Arthritis research & therapy. 2025 Mar 26;27(1):65. doi: 10.1186/s13075-025-03541-8 Q24.42024

Predicting rheumatoid arthritis progression from seronegative undifferentiated arthritis using machine learning: a deep learning model trained on the KURAMA cohort and externally validated with the ANSWER cohort

使用机器学习从血清阴性未分化关节炎预测类风湿性关节炎的进展：在KURAMA队列上训练的深度学习模型并在ANSWER队列中进行外部验证翻译改进

Takayuki Fujii^{1 2}, Koichi Murata^{3 4}, Hirohiko Kohjitani⁵, Akira Onishi³, Kosaku Murakami⁶, Masao Tanaka³, Wataru Yamamoto⁷, Koji Nagai⁸, Ayaka Yoshikawa⁸, Yuki Etani⁹, Yasutaka Okita¹⁰, Naofumi Yoshida¹¹, Hideki Amuro¹¹, Tadashi Okano¹², Yo Ueda¹³, Takaichi Okano¹⁴, Ryota Hara¹⁵, Motomu Hashimoto¹⁶, Akio Morinobu¹⁷, Shuichi Matsuda⁴

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作者单位

¹ Department of Advanced Medicine for Rheumatic Diseases, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawaharacho, Sakyo, Kyoto, Japan. fujiit@kuhp.kyoto-u.ac.jp.

² Department of Orthopaedic Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan. fujiit@kuhp.kyoto-u.ac.jp.

³ Department of Advanced Medicine for Rheumatic Diseases, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawaharacho, Sakyo, Kyoto, Japan.

⁴ Department of Orthopaedic Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

⁵ Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

⁶ Center for Cancer Immunotherapy and Immunobiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

⁷ Department of Health Information Management, Kurashiki Sweet Hospital, Kurashiki, Japan.

⁸ Department of Internal Medicine (IV), Osaka Medical and Pharmaceutical University, Takatsuki, Japan.

⁹ Department of Sports Medical Biomechanics, Osaka University Graduate School of Medicine, Suita, Japan.

¹⁰ Department of Respiratory Medicine and Clinical Immunology, Osaka University, Suita, Japan.

¹¹ First Department of Internal Medicine, Kansai Medical University, Hirakata, Japan.

¹² Center for Senile Degenerative Disorders (CSDD), Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan.

¹³ Department of Rheumatology and Clinical Immunology, Kobe University Graduate School of Medicine, Kobe, Japan.

¹⁴ Department of Clinical Laboratory, Kobe University Hospital, Kobe, Japan.

¹⁵ Department of Orthopaedic Surgery, Nara Medical University, Kashihara, Japan.

¹⁶ Department of Clinical Immunology, Osaka Metropolitan University, Osaka, Japan.

¹⁷ Department of Rheumatology and Clinical Immunology, Kyoto University, Kyoto, Japan.

DOI: 10.1186/s13075-025-03541-8 PMID: 40140918

摘要 Ai翻译

Background: Undifferentiated arthritis (UA) often develops into rheumatoid arthritis (RA), but predicting disease progression from seronegative UA remains challenging because seronegative RA often does not meet the classification criteria. This study aims to build a machine learning (ML) model to predict the progression from seronegative UA to RA using clinical and laboratory parameters.

Methods: KURAMA cohort (training dataset) and ANSWER cohort (validation dataset) were utilized. Patients with seronegative UA were selected based on specific inclusion and exclusion criteria. Clinical and laboratory parameters, including demographic data, acute phase reactants, autoantibodies, and physical examination findings, were collected. Various ML models, including a Feedforward Neural Network (FNN), were developed and compared. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and other metrics. SHapley Additive exPlanations (SHAP) values were computed to interpret the importance of variables.

Results: KURAMA cohort included 210 patients with seronegative UA, of whom 57 (27.1%) progressed to RA. The FNN model demonstrated the highest predictive performance with an AUC of 0.924 and a sensitivity of 80.7% in the training dataset. Validation with ANSWER cohort (140 patients; 32.1% progressed to RA) showed an AUC of 0.777, sensitivity of 77.8%. MMP-3 had the highest impact on the model.

Conclusions: The FNN model exhibited robust performance in predicting the progression of RA from seronegative UA and maintained substantial sensitivity in an independent validation cohort. This model using only clinical and laboratory parameters has potential for predicting RA progression in patients with seronegative UA.

Keywords：machine learning; deep learning model; KURAMA cohort; ANSWER cohort

关键词：机器学习; 深度学习模型; 类风湿关节炎进展; 血清阴性未分化关节炎; KURAMA 队列; ANSWER 队列

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期刊名：Arthritis research & therapy

缩写：ARTHRITIS RES THER

ISSN：1478-6354

e-ISSN：1478-6362

IF/分区：4.4/Q2

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Predicting rheumatoid arthritis progression from seronegative undifferentiated arthritis using machine learning: a deep learning model trained on the KURAMA cohort and externally validated with the ANSWER cohort

使用机器学习从血清阴性未分化关节炎预测类风湿性关节炎的进展：在KURAMA队列上训练的深度学习模型并在ANSWER队列中进行外部验证翻译改进

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