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

数据驱动的共享决策可减轻心房颤动的症状负担

• 批准号: 10607937
• 负责人: Meghan Reading Turchioe
• 金额: $ 24.88万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10607937
• 关键词: Ablation; Academic Medical Centers; Address; Affect; Age; Area; Arrhythmia; Atrial Fibrillation; Award; Benefits and Risks; Cardiac ablation; Cardiovascular system; Caring; Characteristics; Chronic; Clinical; Competence; Complex; Consultations; Data; Data Display; Data Reporting; Data Science; Decision Aid; Devices; Dyspnea; Electronic Health Record; Electrophysiology (science); Enabling Factors; Ensure; Eye; Faculty; Fatigue; Feasibility Studies; Funding; Goals; Gold; Grant; Health; Health Personnel; Healthcare; Impaired health; Impairment; Individual; International; Intervention; Interview; Investigation; Lead; Learning; Life Style; Machine Learning; Measures; Mentors; Methods; Mobile Health Application; Modality; Monitor; Natural Language Processing; Nursing Informatics; Outcome; Palpitations; Patient Outcomes Assessments; Patients; Pattern; Pharmaceutical Preparations; Phase; Population; Positioning Attribute; Predisposing Factor; Prevalence; Procedures; Process; Protocols documentation; Provider; Public Health; Quality of life; Reach Effectiveness Adoption Implementation and Maintenance; Reading; Reinforcing Factor; Reporting; Research; Research Activity; Resources; Risk; Sampling; Scientist; Site; Symptoms; Techniques; Technology; Training; Training Activity; Validation; Visualization; Work; associated symptom; biomedical informatics; career; clinical practice; common symptom; comorbidity; computer human interaction; data visualization; electronic data; experience; health equity; health related quality of life; implementation science; improved; individual patient; innovation; innovative technologies; insight; instrument; mHealth; machine learning method; member; minimally invasive; multidisciplinary; novel strategies; optimal treatments; personalized decision; personalized intervention; pre-doctoral; programs; reduce symptoms; shared decision making; statistics; success; symptom management; symptom science; symptomatic improvement; treatment risk; user centered design

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. ! !

项目摘要 心房颤动（AF）是最常见的心律不齐，症状直接损害与健康有关的症状 生活质量（HRQOL）。经常进行导管消融以减轻AF症状并改善 HRQOL，我们缺乏证据表明哪些症状可能会改善以及患者的患者。消融 自己可能引起并发症，导致HRQOL降低。共享决策（SDM）是一个广泛的 鼓励实践通过使治疗福利和风险与 患者的陈述值。但是，由于缺乏，没有SDM干预措施明确集中于AF症状 关于施加后症状模式的严格证据和交流所需的决策辅助 那些发现。在此K99/R00应用中，我们建议将电子健康记录（EHR）的数据用于 表征燃烧后症状模式，并将其显示在决策aid可视化中以支持 个性化的SDM，介绍了个人患者的AF症状的最佳治疗方式。在K99中 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 来自EHR中叙事笔记的症状数据。我们还将采用严格的，以用户为中心的设计协议 在我的博士后工作中创建，以开发决策AID可视化。在R00阶段，我们将进行 一项可行性研究，在磋商期间介绍了交互式决策AID可视化 临床电生理学实践中的消融。我们的具体目的是：（1）确定在 患有阵发性AF后体育仪消融患者（n> 32,014）； （2）制定和评估决策AID 常见AF症状模式的可视化（n = 50）； （3）评估实施的可行性 临床实践中的决策AID可视化（n = 75）。该项目的培训目标包括掌握 NLP，ML，人类计算机相互作用，症状科学和实施科学的能力。这 长期培训目标是协助Reading Turchioe博士成为独立的教师 研究计划。她试图领导一个科学家和临床医生的跨学科团队 改善患有AF和其他慢性心血管的人的症状管理和HRQOL 条件，着眼于健康公平。确保计划的研究和培训成功 活动，具有互补专业知识的导师的多学科团队，已建立，资金充足的计划 研究以及指导高质量学员的记录将为她提供建议。而且，这项研究将是 在世界一流的学术医学中心进行，具有卓越的建设和实施资源 技术和数据科学方法使用EHR数据。拟议的研究既重要，又 创新：提取EHR数据的NLP和ML方法是决策AID可视化的一种新方法 SDM在AF症状的研究领域。这些技术共同有望增强HRQOL 其他AF治疗方式（例如药物，生活方式改变）和其他慢性心血管疾病。 呢 呢