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. 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. 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. 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. ClinicalTrials.gov: NCT00147498; NCT00413660; NCT00550446; NCT00603512; NCT00687193; NCT01164579; NCT00976599; NCT01059864; NCT01359150; NCT02147587; NCT00960440; NCT00847613; NCT00814307; NCT00856544; NCT00853385; NCT01039688; NCT02187055; NCT02831855; NCT02092467.

中文翻译：

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托法替布治疗类风湿关节炎患者严重感染的机器学习预测和解释模型


与没有 RA 的个体相比，类风湿性关节炎 (RA) 患者发生严重感染 (SI) 的风险更高；预测该患者群体 SI 的工作正在进行中。我们使用托法替尼 RA 临床试验项目的基线数据评估了不同机器学习建模方法预测 SI 的能力。该分析包括 19 项临床试验的数据（2 期，n = 10；3 期，n = 6；3b/4 期，n = 3）。接受托法替布 5 或 10 mg 每日两次 (BID) 治疗的 RA 患者被纳入分析；接受托法替布 11 mg 每日一次的患者被视为托法替布 5 mg BID。提取所有可用的患者水平基线变量。实施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、极端梯度提升树和提升树）来评估基线变量与 SI（仅逻辑回归）的关联，并使用使用5倍交叉验证选择基线变量。每个预测模型单独处理缺失值。共有 8404 名接受托法替布治疗的 RA 患者符合纳入条件（总随访时间为 15,310 患者年），其中 473 名患者报告了 SI。在其他基线因素中，年龄、既往感染情况和皮质类固醇的使用与 SI 显着相关。当对所有研究的数据应用 SI 预测模型时，受试者工作特征 (AUROC) 曲线下面积的范围为 0.656 至 0.739。在 3 期和 3b/4 研究的数据中，AUROC 值的范围为 0.599 至 0.730，仅在 ORAL Surveillance 的数据中，AUROC 值的范围为 0.563 至 0.643。 托法替布 RA 临床试验项目中与 SI 相关的基线因素与与 RA 先进治疗相关的既定 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，但这并未达到准确预测的阈值 (AUROC > 0.85)。因此，预测基线 SI 的发生仍然具有挑战性，并且可能因 RA 病程随时间的变化而变得复杂。为了改善预测，可能需要纳入其他与患者相关和医疗保健提供相关的因素以及协调模型中的研究持续时间。 ClinicalTrials.gov：NCT00147498； NCT00413660； NCT00550446； NCT00603512； NCT00687193； NCT01164579； NCT00976599； NCT01059864； NCT01359150； NCT02147587； NCT00960440； NCT00847613； NCT00814307； NCT00856544； NCT00853385； NCT01039688； NCT02187055； NCT02831855； NCT02092467。