# Leveraging linked registry and electronic health records to examine long-term patient outcomes after peripheral vascular intervention"

> **NIH NIH K01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2024 · $161,321

## Abstract

Leveraging linked registry and electronic health records to examine long-term patient outcomes after
peripheral vascular intervention
Project Summary/Abstract
Peripheral arterial disease (PAD) affects over 200 million people worldwide. Peripheral vascular interventions
(PVI) are the most common procedures that are performed to manage PAD. Existing randomized controlled
trials (RCTs) and observational studies of patient outcomes after PVIs all had limited follow-up lengths due to
difficulties in long-term data collections. In addition, heterogeneity of treatment effect (HTE) for stent placement
vs. percutaneous transluminal angioplasty (PTA) alone has not been well understood with the current
approach of effect modifier assessment. Real-world data (RWD), particularly registries linked with electronic
health data (EHR), are useful for studying long-term outcomes after vascular procedures. However, methods
for working with multiple data sources and analyzing unstructured text data are still evolving. The proposed
research aims to address current evidence gaps in long-term patient outcomes after PVI procedures. This will
be facilitated by innovatively apply and refine data linkage, natural language processing (NLP), and effect
modifier assessment methods. Specifically, this project will link registry and EHR data to 1) examine long-term
major adverse limb events after stent placement vs. PTA alone as well as assess heterogeneity of treatment
effect by patient characteristics; 2) develop an NLP pipeline with machine learning methods to analyze
unstructured text data and examine long-term efficacy endpoints after stent placement vs. PTA alone, and; 3)
establish feasibility and updating requirements for the deployment of the NLP tool for long-term PVI outcome
assessment to other institutions. To support the research activities and the transition toward independence, the
candidate will undertake the following career development activities during the award period: 1) gaining an in-
depth understanding of NLP and machine learning methods; 2) refining data science expertise to integrate
EHR into medical device epidemiologic research; 3) strengthening knowledge in current and novel vascular
disease treatment; 4) developing and improving skills in grant writing and academic leadership; 5) training in
responsible conduct of research. The candidate will be mentored by a team of experts with complementary
strengths in surgical and device outcomes research, natural language processing and machine learning, and
vascular disease and surgery. The proposed career development and research activities will develop the
candidate's skillset and expertise and lead to an R01 level application. The candidate's long-term goal is to
become an independent researcher focusing on the development and application of advanced multidisciplinary
methods in the evaluation of surgical and device outcomes in the vascular disease area, supporting clinical,
patient, and regulatory decision...

## Key facts

- **NIH application ID:** 10904822
- **Project number:** 5K01HL159315-04
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Jialin Mao
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $161,321
- **Award type:** 5
- **Project period:** 2021-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10904822

## Citation

> US National Institutes of Health, RePORTER application 10904822, Leveraging linked registry and electronic health records to examine long-term patient outcomes after peripheral vascular intervention" (5K01HL159315-04). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10904822. Licensed CC0.

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