# A Longitudinal Examination to Predict Quality of Life and Care Transitions for Persons with Alzheimer’s Disease and Related Dementias at End-of-Life

> **NIH NIH R03** · STATE UNIVERSITY OF NEW YORK AT BUFFALO · 2021 · $79,750

## Abstract

ABSTRACT
 Alzheimer's disease and related dementias (AD/ADRD) are a group of progressive, terminal illnesses
that will affect an estimated 14-million people in the United States by the year 2050. Caregivers experience
chronic stress and they report feeling burdened and unprepared for making difficult end-of-life (EOL) care
decisions, which may lead to unnecessary hospitalizations or avoidable transitions in care that affect end-of-life
quality-of-life (EOL-QOL). Advance care planning (ACP), which improves EOL-QOL, is the gold-standard
approach for improving concordance between preferences and actual care received at EOL. However, despite
decades of research aimed at raising their rates, only 50% of those with AD/ADRD have a written ACP.
Furthermore, there is a lack of current research evidence investigating the factors associated with transitions in
care and EOL-QOL for persons with AD/ADRD, which could help guide EOL decision-making. To date, the
state of the science is primarily cross-sectional in nature, and does not account for the influence of trajectories
of decline, the effect these changes have on caregivers, nor how longitudinal changes in caregiving ultimately
affect EOL care outcomes. Therefore, there is a critical need to discover new approaches for preparing
persons with AD/ADRD and their caregivers in making informed, in-the-moment decisions, to ensure high
EOL-QOL care and to support appropriate transitions in care as circumstances change over time. Using the
National Health and Aging Trends Study (NHATS) and National Study on Caregiving (NSOC), we plan to use a
machine learning based framework to identify the key determinants for predicting the risk for EOL care
transitions and the traits of EOL-QOL among older adults residing in the community. This study has two
specific aims: 1) Develop predictive model of factors related to end-of-life care transitions (e.g. inpatient death
versus hospice) in persons with AD/ADRD longitudinally; and 2) Develop a predictive model of factors related
to end-of-life quality-of-life (EOL-QOL) in persons with AD/ADRD. Discovering knowledge in a large population-
level dataset is foundational for the future development of a generalizable/scalable model for guiding persons
with AD/ADRD and their caregivers as they navigate a fragmented healthcare system while making difficult
decisions for their loved ones. Our approach fills a critical gap between the current approaches for improving
EOL-QOL and EOL transitions in care that focus on ACP as a singular outcome, by addressing the
comprehensive needs of individuals with AD/ADRD and their caregivers that change over time. Our study will
provide a predictive model for EOL-QOL and EOL care transitions. This is a critical first step for the future
development of an approach for personalizing care to guide persons with AD/ADRD and their caregivers in
making EOL care decisions. These results will have an important positive impact on EOL care, which aligns
with ...

## Key facts

- **NIH application ID:** 10162470
- **Project number:** 5R03AG067159-02
- **Recipient organization:** STATE UNIVERSITY OF NEW YORK AT BUFFALO
- **Principal Investigator:** Suzanne Sierra Sullivan
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $79,750
- **Award type:** 5
- **Project period:** 2020-05-15 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10162470

## Citation

> US National Institutes of Health, RePORTER application 10162470, A Longitudinal Examination to Predict Quality of Life and Care Transitions for Persons with Alzheimer’s Disease and Related Dementias at End-of-Life (5R03AG067159-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10162470. Licensed CC0.

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