Identification of individuals at high risk of developing rheumatoid arthritis: a balanced random forest model in a cohort of 1544 first-degree relatives

Overall, the results demonstrate that predicting RA by combining genetics, serological biomarkers, epidemiological risk factors and clinical signs is promising, although model generalisation remains challenging. The low prevalence of RA in the cohort complicates the development of highly accurate prediction models. Future efforts should focus on including external validation and potentially incorporating additional biomarkers to enhance the sensitivity and overall performance of the predictive tests.

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Publication Details

Journal
RMD Open
Published
2025-10-01
DOI
https://doi.org/10.1136/rmdopen-2025-005773
Primary Topic
Rheumatoid Arthritis Research and Therapies
Type
article
Field-Weighted Citation Impact
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article

Identification of individuals at high risk of developing rheumatoid arthritis: a balanced random forest model in a cohort of 1544 first-degree relatives

Benoît Gilbert, David Spoerl, Isabel Gehring, Andrea Rubbert‐Roth et al.
RMD Open
Rheumatoid Arthritis Research and Therapies
article

Identification of individuals at high risk of developing rheumatoid arthritis: a balanced random forest model in a cohort of 1544 first-degree relatives

Benoît Gilbert, David Spoerl, Isabel Gehring, Andrea Rubbert‐Roth, Axel Finckh, Ines von Mühlenen, Diego Kyburz, Delphine Sophie Courvoisier, Adrian Ciurea, Sascha Swiniarski, Céline Lamacchia, Laure Brulhart, Olivia Studer, Diana Dan, Jean Dudler, Maresa Grundhuber, Romain Guemara, Burkhard Möller, Zübeyir Salis, Romain Aymon, Ruediger B. Müeller
article en

Abstract

Overall, the results demonstrate that predicting RA by combining genetics, serological biomarkers, epidemiological risk factors and clinical signs is promising, although model generalisation remains challenging. The low prevalence of RA in the cohort complicates the development of highly accurate prediction models. Future efforts should focus on including external validation and potentially incorporating additional biomarkers to enhance the sensitivity and overall performance of the predictive tests.

RMD OpenVol. 11(4)
University of Geneva (CH), Université de Montpellier (FR), University of Zurich (CH), Kantonsspital St. Gallen (CH), University Hospital of Bern (CH), University Hospital of Basel (CH), Physiologie et Médecine Expérimentale du Coeur et des Muscles (FR), University Hospital of Zurich (CH), International Museum of Horology (CH), Department of Biomedicine Basel (CH), Thermo Fisher Scientific (Germany) (DE), Ostschweizer Kinderspital (CH), Geneva College (US), University of Lausanne (CH)
Openalex Percentile: Top 47%
Rheumatoid Arthritis Research and Therapies
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