Generating Reproducible Real-World Evidence with Multi-Source Data to Capture Unstructured Clinical Endpoints for Chronic Diseases

项目摘要/摘要 随机临床试验(RCT)是评估治疗安全性和有效性的金标准，是 支持FDA监管决定的主要证据。然而，随机对照试验有许多限制， 包括缺乏研究结果的概括性，以及评估长期结果的后续行动不足。 随着疾病修正治疗(DMT)的日益普及，需要新的方法来监测 治疗慢性病的药物的长期安全性和有效性。存在电子健康记录(EHR)数据 获取不同患者群体和现实世界中纵向治疗反应的机会 并可用于生成真实世界证据(RWE)，以增强这些药物的随机对照试验数据。 然而，由于缺乏关于疾病的可计算信息，针对DMT的RWE的可用性一直受到限制 进展措施，这是由指导治疗的医生监测的临床结果。这 在临床访问期间，信息通常仅以非结构化文本形式捕获，也可能不一致 在每次遇到时都记录在案，导致数据不完整，即使是劳动密集型的手动抽象也是如此。 此外，至关重要的是，莱茵集团为DMT的强有力的上市后评估提供资源，以确保 医疗保健系统的研究结果。在这项提案中，我们通过开发方法来解决这一未得到满足的需求 针对非结构化疗效和不良事件(AE)终端生成可重复和可推广的RWE 用于类风湿性关节炎和多发性硬化症的治疗评估。我们将创建可扩展的 通过将EHR中的信息链接到注册数据并建立 使用源自评分指南的特征进行有序疾病活动评分的算法。在目标1中，我们 集成来自登记处的疾病活动和进展数据，以生成可扩展的疾病RWE 进度终端利用结构化和自由文本的EHR数据。在目标2中，我们制定了纠正 RWE研究中DMT药物处方中的噪音。AIM 3结合了来自多个 医疗保健系统通过联合学习来确保RWE的普适性。我们打算用这些方法来 为在FDA的药物有效性监管决策中使用RWE建立新的能力，提供 使用真实临床数据支持新药审批的高效、可扩展且强大的方法 DMT的适应症和上市后研究。

PROJECT SUMMARY/ABSTRACT Randomized clinical trials (RCTs) are the gold standard for assessing treatment safety and efficacy and are the primary evidence supporting FDA's regulatory decisions. However, RCTs have a number of limitations, including the lack of generalizability of study findings and insufficient follow-up to assess long-term outcomes. With the growing availability of disease modifying treatments (DMTs), novel approaches are needed to monitor long-term safety and efficacy of agents used in chronic diseases. Electronic health record (EHR) data present the opportunity to capture longitudinal treatment response in heterogeneous patient populations and real-world settings and can be used to generate real-world evidence (RWE) to augment RCT data for these drugs. However, availability of RWE for DMTs has been limited by the lack of computable information on disease progression measures, which are the clinical outcomes monitored by physicians directing therapy. This information is typically captured only in unstructured text during clinical visits and may also not be consistently documented at every encounter, resulting in incomplete data even with labor-intensive manual abstraction. Further, it is critical that RWE resources for robust post-market assessments of DMTs ensure reproducibility of findings across healthcare systems. In this proposal, we address this unmet need by developing methods to generate reproducible and generalizable RWE on unstructured efficacy and adverse event (AE) endpoints used in the evaluation of therapies for rheumatoid arthritis and multiple sclerosis. We will create scalable disease progression endpoints from EHR data by linking information in EHRs to registry data and building algorithms for ordinal disease activity scores using features derived from scoring guidelines. In Aim 1, we integrate disease activity and progression data from registries to generate scalable RWE on disease progression endpoints leveraging structured and free-text EHR data. In Aim 2, we develop strategies to correct for noise in medication prescriptions for DMTs in RWE studies. Aim 3 combines EHR data from multiple healthcare systems through federated learning to ensure generalizability of RWE. We intend for the methods to build new capabilities for use of RWE in FDA's regulatory decisions on drug effectiveness, providing an efficient, scalable, and robust approach to using real-world clinical data to support approval of new drug indications and conduct of postmarket studies for DMTs.