ACTS (AD Clinical Trial Simulation): Developing Advanced Informatics Approaches for an Alzheimer's Disease Clinical Trial Simulation System

ACTS（AD 临床试验模拟）：为阿尔茨海默病临床试验模拟系统开发先进的信息学方法

• 批准号: 10753675
• 负责人: Jiang Bian
• 金额: $ 115.53万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 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 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10753675
• 关键词: Address; Advisory Committees; Affect; Alzheimer' s Disease; Alzheimer' s disease related dementia; American; Brain Diseases; Caregiver Burden; Case Study; Cause of Death; Characteristics; Clinical; Clinical Data; Clinical Investigator; Clinical Trials; Clinical Trials Design; Cognitive; Cohort Studies; Collection; Communities; Computing Methodologies; Controlled Environment; Data; Data Science; Data Set; Dementia; Development; Disease; Effectiveness; Effectiveness of Interventions; Electronic Health Record; Eligibility Determination; Ensure; Environment; Evaluation; Informatics; Learning; Medical; Medicine; Memory Loss; Metadata; Methodology; Methods; Modeling; Natural Language Processing; Nerve Degeneration; Online Systems; Ontology; Outcome; Outcome Measure; Patients; Pattern; Persons; Pharmaceutical Preparations; Phenotype; Physicians; Population; Process; Protocols documentation; Randomized, Controlled Trials; Reproducibility; Research; Research Design; Research Personnel; Resources; Safety; Signal Transduction; Societies; Standardization; Structure; Symptoms; System; Testing; Time; Translating; United States; Work; biomedical ontology; clinical practice; compare effectiveness; computable phenotypes; computer framework; cost; data modeling; data standards; database query; design; drug repurposing; effective therapy; follow-up; improved; interoperability; nervous system disorder; novel; open source; outreach; phenotyping algorithm; pilot test; post-market; prototype; randomized, clinical trials; recruit; simulation; study population; success; tool; trait; trial design; unstructured data; usability; virtual; web app; web interface

ABSTRACT Alzheimer's disease and related dementias (AD/ADRD) are the most common neurodegenerative brain disease and characterized by massive loss of memory and learning. AD/ADRD affects more than 6 million Americans and puts a heavy burden on caregivers in society. However, effective treatment of AD/ADRD is still lacking. While randomized clinical trials (RCT) can provide reliable evidence on the effectiveness of interventions, they also have inherent limitations including high cost and long execution time. In addition, RCTs usually are conducted on selected populations and in specialized environments with limited follow up time. Therefore, they could have limitations in generalizability to real-world clinical practice. Clinical trial simulation is becoming an effective approach to assess feasibility, investigate assumptions, and refine study protocols before conducting the actual trials. Increased availability and granularity of real-world data (RWD) such as electronic health record (EHR) and medical claims data along with advances in data science offer untapped opportunities to leverage RWD for trial simulation studies to generate real world evidence (RWE). Nevertheless, there are methodological barriers and informatics challenges in supporting RWD-based trial simulation studies, especially for AD: (1) clinical trials need to be represented using a formal and standard approach (i.e., ontologies) to capture the entire scope of a trial, especially eligibility criteria and outcome measures (i.e., both effectiveness and safety); (2) such formal and standard representation needs to be made interoperable with RWD standards (e.g., common data models) to identify study cohorts and relevant, important patient characteristics (i.e., via computable phenotypes and natural language processing [NLP] methods as rich AD-related information such as cognitive scores often exist in unstructured clinical notes); and (3) comprehensive and reusable pipelines need to be implemented that can seamlessly align with existing large-scale RWD for generating high-quality analytic-ready datasets for AD clinical trial simulation studies. To address these barriers, we propose create and pilot test the ACTS (Alzheimer's disease Clinical Trial Simulation) system, leveraging three large collections of RWD (~20 million patients from the OneFlorida network, UT Physician Clinical Data Research Warehouse, and the Optum’s Clinformatics data). Specifically, we propose to develop novel informatics approaches to represent the entirety of AD trials while considering the connection of RWD (Aim 1), to use both structured and unstructured RWD to develop robust phenotyping algorithms that will render previously incomputable AD study traits computable (Aim 2), and to develop the ACTS web application, which will provide an integrated environment for AD researchers to construct virtual AD trials using an interactive web interface and obtain analytic-ready datasets for trial simulation studies (Aim 3).

摘要 阿尔茨海默病及相关痴呆（AD/ADRD）是最常见的神经退行性脑疾病 并表现为记忆和学习能力的严重丧失。AD/ADRD影响超过600万美国人 给社会上的看护者带来了沉重的负担。然而，AD/ADRD的有效治疗仍然缺乏。 虽然随机临床试验（RCT）可以为干预措施的有效性提供可靠的证据， 还具有包括高成本和长执行时间的固有限制。此外，RCT通常 在特定人群和特殊环境中开展，随访时间有限。所以他们 在现实世界的临床实践中可能具有可推广性的局限性。临床试验模拟正在成为 评估可行性，调查假设，并在实施前完善研究方案的有效方法 实际的审判。提高真实世界数据（RWD）的可用性和粒度，例如电子健康记录 (EHR)医疗索赔数据沿着数据科学的进步， 用于试验模拟研究的RWD，以生成真实的世界证据（RWE）。然而，有方法论 支持基于RWD的试验模拟研究的障碍和信息学挑战，尤其是AD：（1） 临床试验需要使用正式和标准的方法来表示（即，本体论）来捕获整个 试验范围，尤其是合格标准和结局指标（即，（2）安全性和有效性;（3） 需要使正式的和标准的表示与RWD标准可互操作（例如，公共数据 模型）来识别研究组群和相关的、重要的患者特征（即，通过可计算的表型 和自然语言处理[NLP]方法作为丰富的AD相关信息，如认知分数， 存在于非结构化临床记录中）;以及（3）需要实现全面且可重用的管道， 可与现有的大规模RWD无缝结合，为AD生成高质量的分析就绪型数据集 临床试验模拟研究。为了解决这些障碍，我们建议创建和试点测试 （阿尔茨海默病临床试验模拟）系统，利用三个大型RWD集合（约2000万 来自OneFlorida网络、UT医师临床数据研究仓库和Optum的 临床数据）。具体来说，我们建议开发新的信息学方法来表示整体 在考虑RWD的连接（目标1）的同时，使用结构化和非结构化RWD， 开发强大的表型分型算法，使以前无法计算的AD研究特征变得可计算（Aim 2），并开发了一个可扩展的Web应用程序，为AD研究人员提供一个集成的环境 使用交互式Web界面构建虚拟AD试验，并获得用于试验的分析就绪数据集 模拟研究（目标3）。