# Using the electronic health record to risk-stratify patients with systemic lupus erythematosus

> **NIH NIH K08** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $162,984

## Abstract

PROJECT SUMMARY
Systemic lupus erythematosus (SLE) is a heterogeneous, autoimmune disease with highly variable morbidity
and mortality. Disease heterogeneity is a major challenge to both the clinical care of SLE patients and
successful clinical trials. With heterogeneous diseases, identifying clusters results in improved classification,
biomarkers, and targeted therapies. Electronic health records (EHRs) represent a powerful tool to identify
clusters and risk-stratify SLE patients. We have published an algorithm that accurately identifies SLE patients
in the EHR and have assembled a cohort of 2,376 SLE patients with a mean follow-up of 9 years. This data is
linked to one of the world's largest biobanks, BioVU, with 400 SLE patients already genotyped. The EHR and
BioVU allow for novel methods such as phenome-wide association studies (PheWAS) that use billing codes for
a comprehensive scan of the entire EHR. We have performed the first PheWAS in SLE.
Our overall goal is to use readily available EHR data in an intelligent way to improve outcomes in SLE patients.
We hypothesize that SLE is composed of multiple clusters of patients with different disease courses and
comorbidities, and our EHR-based methodology that incorporates genetic information will serve as novel tools
to risk-stratify SLE patients.
Using PheWAS in Aim 1, we will uncover differences in comorbidities between SLE patients with and without
autoantibodies and with and without pre-specified SLE susceptibility single nucleotide polymorphisms (SNPs).
In Aim 2, we will perform clustering analyses using demographics, autoantibodies, comorbidities, and SLE
SNPs to risk-stratify SLE patients. We will assess renal outcomes, survival, and treatments received among
the clusters. In Aim 3, we will evaluate treatment response to induction therapy for SLE nephritis in the EHR
and compare to published outcomes.
These aims are the necessary first steps to risk-stratify SLE patients and define treatment response in the EHR
to then build models to predict treatment response and conduct EHR-based pragmatic clinical trials of targeted
therapies. Additional mentored training and didactic coursework in genetics, biomedical informatics, and
biostatistics will advance Dr. Barnado's career.
Vanderbilt serves as an exceptional environment to support Dr. Barnado's transition to an independent
physician scientist. Notable strengths include the Synthetic Derivative (SD), a de-identified EHR with over 2.7
million subjects, and BioVU, a genetic biobank linked to the SD. The Department of Medicine and Division of
Rheumatology are in support of Dr. Barnado's career. Her mentors, Drs. Crofford and Denny, are
internationally recognized in rheumatology and biomedical informatics with successful track records of
mentoring. With Vanderbilt's institutional commitment to young investigators and expertise in biomedical
informatics, Dr. Barnado's will successfully leverage her innovative proposal to independent R01 funding.

## Key facts

- **NIH application ID:** 10405057
- **Project number:** 5K08AR072757-05
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** April Lynn Barnado
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $162,984
- **Award type:** 5
- **Project period:** 2018-05-04 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10405057

## Citation

> US National Institutes of Health, RePORTER application 10405057, Using the electronic health record to risk-stratify patients with systemic lupus erythematosus (5K08AR072757-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10405057. Licensed CC0.

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