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Using explainable artificial intelligence to predict and forestall flare in rheumatoid arthritis

Nature Medicine volume 30, pages 925–926 (2024)Cite this article

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Current treatments for rheumatoid arthritis aim to achieve and maintain disease remission, which is characterized by the absence of symptoms of inflammation1. However, remission is often fragile, with about 50% of patients experiencing disease flares after reducing or stopping medication, representing a burden for both patients and clinicians1. Although some predictors of flare exist, they do not confidently predict flare for most patients with rheumatoid arthritis in remission2. Recent studies suggest that deconvolution of patients’ synovial tissue cellular and molecular signatures may offer more accurate prediction models3,4.

Patients with rheumatoid arthritis in sustained remission, defined on the basis of clinical symptoms and ultrasound examination, may have residual synovial tissue inflammation that could determine the sustainability of remission5. Recent advances in single-cell transcriptomics have uncovered a previously unrecognized heterogeneity of rheumatoid arthritis synovial tissue during remission and identified distinct clusters of synovial macrophages and fibroblasts that are predictive of flare or sustained remission4,6.

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Acknowledgements

This study is funded by FOREUM (grant 22RF073).

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Authors and Affiliations

  1. Immunology Research Core Facility, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy

    Stefano Alivernini

  2. Division of Rheumatology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy

    Stefano Alivernini

  3. Division of Rheumatology, Università Cattolica del Sacro Cuore, Rome, Italy

    Stefano Alivernini

  4. Research into Inflammatory Arthritis Centre Versus Arthritis (RACE), University of Glasgow, Glasgow, UK

    Stefano Alivernini & Mariola Kurowska-Stolarska

  5. Rheumatology Department, Hospital Clinic and FCRB-IDIBAPS, Barcelona, Spain

    Juan D. Cañete

  6. Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle, UK

    Jaume Bacardit

Authors
  1. Stefano Alivernini
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  2. Juan D. Cañete
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  3. Jaume Bacardit
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  4. Mariola Kurowska-Stolarska
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Corresponding authors

Correspondence to Stefano Alivernini or Mariola Kurowska-Stolarska.

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The authors declare no competing interests.

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Alivernini, S., Cañete, J.D., Bacardit, J. et al. Using explainable artificial intelligence to predict and forestall flare in rheumatoid arthritis. Nat Med 30, 925–926 (2024). https://doi.org/10.1038/s41591-024-02818-w

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  • DOI: https://doi.org/10.1038/s41591-024-02818-w

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