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.
Highlights
Joint involvement is one of the most common features in patients affected by Systemic Lupus Erythematosus (SLE): a high proportion of patients (69–95%) could experience this manifestation during disease course
Machine learning models in SLE erosive arthritis procedure, we evaluated the relevance of the different factors: this value was higher than 35% for Anti-citrullinated peptide antibodies (ACPA) and anti-CarP
The application of Machine Learning Models allowed to identify factors associated with USdetected erosive bone damage in a large SLE cohort and their relevance in determining this phenotype
Summary
Joint involvement is one of the most common features in patients affected by Systemic Lupus Erythematosus (SLE): a high proportion of patients (69–95%) could experience this manifestation during disease course. A great heterogeneity characterizes this manifestation, moving from arthralgia to more severe arthropathy, with possible development of erosive damage [1]. The presence of an erosive arthritis in SLE patients has been considered a rare condition and generally identified in subjects overlapping with Rheumatoid Arthritis (RA). The introduction of more sensitive imaging techniques in the assessment of inflammatory arthritis, such as ultrasonography (US), allowed the identification of erosive damage in up to 40% of patients with SLE-related arthritis [2]. Few data are available concerning specific biomarkers able to recognize patients at risk to develop erosive damage. Several studies investigated the role of RA specific autoantibodies, moving from their relevance in the identification of individuals at risk to develop RA and in determining erosive arthritis [3]
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