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.
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. - ELETTRONICO. - 13:(2018), pp. 0-0. [10.1371/journal.pone.0207926]
Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models
Sciandrone, Marco;Galvan, Giulio;Galligari, Alessandro;LEVATO, TOMMASO;
2018
Abstract
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.
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