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

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炎 与一般的并发症导致生活质量降低和过度荒谬的并发症 人口。 注意正在转变为临床与临床相关的汇编（例如肾衰竭，感染，心血管疾病） 和以患者为导向的结果。 之所以有限，是因为它们是典型的与“单一疾病框架”隔离的。 了解汇编如何在汇编集群中同时发生。 使用现实世界经验数据和 需要相关结果，例如编译簇，以指导阿曼纳的治疗决策 个性化护理，改善生活质量并降低死亡率。 精确有效地使用利用最新算法组装AAV队列 异类索赔和电子健康记录（EHR）数据。 先进的临床信息方法（即机器学习和自然语言处理）来牙齿化AAV 大数据中的Causs组装大型队列，（2）确定AAV队列中的并发症簇By By By By Bye Bye 应用潜在的过渡分析来实现这些目标 通过在PI的K23期间建立的合作并使用新颖的数据源包括EHR数据 与Medicare和Medicaid索赔有关。 自由文本）EHR数据可用于研究AAV患者AAV患者的临床注意事项和关键词的临床注释中的主题 在这些笔记中可以帮助识别AAV患者，但没有机器学习态度自然语言 处理以前已用于识别AAV案例。 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 倾向于在患者中共同发生的汇编（并发症簇），患者如何过渡 群集随着时间的流逝，哪些因素可以预测一个人的汇编集群的成员资格。 该提案是建立进一步的预预制作数据，以准备在接下来的24个月内进行R01申请。 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 和临床上的面向患者的结果，例如汇编群。 然后在我的K23期间构建的模拟模型中用作输入，以指导最佳的面向患者的治疗 决定最终，该研究计划的目的是改善生活 通过使用数据为AAV治疗的个性化方法告知死亡率。