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.

项目概要 帕帕斯博士是克利夫兰诊所医学研究所的初级教员，在 基于价值的护理研究中心和医院医学部（俄亥俄州克利夫兰）。这 职业发展奖旨在提供倾向方法、深度学习技术和试点方面的培训 干预开发，最终寻求确定个性化的心脏压力测试方法 手术前。非心脏手术存在死亡和发病风险，以及心脏并发症 占围手术期死亡率的最大比例。同时，目前的方法成本高昂，而且可能不会 有效降低心脏病风险。该提案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 可以改变术前风险的干预措施。然后它会寻求确定最有帮助的 心脏负荷测试（如果有）。在这个职业发展奖中，帕帕斯博士提出了三个阶段的调查， 在此过程中，他将获得对于实现成为围手术期风险专家的目标至关重要的新技能 减轻。在目标 1 中，Pappas 博士将使用倾向匹配技术来评估先前的关联 当包含丰富的临床数据时，术前压力测试与术后死亡率改善之间的关系 以前的大型研究无法获得。在目标 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，以及每种压力测试对每个事件的风险的影响 干涉。在目标 3 中，帕帕斯博士将试点一项干预措施，向以下地区的医生提供个性化估计： 术前门诊。除了通过正式课程进行的高级培训外，这种职业发展 该奖项得到了杰出导师团队的支持，其中包括国际公认的专家 围手术期结果研究、心血管疾病、观察性医疗数据的使用和机器 学习。本申请中概述的正式培训和指导研究的结合旨在 确保帕帕斯博士能够从该奖项中脱颖而出，成为个性化领域的独立研究者和专家 围手术期决策。