{"doi":"10.3389/fimmu.2024.1384229","title":"Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model","abstract":"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: , 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.","journal":"Frontiers in Immunology","year":2024,"id":439969,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9602,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":671065,"name":"Ryan Moore","orcid":"0000-0003-2883-9580","position":1,"is_corresponding":false},{"id":671064,"name":"Henry J. Domenico","orcid":"0000-0001-7390-7947","position":2,"is_corresponding":false},{"id":1151578,"name":"Sarah Green","orcid":"0000-0002-8299-4349","position":3,"is_corresponding":false},{"id":786212,"name":"Alex Camai","orcid":null,"position":4,"is_corresponding":false},{"id":1152074,"name":"Ashley Suh","orcid":"0000-0001-6513-8447","position":5,"is_corresponding":false},{"id":1124061,"name":"Bryan Han","orcid":null,"position":6,"is_corresponding":false},{"id":930384,"name":"Katherine Walker","orcid":null,"position":7,"is_corresponding":false},{"id":1251629,"name":"Audrey Anderson","orcid":null,"position":8,"is_corresponding":false},{"id":1251630,"name":"Lannawill Caruth","orcid":null,"position":9,"is_corresponding":false},{"id":1001797,"name":"Anish Katta","orcid":"0009-0008-0254-1069","position":10,"is_corresponding":false},{"id":237701,"name":"Allison B. McCoy","orcid":"0000-0003-2292-9147","position":11,"is_corresponding":false},{"id":671063,"name":"Daniel W. Byrne","orcid":"0000-0001-9330-4334","position":12,"is_corresponding":false},{"id":504542,"name":"April Barnado","orcid":"0000-0002-9573-4335","position":0,"is_corresponding":true}],"reference_count":56,"raw_metadata":null,"created_at":"2026-07-19T02:00:52.633010Z","pmid":"38571954","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}