Objective: Limited 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. Methods: We 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. Results: We 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. Conclusion: The 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.

Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models / Ceccarelli, Fulvia; Sciandrone, Marco; Perricone, Carlo; Galvan, Giulio; Cipriano, Enrica; Galligari, Alessandro; Levato, Tommaso; Colasanti, Tania; Massaro, Laura; Natalucci, Francesco; Spinelli, FRANCESCA ROMANA; Alessandri, Cristiano; Valesini, Guido; Conti, Fabrizio. - In: PLOS ONE. - ISSN 1932-6203. - 13:12(2018). [10.1371/journal.pone.0207926]

Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models.

Fulvia Ceccarelli
;
Marco Sciandrone;Carlo Perricone;Giulio Galvan;Enrica Cipriano;Tania Colasanti;Laura Massaro;Francesco Natalucci;Francesca Romana Spinelli;Cristiano Alessandri;Guido Valesini;Fabrizio Conti
2018

Abstract

Objective: Limited 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. Methods: We 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. Results: We 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. Conclusion: The 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.
2018
systemic lupus erythematosus; arthritis; machine learning
01 Pubblicazione su rivista::01a Articolo in rivista
Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models / Ceccarelli, Fulvia; Sciandrone, Marco; Perricone, Carlo; Galvan, Giulio; Cipriano, Enrica; Galligari, Alessandro; Levato, Tommaso; Colasanti, Tania; Massaro, Laura; Natalucci, Francesco; Spinelli, FRANCESCA ROMANA; Alessandri, Cristiano; Valesini, Guido; Conti, Fabrizio. - In: PLOS ONE. - ISSN 1932-6203. - 13:12(2018). [10.1371/journal.pone.0207926]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1723863
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