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

项目摘要/摘要 候选人兼首席研究员杰弗里·蒂森，医学博士，公共卫生硕士，系助理教授。 加州大学旧金山分校的心脏病学教授Sion说。PI的长期目标是成为一名 独立的临床医生-研究人员，受过必要的培训以进行技术优势的临床研究 既调查又促进心血管疾病的预防。具体地说，该奖项的培训目标是 将允许PI在其现有的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，如移动健康工具，以促进心力衰竭预防。糖果- 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，参加加州大学旧金山分校的医学信息学培训计划。要实现这些目标 根据培训目标，应聘者组建了一支具有广泛专业知识的指导团队 临床试验、流行病学和技术支持的研究(主要导师杰夫·奥尔金博士、马克博士 生物医学/临床信息学和新的数据分析(Atul Butte博士)和 心力衰竭的临床和研究专长(Liviu Klein博士、Veronique Roger博士、John Spertus博士)。这位亲王- Object试图利用我们当前的数字医疗时代远程捕获最新的个性化信息 患者数据和动态风险预测，满足未得到满足的改进远程心力衰竭管理的需求 并减少心力衰竭住院时间。该项目将测试和开发预测动态心力衰竭的工具- URE风险基于在自由生活的心力衰竭人群中测量的实时数据-使用一种新型智能手机- 基于工具和最新EHR数据中的模式。具体目标是：目标1-！检查中的更改 连续6分钟步行试验测定的功能状态作为近期心力衰竭的预测指标 住院治疗。目标2-！开发包含四种最新类型的心力衰竭风险模型 可用的电子病历数据-包括遭遇、药物补充/更改、实验室和生命体征。这 预计研究将产生两种经过验证的方法来估计动态、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，减少住院人数。这个骗局- Attribution有可能改善对心力衰竭患者的远程管理，同时改变临床 CARE范式利用动态的、纵向的和自由生活的数据进行临床决策。这一奖项将 直接使未来的R01级随机实用临床试验验证UP交付的假设 到目前为止，向门诊临床医生提供的最新风险信息可以减少未来的心衰住院人数。该奖项将提供 具有独特技能组合的PI：强大的临床背景，严谨的临床研究基础， 具备机器学习的高级分析技能，能够流利地利用与健康相关的技术获得洞察力 并提供预防性干预措施。

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.