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

被引:3
作者
Barnado, April [1 ,2 ]
Moore, Ryan P. [3 ]
Domenico, Henry J. [3 ]
Green, Sarah [1 ]
Camai, Alex [1 ]
Suh, Ashley [1 ]
Han, Bryan [1 ]
Walker, Katherine [1 ]
Anderson, Audrey [1 ]
Caruth, Lannawill [1 ]
Katta, Anish [1 ]
McCoy, Allison B. [2 ]
Byrne, Daniel W. [2 ,3 ]
机构
[1] Vanderbilt Univ, Med Ctr, Dept Med, Div Rheumatol & Immunol, Nashville, TN 37232 USA
[2] Vanderbilt Univ, Med Ctr, Dept Biomed Informat, Nashville, TN 37203 USA
[3] Vanderbilt Univ, Med Ctr, Dept Biostat, Nashville, TN USA
来源
FRONTIERS IN IMMUNOLOGY | 2024年 / 15卷
关键词
antinuclear antibodies; electronic health record; risk model; autoimmune disease; rheumatology; ELECTRONIC CONSULTATIONS; CLINICAL UTILITY; HEALTH; LUPUS; DIAGNOSIS; PROBABILITY; ACCESS;
D O I
10.3389/fimmu.2024.1384229
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Objective 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.Methods 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. A priori, 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.Results 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.Conclusion 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.
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页数:12
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