Right Ventricular Remodeling in Tetralogy of Fallot

法洛四联症的右心室重构

• 批准号: 10387064
• 负责人: Elizabeth Walker Thompson
• 金额: $ 5.43万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10387064
• 关键词: 3-Dimensional; Adverse event; Affect; Algorithms; Anatomy; Birth; Blood; Cardiac; Cardiac Volume; Cardiology; Cardiovascular Diseases; Cardiovascular Physiology; Cardiovascular system; Caring; Child; Cicatrix; Clinical; Cohort Studies; Consumption; Cross-Sectional Studies; Data; Defect; Dilatation - action; Disease; Engineering; Evaluation; Event; Functional disorder; Genetic; Geometry; Goals; HIF1A gene; Healthcare; Heart; Image; Intervention; Joints; Knowledge; Lead; Life; Liquid substance; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Manuals; Measurement; Mechanics; Medical; Medical Imaging; Mentorship; Modeling; Navier-Stokes equations; Operative Surgical Procedures; Outcome; Outcome Study; Paris, France; Pathologic; Pathway interactions; Patient Care; Patients; Pediatric Hospitals; Phase; Philadelphia; Physicians; Process; Pulmonary Valve Insufficiency; Pulmonary Valve Stenosis; Pulmonary artery structure; Pulmonary valve structure; Quality of life; Radiology Specialty; Research; Research Personnel; Residual state; Resistance; Right Ventricular Dysfunction; Risk; Scientist; Stress; Structure; Technology; Tetralogy of Fallot; Thick; Time; Training; Variant; Ventricular; Ventricular Arrhythmia; Ventricular Function; Work; base; clinical care; congenital heart disorder; experience; follow-up; heart function; hemodynamics; improved; insight; longitudinal analysis; machine learning algorithm; machine learning method; mortality; mortality risk; outcome prediction; patient subsets; pressure; repaired; right ventricular remodeling; serial imaging; shear stress; simulation; skills; success; supervised learning; two-dimensional

Project Summary/Abstract Tetralogy of Fallot (ToF) is the most common cyanotic congenital heart disease, affecting 0.3% of children. Before correction, its four defects lead to increased right heart pressures and mixing of oxygenated and deoxygenated blood. Even after surgical repair, patients may experience elevated right heart pressures and volumes due to residual pulmonary stenosis, pulmonary regurgitation, scar formation, and conduction abnormalities. These changes in geometry, wall thickness, and pressure-volume relationships all contribute to right ventricular (RV) remodeling, which can eventually lead to adverse events such as ventricular arrhythmias, RV dysfunction, and the need for pulmonic valve repair, affecting up to 44% of patients overall. Despite the great advances that have been made in medical and surgical care of ToF patients, there is still limited understanding of which patients will experience adverse RV remodeling and subsequent clinical events. ToF patients’ cardiac function is normally assessed annually using cardiovascular magnetic resonance (CMR) imaging, which provides excellent views of the right heart and its valves. However, manual analysis of these images is time-consuming and subject to inter- and intra-user variability. Additionally, CMR provides anatomic and flow data enabling quantification of pulmonary artery hemodynamics, but has not yet been investigated in post-repair ToF patients. Detailed characterization of pulmonary artery stresses and pressures, through the application of computational fluid dynamics (CFD) simulations, could provide insight into factors affecting RV remodeling. There remains an unmet need to comprehensively identify features that characterize and predict progression from primary ToF repair to adverse RV remodeling and poor outcomes. My objectives in this proposal are to identify the structural and hemodynamic parameters of ToF that are associated with RV remodeling in order to improve both clinical care and quality of life. I plan to approach these objectives using two specific aims. In Aim 1, I will develop a supervised machine learning algorithm to accurately and automatically segment 3D cardiac volumes using CMR images. This algorithm will enable robust and repeatable measurements of cardiac structure and function for both cross-sectional and longitudinal analyses. I hypothesize that this algorithm will achieve accurate and precise segmentation results as assessed by Dice scores and intraclass correlation coefficients. In Aim 2, I will study patient-specific pulmonary artery hemodynamics and determine associations with adverse RV remodeling. Specifically, I will generate 3D and 1D CFD models based upon CMR-derived geometries and phase-contrast flow data. I hypothesize that hemodynamic parameters such as wall shear stress and total pathway resistance will be associated with and provide mechanistic insight into RV remodeling. Overall, I anticipate that this project will provide me with the experience and skills to help achieve my goal of becoming a physician-scientist with expertise in cardiovascular physiology, medical imaging, and fluid dynamics, while leading to validated technologies that will support clinicians and researchers in their understanding of ToF.

项目总结/摘要 法洛四联症（ToF）是最常见的紫绀型先天性心脏病，影响0.3%的儿童。 在纠正之前，其四个缺陷导致右心压力增加，氧合和 脱氧血即使在手术修复后，患者也可能会出现右心压力升高， 残余肺动脉狭窄、肺动脉返流、瘢痕形成和传导导致的体积 异常这些几何形状、壁厚和压力-体积关系的变化都有助于 右心室（RV）重构，其最终可导致不良事件，例如室性心律失常， 右心室功能障碍和肺动脉瓣修复的需要，影响了高达44%的患者。尽管大 尽管ToF患者的医疗和外科护理取得了进展，但对ToF患者的了解仍然有限。 其中患者将经历不利的RV重塑和随后的临床事件。ToF患者心脏 通常每年使用心血管磁共振成像（CMR）评估功能， 右心及其瓣膜的清晰视图。然而，手动分析这些图像是耗时的 并且受到用户间和用户内可变性的影响。此外，CMR提供解剖和血流数据， 肺动脉血流动力学的定量，但尚未在修复后ToF患者中进行研究。 肺动脉应力和压力的详细表征，通过应用计算 流体动力学（CFD）模拟可以深入了解影响RV重塑的因素。仍存在 未满足的需求，全面识别表征和预测主要ToF进展的特征 修复不良RV重塑和不良结局。我在这份提案中的目标是确定结构性的 和与RV重构相关的ToF的血流动力学参数，以改善临床 护理和生活质量。我计划通过两个具体目标来实现这些目标。在目标1中，我将开发一个 使用CMR精确自动分割3D心脏体积的监督机器学习算法 图像.该算法将实现心脏结构和功能的稳健和可重复测量， 横向和纵向分析。我假设这个算法能达到精确的 通过Dice评分和组内相关系数评估分割结果。在目标2中，我将学习 患者特异性肺动脉血流动力学，并确定与不良RV重塑的相关性。 具体来说，我将根据CMR导出的几何形状和相位对比生成3D和1D CFD模型 流量数据我假设血流动力学参数，如壁切应力和总通道阻力， 将与RV重塑相关并提供对RV重塑的机制性见解。总的来说，我预计这个项目 将为我提供经验和技能，以帮助实现我成为一名医生科学家的目标， 心血管生理学，医学成像和流体动力学方面的专业知识，同时导致经过验证的 这些技术将支持临床医生和研究人员对ToF的理解。