Sonomoto Koshiro, Fujino Yoshihisa, Tanaka Hiroaki, Nagayasu Atsushi, Nakayamada Shingo, Tanaka Yoshiya
Department of Clinical Nursing, School of Health Sciences, University of Occupational and Environmental Health, Japan, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, 807-8555, Japan.
The First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health, Japan, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, 807-8555, Japan.
Rheumatol Ther. 2024 Jun;11(3):709-736. doi: 10.1007/s40744-024-00668-z. Epub 2024 Apr 18.
This study aimed to develop low-cost models using machine learning approaches predicting the achievement of Clinical Disease Activity Index (CDAI) remission 6 months after initiation of tumor necrosis factor inhibitors (TNFi) as primary biologic/targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) for rheumatoid arthritis (RA).
Data of patients with RA initiating TNFi as first b/tsDMARD after unsuccessful methotrexate treatment were collected from the FIRST registry (August 2003 to October 2022). Baseline characteristics and 6-month CDAI were collected. The analysis used various machine learning approaches including logistic regression with stepwise variable selection, decision tree, support vector machine, and lasso logistic regression (Lasso), with 48 factors accessible in routine clinical practice for the prediction model. Robustness was ensured by k-fold cross validation.
Among the approaches tested, Lasso showed the advantages in predicting CDAI remission: with a mean area under the curve 0.704, sensitivity 61.7%, and specificity 69.9%. Predicted TNFi responders achieved CDAI remission at an average rate of 53.2%, while only 26.4% of predicted TNFi non-responders achieved remission. Encouragingly, the models generated relied solely on patient-reported outcomes and quantitative parameters, excluding subjective physician input.
While external cohort validation is warranted for broader applicability, this study highlights the potential for a low-cost predictive model to predict CDAI remission following TNFi treatment. The approach of the study using only baseline data and 6-month CDAI measures, suggests the feasibility of establishing regional cohorts to generate low-cost models tailored to specific regions or institutions. This may facilitate the application of regional/in-house precision medicine strategies in RA management.
本研究旨在利用机器学习方法开发低成本模型，以预测肿瘤坏死因子抑制剂（TNFi）作为类风湿关节炎（RA）的一线生物制剂/靶向合成改善病情抗风湿药（b/tsDMARDs）治疗6个月后临床疾病活动指数（CDAI）缓解的情况。
从FIRST注册中心（2003年8月至2022年10月）收集了甲氨蝶呤治疗失败后开始使用TNFi作为首个b/tsDMARD的RA患者的数据。收集了基线特征和6个月时的CDAI。分析使用了多种机器学习方法，包括逐步变量选择的逻辑回归、决策树、支持向量机和套索逻辑回归（Lasso），预测模型采用了常规临床实践中可获得的48个因素。通过k折交叉验证确保了稳健性。
在所测试的方法中，Lasso在预测CDAI缓解方面显示出优势：曲线下平均面积为0.704，敏感性为61.7%，特异性为69.9%。预测的TNFi反应者达到CDAI缓解的平均率为53.2%，而预测的TNFi无反应者中只有26.4%达到缓解。令人鼓舞的是，生成的模型仅依赖于患者报告的结果和定量参数，不包括医生的主观输入。
虽然需要进行外部队列验证以扩大适用性，但本研究强调了低成本预测模型预测TNFi治疗后CDAI缓解的潜力。该研究仅使用基线数据和6个月CDAI测量的方法表明，建立区域队列以生成针对特定区域或机构的低成本模型是可行的。这可能有助于在RA管理中应用区域/内部精准医学策略。
