Personalizing AAV Management by Leveraging Big Data: Targeting Complication Clusters

利用大数据个性化 AAV 管理：针对并发症集群

• 批准号: 10198103
• 负责人: Zachary Scott Wallace
• 金额: $ 10.58万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10198103
• 关键词: ANCA vasculitis; Address; Algorithms; Antineutrophil Cytoplasmic Antibodies; Attention; Big Data; Cardiovascular Diseases; Cardiovascular system; Caring; Cessation of life; Chronic; Clinical; Clinical Informatics; Code; Collaborations; Complication; Data; Data Set; Data Sources; Decision Making; Development; Diabetes Mellitus; Diagnosis; Diagnostic; Disease; Disease Clusterings; Electronic Health Record; Epidemiology; Excess Mortality; Flare; General Population; Goals; Head; Healthcare Systems; Hypertension; Individual; Infection; Inflammation; Informatics; Kidney; Kidney Diseases; Kidney Failure; Knowledge; Left; Link; Logistic Regressions; Lung diseases; Machine Learning; Medicaid; Medicare; Medicare claim; Medicare/Medicaid; Metabolic; Methodology; Methods; Modification; Natural Language Processing; Obesity; Organ; Outcome; Outcome Measure; Patients; Performance; Persons; Pharmaceutical Preparations; Phenotype; Positioning Attribute; Predictive Factor; Preparation; Provider; Publishing; Quality of life; Remission Induction; Research; Respiratory Tract Infections; Rheumatism; Risk; Risk Factors; Sampling; Structure; Test Result; Text; Time; Treatment Effectiveness; Treatment-related toxicity; Vasculitis; Work; base; case finding; clinically relevant; cohort; comparative effectiveness study; comparative efficacy; data resource; experience; high risk; improved; improved outcome; models and simulation; mortality; mortality risk; novel; patient oriented; patient population; person centered; personalized approach; personalized care; personalized medicine; prevent; programs; respiratory; structured data; treatment comparison; treatment strategy; unstructured data

PROJECT SUMMARY ANCA-associated vasculitis (AAV) is a small vessel vasculitis associated with disease- and treatment-related complications that contribute to reduced quality of life and excess mortality compared to the general population. In the context of improving rates of flare and mortality with contemporary treatments, increasing attention is shifting to complications (e.g., renal failure, infection, cardiovascular disease) as clinically-relevant and patient-oriented outcomes. However, our understanding of how best to address and prevent complications is limited because they are typically studied in isolation from a “single disease framework.” We do not understand how complications tend to co-occur in individuals in complication clusters. Moreover, with several available treatment options for AAV, comparative effectiveness studies using real-world experience data and relevant outcomes like complication clusters are needed to guide treatment decisions in a manner that personalizes care, improves quality of life, and reduces mortality. However, we do not have the methods to accurately and efficiently assemble an AAV cohort using state-of-the-art algorithms that leverage heterogeneous claims and electronic health record (EHR) data. The aims of this proposal are to (1) apply advanced clinical informatics methods (i.e., machine learning and natural language processing) to identify AAV cases in big data to assemble a large cohort and (2) determine complication clusters in an AAV cohort by applying latent transition analysis. To achieve these aims, we will leverage methodologic expertise developed through collaborations established during the PI’s K23 and use a novel data source that includes EHR data linked to Medicare and Medicaid claims. The PI’s team has previously demonstrated that unstructured (i.e., free-text) EHR data can be used to study topics mentioned in clinical notes of AAV patients and that keywords in these notes can help identify AAV patients but neither machine learning nor sophisticated natural language processing have been previously used to identify AAV cases. In addition, our prior work has examined AAV complications in isolation (e.g., renal disease, cardiovascular disease) but here we seek to identify phenotypes of complications (complication clusters) that tend to co-occur in patients, how patients transition between clusters over time, and what factors predict a person’s membership in a complication cluster. The major goal of this proposal is to build further preliminary data in preparation for an R01 application over the next 24 months. The planned R01 will focus on comparative effectiveness studies in AAV using cohorts assembled in big data and clinically-relevant, patient-oriented outcomes, like complication clusters. The results of these studies can then be used as inputs in simulation models built during my K23 to guide optimal patient-oriented treatment decisions. Ultimately, the goal of this research program is to improve quality of life and reduce complications and mortality by using data to inform personalized approaches to AAV treatment.

项目总结 ANCA相关性血管炎(AAV)是一种与疾病和治疗相关的小血管炎。 与一般情况相比，导致生活质量下降和死亡率过高的并发症 人口。在用现代治疗方法改善红斑率和死亡率的背景下， 注意力正在转移到与临床相关的并发症(例如，肾功能衰竭、感染、心血管疾病)上 和以病人为导向的结果。然而，我们对如何最好地处理和预防并发症的理解 是有限的，因为它们通常是从“单一疾病框架”中孤立地进行研究的。我们不会 了解并发症如何在并发症集群中的个体中同时发生。此外，有几个 AAV的可用治疗方案，使用真实世界经验数据和 需要相关的结果，如并发症组，以指导治疗决定 个性化护理，提高生活质量，降低死亡率。然而，我们没有方法来 使用先进的算法准确高效地组装AAV队列，该算法利用 异类索赔和电子健康记录(EHR)数据。本提案的目的是(1)适用于 先进的临床信息学方法(即机器学习和自然语言处理)来识别AAV 通过以下方式确定AAV队列中的并发症群： 应用潜伏期分析。为了实现这些目标，我们将利用开发的方法专业知识 通过在PI的K23期间建立的协作，并使用包括EHR数据的新数据源 与医疗保险和医疗补助申请有关。PI的团队先前已经证明了非结构化(即， 自由文本)EHR数据可用于研究AAV患者临床笔记中提到的主题和关键词 这些笔记可以帮助识别AAV患者，但无论是机器学习还是复杂的自然语言 以前曾使用处理程序来识别AAV病例。此外，我们之前的工作已经检查了AAV 隔离的并发症(例如，肾脏疾病、心血管疾病)，但在这里我们试图确定表型 容易在患者身上同时发生的并发症(并发症群)，患者如何在 随着时间的推移而聚集，以及什么因素预测一个人在复杂聚集中的成员资格。的主要目标是 这项提议是为了建立更多的初步数据，为未来24个月的R01申请做准备。 计划中的R01将专注于使用大数据中集合的队列进行AAV的比较有效性研究 以及与临床相关的、以患者为导向的结果，如并发症集群。这些研究的结果可以 然后在我的K23期间建立的模拟模型中用作输入，以指导以患者为中心的最佳治疗 决定。归根结底，这个研究项目的目标是提高生活质量，减少并发症 通过使用数据为AAV治疗的个性化方法提供信息，从而降低死亡率。