Bridging clinical trial and real-world data via machine learning to advance rheumatoid arthritis treatment strategies

通过机器学习连接临床试验和真实世界数据，以推进类风湿性关节炎的治疗策略

• 批准号: 10339668
• 负责人: TIANXI CAI
• 金额: $ 74.84万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10339668
• 关键词: Aftercare; Autoimmune; Clinical; Clinical Data; Clinical Management; Clinical Trials; Clinical assessments; Data; Data Set; Disease; Electronic Health Record; Genetic Crossing Over; Gold; Healthcare Systems; Hydroxychloroquine; Inflammatory; Institution; Joints; Link; Machine Learning; Measurement; Methodology; Methods; Methotrexate; Modeling; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Outcome; Participant; Patients; Physicians; Population; Prediction of Response to Therapy; Randomized; Randomized Controlled Clinical Trials; Registries; Research Design; Resources; Rheumatoid Arthritis; Sample Size; Site; Strategic Planning; Subgroup; Sulfasalazine; Supervision; TNF gene; Testing; Therapy trial; Treatment Effectiveness; United States Department of Veterans Affairs; Work; arm; arthritis registry; arthritis therapy; arthropathies; base; causal model; compare effectiveness; data registry; effective therapy; functional status; improved; inhibitor; innovation; machine learning method; novel; optimal treatments; patient population; patient subsets; performance tests; personalized medicine; predicting response; recruit; response; rheumatologist; supervised learning; treatment effect; treatment response; treatment strategy

PROJECT SUMMARY/ABSTRACT Rheumatoid arthritis (RA) is the most common autoimmune joint disease with over 15 treatment options, reflecting both advances in therapy as well as the heterogenous response to therapy. After the first line therapy methotrexate (MTX), patients and their rheumatologist proceed on a trial-and-error approach to identify the optimal treatment. A landmark randomized controlled clinical trial (RCT), RACAT, compared the effectiveness of triple therapy-MTX, sulfasalazine, and hydroxychloroquine vs MTX and a tumor necrosis factor inhibitor (TNFi). The RACAT subgroup analyses observed that some patients had a better response to one treatment strategy vs the other. However, like most RCTs, it was underpowered to better characterize these subgroups. Real-world data (RWD), such as electronic health record (EHR) and registry data, have a larger sample size but lack the randomization and precise clinical measurements performed as part of clinical trials. The objective of this proposal is to apply and rigorously test state-of-the-art methods that can combine the strengths of RCT and RWD to extend RCT findings. RACAT was a Veterans Affairs (VA) based clinical trial and thus many of their subjects also have EHR data in parallel, providing an ideal study design to test methods to understand how well we can replicated RCT using RWD. In Aim 1, we test methods using semi-supervised machine learning methods to impute RACAT clinical endpoints using EHR data; the linked RACAT data will be used as the gold standard comparison. Next, we apply causal inference modeling comparing triple therapy vs TNFi using EHR data with the imputed endpoints and validate results using the linked RACAT data. In Aim 2, we apply novel causal modeling methods that enable us to examine subgroup findings using RWD. We will identify subjects in the larger EHR and registries similar to RACAT subgroups, i.e. patients who benefitted more from triple therapy vs TNFi or vice versa, and subjects who remained on TNFi throughout the trial and did well. These larger populations will provide improved power to study potential predictors of treatment response. Moreover, the integration of EHR data allows us to study a broader set of potential predictors not collected in RCT or registry data. Our overarching hypothesis is that we will identify the clinical subgroups observed in RACAT with differing response to treatments within the larger populations of RA patients in EHR and registry. We will also identify novel predictors of response by using a broader set of clinical data available in EHR. This study is significant because it will provide a blueprint for studies for extending RCT findings in datasets with linked RCT and RWD, applicable to many treatments and conditions. This study is innovative because of its approach to maximize the data available from RCTs with existing RWD using linked datasets, powering studies to optimize RA therapy for different patients. This proposal also anticipates the growing ability of patients and institutions to access EHR data, enabling previously siloed datasets to become part of data-driven studies to advance clinical management of RA and other conditions.

项目摘要/摘要 类风湿关节炎（RA）是最常见的自身免疫性关节疾病，有15多种治疗选择， 反映了治疗方面的进步以及对治疗的异质反应。第一行之后 甲氨蝶呤治疗（MTX），患者及其风湿病学家采用试验方法进行识别 最佳处理。具有里程碑意义的随机对照临床试验（RCT），Racat，比较了 三重疗法MTX，磺胺贺嗪和羟基氯喹与MTX的有效性和肿瘤坏死因子 抑制剂（TNFI）。 Racat子组分析观察到，有些患者对一个患者的反应更好 治疗策略与另一个。但是，像大多数RCT一样，它不足以更好地描述这些 亚组。现实世界数据（RWD），例如电子健康记录（EHR）和注册表数据，具有较大的数据 样本量，但缺乏作为临床试验的一部分进行的随机分配和精确的临床测量。 该提案的目的是应用和严格测试可以结合使用的最先进方法 RCT和RWD的优势扩展了RCT发现。 Racat是基于退伍军人事务（VA）的临床试验 因此，他们的许多受试者也具有并行的EHR数据，为测试方法提供了理想的研究设计 要了解我们如何使用RWD复制RCT。在AIM 1中，我们使用半监督者测试方法 使用EHR数据将赛车临床终点的机器学习方法；链接的Racat数据将是 用作黄金标准比较。接下来，我们采用比较三重治疗与 TNFI使用EHR数据与估算的端点一起使用链接的Racat数据验证结果。在AIM 2中， 我们采用新颖的因果建模方法，使我们能够使用RWD检查亚组发现。我们将 识别较大的EHR和类似Racat子组的注册表中的受试者，即受益的患者 来自三重治疗与TNFI的更多信息，反之亦然，在整个试验过程中一直呆在TNFI上的受试者 出色地。这些较大的人群将提供改进的能力来研究治疗反应的潜在预测指标。 此外，EHR数据的集成使我们能够研究未在 RCT或注册表数据。我们的总体假设是，我们将确定在 在EHR和注册表中，对RA患者较大的患者种群中对治疗的反应不同。 我们还将通过使用EHR中提供的更广泛的临床数据来确定响应的新颖预测指标。这 研究之所以重要，是因为它将为研究扩展数据集的RCT调查结果提供蓝图 链接的RCT和RWD，适用于许多治疗和条件。这项研究是创新的，因为它 使用链接的数据集，使用现有RWD最大化RCT可用的数据，为研究提供动力 为不同患者优化RA疗法。该建议还预计患者的能力不断增长 访问EHR数据的机构，使以前的孤立数据集成为数据驱动研究的一部分 提高RA和其他条件的临床管理。