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

本提案的目的是为申请人提供基于图像的外科手术方面的示范性培训 规划和成果数据收集，并准备申请人的职业生涯作为一个独立的研究 科学家为了实现这一目标，在二叶主动脉瓣手术治疗范围内的培训计划， 阀（BAV）的开发。主动脉瓣关闭不全（AI）是BAV的常见并发症， 最近一直接受主动脉瓣置换手术。由于出现AI的BAV患者 他们通常年轻（20至50岁），由于担心瓣膜置换术， 与假体耐久性和与抗凝治疗需求相关的生活方式限制有关。BAV修复 是一种新兴的替代瓣膜置换术的治疗方法，但BAV修复的手术方法 婴儿期，长期结果的报告很少。此外，往往不确定的是， AI的机制是，由于外科医生必须在心脏处于非手术状态时在术中检查瓣膜， 逮捕国。因此，有两个需求没有得到满足。首先需要的是多中心临床结局数据 第二个是识别人工智能在BAV修复候选者中的精确机制的技术， 促进患者特定的修复计划。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导致AI的动态解剖异常， 增加术中BAV检查。该奖项下提出的实验旨在：（1） 开发并验证术前多模态图像分析和BAV可视化技术， 并在手术室中测试这些能力，（2）使用预处理确定BAV患者的AI机制， 手术图像分析和可视化，（3）建立多机构的信息化平台 BAV修复结局数据共享。拟议的研究将通过启动多中心研究产生积极影响。 BAV维修的长期数据采集，并通过引入前所未有的BAV分析功能， 手术室最终，如果成功，该研究可能会导致BAV修复的更大利用，并减少 BAV相关AI需要再次手术。开展这项原始研究将提供五个方面的培训 领域：生物医学信息学，人机交互，多中心研究的领导，多模式 成像和手术计划。这种培训将辅以教学课程，观察 在手术室的经验，参加会议和研讨会，并在负责培训 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 由外科、麻醉学、生物医学信息学和数据存储领域的多学科专家组成的团队， 分享会指导候选人。最终，此培训将为候选人提供领导基础 一个基于图像的手术计划和结果数据收集和分析的研究项目。

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.