Personalizing Preoperative Stress Testing Using Machine Learning

使用机器学习个性化术前压力测试

• 批准号: 10322029
• 负责人: MATTHEW PAPPAS
• 金额: $ 16.1万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-15 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322029
• 关键词: Appointment; Award; Cardiac; Cardiovascular Diseases; Caring; Cessation of life; Characteristics; Clinic; Clinical; Clinical Data; Consultations; Data; Data Set; Decision Making; Diagnostic; Diagnostic tests; Ensure; Event; Faculty; Goals; Grant; Guidelines; Healthcare; Heart; Hospital Departments; Information Theory; Institute of Medicine (U.S.); Internal Medicine; International; Intervention; Investigation; K-Series Research Career Programs; Learning; Machine Learning; Medicine; Mentors; Mentorship; Methods; Modality; Modeling; Morbidity - disease rate; Observational Study; Occupations; Operative Surgical Procedures; Outcome; Outcomes Research; Pathway interactions; Patients; Perioperative; Perioperative complication; Phase; Physicians; Postoperative Period; Probability; Recommendation; Research; Research Personnel; Risk; Risk Assessment; Role; Stress Tests; Techniques; Testing; Thallium Myocardial Perfusion Imaging Stress Test; Therapeutic; Therapeutic Intervention; Thinking; Training; Weight; base; clinical decision support; cost; deep learning; design; functional status; implementation science; improved; improved outcome; large datasets; member; mortality; mortality risk; perioperative mortality; personalized approach; personalized decision; prognostic value; randomized trial; risk mitigation; skills; surgical risk; therapy development; treatment center

Project Summary Dr. Pappas is a junior faculty member in the Medicine Institute at the Cleveland Clinic, with appointments in the Center for Value-Based Care Research and the Department of Hospital Medicine (Cleveland, OH). This career development award aims to provide training in propensity methods, deep learning techniques, and pilot intervention development, ultimately seeking to identify personalized approaches to cardiac stress testing before surgery. Noncardiac surgery carries risk of mortality and morbidity, and cardiac complications account for the largest share of perioperative mortality. Meanwhile, current approaches are expensive and may not effectively reduce cardiac risk. This proposal uses machine learning techniques to define the value of information provided by each different kind of stress test and the expected benefit of different therapeutic interventions through which preoperative risks might be modified. It then seeks to identify the most helpful cardiac stress test, if any. In this career development award, Dr. Pappas proposes three phases of investigation, and in so doing will acquire new skills critical to achieving his goal of becoming an expert in perioperative risk mitigation. In Aim 1, Dr. Pappas will use propensity matching techniques to evaluate prior associations between preoperative stress testing and improved postoperative mortality, when including rich clinical data not available to previous large studies. In Aim 2, he will use machine learning techniques to estimate the value of information provided by each modality of stress testing, and the impact on the risk of each event from each intervention. In Aim 3, Dr. Pappas will pilot an intervention presenting personalized estimates to physicians in the preoperative clinic. In addition to advanced training through formal coursework, this career development award is supported by an extraordinary mentorship team, including internationally-recognized experts in perioperative outcomes research, cardiovascular disease, use of observational healthcare data, and machine learning. The combination of formal training and mentored research outlined in this application is designed to ensure that Dr. Pappas will emerge from this award as an independent investigator and expert in personalized perioperative decision-making.

项目摘要 Pappas博士是克利夫兰诊所医学研究所的初级教职员工，任命 基于价值的护理研究中心和医院医学系（俄亥俄州克利夫兰）。这 职业发展奖旨在提供倾向方法，深度学习技术和飞行员的培训 干预开发，最终寻求确定心脏应激测试的个性化方法 手术前。非心脏手术具有死亡率和发病率的风险，心脏并发症的说明 对于围手术期死亡率的最大份额。同时，当前的方法很昂贵，可能不会 有效降低心脏风险。该建议使用机器学习技术来定义 每种不同类型的压力测试提供的信息以及不同治疗的预期益处 可以修改术前风险的干预措施。然后，它试图确定最有帮助的 心脏应激测试（如果有）。在这个职业发展奖中，帕帕斯博士提出了三个调查阶段， 这样做将获得新技能，这对于实现他成为围手术期风险专家的目标至关重要 减轻。在AIM 1中，Pappas博士将使用倾向匹配技术来评估先前的关联 在包括丰富的临床数据时，术前应力测试和改善术后死亡率 以前的大型研究不可用。在AIM 2中，他将使用机器学习技术来估计价值 每种压力测试方式提供的信息，以及每个事件对每个事件的风险的影响 干涉。在AIM 3中，Pappas博士将进行干预措施，向医生提供个性化估计。 术前诊所。除了通过正式课程的高级培训外，这项职业发展 奖项得到了一个非凡的指导团队的支持，包括国际公认的专家 围手术期结果研究，心血管疾病，观察性医疗保健数据和机器的使用 学习。本应用程序中概述的正式培训和指导研究的结合旨在 确保Pappas博士将作为独立研究者和个性化专家从该奖项中获得 围手术期决策。