在现实世界肺癌人口中验证与免疫相关的不良事件 (irAE) 病例定义
James S Heyward1,2, Jodi B Segal1,3, Hemalkumar B Mehta1
1Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
这项研究验证了在接受免疫检查点抑制剂 (ICI) 治疗的癌症患者中识别免疫相关不良事件 (irAEs) 的算法. 使用诊断和药物代码的算法有效地识别了严重的irAEs,仅药物代码就显示了严重事件的强大性能.
科学领域:
- 在瘤学瘤学.
- 药物监督 药物监督 药物监督
- 现实世界的数据分析.
背景情况:
- 越来越多地使用现实世界数据 (RWD) 来研究免疫检查点抑制剂 (ICI) 用户的免疫相关不良事件 (irAEs).
- 需要验证的算法来准确地从电子健康记录 (EHR) 中识别irAE发病率和风险因素.
研究的目的:
- 为了验证irAEs的五个不同的案例定义算法.
- 用诊断 (Dx) 和药物 (Rx) 代码对图表审查数据进行算法性能评估.
- 评估用于识别特定的IRE和不同严重程度的算法实用性.
主要方法:
- 使用链接的EHR和癌症注册表数据 (2013-2020) 的回顾性队列研究.
- 开发和比较五种不同严格度的算法 (Dx+Rx,只有Rx,只有Dx).
- 性能指标 (灵敏度,特异性,PPV,NPV,C-统计) 与图表审查确定的irAE状态相比较.
主要成果:
- 队列包括354名ICI暴露患者;25.1%的患者经历了irAEs.
- 算法显示了基于严格性的敏感性和特异性之间的权衡.
- 仅使用Rx代码或Dx+Rx代码的算法显示出更高的C统计,特别是在严重的irAEs (CTCAE等级3-5).
结论:
- 综合ICD-10和药物代码有效地识别严重的irAEs,特别是肺炎,肝炎和结肠炎.
- 仅仅药物编码对于严重的irAE检测是有效的;严格的算法提供更高的特异性和PPV.
- 经过验证的算法对于在不同治疗方案和患者子组中比较irAE风险是有用的.
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