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; 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).

抽象的 阿尔茨海默氏病和相关痴呆症（AD/ADRD）是最常见的神经退行性脑疾病 并以大规模的记忆和学习为特征。 AD/ADRD影响超过600万美国人 并对社会的看护人造成了沉重的烧伤。但是，仍然缺乏对AD/ADRD的有效处理。 尽管随机临床试验（RCT）可以提供有关干预措施有效性的可靠证据，但 还具有固有的限制，包括高成本和长时间执行时间。此外，RCT通常是 在选定人群和随访时间有限的专业环境中进行。因此，他们 可以在现实世界临床实践的普遍性方面存在局限性。临床试验模拟已成为 有效评估可行性，调查假设和完善研究方案的方法 实际试验。实际数据（RWD）（例如电子健康记录）的可用性和粒度增加 （EHR）和医疗索赔数据以及数据科学的进步提供了尚未开发的机会来利用 用于产生现实世界证据（RWE）的试验模拟研究的RWD。然而，有方法论 在支持基于RWD的试验仿真研究方面遇到的障碍和信息挑战，尤其是针对AD：（1） 需要使用正式和标准方法（即本体学）来代表临床试验以捕获整个 试验的范围，特别是合格的标准和结果指标（即有效性和安全性）； （2）这样 需要与RWD标准互操作正式和标准表示（例如，常见数据 模型）确定研究队列和相关的重要患者特征（即，通过可计算的表型 和自然语言处理[NLP]作为丰富的广告相关信息，例如认知分数 存在于非结构化临床注释中）； （3）需要实施全面和可重复使用的管道 可以与现有的大规模RWD无缝保持一致，以生成用于AD的高质量分析的数据集 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行为 （阿尔茨海默氏病临床试验模拟）系统，利用三个大型RWD（约2000万） Oneflorida网络，UT医师临床数据研究仓库和Optum的患者 临床数据数据）。特别是，我们建议开发新颖的信息方法以表示整个 在考虑RWD的连接（AIM 1）的同时，使用结构化和非结构化RWD 开发强大的表型算法，该算法将使以前无法计算的AD研究特征可计算（AIM 2），并开发ACTS Web应用程序，该应用程序将为广告研究人员提供一个集成的环境 使用交互式Web界面构建虚拟广告试验并获取用于试验的分析数据集 模拟研究（目标3）。