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: 10369732
• 负责人: Zachary Scott Wallace
• 金额: $ 8.32万
• 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/10369732
• 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; machine learning algorithm; 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; 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; machine learning algorithm; 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.

项目摘要 与ANCA相关的血管炎（AAV）是与疾病和治疗有关的小血管血管炎 与一般的并发症导致生活质量降低和过剩死亡率的并发症 人口。在通过当代治疗提高耀斑和死亡率的背景下，增加 注意并发症（例如，肾衰竭，感染，心血管疾病）的并发症转移为临床上的相关性 和面向患者的结果。但是，我们对如何最好地解决和预防并发症的理解 之所以受到限制，是因为它们通常是从“单个疾病框架”隔离的。我们没有 了解并发症如何在并发症簇中的个体中共发生。而且，有几个 使用现实世界经验数据和 需要相关结果，例如并发簇，以指导治疗决策的方式 个性化护理，改善生活质量并降低死亡率。但是，我们没有方法 精确有效地使用利用最先进的算法组装AAV队列 异质索赔和电子健康记录（EHR）数据。该提案的目的是（1）申请 先进的临床信息方法（即机器学习和自然语言处理）来识别AAV 大数据中的情况以组装大型队列，（2）通过 应用潜在过渡分析。为了实现这些目标，我们将利用开发的方法论专业知识 通过在PI的K23期间建立的合作，并使用包含EHR数据的新型数据源 与Medicare和Medicaid索赔有关。 PI的团队以前已经证明了这一点（即 自由文本）EHR数据可用于研究AAV患者临床注释中提到的主题和关键词 在这些笔记中可以帮助识别AAV患者，但没有机器学习或精致的自然语言 处理以前已用于识别AAV案例。此外，我们先前的工作已经检查了AAV 孤立的并发症（例如肾脏疾病，心血管疾病），但在这里我们试图识别表型 患者倾向于同时发生的并发症（并发症簇），患者如何过渡 群集会随着时间的流逝，以及哪些因素可以预测一个人在并发症集群中的成员资格。主要目标 该建议是建立进一步的初步数据，以准备在接下来的24个月内进行R01应用程序。 计划中的R01将使用大数据组装的同类群体专注于AAV中的比较有效性研究 以及临床上的，以患者为导向的结果，例如并发症簇。这些研究的结果可以 然后在我的K23期间构建的模拟模型中用作输入，以指导最佳的以患者为导向的治疗 决定。最终，该研究计划的目标是改善生活质量并减少并发症 通过使用数据为AAV治疗的个性化方法告知死亡率。