spelling pubmed-97010632022-11-29 Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach Sullivan, Suzanne S Bo, Wei Li, Chin-Shang Xu, Wenyao Chang, Yu-Ping Innov Aging Special Issue: Nursing Science Interventions in Aging BACKGROUND AND OBJECTIVES: Hospice programs assist people with serious illness and their caregivers with aging in place, avoiding unnecessary hospitalizations, and remaining at home through the end-of-life. While evidence is emerging of the myriad of factors influencing end-of-life care transitions among persons living with dementia, current research is primarily cross- sectional and does not account for the effect that changes over time have on hospice care uptake, access, and equity within dyads. RESEARCH DESIGN AND METHODS: Secondary data analysis linking the National Health and Aging Trends Study to the National Study of Caregiving investigating important social determinants of health and quality-of-life factors of persons living with dementia and their primary caregivers (n = 117) on hospice utilization over 3 years (2015–2018). We employ cutting-edge machine learning approaches (correlation matrix analysis, principal component analysis, random forest [RF], and information gain ratio [IGR]). RESULTS: IGR indicators of hospice use include persons living with dementia having diabetes, a regular physician, a good memory rating, not relying on food stamps, not having chewing or swallowing problems, and whether health prevents them from enjoying life (accuracy = 0.685; sensitivity = 0.824; specificity = 0.537; area under the curve (AUC) = 0.743). RF indicates primary caregivers’ age, and the person living with dementia’s income, census division, number of days help provided by caregiver per month, and whether health prevents them from enjoying life predicts hospice use (accuracy = 0.624; sensitivity = 0.713; specificity = 0.557; AUC = 0.703). DISCUSSION AND IMPLICATIONS: Our exploratory models create a starting point for the future development of precision health approaches that may be integrated into learning health systems that prompt providers with actionable information about who may benefit from discussions around serious illness goals-for-care. Future work is necessary to investigate those not considered in this study—that is, persons living with dementia who do not use hospice care so additional insights can be gathered around barriers to care. Oxford University Press 2022-08-11 /pmc/articles/PMC9701063/ /pubmed/36452051 http://dx.doi.org/10.1093/geroni/igac051 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of The Gerontological Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Special Issue: Nursing Science Interventions in Aging
Sullivan, Suzanne S
Bo, Wei
Li, Chin-Shang
Xu, Wenyao
Chang, Yu-Ping
Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach
title Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach
title_full Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach
title_fullStr Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach
title_full_unstemmed Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach
title_short Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach
title_sort predicting hospice transitions in dementia caregiving dyads: an exploratory machine learning approach
topic Special Issue: Nursing Science Interventions in Aging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9701063/
https://www.ncbi.nlm.nih.gov/pubmed/36452051
http://dx.doi.org/10.1093/geroni/igac051
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