Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models
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 stu...
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| Published in | PloS one Vol. 13; no. 12; p. e0207926 |
|---|---|
| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
04.12.2018
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0207926 |
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| 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.
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.
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.
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. |
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| AbstractList | 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. 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. 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. 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. 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. 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.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.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.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.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.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.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.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. 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. 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. 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. 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. 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. 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. |
| Audience | Academic |
| Author | Galvan, Giulio Levato, Tommaso Massaro, Laura Natalucci, Francesco Alessandri, Cristiano Conti, Fabrizio Colasanti, Tania Ceccarelli, Fulvia Galligari, Alessandro Cipriano, Enrica Spinelli, Francesca Romana Valesini, Guido Perricone, Carlo Sciandrone, Marco |
| AuthorAffiliation | University College London, UNITED KINGDOM 1 Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy 2 Dipartimento di Ingegneria dell'Informazione, Università di Firenze, Florence, Italy |
| AuthorAffiliation_xml | – name: University College London, UNITED KINGDOM – name: 1 Lupus Clinic, Rheumatology, Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy – name: 2 Dipartimento di Ingegneria dell'Informazione, Università di Firenze, Florence, Italy |
| Author_xml | – sequence: 1 givenname: Fulvia orcidid: 0000-0001-5026-8783 surname: Ceccarelli fullname: Ceccarelli, Fulvia – sequence: 2 givenname: Marco surname: Sciandrone fullname: Sciandrone, Marco – sequence: 3 givenname: Carlo surname: Perricone fullname: Perricone, Carlo – sequence: 4 givenname: Giulio orcidid: 0000-0002-0384-0334 surname: Galvan fullname: Galvan, Giulio – sequence: 5 givenname: Enrica surname: Cipriano fullname: Cipriano, Enrica – sequence: 6 givenname: Alessandro orcidid: 0000-0002-8599-642X surname: Galligari fullname: Galligari, Alessandro – sequence: 7 givenname: Tommaso orcidid: 0000-0002-4616-8277 surname: Levato fullname: Levato, Tommaso – sequence: 8 givenname: Tania surname: Colasanti fullname: Colasanti, Tania – sequence: 9 givenname: Laura surname: Massaro fullname: Massaro, Laura – sequence: 10 givenname: Francesco surname: Natalucci fullname: Natalucci, Francesco – sequence: 11 givenname: Francesca Romana surname: Spinelli fullname: Spinelli, Francesca Romana – sequence: 12 givenname: Cristiano surname: Alessandri fullname: Alessandri, Cristiano – sequence: 13 givenname: Guido surname: Valesini fullname: Valesini, Guido – sequence: 14 givenname: Fabrizio surname: Conti fullname: Conti, Fabrizio |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30513105$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2018 Public Library of Science 2018 Ceccarelli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2018 Ceccarelli et al 2018 Ceccarelli et al |
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| References_xml | – volume: 38 start-page: 894 year: 2008 ident: ref10 article-title: Wrapper filtering criteria via linear neuron and kernel approaches publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2008.05.005 – volume: 2015 start-page: 712490 year: 2015 ident: ref23 article-title: The Role of Posttranslational Protein Modifications in Rheumatological Diseases: Focus on Rheumatoid Arthritis publication-title: J Immunol Res doi: 10.1155/2015/712490 – volume: 29 start-page: 288 year: 2002 ident: ref16 article-title: Systemic lupus erythematosus disease activity index 2000 publication-title: J Rheumatol – volume: 32 start-page: 2485 year: 2005 ident: ref20 article-title: Musculoskeletal ultrasound including definitions for ultrasonographic pathology publication-title: J Rheumatol – volume: 40 start-page: 1725 year: 1997 ident: ref13 article-title: Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus publication-title: Arthritis Rheum doi: 10.1002/art.1780400928 – volume: 64 start-page: 321 year: 2012 ident: ref4 article-title: Anti-cyclic citrullinated peptide antibodies in systemic lupus erythematosus patients with articular involvement: a predictive marker for erosive disease? publication-title: Reumatismo doi: 10.4081/reumatismo.2012.321 – volume: 12 start-page: e0174200 year: 2017 ident: ref8 article-title: Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models publication-title: PLoS One doi: 10.1371/journal.pone.0174200 – volume: 27 start-page: 105 year: 2018 ident: ref24 article-title: Anti-carbamylated protein antibodies in systemic lupus erythematosus patients with articular involvement publication-title: Lupus doi: 10.1177/0961203317713141 – volume: 35 start-page: 674 year: 2017 ident: ref25 article-title: Jaccoud's arthropathy in systemic lupus erythematosus: clinical, laboratory and ultrasonographic features publication-title: Clin Exp Rheumatol – volume: 67 start-page: 62 year: 2015 ident: ref15 article-title: Joint involvement in patients affected by systemic lupus erythematosus: application of the swollen to tender joint count ratio publication-title: Reumatismo doi: 10.4081/reumatismo.2015.828 – volume: 60 start-page: 641 year: 2001 ident: ref19 article-title: Guidelines for musculoskeletal ultrasound in rheumatology publication-title: Ann Rheum Dis doi: 10.1136/ard.60.7.641 – volume: 8 start-page: 68 year: 2002 ident: ref11 article-title: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning publication-title: Nat Med doi: 10.1038/nm0102-68 – volume: 8 start-page: R144 year: 2006 ident: ref5 article-title: Presence of antibodies against cyclic citrullinated peptides in patients with 'rhupus': across-sectional study publication-title: Arthritis Res Ther doi: 10.1186/ar2036 – volume: 3 start-page: 1157 year: 2003 ident: ref22 article-title: An introduction to variable and feature selection publication-title: J. 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| Snippet | Limited evidences are available on biomarkers to recognize Systemic Lupus erythematosus (SLE) patients at risk to develop erosive arthritis. Anti-citrullinated... Objective Limited evidences are available on biomarkers to recognize Systemic Lupus erythematosus (SLE) patients at risk to develop erosive arthritis.... ObjectiveLimited evidences are available on biomarkers to recognize Systemic Lupus erythematosus (SLE) patients at risk to develop erosive arthritis.... Objective Limited evidences are available on biomarkers to recognize Systemic Lupus erythematosus (SLE) patients at risk to develop erosive arthritis.... |
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| SubjectTerms | Adult Alzheimer's disease Anti-Citrullinated Protein Antibodies - blood Antibodies Arthralgia Arthritis Arthritis - diagnostic imaging Arthritis - etiology Arthritis - immunology Artificial intelligence Autoantibodies - blood Autoimmune diseases Biological markers Biology and Life Sciences Biomarkers Biomarkers - blood Carp Chronic conditions Citrulline Cohort Studies Computer and Information Sciences Cross-Sectional Studies Damage detection Decision Trees Development and progression Diagnosis Diagnostic imaging Engineering and Technology Family medical history Female Humans Immunoglobulins Joint diseases Laboratories Learning algorithms Logistic Models Lupus Lupus erythematosus Lupus Erythematosus, Systemic - complications Lupus Erythematosus, Systemic - immunology Lymphoma Machine Learning Male Medicine and Health Sciences Middle Aged NMR Nuclear magnetic resonance Patients Peptides Phenotypes Protein Carbamylation - immunology Proteins Research and Analysis Methods Rheumatoid arthritis Rheumatoid factor Rheumatoid Factor - blood Rheumatology Risk Factors Systemic lupus erythematosus Ultrasonic imaging Ultrasonography Ultrasound Ultrasound imaging |
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| Title | Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models |
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