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

利用多源数据生成可重复的真实世界证据，以捕获慢性病的非结构化临床终点

• 批准号: 10797849
• 负责人: FLORENCE BOURGEOIS
• 金额: $ 105.45万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10797849
• 关键词: Generating; Reproducible; Real; World; Evidence

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.

项目总结/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， 支持FDA监管决定的主要证据。然而，随机对照试验有许多局限性， 包括研究结果缺乏普遍性和后续行动不足，无法评估长期结果。 随着疾病修饰治疗（DMT）的日益普及，需要新的方法来监测 用于慢性病的药物的长期安全性和有效性。电子健康记录（EHR）数据存在 在异质患者人群和真实世界中捕获纵向治疗反应的机会 这些数据可用于生成真实世界证据（RWE），以增加这些药物的RCT数据。 然而，由于缺乏关于疾病的可计算信息， 进展指标，即指导治疗的医生监测的临床结局。这 信息通常在临床访问期间仅以非结构化文本捕获 在每次遇到时都记录，即使使用劳动密集型手动抽象也会导致数据不完整。 此外，用于DMT稳健上市后评估的RWE资源必须确保 医疗保健系统的发现。在本提案中，我们通过开发方法来解决这一未满足的需求， 生成关于非结构化疗效和不良事件（AE）终点的可重现和可推广的RWE 用于评估类风湿性关节炎和多发性硬化症的治疗。我们将创建可扩展的 通过将EHR中的信息与登记数据相关联， 使用从评分指南导出的特征的有序疾病活动性评分的算法。目标1： 整合来自登记研究的疾病活动和进展数据，生成可扩展的疾病RWE 利用结构化和自由文本EHR数据的进展终点。在目标2中，我们制定策略， 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邦学习，以确保RWE的普遍性。我们打算用这些方法来 在FDA关于药物有效性的监管决策中建立使用RWE的新能力， 使用真实世界临床数据支持新药审批的高效、可扩展且稳健的方法 适应症和DMT上市后研究的开展。

项目成果

LATTE: Label-efficient incident phenotyping from longitudinal electronic health records.

• DOI: 10.1016/j.patter.2023.100906
• 发表时间: 2024-01-12
• 期刊: PATTERNS
• 影响因子: 6.5
• 作者: Wen, Jun;Hou, Jue;Bonzel, Clara -Lea;Zhao, Yihan;Castro, Victor M.;Gainer, Vivian S.;Weisenfeld, Dana;Cai, Tianrun;Ho, Yuk-Lam;Panickan, Vidul A.;Costa, Lauren;Hong, Chuan;Gaziano, J. Michael;Liao, Katherine P.;Lu, Junwei;Cho, Kelly;Cai, Tianxi
• 通讯作者: Cai, Tianxi