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: 10179450
• 负责人: Alison Marie Pouch
• 金额: $ 10.6万
• 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/10179450
• 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; 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; Visualization; 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; repair strategy; repaired; responsible research conduct; sharing platform; 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.

该提案的目的是为申请人提供基于图像的外科手术的示范性培训 规划和结果数据收集，并为申请人的独立研究生涯做好准备 科学家。为了实现这一目标，二尖瓣主动脉手术治疗范围内的训练计划 开发了阀门（BAV）。主动脉瓣关闭不全 (AI) 是 BAV 的常见并发症，直到 最近总是接受主动脉瓣置换手术治疗。由于出现 AI 的 BAV 患者 通常很年轻（20 至 50 岁），由于担心，他们不是瓣膜置换术的理想人选 与假体耐用性和与抗凝需要相关的生活方式限制有关。 BAV修复 是瓣膜置换术的一种新兴替代治疗方法，但 BAV 修复的手术方法在其 婴儿期，长期结果的报告很少。此外，通常不确定底层的内容是什么。 AI 的机制是，因为外科医生必须在术中检查心脏瓣膜（当心脏处于紧急状态时）。 逮捕状态。因此，存在两个未满足的需求。首先需要的是多中心临床结果数据 第二个是识别 BAV 修复候选者中 AI 精确机制的技术 促进针对患者的特定修复计划。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 增强术中 BAV 检查。该奖项提出的实验旨在：（1） 开发和验证术前多模态图像分析和 BAV 可视化技术， 并在手术室测试这些能力，（2）使用预识别 BAV 患者的 AI 机制 仅手术图像分析和可视化，以及（3）建立多机构信息学平台 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影响 为 BAV 修复提供长期数据采集，并通过向 BAV 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。开展这项原创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、多中心研究领导力、多模式 影像学和手术计划。该培训将辅以教学课程、观察性课程 手术室经验、参加会议和研讨会以及负责人的培训 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 由外科、麻醉学、生物医学信息学和数据存储方面的多学科专家组成的团队 分享将为候选人提供指导。最终，这种培训将为候选人提供领导的基础 基于图像的手术计划和结果数据收集和分析的研究计划。