Personalizing AAV Management by Leveraging Big Data: Targeting Complication Clusters

项目摘要 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，导致生活质量降低和死亡率增加的并发症 人口在当代治疗改善发作率和死亡率的背景下， 注意力转移到并发症（例如，肾衰竭、感染、心血管疾病）临床相关 和以病人为导向的结果。然而，我们对如何最好地解决和预防并发症的理解 是有限的，因为它们通常是孤立于“单一疾病框架”进行研究的。我们不 了解并发症如何在并发症集群中共同发生。此外，与几个 AAV的可用治疗方案，使用真实世界经验数据的比较有效性研究， 需要相关结果（如并发症群集）来指导治疗决策， 个性化护理，提高生活质量，降低死亡率。但是，我们没有办法 使用最先进的算法准确有效地组装AAV群组， 异构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 大数据中的病例以组装大型队列，以及（2）通过以下方式确定AAV队列中的并发症集群： 应用潜在转换分析。为了实现这些目标，我们将利用开发的方法学专业知识 通过在PI的K23期间建立的合作，并使用包括EHR数据的新数据源 与医疗保险和医疗补助索赔有关。PI的团队以前已经证明了非结构化（即， EHR数据可用于研究AAV患者临床记录中提到的主题， 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， 先前已经使用这种处理来鉴定AAV病例。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，我们寻求确定表型 并发症（并发症集群）倾向于在患者中同时发生，患者如何在 随着时间的推移，什么因素预测一个人在并发症集群中的成员资格。的主要目标 该提案旨在为未来24个月的R01申请做进一步的初步数据准备。 计划的R01将侧重于使用大数据中收集的队列进行AAV的比较有效性研究 和临床相关的、以患者为导向的结果，如并发症群。这些研究的结果可以 然后用作K23期间建立的模拟模型的输入，以指导最佳的以患者为导向的治疗 决策最终，这项研究计划的目标是提高生活质量和减少并发症 通过使用数据来告知AAV治疗的个性化方法。

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.