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.

项目总结