Evidence map›Paper›PMID 36452051›Full record

ArticleInnovation in aging2022

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

Suzanne S Sullivan, Wei Bo, Chin-Shang Li, Wenyao Xu, Yu-Ping Chang

Open access · goldAbstract read
In one paragraph

Article in Innovation in aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Nursing Science Interventions in Aging.Innovation in aging · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Suzanne S SullivanSchool of Nursing, University at Buffalo, Buffalo, New York, USA.ORCID https://orcid.org/0000-0003-3064-6884
Wei BoDepartment of Computer Science Engineering, University at Buffalo, Buffalo, New York, USA.ORCID https://orcid.org/0000-0002-8064-496X
Chin-Shang LiSchool of Nursing, University at Buffalo, Buffalo, New York, USA.ORCID https://orcid.org/0000-0002-0054-4476
Wenyao XuDepartment of Computer Science Engineering, University at Buffalo, Buffalo, New York, USA.ORCID https://orcid.org/0000-0001-6444-9411
Yu-Ping ChangSchool of Nursing, University at Buffalo, Buffalo, New York, USA.ORCID https://orcid.org/0000-0003-2328-6876
University at Buffalo, State University of New York · US

Funding

University of Buffalo Clinical and Translational Science Institute - Supplement SchulyerUL1TR001412 · NCATS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI MURPHY, TIMOTHY F · 2015 to 2024
$33.8M
A Longitudinal Examination to Predict Quality of Life and Care Transitions for Persons with Alzheimer’s Disease and Related Dementias at End-of-LifeR03AG067159 · NIA · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI SULLIVAN, SUZANNE SIERRA · 2020 to 2021
$160k
NCATS NIH HHS UL1 TR001412NIA NIH HHS R03 AG067159
6 · The paper itself

Abstract

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 ( 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.

Indexed as

DementiaNHATSPrecision healthSerious illness planningSocial determinants of health

Identifiers

PMID36452051
PMCPMC9701063
OpenAlexW4291000539

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.