Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model

• Published in: Frontiers in immunology 2024-03, Vol.15, p.1384229-1384229
• Main Authors: Barnado, April, Moore, Ryan P, Domenico, Henry J, Green, Sarah, Camai, Alex, Suh, Ashley, Han, Bryan, Walker, Katherine, Anderson, Audrey, Caruth, Lannawill, Katta, Anish, McCoy, Allison B, Byrne, Daniel W
• Format: Article
• Language: English
• Subjects: Antibodies, Antinuclear; antinuclear antibodies; Autoantibodies; autoimmune disease; Autoimmune Diseases - diagnosis; electronic health record; Electronic Health Records; Female; Humans; Immunology; Male; Rheumatology; risk model
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2024.1384229

Positive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals. Using a de-identified electronic health record (EHR), we randomly chart reviewed 2,000 positive ANA individuals to determine if a systemic autoimmune disease was diagnosed by a rheumatologist. , we considered demographics, billing codes for autoimmune disease-related symptoms, and laboratory values as variables for the risk model. We performed logistic regression and machine learning models using training and validation samples. We assembled training (n = 1030) and validation (n = 449) sets. Positive ANA individuals who were younger, female, had a higher titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes related to symptoms of autoimmune diseases were all more likely to develop autoimmune diseases. The most important variables included having a disease-specific autoantibody, number of billing codes for autoimmune disease-related symptoms, and platelet count. In the logistic regression model, AUC was 0.83 (95% CI 0.79-0.86) in the training set and 0.75 (95% CI 0.68-0.81) in the validation set. We developed and validated a risk model that predicts risk for developing systemic autoimmune diseases and can be deployed easily within the EHR. The model can risk stratify positive ANA individuals to ensure high-risk individuals receive urgent rheumatology referrals while reassuring low-risk individuals and reducing unnecessary referrals.