# Physical Resilience Prediction in Advanced Renal Disease

> **NIH VA I01** · DURHAM VA MEDICAL CENTER · 2022 · —

## Abstract

ABSTRACT
Background: Older Veterans with advanced chronic kidney disease (CKD) face complex decisions to initiate
or forgo dialysis in the context of uncertainty about their future health and physical function. Making these
decisions is complicated by the course of advanced CKD which is characterized by frequent health events that
further worsen function. Decisions support tools are needed that are specific to the clinical course of advanced
CKD and predict outcomes that matter most to these patients, such as physical function. Characterizing how
patients ‘bounce back’ from health events, such as illnesses or injuries that result in emergency department
(ED) visits or hospitalizations may be key to predicting future functional status. This approach draws from the
novel geriatric concept of physical resilience, defined as one’s ability to resist or recover from functional decline
following a ‘health stressor.’ Objectives: To help older Veterans make informed decisions about kidney
disease treatment by better characterizing physical resilience and identifying patient factors associated with
physical resilience to develop a prediction tool for physical resilience in advanced CKD. This addresses the
HSR&D priority of Patient-Centered Care domain. To do this, we propose Physical REsilience Prediction in
Advanced REnal Disease (PREPARED), a prospective cohort study of older Veterans with advanced CKD with
the following Aims:
1. To characterize physical function trajectories before and after an acute health stressor in order to define
 physical resilience among older Veterans with advanced CKD.
2. To identify associations between patient characteristics and physical resilience trajectory and potential
 candidate variables for prediction model development.
3. To develop a prediction tool for physical resilience (where this quantity has been defined in Aim 1).
4. To determine the association of physical resilience with short-term mortality.
Methods: We will conduct a longitudinal cohort study of 800 Veterans ≥ 70 years old, with an estimated
glomerular filtration rate (eGFR) < 30 ml/min/1.73 m2 (excluding dialysis or transplant), and 90-day probability
of hospitalization ≥ 50% (based on the Care Assessment Needs [CAN] score). Telephone assessments will
include brief validated measures of function every 8 weeks, and within 14 days following a stressor for up to 6
calls. In Aim 1, we will characterize physical resilience, first by identifying latent classes of physical resilience
trajectories using general growth mixture modeling. Next, among the subset from the physical resilience latent
trajectory class we will fit a piecewise linear mixed effects model to quantify resilience. In Aim 2, we will
determine how the physical function trajectory is moderated by person-level health and psychosocial factors
and organ system-level physiologic factors. This information will be used to identify potential candidate
variables for our prediction model in Aim 3. The pu...

## Key facts

- **NIH application ID:** 10186553
- **Project number:** 5I01HX002704-03
- **Recipient organization:** DURHAM VA MEDICAL CENTER
- **Principal Investigator:** Christopher Barrett Bowling
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-03-01 → 2024-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10186553

## Citation

> US National Institutes of Health, RePORTER application 10186553, Physical Resilience Prediction in Advanced Renal Disease (5I01HX002704-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10186553. Licensed CC0.

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