# Predicting risk of systemic autoimmune disease in patients with positive antinuclear antibodies

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2024 · $401,415

## Abstract

Project Summary
Providers across multiple specialties face challenges in determining the clinical significance of a
positive antinuclear antibody (ANA). While a positive ANA is highly sensitive for autoimmune
disease, it is non-specific with up to 20% of the general population having a positive ANA
without having autoimmune disease. Risk models to aide clinicians in stratifying positive ANA
patients do not currently exist. By identifying high-risk patients, providers could properly triage
patients for prompt treatment to reduce autoimmune disease-related morbidity and mortality.
Our long-term goal is to build risk models in the electronic health record (EHR) for autoimmune
diseases that improve outcomes. The overall objective of this proposal is to identify positive
ANA patients who are at high risk for developing autoimmune disease to facilitate appropriate
triage to rheumatology for earlier diagnosis and treatment. Our institution with expertise in
biostatistics, biomedical informatics, and implementation science has demonstrated success in
building and testing robust EHR risk models. Building upon this well-established infrastructure,
we hypothesize that we can use available EHR data to identify positive ANA patients that are
high risk for autoimmune disease. We hypothesize that using tailored risk assessments in real-
time in the EHR can reduce time to autoimmune disease diagnosis and treatment. We will test
these hypotheses with the following specific aims: (1) Refine and validate features available in
the EHR to distinguish positive ANA patients who develop autoimmune disease from positive
ANA patients who do not develop autoimmune disease and (2) Conduct an adaptive,
randomized, pragmatic evaluation of an autoimmune disease risk model in the EHR to risk-
stratify patients with a positive ANA. For Aim 1, we will validate a risk model for autoimmune
disease in positive ANA patients using EHR data with logistic regression and machine learning
methods. For Aim 2, we will deploy a risk model for autoimmune disease in real-time in the
EHR. We will randomize positive ANA patients to either have a risk score from the model
displayed and acted upon vs. not having a risk score displayed or usual care. We will assess if
having this EHR system change with a risk score calculated and shared with the ordering
provider compared to usual care affects time to autoimmune disease diagnosis and treatment.
Our proposal is innovative in that it not only builds a predictive risk model for autoimmune
disease but also deploys and assesses if the model impacts patient outcomes. For expected
outcomes, we anticipate deploying an EHR risk model that identifies positive ANA patients at
high risk for developing autoimmune disease and decreases time to diagnosis and treatment.

## Key facts

- **NIH application ID:** 10811642
- **Project number:** 5R01AR080629-03
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** April Lynn Barnado
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $401,415
- **Award type:** 5
- **Project period:** 2022-04-01 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10811642

## Citation

> US National Institutes of Health, RePORTER application 10811642, Predicting risk of systemic autoimmune disease in patients with positive antinuclear antibodies (5R01AR080629-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10811642. Licensed CC0.

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