# Addressing variability in peripheral arterial disease outcomes using machine learning techniques

> **NIH NIH F32** · DUKE UNIVERSITY · 2021 · $47,909

## Abstract

Project Summary/Abstract:
Peripheral arterial disease (PAD) is a major cause of morbidity and mortality in the United States, affecting over
eight million Americans, of whom 100,000 a year suffer major amputation. Current guidelines dictate medical
treatment and aggressive risk factor modification for all PAD patients, whether symptomatic or not, with
revascularization attempts for patients with chronic limb threatening ischemia (CLTI) or lifestyle-limiting
claudication. Despite strongly-worded standards of care, variability in PAD outcomes persists. Prior research
has demonstrated that some demographic factors such as gender, race, and socioeconomic status are
associated with worse PAD care and outcomes even when controlling for comorbidities. It is unknown what
specific patient, provider, and healthcare system factors lead to these disparities.
Efforts to understand which patients will suffer worse outcomes and disease progression have been hampered
by contemporary outcomes research techniques. The majority of PAD outcomes research relies on
administrative claims databases, procedural registries, or single center retrospective reviews. While each of
these methods has some advantages, none offer the combination of patient- and disease-specific data,
information about care provision on a provider and health-system level, and outcomes across a range of possible
locations. Furthermore, use of any of these methods at the scale necessary to draw powerful conclusions is
prohibitively time- and resource-intensive. The overall objective of this research is to use a novel natural
language processing model to build a combined EHR/CMS database and to use that database to predict which
PAD patients are at highest risk of poor outcomes with improved power and precision.
This proposal contains plans for collaboration with Duke Forge, who bring expertise in natural language
processing and machine learning in order to efficiently identify PAD patients within our EHR and efficiently
abstract information about them. Once identified, these patients can be linked to their CMS outcomes, allowing
for assessment of how patient-, physician-, and healthcare-specific factors affect PAD outcomes. Our central
hypothesis is that natural language processing powered by machine learning will permit efficient identification of
patients with PAD, thereby facilitating higher-powered and higher-quality investigation into disparities in PAD
outcomes.
This research will pave the way for future interventions targeting sources of outcome inequality, possibly
including access to care, physician adherence to national guidelines, and patient preferences or health literacy.

## Key facts

- **NIH application ID:** 10464976
- **Project number:** 5F32HL151181-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Elizabeth Hope Weissler
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $47,909
- **Award type:** 5
- **Project period:** 2020-09-30 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10464976

## Citation

> US National Institutes of Health, RePORTER application 10464976, Addressing variability in peripheral arterial disease outcomes using machine learning techniques (5F32HL151181-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10464976. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*