Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus
ObjectivesDiagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.Met...
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| Published in | Annals of the rheumatic diseases Vol. 80; no. 6; pp. 758 - 766 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BMJ Publishing Group Ltd and European League Against Rheumatism
01.06.2021
Elsevier Limited BMJ Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0003-4967 1468-2060 1468-2060 |
| DOI | 10.1136/annrheumdis-2020-219069 |
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| Abstract | ObjectivesDiagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.MethodsFrom a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls).ResultsA novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy.ConclusionsWe have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes. |
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| AbstractList | ObjectivesDiagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.MethodsFrom a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls).ResultsA novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy.ConclusionsWe have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes. Diagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis. From a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls). A novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy. We have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes. Diagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.OBJECTIVESDiagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.From a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls).METHODSFrom a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls).A novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy.RESULTSA novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy.We have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes.CONCLUSIONSWe have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes. |
| Author | Adamichou, Christina Nikolopoulos, Dionysis Nikoloudaki, Myrto Genitsaridi, Irini Repa, Argyro Bortoluzzi, Alessandra Bertsias, George K Sidiropoulos, Prodromos Boumpas, Dimitrios T Fanouriakis, Antonis |
| AuthorAffiliation | 1 Rheumatology, Clinical Immunology and Allergy , University of Crete School of Medicine , Heraklion , Crete , Greece 6 Laboratory of Immune Regulation and Tolerance, Autoimmunity and Inflammation , Biomedical Research Foundation of the Academy of Athens , Athens , Attica , Greece 3 Section of Rheumatology, Department of Medical Sciences , Azienda Ospedaliero Universitaria di Ferrara Arcispedale Sant'Anna , Cona , Emilia-Romagna , Italy 2 Rheumatology and Clinical Immunology Unit, 4th Department of Internal Medicine , Attikon University Hospital, National and Kapodistrian University of Athens , Athens , Greece 4 Rheumatology , “Asklepieion” General Hospital , Athens , Greece 5 Institute of Molecular Biology and Biotechnology , Foundation of Research and Technology—Hellas , Heraklion , Crete , Greece |
| AuthorAffiliation_xml | – name: 6 Laboratory of Immune Regulation and Tolerance, Autoimmunity and Inflammation , Biomedical Research Foundation of the Academy of Athens , Athens , Attica , Greece – name: 4 Rheumatology , “Asklepieion” General Hospital , Athens , Greece – name: 2 Rheumatology and Clinical Immunology Unit, 4th Department of Internal Medicine , Attikon University Hospital, National and Kapodistrian University of Athens , Athens , Greece – name: 1 Rheumatology, Clinical Immunology and Allergy , University of Crete School of Medicine , Heraklion , Crete , Greece – name: 5 Institute of Molecular Biology and Biotechnology , Foundation of Research and Technology—Hellas , Heraklion , Crete , Greece – name: 3 Section of Rheumatology, Department of Medical Sciences , Azienda Ospedaliero Universitaria di Ferrara Arcispedale Sant'Anna , Cona , Emilia-Romagna , Italy |
| Author_xml | – sequence: 1 givenname: Christina surname: Adamichou fullname: Adamichou, Christina organization: Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine, Heraklion, Crete, Greece – sequence: 2 givenname: Irini surname: Genitsaridi fullname: Genitsaridi, Irini organization: Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine, Heraklion, Crete, Greece – sequence: 3 givenname: Dionysis orcidid: 0000-0002-9894-6966 surname: Nikolopoulos fullname: Nikolopoulos, Dionysis organization: Rheumatology and Clinical Immunology Unit, 4th Department of Internal Medicine, Attikon University Hospital, National and Kapodistrian University of Athens, Athens, Greece – sequence: 4 givenname: Myrto surname: Nikoloudaki fullname: Nikoloudaki, Myrto organization: Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine, Heraklion, Crete, Greece – sequence: 5 givenname: Argyro surname: Repa fullname: Repa, Argyro organization: Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine, Heraklion, Crete, Greece – sequence: 6 givenname: Alessandra surname: Bortoluzzi fullname: Bortoluzzi, Alessandra organization: Section of Rheumatology, Department of Medical Sciences, Azienda Ospedaliero Universitaria di Ferrara Arcispedale Sant'Anna, Cona, Emilia-Romagna, Italy – sequence: 7 givenname: Antonis orcidid: 0000-0003-2696-031X surname: Fanouriakis fullname: Fanouriakis, Antonis organization: Rheumatology, “Asklepieion” General Hospital, Athens, Greece – sequence: 8 givenname: Prodromos orcidid: 0000-0001-9607-0326 surname: Sidiropoulos fullname: Sidiropoulos, Prodromos organization: Institute of Molecular Biology and Biotechnology, Foundation of Research and Technology—Hellas, Heraklion, Crete, Greece – sequence: 9 givenname: Dimitrios T orcidid: 0000-0002-9812-4671 surname: Boumpas fullname: Boumpas, Dimitrios T organization: Laboratory of Immune Regulation and Tolerance, Autoimmunity and Inflammation, Biomedical Research Foundation of the Academy of Athens, Athens, Attica, Greece – sequence: 10 givenname: George K orcidid: 0000-0001-5299-1406 surname: Bertsias fullname: Bertsias, George K email: gbertsias@uoc.gr organization: Institute of Molecular Biology and Biotechnology, Foundation of Research and Technology—Hellas, Heraklion, Crete, Greece |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33568388$$D View this record in MEDLINE/PubMed |
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| Keywords | autoimmune diseases systemic lupus erythematosus autoantibodies |
| Language | English |
| License | This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. cc-by-nc |
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| Snippet | ObjectivesDiagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against... Diagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing... |
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| SubjectTerms | Accuracy Adult Algorithms Antibodies, Antinuclear Antinuclear antibodies Artificial intelligence Atrophy autoantibodies autoimmune diseases Classification Clinical medicine Datasets Diagnosis Disease Feature selection Hemolytic anemia Humans Immunological diseases Learning algorithms Lupus lupus erythematosus Lupus Erythematosus, Systemic Machine Learning Medical diagnosis Mental disorders Nephritis Patients Probability Proteinuria Rheumatology Serology systemic Systemic Lupus Erythematosus Thrombocytopenia |
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| Title | Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus |
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