Dynamic prediction of heart failure using real-time functional status and EHR data in the ambulatory setting

项目总结/摘要 候选人和首席研究员（PI）杰弗里·蒂森，医学博士，公共卫生硕士，是在Divi助理教授， 加州大学旧金山分校弗朗西斯科心脏病学锡永。PI的长期目标是成为 独立的临床医生-研究者，经过必要的培训，以进行技术杠杆临床研究 研究和促进心血管疾病的预防。具体而言，该奖项的培训目标 将允许PI在其现有临床研究和数据分析技能的基础上使用机器学习 以及其他基于技术的解决方案，如移动的健康工具，以促进心力衰竭的预防。坎迪- date将完成课程，以发展他在机器学习，医学信息学和临床试验方面的技能 设计和实施，参加了UCSF医学信息学培训计划。实现这些 培训目标，候选人已经组建了一个指导团队，在以下方面具有广泛的互补专业知识： 临床试验，流行病学和技术支持的研究（杰夫奥尔金博士，主要导师，马克博士 Pletcher，Veronique Roger博士），生物医学/临床信息学和新数据分析（Atul Butte博士），以及 心力衰竭临床和研究专业知识（Liviu Klein博士、Veronique Roger博士、John Spertus博士）。这个亲- ject旨在利用我们当前的数字医疗时代，远程捕获个性化的最新信息， 患者数据和预测动态风险，解决未满足的改善远程心力衰竭管理的需求 并减少心力衰竭住院率。该项目将测试和开发预测动态心力衰竭的工具- 基于自由生活心力衰竭人群中测量的实时数据的风险-使用新型智能手机- 基于工具和最新EHR数据中的模式。具体目标是：目标1-！检查 功能状态，通过连续自我管理6分钟步行试验测量，作为近期HF的预测因子 住院瞄准2-！开发一个“动态”心力衰竭风险模型，该模型包含四种最新的 EHR数据，因为它变得可用-包括遭遇，药物补充/变化，实验室和生命体征。这 研究预计将产生两种经过验证的方法来估计动态的、最新的心力衰竭风险， 能够提供更早、更有效的门诊干预措施，减少住院。这个骗局 物联网有可能改善心力衰竭患者的远程管理，同时改变临床 护理范例利用动态，纵向和自由生活的数据进行临床决策。这个奖项将 直接使未来的R 01级随机化实用临床试验测试的假设，提供了- 向门诊医生提供最新的风险信息可以减少未来的HF住院治疗。该奖项将提供 PI具有独特的技能组合：强大的临床背景，严谨的临床研究基础， 先进的机器学习分析技能和流畅性，以利用健康相关技术来获得见解 并采取预防性干预措施。

Project Summary/Abstract The candidate and principal investigator (PI) Geoffrey Tison, MD, MPH, is an Assistant Professor in the Divi- sion of Cardiology at the University of California, San Francisco. The long-term goal of the PI is to become an independent clinician-investigator with the training necessary to perform technology-leveraged clinical research to both investigate and facilitate cardiovascular disease prevention. Specifically, the training aims of this award will allow the PI to build upon his existing clinical research and data analysis skills to employ machine learning and other technology-based solutions, like mobile health tools, to advance heart failure prevention. The candi- date will complete coursework to develop his skills in machine learning, medical informatics, and clinical trial design and implementation, taking part in the UCSF Medical Informatics Training Program. To achieve these training goals, the candidate has assembled a mentoring team with extensive and complimentary expertise in clinical trials, epidemiology, and technology-enabled research (Dr. Jeff Olgin, the primary mentor, Dr. Mark Pletcher, Dr. Veronique Roger), biomedical/clinical informatics and novel data analysis (Dr. Atul Butte) and heart failure clinical and research expertise (Dr. Liviu Klein, Dr. Veronique Roger, Dr. John Spertus). This pro- ject seeks to take advantage of our current digital medical era to remotely capture individualized up-to-date patient data and predict dynamic risk, addressing the unmet need to improve remote heart failure management and decrease heart failure hospitalization. The project will test and develop tools to predict dynamic heart fail- ure risk based on real-time data measured in a free-living heart failure population—using a novel smartphone- based tool—and from patterns in up-to-date EHR data. The specific aims are: Aim 1–! Examine changes in functional status, measured by serial Self-Administered 6 Minute Walk Test, as a predictor of near-term HF hospitalization. Aim 2–! Develop a “dynamic” heart failure risk model that incorporates four types of up-to-date EHR data as it becomes available—including encounters, medication refills/changes, labs and vital signs. This research is expected to produce two validated methods to estimate dynamic, up-to-date heart failure risk to enable the provision of earlier, more effective outpatient interventions that decrease hospitalization. This con- tribution has the potential to improve remote management for heart failure patients, while shifting the clinical care paradigm to utilize dynamic, longitudinal and free-living data for clinical decision-making. This award will directly enable a future R01-level randomized pragmatic clinical to trial test the hypothesis that delivery of up- to-date risk information to outpatient clinicians can decrease future HF hospitalizations. This award will provide the PI with a unique combination of skills: a strong clinical background, a rigorous clinical research foundation, advanced analytic skills in machine learning and fluency to utilize health-related technologies to derive insights and deliver preventive interventions.