Uncertainty aware virtual treatment planning for peripheral pulmonary artery stenosis

外周肺动脉狭窄的不确定性虚拟治疗计划

• 批准号: 10734008
• 负责人: Jeffrey A. Feinstein
• 金额: $ 74.23万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10734008
• 关键词: Affect; Aftercare; Age; Alagille Syndrome; Angioplasty; Arteries; Awareness; Balloon Angioplasty; Basic Science; Bayesian Method; Bayesian Modeling; Biological; Biomechanics; Biomedical Engineering; Blood Vessels; Blood flow; Cardiac Catheterization Procedures; Cardiovascular system; Catheters; Cessation of life; Characteristics; Childhood; Clinical; Clinical Data; Complex; Complication; Computer Models; Coupling; Data; Data Collection; Disease; Disease Progression; Distal; Engineering; Ensure; Formulation; Foundations; Goals; Growth; Histologic; Histology; Hour; Human; Hypertension; Infant Mortality; Institution; Intervention; Lesion; Liquid substance; Literature; Lobar; Lung; Machine Learning; Mechanics; Membrane; Methods; Modeling; Morbidity - disease rate; Morphology; Operative Surgical Procedures; Outcome; Pathologic; Patient-Focused Outcomes; Patients; Pediatric cardiology; Peripheral; Physics; Physiologic intraventricular pressure; Physiological; Population; Postoperative Period; Prediction of Response to Therapy; Procedures; Property; Pulmonary Valve Stenosis; Pulmonary arterial remodeling; Pulmonary artery structure; Reconstructive Surgical Procedures; Severities; Solid; Statistical Models; Stenosis; Stents; Structure; Testing; Tetanus Helper Peptide; Tetralogy of Fallot; Time; Tissue Model; Tissues; Training; Treatment outcome; Trees; Uncertainty; Unnecessary Procedures; Ventricular; Work; artery stenosis; clinical decision support; clinical decision-making; clinically relevant; congenital heart disorder; digital twin; experimental study; hemodynamics; homograft; human data; human disease; improved; improved outcome; individual patient; intervention effect; mechanical properties; mortality; multi-scale modeling; novel; optimal treatments; patient population; pediatric patients; personalized medicine; post intervention; predictive modeling; pulmonary arterial pressure; reconstruction; right ventricular failure; simulation; soft tissue; standard of care; success; support tools; theories; treatment comparison; treatment optimization; treatment planning; treatment strategy; vascular abnormality; virtual model; virtual therapy

Congenital heart disease (CHD) affects 1/100 babies and is the leading cause of infant mortality in the U.S. Pulmonary artery (PA) stenosis is common in CHD patients and is particularly challenging to treat when occurring in the periphery of the PA tree. Peripheral pulmonary stenosis (PPS), often consisting of numerous vessel narrowings at proximal and distal bifurcation levels, can lead to persistent RV hypertension, RV failure, and even death. Most institutions treat PPS patients with stenting and angioplasty limited to the proximal (central and lobar) PAs only. These catheter-based interventions, however, are often ineffective at reducing right ventricular pressures and are associated with poor and unpredictable outcomes. Comprehensive surgical reconstruction, involving patch augmentation of ALL stenoses (central, lobar, segmental PAs), can achieve long-term RV pressure reduction with low morbidity and mortality, but requires >10-hour procedures and specialized expertise available only at select institutions. Because treatment strategies continue to be debated nationally, and outcomes remain poor, there is a pressing unmet need for novel clinical decision support tools. We aim to develop two complementary modeling methods to support clinical decision making in CHD patients with PPS: 1) a mechanistic multiscale model of pulmonary fluid solid growth melding fluid structure interaction (FSI) and vascular growth and remodeling (G&R), and 2) a real-time uncertainty-aware digital twin model for virtual treatment planning to aid clinicians in identifying optimal treatment strategies. To accomplish these goals, we propose three specific aims: (1) Characterize mechanical and immunohistochemical properties of PA tissue in human PPS patients via biaxial testing and histology; (2) Develop and validate a computational modeling framework (melding hemodynamics and G&R) capable of predicting post-treatment hemodynamics in PPS; and (3) Develop and validate a fast, interactive Bayesian modeling framework for virtual treatment planning under uncertainty to aid near real-time clinical decision making for PPS, leveraging reduced order models. Our proposed study will tightly integrate modeling and experiments to improve physiological fidelity and clinical relevance of patient-specific models in an understudied patient population. The biaxial mechanical characterization of pediatric human PA tissue will provide much needed data on tissue properties in CHD which are currently absent from the literature. This project assembles an interdisciplinary team of engineers with expertise in hemodynamics modeling, cardiovascular biomechanics, mechanical characterization of biological tissues, and uncertainty quantification, and clinicians with expertise in pediatric cardiology/pulmonary vascular abnormalities, cardiothoracic surgery, and cardiac catheterization. Our translational objectives are to: (1) systematically compare treatment options for PPS and thus challenge the current standard of care and, (2) ultimately optimize treatments for individual patients, thus reducing unnecessary procedures and potential harm and improving long-term outcomes in this complex pediatric population.

