Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Semin Arthritis Rheum. 2022 Oct;56:152050. doi: 10.1016/j.semarthrit.2022.152050. Epub 2022 Jun 15.
Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy.
Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum® De-identified Clinformatics® Data Mart Database using L1-regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history. Models were externally validated on all other databases. Performance was assessed using the area under the receiver operator characteristic curve (AUC) and calibration plots.
Models were developed and internally validated on 21,547 RA patients and externally validated on 131,928 RA patients. Models for serious infection (AUC: internal 0.74, external ranging from 0.62 to 0.83), MI (AUC: internal 0.76, external ranging from 0.56 to 0.82), and stroke (AUC: internal 0.77, external ranging from 0.63 to 0.95), showed good discrimination and adequate calibration. Models for the other outcomes showed modest internal discrimination (AUC < 0.65) and were not externally validated.
We developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. Final models for serious infection, MI, and stroke demonstrated good performance across multiple databases and can be studied for clinical use.
This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA.
识别类风湿关节炎（RA）患者发生不良健康结局的高风险仍然是一个主要挑战。我们旨在开发和验证接受一线甲氨蝶呤（MTX）单药治疗的 RA 患者发生各种不良健康结局的预测模型。
使用来自 9 个国家的 15 个索赔和电子健康记录数据库的数据。使用 Optum®去识别的 Clinformatics®Data Mart 数据库中的 L1 正则化逻辑回归模型来开发和内部验证模型，以估计在 3 个月内（白细胞减少症、全血细胞减少症、感染）、2 年内（心肌梗死（MI）和中风）以及 5 年内（治疗开始后发生的癌症[结直肠癌、乳腺癌、子宫癌]）发生不良健康结局的风险。候选预测因子包括人口统计学变量和既往病史。模型在所有其他数据库上进行外部验证。使用接受者操作特征曲线（ROC）下面积（AUC）和校准图来评估性能。
模型在 21547 例 RA 患者中进行了开发和内部验证，在 131928 例 RA 患者中进行了外部验证。严重感染（AUC：内部 0.74，外部范围为 0.62 至 0.83）、MI（AUC：内部 0.76，外部范围为 0.56 至 0.82）和中风（AUC：内部 0.77，外部范围为 0.63 至 0.95）的模型具有良好的区分度和适度的校准度。其他结局的模型内部区分度较低（AUC<0.65），并且未进行外部验证。
我们开发和验证了接受一线 MTX 单药治疗的 RA 患者发生各种不良健康结局的预测模型。严重感染、MI 和中风的最终模型在多个数据库中表现良好，可以进行临床研究。
在欧洲健康数据和证据网络（EHDEN）下的这项活动得到了创新药物倡议 2 联合企业的资助，该联合企业根据第 806968 号协议获得了资助。该联合企业得到了欧洲联盟地平线 2020 研究和创新计划以及 EFPIA 的支持。
