Data-driven shared decision-making to reduce symptom burden in atrial fibrillation

项目总结 房颤是最常见的心律失常，其症状直接损害健康。 生活质量(HRQOL)。虽然常规进行导管消融以减少房颤症状和改善 HRQOL，我们缺乏证据表明哪些症状可能会改善，哪些患者可以。烧蚀 本身可能会引起并发症，从而导致较低的HRQOL。共享决策(SDM)是一种广泛的 鼓励实践通过将治疗收益和风险与 病人陈述的价值。然而，由于缺乏，没有SDM干预措施明确地专注于房颤症状 关于消融后症状模式的严格证据和沟通所需的决策辅助 这些发现。在此K99/R00应用程序中，我们建议使用电子健康记录(EHR)中的数据来 描述消融后症状模式，并将其显示在决策辅助可视化中，以支持 个性化的SDM关于个人患者房颤症状的最佳治疗方式。在K99公路上 阶段，我们将使用自然语言处理(NLP)和机器学习(ML)来提取和分析 电子病历中叙述性笔记中的症状数据。我们还将采用严格的、以用户为中心的设计协议 在我的博士后工作期间创建的，以开发决策辅助可视化。在R00阶段，我们将进行 一项可行性研究，其中在咨询期间引入了交互式决策辅助可视化 消融在临床电生理实践中的应用。我们的具体目标是：(1)确定常见的症状模式 导管消融后阵发性房颤患者(n&gt；32,014)；(2)开发和评估决策辅助 对常见房颤症状模式进行可视化(n=50)；以及(3)评估实施 临床实践中的决策辅助可视化(n=75)。本项目的培训目标包括掌握 具备NLP、ML、人机交互、症状科学和实施科学方面的能力。这个 长期的培训目标是帮助雷丁·图尔基奥博士成为一名独立的教职员工 研究计划。她寻求领导一个由科学家和临床医生组成的跨学科团队，致力于 改善房颤和其他慢性心血管疾病患者的症状管理和HRQOL 条件，着眼于卫生公平。确保计划的研究和培训取得成功 活动，一个多学科的导师团队，具有互补的专业知识，建立的，资金充足的计划 研究，以及指导高素质学员的记录将为她提供建议。此外，这项研究将是 在世界级学术医疗中心进行，拥有卓越的建设和实施资源 使用电子病历数据的技术和数据科学方法。拟议的研究既有重要意义，又有 创新：NLP和ML方法提取用于辅助决策可视化的EHR数据是一种新的方法 房颤症状研究不足地区的SDM。总而言之，这些技术有望提高以下方面的HRQOL 其他房颤治疗方式(例如，药物、生活方式的改变)和其他慢性心血管疾病。 好了！ 好了！

PROJECT SUMMARY Atrial fibrillation (AF) is the most common cardiac arrhythmia with symptoms that directly impair health-related quality of life (HRQoL). While catheter ablation is routinely performed to reduce AF symptoms and improve HRQoL, we lack evidence about which symptoms are likely to improve and for which patients. Ablations themselves may cause complications that lead to lower HRQoL. Shared decision-making (SDM) is a widely encouraged practice to navigate such complex choices by aligning treatment benefits and risks with the patient's stated values. However, no SDM interventions have focused explicitly on AF symptoms due to a lack of rigorous evidence about post-ablation symptom patterns and the decision aids necessary to communicate those findings. In this K99/R00 application, we propose to use data from electronic health records (EHRs) to characterize post-ablation symptom patterns, and display them in decision-aid visualizations to support personalized SDM about the best treatment modalities for an individual's patient's AF symptoms. In the K99 phase, we will use natural language processing (NLP) and machine learning (ML) to extract and analyze symptom data from narrative notes in EHRs. We will also employ a rigorous, user-centered design protocol created during my postdoctoral work to develop decision-aid visualizations. In the R00 phase, we will conduct a feasibility study in which the interactive decision-aid visualizations are introduced during consultations about ablation in clinical electrophysiology practices. Our specific aims are: (1) identify common symptom patterns in patients with paroxysmal AF post-catheter ablation (n>32,014); (2) develop and evaluate decision-aid visualizations of common AF symptom patterns (n=50); and (3) evaluate the feasibility of implementing the decision-aid visualizations in clinical practice (n=75). The training objectives of this project include mastering competencies in NLP, ML, human-computer interaction, symptom science, and implementation science. The long-term training goal is to assist Dr. Reading Turchioe to become a faculty member with an independent program of research. She seeks to lead an interdisciplinary team of scientists and clinicians committed to improving symptom management and HRQoL for individuals living with AF and other chronic cardiovascular conditions, with an eye towards health equity. To ensure success for the planned research and training activities, a multidisciplinary team of mentors with complementary expertise, established, well-funded programs of research, and a record of mentoring high-quality trainees will advise her. Moreover, this research will be conducted in a world-class academic medical center with exceptional resources for building and implementing technology and data science methods using EHR data. The proposed research is both significant and innovative: NLP and ML methods to extract EHR data for decision-aid visualizations are a novel approach to SDM in the understudied area of AF symptoms. Together, these techniques promise to enhance HRQoL for other AF treatment modalities (e.g. medications, lifestyle changes) and other chronic cardiovascular conditions. ! !