Uncertainty aware virtual treatment planning for peripheral pulmonary artery stenosis

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，并且在发生时治疗特别具有挑战性 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的分叉可导致持续性RV高血压、RV衰竭，甚至 死亡大多数机构治疗PPS患者的支架植入术和血管成形术仅限于近端（中央和肺叶） 仅限私人助理。然而，这些基于导管的干预通常在减少右心室收缩方面无效。 压力，并与穷人和不可预测的结果。全面的外科重建， 涉及ALL狭窄（中央、肺叶、节段性PA）的补片增强，可实现长期RV 减压，发病率和死亡率低，但需要>10小时的手术和专业知识 仅在特定机构提供。因为治疗策略仍在全国范围内争论， 结果仍然很差，对新的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，以支持CHD患者的临床决策， PPS：1）肺流体固体生长融合流体结构的多尺度力学模型 交互（FSI）和血管生长和重塑（G&R），以及2）实时不确定性感知数字 虚拟治疗计划的孪生模型，以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）表征机械和免疫组化 通过双轴测试和组织学研究人类PPS患者PA组织的特性;（2）开发和验证 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;以及（3）开发并验证用于虚拟的快速，交互式贝叶斯建模框架 不确定性下的治疗计划，以帮助PPS的近实时临床决策， 订单模型我们提出的研究将紧密结合建模和实验，以提高生理逼真度 以及在未充分研究的患者人群中患者特异性模型的临床相关性。双轴机械 儿科人PA组织的表征将提供关于CHD中组织性质的急需数据， 目前在文献中还没有。该项目汇集了一个跨学科的工程师团队， 在血液动力学建模、心血管生物力学、生物力学特性、 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的治疗选择，从而挑战当前的护理标准，（2） 最终优化个体患者的治疗，从而减少不必要的程序和潜在的伤害 并改善这一复杂儿科人群的长期结局。

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.