ArticlePloS one2018
Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed, 42 citations in OpenAlex.
- Machine learning models identify prognostic factors in systemic lupus erythematosus patients with epstein-barr virus infection.Clinical rheumatology · 2026Article
- A decision tree-based algorithm for structured risk stratification of rare rheumatic diseases in a tertiary referral setting.Frontiers in medicine · 2026Article
- Ultrasound clusters of joint inflammation in systemic lupus erythematosus: a cross-sectional study.Rheumatology international · 2025Article
- Prediction of 1-Year Activity in Systemic Lupus Erythematosus: Hierarchical Machine Learning Approach.JMIR formative research · 2025Article
- Examining the clinical and radiological landscape of rhupus: navigating the challenges in disease classification.Rheumatology international · 2024Review
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Article
- How AI May Transform Musculoskeletal Imaging.Radiology · 2024Review
- Application of Machine Learning Models in Systemic Lupus Erythematosus.International journal of molecular sciences · 2023Review
- Anti-Carbamylated Protein (Anti-CarP) Antibodies in Patients Evaluated for Suspected Rheumatoid Arthritis.Diagnostics (Basel, Switzerland) · 2022Article
- Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus.BioData mining · 2021Article
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- Machine Learning in Rheumatic Diseases.Clinical reviews in allergy & immunology · 2021Review
- Coupling of Co-expression Network Analysis and Machine Learning Validation Unearthed Potential Key Genes Involved in Rheumatoid Arthritis.Frontiers in genetics · 2021Article
- Comparison of Role of Hand and Wrist Ultrasound in Diagnosis of Subclinical Synovitis in Patients with Systemic Lupus Erythematosus and Rheumatoid Arthritis: A Retrospective, Single-Center Study.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- Computer-aided diagnosis in rheumatic diseases using ultrasound: an overview.Clinical rheumatology · 2020Review
- Applied machine learning and artificial intelligence in rheumatology.Rheumatology advances in practice · 2020Review
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- An Overview of the Intrinsic Role of Citrullination in Autoimmune Disorders.Journal of immunology research · 2019Review
- Correction: Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models.PloS one · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
14 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveLimited evidences are available on biomarkers to recognize Systemic Lupus erythematosus (SLE) patients at risk to develop erosive arthritis. Anti-citrullinated peptide antibodies (ACPA) have been widely investigated and identified in up to 50% of X-ray detected erosive arthritis; conversely, few studies evaluated anti-carbamylated proteins antibodies (anti-CarP). Here, we considered the application of machine learning models to identify relevant factors in the development of ultrasonography (US)-detected erosive damage in a large cohort of SLE patients with joint involvement.
methodsWe enrolled consecutive SLE patients with arthritis/arthralgia. All patients underwent joint (DAS28, STR) and laboratory assessment (detection of ACPA, anti-CarP, Rheumatoid Factor, SLE-related antibodies). The bone surfaces of metacarpophalangeal and proximal interphalangeal joints were assessed by US: the presence of erosions was registered with a dichotomous value (0/1), obtaining a total score (0-20). Concerning machine learning techniques, we applied and compared Logistic Regression and Decision Trees in conjunction with the feature selection Forward Wrapper method.
resultsWe enrolled 120 SLE patients [M/F 8/112, median age 47.0 years (IQR 15.0); median disease duration 120.0 months (IQR 156.0)], 73.3% of them referring at least one episode of arthritis. Erosive damage was identified in 25.8% of patients (mean±SD 0.7±1.6), all of them with clinically evident arthritis. We applied Logistic Regression in conjunction with the Forward Wrapper method, obtaining an AUC value of 0.806±0.02. As a result of the learning procedure, we evaluated the relevance of the different factors: this value was higher than 35% for ACPA and anti-CarP.
conclusionThe application of Machine Learning Models allowed to identify factors associated with US-detected erosive bone damage in a large SLE cohort and their relevance in determining this phenotype. Although the scope of this study is limited by the small sample size and its cross-sectional nature, the results suggest the relevance of ACPA and anti-CarP antibodies in the development of erosive damage as also pointed out in other studies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.