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

这项建议的目的是为申请者提供模范的影像外科培训。 计划和结果数据收集，并为申请人作为独立研究人员的职业生涯做好准备 科学家。为了实现这一目标，在二尖瓣主动脉外科治疗的范围内制定了培训计划。 阀门(BAV)已被开发出来。主动脉瓣关闭不全(AI)是BAV的常见并发症，直到 最近一直在接受主动脉瓣置换术。由于出现AI的BAV患者 他们通常很年轻(20到50岁)，由于担心，他们不是瓣膜置换的理想人选 与假体耐用性和与抗凝需求相关的生活方式限制有关。BAV维修 是一种新兴的瓣膜置换术的替代疗法，但BAV修复的外科方法还在其 婴儿期，关于长期结果的报告很少。此外，通常不确定潜在的 人工智能的机制是，因为外科医生必须在手术中检查心脏处于 被逮捕的州。因此，有两个未得到满足的需求。第一个需求是多中心临床结果数据 第二个是识别BAV维修候选者中人工智能的精确机制的技术 促进针对患者的修复计划。中心假设是自动化的术前4D图像 分析和可视化可以重复地识别导致AI的动态解剖异常，从而 加强术中BAV检查。根据本奖项提出的实验旨在：(1) 开发和验证BAV术前多模式图像分析和可视化技术， 并在手术室测试这些能力，(2)确定人工智能在BAV患者中的机制 只进行手术图像分析和可视化，以及(3)建立多机构的信息化平台 BAV维修成果数据共享。拟议的研究将通过发起多中心产生积极影响 用于BAV维修的长期数据采集，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，这项研究可能会导致BAV修复的更大利用率，并减少 BAV相关人工智能再手术的必要性。开展这项原创性研究将在五年内提供培训 领域：生物医学信息学、人机交互、多中心研究的领导力、多模式 成像和手术计划。本培训还将辅以教学课程、观察性课程 在手术室的经验，参加会议和研讨会，以及对负责人的培训 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。一个 多学科专家团队，包括外科、麻醉学、生物医学信息学、数据存储和 分享将对候选人起到指导作用。最终，这一培训将为候选人提供领导能力的基础 以影像为基础的手术计划和结果数据收集和分析的研究计划。

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.