# Leveraging Electronic Health Records and Genomic Biobanks for Kidney Stone Disease

> **NIH NIH R21** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $216,250

## Abstract

PROJECT SUMMARY
Kidney stones are highly prevalent and recurrent. Our current understanding of kidney stone disease risk factors
and disease associations has relied primarily on data from chart review, nonspecific administrative datasets, and
secondary analyses of observation studies. Current study designs suffer from small sample sizes, heterogenous
patient groups, and lack of standardized accuracy data and outcome definitions. The widespread adoption of
electronic health records (EHRs) provides novel research opportunities for kidney stone disease. EHRs contain
a robust clinical repository of data collected over time from clinical care. However, there are currently limited
tools to identify and characterize kidney stone patients in the EHR. The objective of this study is to establish
feasibility of utilizing EHR data to investigate kidney stone disease. To structure EHR data in an efficient and
cost-effective manner, natural language processing and deep learning methods can be designed for identifying
and phenotyping kidney stone patients and clinical outcomes. Our de-identified EHR is linked to a DNA biobank
that can enable investigation of genetic associations with disease. This project has two specific aims. In Aim 1,
we will perform genetic association studies in our EHR and linked DNA biobank. We will replicate previously
described associations with genetic variants and kidney stone disease. We will then perform a genome-wide
association study to discover novel associations. In Aim 2, our goal is to develop and validate a computable
framework to extract clinical outcomes of kidney stone disease from the EHR. Clinically meaningful outcomes
include symptomatic stone passage and radiographic stone characterization. We will develop and test natural
language processing and deep learning algorithms to extract keywords and context-based information in clinical
notes and reports. We will train and test these algorithms using manual annotation as the gold standard. This
aim will enable rigorous phenotyping of each kidney stone patient using structured and unstructured EHR data.
Successful completion of this project will lay the groundwork towards advancing genomic medicine and precision
health to support clinical decision-making in kidney stone patients.

## Key facts

- **NIH application ID:** 10395992
- **Project number:** 5R21DK127075-02
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Cosmin Adrian Bejan
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $216,250
- **Award type:** 5
- **Project period:** 2021-05-01 → 2023-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10395992

## Citation

> US National Institutes of Health, RePORTER application 10395992, Leveraging Electronic Health Records and Genomic Biobanks for Kidney Stone Disease (5R21DK127075-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10395992. Licensed CC0.

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