# Improving the patient experience of hemodialysis vascular access decision making

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $439,934

## Abstract

Project Summary/Abstract
Patients with end-stage kidney disease (ESKD), who use hemodialysis as their kidney replacement method,
require vascular access in the form of an arteriovenous fistula, arteriovenous graft, or central venous catheter to
receive life-sustaining hemodialysis. Providers and patients face selection of a vascular access type without
adequate evidence of likely outcomes. To overcome this key barrier, the goal of this R01 proposal is to optimize
the patient experience of vascular access decision-making by a) developing an interactive, evidence-based
guide to vascular access outcomes that incorporates a prognostic model for short and long-term outcomes of
vascular access and b) identifying best practices for utilization of the guide during the clinician-patient encounter.
To do so, a novel, large-scale data source that contains multi-institutional granular data regarding vascular
access operations and their short and long-term outcomes will be created by linking the Vascular Quality Initiative
Vascular Access Registry (VQIVAR) to the United States Renal Data Systems Registry (USRDS) and Medicare
claims. Prognostic models will be developed, by using traditional statistical approaches (e.g., logistic regression,
Kaplan-Meier estimates) and machine learning methods (e.g., Bayesian networks, random forests) to predict
outcomes that are meaningful to patients (revision procedures, repeat vascular access operation), and compare
these models using technical metrics (e.g., sensitivity/specificity). The best-performing models will be selected
and tested for external validity in a local UCLA population.
Simultaneously, a mixed-methods approach will be used to engage patient and provider stakeholders to
collaborate in creation and implementation of the proposed guide to vascular access outcomes, assessing the:
1) preferred means of communication with the clinician during the vascular access decision-making encounter;
2) optimal methods for incorporating the guide (including the prognostic model) into the decision-making process;
and 3) satisfaction with iterative versions of the guide. The Specific Aims are:
Aim 1 Design, evaluate and test the externally validity of the prognostic models for hemodialysis vascular
access outcomes, to be used in vascular access decision-making, generated from VQIVAR data linked to
USRDS and Medicare claims using statistical and machine learning methods and validated in a UCLA cohort
with model calibration.
Aim 2 Identify best practices for the clinician-patient vascular access decision-making interaction by
using a mixed methods approach that includes individual interviews, direct observation, and quantitative
satisfaction and preference scales.
Aim 3 Create and refine an interactive guide to vascular access outcomes based on the best-performing
prognostic model created in Aim 1, that allows for personalization with each patient’s characteristics, by engaging
patient and provider stakeholders in an iterative ...

## Key facts

- **NIH application ID:** 10693330
- **Project number:** 5R01DK129810-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Karen Woo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $439,934
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10693330

## Citation

> US National Institutes of Health, RePORTER application 10693330, Improving the patient experience of hemodialysis vascular access decision making (5R01DK129810-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10693330. Licensed CC0.

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