Using Large Electronic Health Records and Advanced Analytics to Develop Predictive Frailty Trajectories in Patients with Heart Failure

候选人目标：我的目标是成为一名分析领域的独立定量科学家 通过有组织的培训和有指导的研究经验进行临床研究。我的目标是成为一名 使用电子健康的健康轨迹高级预测模型的学术领导者和开发者 记录(EHR)。培训目标：作为一名临床定量科学家，我希望通过临床应用提高自己的技能 信息学、EHR数据仓库和高级计算模型。我将使用提供的保护时间 该奖项旨在精通患者与临床医生的互动、临床信息学、自然语言处理、 和先进的生存分析来实现我的研究目标。背景：虚弱是一种复杂的临床表现 与衰老和慢性病相关的综合症。它减少了生理储备，增加了 易受压力源影响。心力衰竭患者中虚弱的患病率为74%。脆弱和脆弱的相互作用 心力衰竭会增加死亡、住院时间延长和功能依赖的风险。一个概念 实施脆弱性的框架是赤字累积：脆弱性指数(FI)。FI提供风险分值 基于这样一种假设，即患者的疾病越多，不良后果的风险就越高，包括 死亡率。由于以下限制，以前的FI模型尚未在常规临床实践中使用： 临床变量的数量和范围不足，缺乏个性化缺陷检测，数据使用不充分 在EHR中常见，不充分使用包括生存分析在内的纵向分析模型 技术，以及将FI减少到横断面健康状态，而不是健康轨迹。研究 目的：此应用程序的首要目标是为心力衰竭患者开发一种脆弱轨迹(FT)， 提供综合既往功能损害、当前功能状态和未来死亡风险的信息。 在目标1中，我们将开发一种新的横断面FI，它使用全范围的门诊EHR数据和 创新的机器学习数据科学方法来预测死亡率。在目标2中，我们将使用连续横截面 FIS建立FTs，并识别随着时间的推移出现类似脆弱性的个体集群。在《目标3》中， 我们将比较横断面FI和FT的预后价值。退伍军人事务部国家电子病历提供了理想的 这项研究的背景，因为它提供了1999年以来的纵向数据，并可以链接到来自非 退伍军人管理局来源，包括链接的联邦医疗保险数据库。辅导与环境：多学科辅导 团队将监督我的培训，并监督我指导的研究项目、正式课程工作、指导 阅读，和职业发展。拟议的活动将为过渡到 独立的定量数据科学家，开发临床决策辅助工具，以指导患者护理。贝勒学院 医学和质量、有效性和安全创新中心在全国享有声誉 指导和支持从不同学术背景到独立职业的初级教职员工 作为临床研究人员。

Candidate objective: My objective for this award is to become an independent quantitative scientist in analytical clinical research through structured training and mentored research experience. My goal is to become an academic leader and developer of advanced predictive models of health trajectories using electronic health records (EHR). Training objectives: I seek to sharpen my skill set as a clinical quantitative scientist using clinical informatics, EHR data warehouses, and advanced computational models. I will use the protected time provided by this award to gain proficiency in patient-clinician interactions, clinical informatics, natural language processing, and advanced survival analysis to accomplish my research aims. Background: Frailty is a complex clinical syndrome associated with aging and chronic illness. It decreases physiological reserves and increases vulnerability to stressors. The prevalence of frailty in patients with heart failure is 74%. The interplay of frailty and heart failure increases the risk for death, prolonged hospital stays, and functional dependence. One conceptual framework to operationalize frailty is accumulation of deficits: the frailty index (FI). The FI provides a risk score based on the assumption that the more ailments a patient has, the higher the risk of adverse outcomes, including mortality. Prior FI models have not been used in routine clinical practice due to the following limitations: insufficient number and range of clinical variables, lack of personalized deficit detection, use of data not commonly found in EHRs, insufficient use of longitudinal analytical models including survival analysis techniques, and the reduction of FI to a cross-sectional health status rather than a health trajectory. Research Aim: The overarching goal of this application is to develop a frailty trajectory (FT) for heart failure patients that provides information integrating prior functional impairment, current functional status, and future risk of mortality. In Aim 1, we will develop a novel cross-sectional FI that uses the full breadth of outpatient EHR data and innovative machine learning data science methods to predict mortality. In Aim 2, we will use serial cross-sectional FIs to build FTs and identify clusters of individuals following a similar progression of frailty over time. In Aim 3, we will compare the prognostic value of cross-sectional FI versus FT. The VA national EHR offers the ideal context for this study, as it provides longitudinal data since 1999 and can link to administrative data from non- VA sources, including linked Medicare databases. Mentoring & environment: A multidisciplinary mentoring team will supervise my training and will oversee my mentored research projects, formal coursework, directed reading, and career development. The proposed activities will provide a foundation for transitioning to an independent quantitative data scientist developing clinical decision aids to guide patient care. Baylor College of Medicine and the Center for Innovations in Quality, Effectiveness, and Safety have a national reputation of mentoring and supporting junior faculty members from diverse academic backgrounds to independent careers as clinical-investigators.