A Novel computational approach to optimize Fontan and improve surgical predictability

一种优化 Fontan 并提高手术可预测性的新型计算方法

基本信息

批准号：
10346020
负责人：
Vijay Govindarajan
金额：
$ 62.75万
依托单位：
BOSTON CHILDREN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2027-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10346020
关键词：
Adult Affect Algorithms Anatomy Aneurysm Arteriovenous malformation Assessment tool Biochemistry Blood Blood Circulation Blood Platelets Blood coagulation Blood flow Caliber Cardiac Cardiac Surgery procedures Caring Childhood Clinical Coagulation Process Collection Complex Consumption Coupled Custom Data Deposition Distal Engineering Environment Equilibrium Evaluation Failure Family suidae Fibrin Flare Fontan Procedure Gases Geometry Heart Hematologist Hepatic Image In Vitro Intervention Lead Liquid substance Liver Lung Magnetic Resonance Imaging Manuals Methodology Methods Modeling Monitor Morbidity - disease rate Operative Surgical Procedures Organ Outcome Palliative Care Pathway interactions Patients Physiology Play Polytetrafluoroethylene Postoperative Period Proteins Protocols documentation Research Risk Role Route Shapes Single ventricle congenital heart disease Surgeon Techniques Thrombin Thrombosis Thrombus Time Validation Variant Venous Ventricular Work aortic arch base biochemical model blood pump clinical application cohort comparative computerized tools congenital heart disorder design hemodynamics improved in silico in vitro Model insight kidney dysfunction mortality multidisciplinary novel palliative pressure prevent prospective reconstruction repaired success surgery outcome thrombotic tool virtual virtual surgery

项目摘要

An efficient hemodynamics with minimal thrombosis risk post-surgery is essential for short- and long-term success of a cardiac surgery. Achieving this is a challenge in cardiac surgery that involves the design of a complex flow pathway. Aortic arch reconstruction, aneurysm repair, Fontan surgeries are a few examples. The Fontan surgical procedure is the most effective palliative treatment for patients with single ventricle defects (SVD). SVD refers to a collection of congenital heart diseases where one of the lower ventricular chambers of the heart remains underdeveloped. Fontan procedure involves re-routing of deoxygenated blood from upper and lower body to flow directly to lungs allowing the single functioning ventricle to pump blood for systemic circulation. Though lifesaving, the Fontan physiology creates a non-natural pathway for venous return of the blood to the lungs thus producing a non-physiological blood flow. A successful Fontan procedure should involve 1) well-balanced overall and hepatic venous flow return to lungs to prevent pulmonary arteriovenous malformations (PAVMs) that can lead to poor gas exchange, 2) minimal energy loss, and 3) minimal thrombosis (blood clot) risk. Complications such as PAVMs and thrombosis post-surgery can result in a Fontan failure. To improve Fontan surgical planning, its efficacy and predictability, we propose to develop an automated image-based computational fluid dynamics (CFD) workflow capable of optimizing and predicting all the above determinants for a successful Fontan physiology. CFD models have been developed in the past to assess energy loss and hepatic venous flow distribution, but an automated computational tool for rapidly optimizing the patients' Fontan physiology in terms of factors affecting success does not exist. To fill this gap, we will integrate our existing patient-specific Fontan surgical planning protocol to predict energy loss and hepatic venous flow distribution with 1) a shape optimization algorithm and 2) our validated model of blood coagulation to provide a computational tool to virtually improve the planned Fontan physiology for optimal hepatic and overall venous return to lungs, minimal energy loss and thrombotic potential and quantitatively predict thrombosis risk. We will completely automate our workflow with custom scripts to minimize errors and user intervention. Our biochemical model of blood coagulation has all the components representing platelet and fibrin deposition and is 2-way coupled with blood flow. The continuum-based approach of this model allows it to be used in large geometries. After rigorous validation of our surgical optimization workflow using MRI-based patient specific in-vitro models, we will perform virtual surgeries using our tool and retrospective patient data to establish clinical applicability. Our tool could potentially be 1) included in the current surgical planning workflow to perform virtual surgeries using patient pre-op data to improve and predict surgical outcomes, and 2) used to evaluate risk of clotting post- Fontan so that patients can be selectively monitored. Our long-term objective is to provide a prospective surgical planning tool for Fontan and then extend it other surgeries where such optimization can improve surgical efficacy.

术后血栓形成风险最小化的有效血流动力学对于短期和长期治疗至关重要心脏手术的成功。实现这一目标是心脏手术中的一个挑战，其中涉及设计复杂的流动路径。主动脉弓重建、动脉瘤修复、Fontan 手术就是几个例子。 Fontan 手术是单心室患者最有效的姑息治疗缺陷（SVD）。 SVD 是指一组先天性心脏病，其中一个下心室心室仍不发达。 Fontan 手术涉及脱氧血液的重新输送从上半身和下半身直接流向肺部，使单一功能的心室泵血体循环。虽然可以挽救生命，但 Fontan 生理学为静脉回流创造了一条非自然途径血液流向肺部，从而产生非生理性血流。成功的 Fontan 手术应该 1）整体平衡良好，肝静脉血流回流肺部，防止肺动静脉可导致气体交换不良的畸形 (PAVM)，2) 最小的能量损失，以及 3) 最小的血栓形成（血栓）风险。 PAVM 和术后血栓形成等并发症可能会导致 Fontan 失败。为了改善 Fontan 手术计划、其有效性和可预测性，我们建议开发一种自动化的基于图像的计算流体动力学 (CFD) 工作流程能够优化和预测上述所有内容成功的 Fontan 生理学的决定因素。过去开发了 CFD 模型来评估能源损失和肝静脉流量分布，而是一种用于快速优化患者的自动化计算工具丰坦生理学而言，影响成功的因素是不存在的。为了填补这一空白，我们将整合我们的现有的患者特异性 Fontan 手术计划方案可预测能量损失和肝静脉流量分布具有 1) 形状优化算法和 2) 我们经过验证的凝血模型，以提供计算工具实际上可以改善计划的 Fontan 生理学，以实现最佳的肝脏和整体静脉返回肺部、最小的能量损失和血栓形成潜力，并定量预测血栓形成风险。我们将使用自定义脚本完全自动化我们的工作流程，以最大限度地减少错误和用户干预。我们的生化凝血模型具有代表血小板和纤维蛋白沉积的所有成分，并且是双向的加上血流量。该模型基于连续体的方法使其可以用于大型几何形状。使用基于 MRI 的患者特定体外模型对我们的手术优化工作流程进行严格验证后，我们将使用我们的工具和回顾性患者数据进行虚拟手术，以确定临床适用性。我们的工具可能 1) 包含在当前的手术计划工作流程中以执行虚拟手术使用患者术前数据来改善和预测手术结果，2) 用于评估术后凝血风险 Fontan 使患者能够得到选择性监测。我们的长期目标是提供前瞻性的手术 Fontan 的规划工具，然后将其扩展到其他手术，这种优化可以提高手术效果。