AI 中文总结
该研究开发了一种联邦学习方法，通过构建非参数累积发生率曲线，在不共享患者数据的情况下，分析10个站点1万余名癌症患者数据，揭示自身免疫病患者接受免疫检查点抑制剂治疗后的内分泌不良事件风险，为临床提供新依据。
AI 中文摘要
整合来自多个机构的电子健康记录（EHR）数据是开展医疗产品上市后安全性监测的重要策略，但隐私问题限制了个体层面数据的共享。我们开发了一种新颖的联邦学习（FL）方法，用于基于竞争风险数据开展多机构医疗产品上市后安全性监测。将该方法应用于研究自身免疫病（AID）患者接受免疫检查点抑制剂（ICIs）治疗后的免疫相关不良事件（irAEs）。我们提供了一种用于构建竞争事件类型非参数累积发生率曲线的算法，可在无需跨机构共享患者层面数据的情况下比较暴露组（如治疗组与未治疗组）。我们通过逆倾向评分加权纳入协变量调整，并利用累积发生率曲线下面积（即受限平均损失时间）开展信息性因果比较。将方法应用于来自OneFlorida+网络K=10个站点的N=10281例无预先存在内分泌相关AID且接受ICIs治疗的癌症患者，比较预先存在非内分泌AID的患者与无预先存在AID的患者。经协变量调整后，我们发现预先存在非内分泌AID的患者在治疗后前18个月内因内分泌irAEs损失了4.8个月[95%置信区间（CI）：4.3,5.2]的无事件生存时间，而无预先存在AID的组损失了3.2个月[95% CI：3.1,3.3]。由于预先存在AID的患者最初被排除在ICIs的临床试验之外，我们的发现为接受或考虑ICIs治疗的临床医生和患者提供了重要的新信息。我们提出的非参数联邦算法是首个允许研究人员使用部分最关键的非参数工具开展多机构上市后安全性监测的算法。
英文摘要
Combining electronic health record (EHR) data from multiple institutions is a valuable strategy for conducting post-market safety surveillance of medical products, but privacy concerns limit sharing individual-level data. We develop a novel federated learning (FL) method for multi-site post-market safety surveillance of medical products using competing risks data. We apply this method to study immune-related adverse events (irAEs) following treatment with immune checkpoint inhibitors (ICIs) in patients with auto-immune disease (AID). We provide an algorithm for constructing non-parametric cumulative incidence curves for competing event types, which can be used to compare exposure groups (e.g. treated and untreated) with no sharing of patient-level data across institutions. We incorporate covariate adjustment via inverse propensity weighting, and informative causal comparison using the area under cumulative incidence curves, known as restricted mean time lost. We apply our method to $N=10,281$ cancer patients with no pre-existing endocrine-related AID receiving ICIs across $K=10$ sites from the OneFlorida+ network, comparing patients with a pre-existing non-endocrine AID to those with no pre-existing AID. After covariate adjustment, we found that patients with a pre-existing non-endocrine AID lost 4.8 [95% CI: 4.3,5.2] months of event-free survival time to endocrine irAEs in the first 18 months following treatment, compared to 3.2 [95% CI: 3.1,3.3] months in the group without prior AID. As patients with prior AID were initially excluded from clinical trials of ICIs, our findings provide important new information to clinicians and patients receiving or considering ICI treatment. Our proposed non-parametric federated algorithm is the first to allow investigators to use some of the most crucial non-parametric tools for conducting postmarket safety surveillance across multiple institutions.