先天性心脏病 (CHD) 影响 1/100 的婴儿，是美国婴儿死亡的主要原因。 肺动脉 (PA) 狭窄在 CHD 患者中很常见，发生时治疗起来特别困难 在PA树的外围。周围性肺动脉狭窄 (PPS)，通常由大量血管组成 近端和远端分叉水平变窄，可导致持续性右心室高血压、右心室衰竭，甚至 死亡。大多数机构通过仅限于近端（中央和肺叶）的支架置入术和血管成形术来治疗 PPS 患者 仅 PA。然而，这些基于导管的干预措施通常不能有效地减少右心室收缩压。 压力，并与不良和不可预测的结果相关。综合手术重建， 涉及所有狭窄（中央、肺叶、节段 PA）的补片增强，可以实现长期 RV 减压具有低发病率和死亡率，但需要超过 10 小时的手术和专业知识 仅在选定的机构提供。因为治疗策略在全国范围内仍在争论，并且 结果仍然不佳，对新型临床决策支持工具的迫切需求尚未得到满足。我们的目标是 开发两种互补的建模方法来支持冠心病患者的临床决策 PPS：1）肺液固体生长融合流体结构的机械多尺度模型 相互作用 (FSI) 和血管生长和重塑 (G&R)，以及 2) 实时不确定性感知数字 用于虚拟治疗计划的双胞胎模型，可帮助临床医生确定最佳治疗策略。到 为了实现这些目标，我们提出了三个具体目标：（1）表征机械和免疫组织化学 通过双轴测试和组织学研究人类 PPS 患者的 PA 组织特性； (2) 开发并验证 能够预测治疗后的计算建模框架（融合血流动力学和 G&R） PPS 中的血流动力学； (3) 开发并验证一个快速、交互式的虚拟贝叶斯建模框架 在不确定的情况下制定治疗计划，以帮助近实时的 PPS 临床决策，利用减少的 订购型号。我们提出的研究将紧密结合建模和实验以提高生理保真度 以及未充分研究的患者群体中患者特异性模型的临床相关性。双轴机械 儿科人类 PA 组织的表征将为先天性心脏病 (CHD) 的组织特性提供急需的数据。 目前文献中尚不存在。该项目组建了一支跨学科的工程师团队 血流动力学建模、心血管生物力学、生物力学表征方面的专业知识 组织和不确定性量化，以及具有儿科心脏病学/肺血管专业知识的临床医生 异常、心胸外科手术和心导管插入术。我们的翻译目标是：(1) 系统地比较 PPS 的治疗方案，从而挑战当前的护理标准，(2) 最终优化个体患者的治疗，从而减少不必要的手术和潜在的伤害 并改善这个复杂的儿科人群的长期结果。