Eligibility criteria design for Alzheimer's trials with real-world data and explainable AI

利用真实数据和可解释的人工智能设计阿尔茨海默病试验的资格标准

基本信息

批准号：
10608470
负责人：
Jiang Bian
金额：
$ 82.02万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-02-01 至 2027-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10608470
关键词：
Adoption Alabama Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease patient Artificial Intelligence Characteristics Clinical Clinical Research Clinical Trials Collection Data Data Science Detection Documentation Effectiveness Eligibility Determination Ensure Event Florida General Population Individual Libraries Machine Learning Methodology Methods Modeling Natural Language Processing Online Systems Outcome Participant Patient-Focused Outcomes Patients Pharmaceutical Preparations Population Process Research Research Personnel Safety Serious Adverse Event Site Target Populations Treatment Effectiveness Treatment Efficacy Work cohort computable phenotypes data resource design electronic health record system high dimensionality improved interest novel patient oriented patient safety privacy preservation prototype study population success tool trait trial design trial enrollment user centered design

项目摘要

ABSTRACT Clinical trials are often conducted under idealized and rigorously controlled conditions to ensure internal validity (maximizing potential treatment efficacy) while balancing patient safety (e.g., serious adverse events [SAEs]); but these conditions—often reflected in trials’ eligibility criteria—paradoxically, limits (1) the ability to identify the “right” study populations of the trials, and (2) the trials’ generalizability to the target population in real-world settings. Low generalizability has long been a concern, including for Alzheimer's disease (AD) trials. AD trial participants are systematically younger than AD patients in the general population, where eligibility criteria design issues are arguably the biggest yet modifiable barriers. The FDA has launched numerous initiatives to improve trial design and enrollment practices, such as using enrichment strategies (e.g., “use patient characteristic to select a study population in which detection of a drug effect [or safety event] is more likely than it would be in an unselected population”), so that the trial participants can better reflect the real-world target population and the trials are more likely to succeed. However, there are significant gaps between the need to improve AD trial eligibility criteria design and ways available to fulfill the need in practice. On the other hand, rapid adoption of electronic health record (EHR) systems has made large collections of real-world data (RWD) that reflect the characteristics and outcomes of the patients being treated in real-world settings, available for research. The increasing availability of RWD combined with the advancements in artificial intelligence (AI), especially machine learning (ML) offer untapped opportunities to generate real-world evidence (RWE) to support eligibility criteria design for AD trials, due to a number of key methodological gaps: (1) the lack of validated computable phenotyping (CP) and natural language processing (NLP) algorithms and tools that can accurately define the populations (e.g., AD patients) of interest and extract key relevant patient characteristics and outcomes of interest (e.g., trial endpoints such as MoCA and safety profile such as SAEs) from RWD, (2) the lack of ways to identify the desired study populations (and corresponding eligibility criteria), considering the impact of criteria to potential treatment effectiveness, patient safety, and study generalizability, and (3) the need of an easy-to-use toolbox to support trialists’ eligibility criteria design process. We propose (1) novel causal- principled, explainable AI (XAI) approaches to generate RWE to facilitate AD trial eligibility criteria design, and (2) to create the web-based ALZHEIMER'S DISEASE ELIGIBILITY EXPLAINER (ADEP) tool. We will leverage two large RWD resources, the OneFlorida+ (~19 million patients from Florida, Georgia, and Alabama) and INSIGHT (~12 million New Yorkers) clinical research networks (CRNs) contributing to the national Patient-Centered Clinical Research Network (PCORnet). The success of this project will establish (1) a novel, generalizable, and XAI-based trial enrichment framework with large collections of distributed RWD, and (2) a prototype toolbox that can provide RWE to eligibility criteria design, balancing effectiveness and patient safety in the target population.

抽象的临床试验通常在理想化和严格控制的条件下进行，以确保内部有效性（最大化潜在治疗效果），同时平衡患者安全（例如严重不良事件 [SAE]）；但这些条件——通常反映在试验的资格标准中——矛盾的是，限制了（1）识别试验的“正确”研究人群，以及（2）试验对现实世界中目标人群的普遍性长期以来，普遍性低一直是一个问题，包括阿尔茨海默病 (AD) 试验。参与者总体上比一般人群中的 AD 患者年轻，其中资格标准设计这些问题可以说是最大但可以改变的障碍，FDA 已启动了许多改进措施。试验设计和入组实践，例如使用丰富策略（例如，“利用患者特征来选择一个研究人群，在该人群中检测到药物效应[或安全事件]的可能性比在其他人群中检测到的可能性高。未选择的人群”），以便试验参与者能够更好地反映现实世界的目标人群和试验更有可能成功，但是，改进 AD 试验的需要之间存在显着差距。另一方面，资格标准的设计和可满足实践需要的方式。电子健康记录 (EHR) 系统收集了大量真实世界数据 (RWD)，这些数据反映了在现实环境中接受治疗的患者的特征和结果，可用于研究。 RWD 的可用性不断提高，再加上人工智能 (AI) 的进步，尤其是机器学习（ML）提供了尚未开发的机会来生成现实世界证据（RWE）来支持 AD 试验的资格标准设计，由于一些关键的方法学缺陷：（1）缺乏经过验证的可计算表型分析 (CP) 和自然语言处理 (NLP) 算法和工具准确定义感兴趣的人群（例如 AD 患者）并提取关键的相关患者特征以及来自 RWD 的感兴趣的结果（例如 MoCA 等试验终点和 SAE 等安全性概况），(2) 缺乏方法来确定所需的研究人群（以及相应的资格标准），考虑到标准对潜在治疗效果、患者安全和研究普遍性的影响，以及 (3) 需要我们提出了一个易于使用的工具箱来支持试验者的资格标准设计过程。解释了生成 RWE 的可解释 AI (XAI) 原理方法，以促进 AD 试验资格标准设计，以及 (2) 创建基于网络的阿尔茨海默病资格解释器 (ADEP) 我们将利用两个工具。大量 RWD 资源、OneFlorida+（来自佛罗里达州、佐治亚州和阿拉巴马州的约 1900 万患者）和 INSIGHT （约 1200 万纽约人）临床研究网络 (CRN) 为国家以患者为中心做出贡献临床研究网络（PCORnet）。该项目的成功将建立（1）一个新颖的、可推广的、并且基于 XAI 的试验丰富框架，具有大量分布式 RWD 集合，以及 (2) 一个原型工具箱，可以为 RWE 提供资格标准设计，平衡目标人群的有效性和患者安全。