Idiopathic inflammatory myopathies (IIMs) are heterogeneous conditions, and the optimal way to classify patients and divide them into subgroups remains unclear. Could machine learning techniques be the answer to the problem of defining homogeneous disease phenotypes, enabling stratified treatment approaches and the formulation of future IIM classification criteria?
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Bohan, A. & Peter, J. B. Polymyositis and dermatomyositis (first of two parts). N. Engl. J. Med. 292, 344–347 (1975).
Bohan, A. & Peter, J. B. Polymyositis and dermatomyositis (second of two parts). N. Engl. J. Med. 292, 403–407 (1975).
Bohan, A. et al. Computer-assisted analysis of 153 patients with polymyositis and dermatomyositis. Medicine (Baltimore) 56, 255–286 (1977).
Lundberg, I. E. et al. 2017 European League Against Rheumatism/American College of Rheumatology classification criteria for adult and juvenile idiopathic inflammatory myopathies and their major subgroups. Arthritis Rheumatol. 69, 2271–2282 (2017).
Eng, S. W. M. et al. A clinically and biologically based subclassification of the idiopathic inflammatory myopathies using machine learning. ACR Open Rheumatol. 2, 158–166 (2020).
Mariampillai, K. et al. Development of a new classification system for idiopathic inflammatory myopathies based on clinical manifestations and myositis-specific autoantibodies. JAMA Neurol. 75, 1528–1537 (2018).
Spielmann, L. et al. Anti-Ku syndrome with elevated CK and anti-Ku syndrome with anti-dsDNA are two distinct entities with different outcomes. Ann. Rheum. Dis. 78, 1101–1106 (2019).
Pinal-Fernandez, I. & Mammen, A. L. On using machine learning algorithms to define clinically meaningful patient subgroups. Ann. Rheum. Dis. https://doi.org/10.1136/annrheumdis-2019-215852 (2019).
Meyer, A., Spielmann, L. & Séverac, F. On how to not misuse hierarchical clustering on principal components to define clinically meaningful patient subgroups. Response to: ‘On using machine learning algorithms to define clinical meaningful patient subgroups’ by Pinal-Fernandez and Mammen. Ann. Rheum. Dis. https://doi.org/10.1136/annrheumdis-2019-215868 (2019).
Oddis, C. V. et al. Rituximab in the treatment of refractory adult and juvenile dermatomyositis and adult polymyositis: a randomized, placebo-phase trial. Arthritis Rheum. 65, 314–324 (2013).
Acknowledgements
The work of H.C. is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre Funding Scheme. J.B.L. holds an NIHR Clinical Lectureship in Neurology (NWN/006/025/A). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Lilleker, J.B., Chinoy, H. Can machine learning unravel the complex IIM spectrum?. Nat Rev Rheumatol 16, 299–300 (2020). https://doi.org/10.1038/s41584-020-0412-6
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41584-020-0412-6
This article is cited by
-
Slicing and dicing myositis for cures and prevention
Nature Reviews Rheumatology (2021)