Right Ventricular Remodeling in Tetralogy of Fallot

法洛四联症的右心室重构

• 批准号: 10708738
• 负责人: Elizabeth Walker Thompson
• 金额: $ 5.52万
• 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/10708738
• 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; Tetralogy of Fallot; Thick; Time; Training; Variant; Ventricular; Ventricular Arrhythmia; Ventricular Function; Work; automated segmentation; care burden; 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; technology validation; 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.

项目摘要/摘要 法洛（Fallot）技术（TOF）是最常见的氰基先天性心脏病，影响0.3％的儿童。 在校正之前，其四个缺陷导致右心压增加并混合氧化和混合 脱氧血液。即使经过手术修复，患者也可能会遇到右心压的升高和 由于残留的肺部狭窄，肺反流，疤痕形成和传导而引起的体积 异常。这些几何形状，壁厚和压力量关系的变化都有助于 右心（RV）重塑，有时会导致不良事件，例如心室心律不齐， RV功能障碍以及对肺门瓣膜修复的需求，总体上影响了44％的患者。尽管很棒 在TOF患者的医疗和手术护理中已取得的进步，了解仍然有限 其中患者将经历不良的RV重塑和随后的临床事件。 TOF患者的心脏 通常使用心血管磁共振（CMR）成像每年评估功能 右心及其阀门的美好景色。但是，这些图像的手动分析是耗时的 并受到用户间和用户内变异性的约束。此外，CMR提供解剖和流数据启用 肺动脉血流动力学的定量，但尚未在后修复后TOF患者中研究。 通过应用计算，肺动脉应力和压力的详细表征 流体动力学（CFD）模拟可以提供有关影响RV重塑的因素。仍然有一个 未满足的需要全面识别表征和预测主要TOF进展的特征 维修不良的RV重塑和不良预后。我在此提案中的目标是确定结构性 与RV重塑相关的TOF的血液动力学参数，以改善这两个临床 照顾和生活质量。我计划使用两个具体目标来实现这些目标。在AIM 1中，我将发展一个 有监督的机器学习算法可准确，自动使用CMR细分3D心脏体积 图像。该算法将实现对心脏结构和功能的鲁棒和可重复的测量 横截面和纵向分析。我假设该算法将达到准确，精确 分割结果通过骰子得分评估并影响相关系数。在AIM 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重塑的关联。 具体而言，我将基于CMR衍生的几何形状和相位对比生成3D和1D CFD模型 流数据。我假设血液动力学参数，例如壁剪应力和总途径阻力 将与RV重塑相关联并提供机械洞察力。总的来说，我预计这个项目 将为我提供经验和技能，以帮助我实现成为与身体科学家的目标 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 将支持临床医生和研究人员对TOF的理解的技术。