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.

项目摘要/摘要 随机临床试验（RCT）是评估治疗安全性和功效的黄金标准，是 支持FDA监管决定的主要证据。但是，RCT有许多局限性， 包括缺乏研究结果的普遍性和评估长期结果的随访不足。 随着疾病修饰治疗（DMT）的日益增长的可用性，需要采取新颖的方法来监测 慢性疾病中使用的药物的长期安全性和功效。电子健康记录（EHR）数据 捕获异类患者人群和现实世界中纵向治疗反应的机会 设置，可用于生成真实的证据（RWE），以增加这些药物的RCT数据。 但是，DMT的RWE的可用性受到疾病的可计算信息的限制 进展措施是由指导治疗的医生监测的临床结果。这 信息通常仅在临床访问期间仅在非结构化文本中捕获，并且可能不会始终如一 在每次相遇中都记录下来，即使通过劳动密集型手动抽象也会产生不完整的数据。 此外，至关重要的是，RWE资源用于强大的DMT市场后市场评估，确保可重复性的可重复性 跨医疗保健系统的发现。在此建议中，我们通过开发方法来满足这种未满足的需求 在非结构化功效和不良事件（AE）端点上生成可重现和可推广的RWE 用于评估类风湿关节炎和多发性硬化症的疗法。我们将创建可扩展的 通过将EHR中的信息链接到注册表数据和构建，疾病进步终点是来自EHR数据的终点 使用来自评分指南的特征的顺序疾病活动评分算法。在AIM 1中，我们 整合疾病活动和从注册表中的进展数据，以产生可扩展的RWE疾病 利用结构和自由文本EHR数据的进程终点。在AIM 2中，我们制定了纠正策略 用于RWE研究中DMT的药物处方中的噪声。 AIM 3结合了来自多个的EHR数据 通过联合学习，医疗保健系统，以确保RWE的普遍性。我们打算使用的方法 在FDA关于药物有效性的监管决定中建立新的功能，以提供 有效，可扩展性和可靠的方法使用现实世界中的临床数据来支持新药的批准 DMTS的后市场研究的适应症和进行。

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