A Platform for Outcomes Data Sharing and Pre-Operative Image-Guided Mechanistic Assessment for Bicuspid Aortic Valve Repair Surgery

二叶式主动脉瓣修复手术结果数据共享和术前图像引导机械评估平台

• 批准号: 9766832
• 负责人: Alison Marie Pouch
• 金额: $ 10.7万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9766832
• 关键词: 3-Dimensional; 4D Imaging; Adopted; Adoption; Agreement; Anesthesiology; Anticoagulation; Aortic Valve Insufficiency; Area; Award; Biological Markers; Bioprosthesis device; Cardiac; Cessation of life; Clinical; Collaborations; Complication; Congenital Heart Defects; Data; Data Analyses; Data Collection; Data Storage and Retrieval; Deformity; Development; Event; Foundations; Freedom; Funding; Future; Goals; Grant; Heart; Heart Valves; Image; Image Analysis; Imagery; Industry; Informatics; Institution; Intuition; Lead; Leadership; Left; Life; Life Style; Link; Measures; Mechanics; Medical center; Mentors; Modeling; Morbidity - disease rate; Multicenter Studies; Multimodal Imaging; Operating Rooms; Operative Surgical Procedures; Outcome; Outcomes Research; Patients; Pennsylvania; Perioperative; Physiological; Prosthesis; Quality of life; Repeat Surgery; Reporting; Research; Risk; Scientist; Standardization; Surgeon; Techniques; Technology; Testing; Time; Tissues; Training; Translating; Two-Dimensional Echocardiography; Universities; University Hospitals; Ventricular; Work; X-Ray Computed Tomography; alternative treatment; aortic valve; aortic valve disorder; aortic valve replacement; ascending aorta; base; bicuspid aortic valve; biomedical informatics; care costs; career; computer human interaction; congenital heart disorder; data acquisition; data sharing; design; evidence base; experience; experimental study; follow-up; image guided; image visualization; imaging Segmentation; improved; infancy; medical schools; multidisciplinary; novel; preservation; programs; repaired; responsible research conduct; symposium; valve replacement; web portal; young adult

The objective of this proposal is to provide the applicant with exemplary training in image-based surgical planning and outcomes data collection, and to prepare the applicant for a career as an independent research scientist. To achieve this objective, a training plan within the scope of surgical treatment for the bicuspid aortic valve (BAV) has been developed. Aortic insufficiency (AI) is a common complication of BAV which until recently was always treated with aortic valve replacement surgery. Since BAV patients presenting with AI are typically young (20 to 50 years old), they are not ideal candidates for valve replacement because of concerns related to prosthesis durability and lifestyle restrictions associated with the need for anticoagulation. BAV repair is an emerging alternative treatment to valve replacement, but the surgical approach to BAV repair is in its infancy, and reports of long-term outcomes are scarce. Furthermore, it is often uncertain what the underlying mechanisms of AI are, since the surgeon must exam the valve intra-operatively when the heart is in an arrested state. Therefore, there are two unmet needs. The first need is for multicenter clinical outcomes data and the second is for technology that identifies the precise mechanism of AI in BAV repair candidates to facilitate patient-specific repair planning. The central hypothesis is that automated pre-operative 4D image analysis and visualization can reproducibly identify dynamic anatomical abnormalities causing AI and thereby augment intra-operative BAV inspection. The experiments proposed under this award are designed to: (1) develop and validate techniques for pre-operative multi-modal image analysis and visualization of the BAV, and test these capabilities in the operating room, (2) identify the mechanism of AI in BAV patients using pre- operative image analysis and visualization alone, and (3) establish an informatics platform for multi-institutional BAV repair outcomes data sharing. The proposed research will have a positive impact by initiating multicenter long-term data acquisition for BAV repair and by introducing unprecedented BAV analysis capabilities to the operating room. Ultimately, if successful, the research may lead to greater utilization of BAV repair, and reduce the need for reoperation for BAV-associated AI. Carrying out this original research will provide training in five areas: biomedical informatics, human computer interaction, leadership of multicenter studies, multi-modal imaging, and surgical planning. This training will be supplemented by didactic coursework, observational experience in the operating room, attendance at conferences and seminars, and training in the responsible conduct of research. The proposal will be carried out primarily at the Hospital of the University of Pennsylvania in collaboration with the University of Pittsburgh Medical Center and Stanford University School of Medicine. A multi-disciplinary team of experts in surgery, anesthesiology, biomedical informatics, and data storage and sharing will mentor the candidate. Ultimately, this training will provide the candidate with the foundation to lead a research program in image-based surgical planning and outcomes data collection and analysis.

该建议的目的是为申请人提供基于图像的外科手术的示例性培训 计划和结果收集数据，并为申请人做好准备作为独立研究的职业 科学家。为了实现这一目标，在双质主动脉的手术治疗范围内制定了培训计划 阀（BAV）已开发。主动脉功能不全（AI）是BAV的常见并发症，直到 最近，始终接受主动脉瓣置换手术的治疗。由于出现AI的BAV患者是 通常是年轻的（20至50岁），由于担心 与抗凝需求相关的假体耐用性和生活方式限制有关。 BAV维修 是替换阀门的新兴替代方法，但是BAV修复的手术方法是 婴儿期和长期结局的报告很少。此外，通常不确定基础 AI的机制是，因为外科医生必须在心脏处于心脏中的术中检查瓣膜 被捕的国家。因此，有两个未满足的需求。第一个需求是多中心临床结果数据 第二个是针对识别BAV维修候选中AI的确切机制的技术 促进特定于患者的维修计划。中心假设是自动术前4D图像 分析和可视化可以可重复地识别引起AI的动态解剖异常，从而 增强术中BAV检查。根据该奖项提出的实验旨在：（1） 开发和验证技术用于术前的多模式图像分析和BAV的可视化， 并在手术室中测试这些功能，（2）使用Pre- 单独的手术图像分析和可视化，（3）为多机构建立信息学平台 BAV修复结果数据共享。拟议的研究将通过启动多中心产生积极的影响 长期数据获取用于BAV维修，并通过将前所未有的BAV分析功能引入 手术室。最终，如果成功的话，研究可能会导致更大的BAV维修利用，并减少 需要重新操作BAV相关的AI。进行这项原始研究将提供五个 领域：生物医学信息学，人类计算机互动，多中心研究的领导，多模式 成像和手术计划。这项培训将由教学课程，观察力补充 在手术室，会议和研讨会的出席以及负责任的培训中的经验 进行研究。该提案将主要在宾夕法尼亚大学医院进行 与匹兹堡大学医学中心和斯坦福大学医学院合作。一个 多学科手术专家团队，麻醉学，生物医学信息学和数据存储以及 分享将指导候选人。最终，这项培训将为候选人提供领导的基础 基于图像的外科计划和结果的研究计划，可以收集数据和分析。