RMD open · 2025
Identification of individuals at high risk of developing rheumatoid arthritis: a balanced random forest model in a cohort of 1544 first-degree relatives.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Aymon Romain, Lamacchia Céline, Gilbert Benoit Thomas P, Grundhuber Maresa, Gehring Isabel, Swiniarski Sascha, Studer Olivia, Salis Zubeyir, Guemara Romain, Spoerl David, Dudler Jean, Möller Burkhard, Dan Diana, Brulhart Laure, Von Mühlenen Ines, Kyburz Diego, Rubbert-Roth Andrea, Ciurea Adrian, Mueller Ruediger, Courvoisier Delphine S, Finckh Axel
Аннотация
OBJECTIVES: To identify in a genetically susceptible population individuals at higher risk of developing rheumatoid arthritis (RA) using a classification approach combining known epidemiological risk factors, serological biomarkers, genetics, clinical signs and symptoms.
METHODS: We used data from the prospective SCREEN-RA (Evaluation of a SCREENing strategy for Rheumatoid Arthritis) cohort of 1540 first-degree relatives of RA patients (RA-FDRs). The primary outcome was the development of RA. Additionally, we used seropositive inflammatory arthritis (IA) as a secondary outcome for exploratory analyses. Balanced random forest (BRF) models were fit and evaluated through fivefold cross-validation to avoid overfitting. We chose a classification threshold that targeted high sensitivity.
RESULTS: After a mean follow-up of 7.1 years, 27 participants developed RA and 126 developed seropositive IA. The BRF demonstrated moderate predictive performance, characterised by high sensitivity (≥0.85) but modest specificity. Rheumatoid factors (RFs) had the highest importance in RA prediction, followed by symptoms of 'clinically suspected arthralgia' (CSA) scale. Age, gender and anti-RA33 autoantibodies were the main variables for the prediction of seropositive IA.
CONCLUSIONS: 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.
METHODS: We used data from the prospective SCREEN-RA (Evaluation of a SCREENing strategy for Rheumatoid Arthritis) cohort of 1540 first-degree relatives of RA patients (RA-FDRs). The primary outcome was the development of RA. Additionally, we used seropositive inflammatory arthritis (IA) as a secondary outcome for exploratory analyses. Balanced random forest (BRF) models were fit and evaluated through fivefold cross-validation to avoid overfitting. We chose a classification threshold that targeted high sensitivity.
RESULTS: After a mean follow-up of 7.1 years, 27 participants developed RA and 126 developed seropositive IA. The BRF demonstrated moderate predictive performance, characterised by high sensitivity (≥0.85) but modest specificity. Rheumatoid factors (RFs) had the highest importance in RA prediction, followed by symptoms of 'clinically suspected arthralgia' (CSA) scale. Age, gender and anti-RA33 autoantibodies were the main variables for the prediction of seropositive IA.
CONCLUSIONS: 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.