Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach
Objectives In axial spondyloarthritis (axSpA), early diagnosis is crucial, but diagnostic delay remains long and diagnostic criteria do not exist. We aimed to identify a diagnostic model that distinguishes patients with axSpA from patients without axSpA with chronic back pain based on clinical data...
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BMJ Publishing Group
2024-11-01
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| Series: | RMD Open |
| Online Access: | https://rmdopen.bmj.com/content/10/4/e004702.full |
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| author | Xenofon Baraliakos Uta Kiltz Philipp Sewerin Imke Redeker David Kiefer Ioana Andreica Styliani Tsiami Jan Eicker |
| author_facet | Xenofon Baraliakos Uta Kiltz Philipp Sewerin Imke Redeker David Kiefer Ioana Andreica Styliani Tsiami Jan Eicker |
| author_sort | Xenofon Baraliakos |
| collection | DOAJ |
| description | Objectives In axial spondyloarthritis (axSpA), early diagnosis is crucial, but diagnostic delay remains long and diagnostic criteria do not exist. We aimed to identify a diagnostic model that distinguishes patients with axSpA from patients without axSpA with chronic back pain based on clinical data in routine care.Methods Clinical data from patients with chronic back pain were used, with information on rheumatological examinations based on clinical indications. The total dataset was randomly divided into training and test datasets at a 7:3 ratio. A machine learning-based model was built to distinguish axSpA from non-axSpA using the random forest algorithm. Overall accuracy, sensitivity, specificity and the area under the receiver operating characteristic curve-area under the curve (ROC-AUC) in the test dataset were calculated. The contribution of each variable to the accuracy of the model was assessed.Results Data from 939 randomly selected patients were available: 659 diagnosed with axSpA and 280 with non-axSpA. In the test dataset, the model reached an accuracy of 0.9234, a sensitivity of 0.9586, a specificity of 0.8438 and a ROC-AUC of 0.9717. Human leucocyte antigen B27 (HLA-B27) contributed most to the accuracy of the model; that is, the accuracy would suffer most from not using HLA-B27, followed by insidious onset of back pain and erosions in the sacroiliac joint.Conclusions We provide a machine learning-based model that reveals high performance in diagnosing patients with chronic back pain with axSpA versus without axSpA based on information from a tertiary rheumatology practice. This model has the potential to improve diagnostic delay in patients with axSpA in daily routine settings. |
| format | Article |
| id | doaj-art-0733b4472b6b4e56893cc002db96a615 |
| institution | DOAJ |
| issn | 2056-5933 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | RMD Open |
| spelling | doaj-art-0733b4472b6b4e56893cc002db96a6152025-08-20T03:07:27ZengBMJ Publishing GroupRMD Open2056-59332024-11-0110410.1136/rmdopen-2024-004702Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approachXenofon Baraliakos0Uta Kiltz1Philipp Sewerin2Imke Redeker3David Kiefer4Ioana Andreica5Styliani Tsiami6Jan Eicker7Rheumazentrum Ruhrgebiet, Herne, GermanyRuhr University Bochum, Bochum, GermanyRheumazentrum Ruhrgebiet, Ruhr-Universität Bochum, Herne, GermanyRheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, GermanyRheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, GermanyRheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, GermanyRheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, GermanyRheumatology, Ruhr-Universität Bochum and Rheumazentrum Ruhrgebiet, Bochum, GermanyObjectives In axial spondyloarthritis (axSpA), early diagnosis is crucial, but diagnostic delay remains long and diagnostic criteria do not exist. We aimed to identify a diagnostic model that distinguishes patients with axSpA from patients without axSpA with chronic back pain based on clinical data in routine care.Methods Clinical data from patients with chronic back pain were used, with information on rheumatological examinations based on clinical indications. The total dataset was randomly divided into training and test datasets at a 7:3 ratio. A machine learning-based model was built to distinguish axSpA from non-axSpA using the random forest algorithm. Overall accuracy, sensitivity, specificity and the area under the receiver operating characteristic curve-area under the curve (ROC-AUC) in the test dataset were calculated. The contribution of each variable to the accuracy of the model was assessed.Results Data from 939 randomly selected patients were available: 659 diagnosed with axSpA and 280 with non-axSpA. In the test dataset, the model reached an accuracy of 0.9234, a sensitivity of 0.9586, a specificity of 0.8438 and a ROC-AUC of 0.9717. Human leucocyte antigen B27 (HLA-B27) contributed most to the accuracy of the model; that is, the accuracy would suffer most from not using HLA-B27, followed by insidious onset of back pain and erosions in the sacroiliac joint.Conclusions We provide a machine learning-based model that reveals high performance in diagnosing patients with chronic back pain with axSpA versus without axSpA based on information from a tertiary rheumatology practice. This model has the potential to improve diagnostic delay in patients with axSpA in daily routine settings.https://rmdopen.bmj.com/content/10/4/e004702.full |
| spellingShingle | Xenofon Baraliakos Uta Kiltz Philipp Sewerin Imke Redeker David Kiefer Ioana Andreica Styliani Tsiami Jan Eicker Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach RMD Open |
| title | Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach |
| title_full | Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach |
| title_fullStr | Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach |
| title_full_unstemmed | Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach |
| title_short | Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach |
| title_sort | identification of a machine learning based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach |
| url | https://rmdopen.bmj.com/content/10/4/e004702.full |
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