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

项目摘要 心房颤动（AF）是最常见的心律失常，其症状直接损害健康相关的 生活质量（HRQoL）。虽然常规进行导管消融以减少AF症状并改善 HRQoL，我们缺乏关于哪些症状可能改善以及哪些患者的证据。消融 它们本身可能引起导致HRQoL降低的并发症。共享决策（SDM）是一种广泛的 鼓励实践通过调整治疗获益和风险， 患者的价值观。然而，由于缺乏对房颤症状的研究， 关于消融后症状模式的严格证据和沟通所需的决策辅助工具 这些发现。在此K99/R 00应用程序中，我们建议使用电子健康记录（EHR）中的数据， 表征消融后症状模式，并在决策辅助可视化中显示它们，以支持 个性化SDM，了解针对个体患者AF症状的最佳治疗方式。在K99 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 症状数据来自EHR中的叙述性记录。我们还将采用严格的、以用户为中心的设计协议 在我博士后工作期间创建的，用于开发决策辅助可视化。在R 00阶段，我们将进行 一项可行性研究，其中在协商期间引入了交互式决策辅助可视化， 临床电生理学实践中的消融。我们的具体目标是：（1）确定常见的症状模式， 导管消融术后阵发性房颤患者（n> 32，014）;（2）开发和评价决策辅助 常见AF症状模式的可视化（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 NLP，ML，人机交互，症状科学和实施科学的能力。的 长期的培训目标是协助博士阅读Turchioe成为一名教师与独立的 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AF和其他慢性心血管疾病患者的症状管理和HRQoL 条件，着眼于健康公平。确保计划的研究和培训取得成功 活动，具有互补专业知识的多学科导师团队，建立，资金充足的计划 研究，并指导高质量的学员记录将建议她。此外，这项研究将是 在世界一流的学术医疗中心进行，拥有卓越的资源来建立和实施 使用EHR数据的技术和数据科学方法。这项研究具有重要意义， 创新：NLP和ML方法提取EHR数据用于决策辅助可视化是一种新颖的方法， SDM在AF症状的未充分研究领域。总之，这些技术有望提高HRQoL， 其他AF治疗方式（例如药物治疗、生活方式改变）和其他慢性心血管疾病。 ! !

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. ! !