背景:类风湿性关节炎(RA)患者发生严重感染(SI)的风险增加没有RA的个体;在该患者组中预测SI的努力正在进行中。我们使用托法替尼RA临床试验计划的基线数据评估了不同机器学习建模方法预测SI的能力。
方法:该分析包括来自19项临床试验的数据(2期,n=10;3期,n=6;3b/4期,n=3)。每天两次(BID)接受托法替尼5或10mg的RA患者被纳入分析;每天一次接受托法替尼11mg的患者被视为托法替尼5mgBID。提取所有可用的患者水平基线变量。统计和机器学习方法(逻辑回归,具有线性核的支持向量机,随机森林,极端梯度增强树,和增强的树)被实施以评估基线变量与SI(仅逻辑回归)的关联,并使用5倍交叉验证选择的基线变量来预测SI。每个预测模型单独处理缺失值。
结果:共有8404例接受托法替尼治疗的RA患者符合纳入条件(总随访15,310例患者-年),其中473例患者报告了SI。在其他基线因素中,年龄,以前的感染,皮质类固醇的使用与SI显著相关。在对来自所有研究的数据应用SI预测建模时,受试者工作特征曲线下面积(AUROC)范围为0.656~0.739.在3期和3b/4期研究的数据中,AUROC值范围为0.599至0.730,以及仅来自口腔监测的数据从0.563到0.643。
结论:托法替尼RA临床试验项目中与SI相关的基线因素与已确定的RA晚期治疗相关的SI危险因素相似。此外,虽然预测SI的模型性能与其他已发布的模型相似,未达到准确预测的阈值(AUROC>0.85).因此,在基线时预测SIs的发生仍然具有挑战性,并且随着时间的推移,RA的病程可能会发生变化。可能需要包括其他与患者相关和医疗保健交付相关的因素,并协调模型中包括的研究持续时间,以改善预测。
背景:ClinicalTrials.gov:NCT00147498;NCT00413660;NCT00550446;NCT00603512;NCT00687193;NCT01164579;NCT00976599;NCT01059864;NCT01318135150;NCT021475445;NCT
BACKGROUND: Patients with rheumatoid arthritis (RA) have an increased risk of developing serious infections (SIs) vs. individuals without RA; efforts to predict SIs in this patient group are ongoing. We assessed the ability of different machine learning modeling approaches to predict SIs using baseline data from the tofacitinib RA clinical trials program.
METHODS: This analysis included data from 19 clinical trials (phase 2, n = 10; phase 3, n = 6; phase 3b/4, n = 3). Patients with RA receiving tofacitinib 5 or 10 mg twice daily (BID) were included in the analysis; patients receiving tofacitinib 11 mg once daily were considered as tofacitinib 5 mg BID. All available patient-level baseline variables were extracted. Statistical and machine learning methods (logistic regression, support vector machines with linear kernel, random forest, extreme gradient boosting trees, and boosted trees) were implemented to assess the association of baseline variables with SI (logistic regression only), and to predict SI using selected baseline variables using 5-fold cross-validation. Missing values were handled individually per prediction model.
RESULTS: A total of 8404 patients with RA treated with tofacitinib were eligible for inclusion (15,310 patient-years of total follow-up) of which 473 patients reported SIs. Amongst other baseline factors, age, previous infection, and corticosteroid use were significantly associated with SI. When applying prediction modeling for SI across data from all studies, the area under the receiver operating characteristic (AUROC) curve ranged from 0.656 to 0.739. AUROC values ranged from 0.599 to 0.730 in data from phase 3 and 3b/4 studies, and from 0.563 to 0.643 in data from ORAL Surveillance only.
CONCLUSIONS: Baseline factors associated with SIs in the tofacitinib RA clinical trial program were similar to established SI risk factors associated with advanced treatments for RA. Furthermore, while model performance in predicting SI was similar to other published models, this did not meet the threshold for accurate prediction (AUROC > 0.85). Thus, predicting the occurrence of SIs at baseline remains challenging and may be complicated by the changing disease course of RA over time. Inclusion of other patient-associated and healthcare delivery-related factors and harmonization of the duration of studies included in the models may be required to improve prediction.
BACKGROUND: ClinicalTrials.gov: NCT00147498; NCT00413660; NCT00550446; NCT00603512; NCT00687193; NCT01164579; NCT00976599; NCT01059864; NCT01359150; NCT02147587; NCT00960440; NCT00847613; NCT00814307; NCT00856544; NCT00853385; NCT01039688; NCT02187055; NCT02831855; NCT02092467.