Image-based Multi-scale Modeling Framework of the Cardiopulmonary System: Longitudinal Calibration and Assessment of Therapies in Pediatric Pulmonary Hypertension

基于图像的心肺系统多尺度建模框架：小儿肺动脉高压治疗的纵向校准和评估

• 批准号: 9450536
• 负责人: Seungik Baek
• 金额: $ 59.57万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-15 至 2021-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9450536
• 关键词: Anatomy; Animal Model; Animals; Assimilations; Bayesian Analysis; Biological Markers; Biomechanics; Blood Circulation; Blood Vessels; Calibration; Cardiac; Cardiac Catheterization Procedures; Cardiopulmonary; Cardiovascular system; Cessation of life; Child; Childhood; Chronic; Clinical; Clinical Data; Clinical Research; Clinical assessments; Complex; Computer Simulation; Coronary artery; Coupled; Coupling; Data; Diagnosis; Disease; Disease Progression; Early Diagnosis; Engineering; Etiology; Evolution; Failure; Goals; Gold; Growth; Heart; Heart failure; Human; Hypoxia; Image; Individual; Intervention; Intravenous; Kinetics; Lead; Lung; Magnetic Resonance Imaging; Measurement; Mechanics; Medication Management; Methods; Microcirculation; Modeling; Modernization; Monitor; Operative Surgical Procedures; Oral; Organ; Outcome; Patients; Peripheral; Pharmacology; Physical Examination; Physics; Prevalence; Pulmonary Circulation; Pulmonary Hypertension; Pulmonary Vascular Resistance; Pulmonary artery structure; Right ventricular structure; Statistical Methods; Statistical Models; Study models; System; Systemic hypertension; Techniques; Time; Transplantation; Uncertainty; Vasodilator Agents; Ventricular; Ventricular Septal Defects; Work; base; blood pressure regulation; cardiopulmonary system; cohort; computer framework; diagnosis standard; dosage; follow-up; heart function; hemodynamics; hypertension control; insight; mortality; multi-scale modeling; outcome forecast; patient population; pressure; primary pulmonary hypertension; prospective; simulation; treatment strategy

ABSTRACT Pulmonary hypertension (PH) is a complex disorder associated with elevated pulmonary arterial pressure. Unlike systemic hypertension, PH is difficult to detect in routine physical examinations and the current gold standard for diagnosing PH is through invasive right heart catheterization. Unlike in systemic hypertension, for which patients have effective pharmacological management of blood pressure for decades, PH prognosis remains poor with 15% mortality within 1 year on modern therapy. Challenges in early detection of PH, as well as structural differences in the cardio-pulmonary system (e.g., thinner ventricular wall, more distributed compliance and larger number of peripheral vessels) may explain the stark differences in clinical outcomes between systemic hypertension and PH. Our current understanding of PH has largely been obtained through animal models, clinical studies and computational modeling. However, surgical banding or chronic hypoxia animal models do not fully reproduce the etiology of human PH. Invasive clinical measurements of pulmonary vascular resistance (PVR), stiffness and ventricular elastance provide limited insight into disease progression. Computational models have been developed to study growth and remodeling (G&R) in the ventricles and hemodynamics in PH. However, these models are incomplete: ventricular G&R models lack coupling with evolving pulmonary hemodynamics, whereas pulmonary hemodynamic models have not included ventricular-arterial coupling. Given that the interactions between RV and the pulmonary vasculature are a key determinant of the clinical course of PH, specifically, the transition from compensated to decompensated remodeling, we submit that there is a pressing need to develop a multi-scale (MS) computational model that can couple the short term (e.g. hemodynamics) and long-term G&R interactions between the RV and the pulmonary circulation. In this project, we propose to develop a multi-scale, multi-physics computational model of the cardio-pulmonary circulation and calibrate it using longitudinal data acquired on cohorts of pediatric pulmonary hypertension and control (e.g., cardiac transplant) subjects. The model will be the first of its kind because it will be able to describe the bi-directional interactions between evolving ventricular and vascular biomechanics and hemodynamics using human pulmonary hypertension data.

抽象的 肺动脉高压（pH）是一种与肺动脉升高有关的复杂疾病。与众不同 全身性高血压，pH值很难在常规身体检查中检测到 诊断pH是通过侵入性右心导管插入术。与全身性高血压不同，患者 几十年来，对血压进行有效的药理治疗，pH预后仍然很差 现代疗法1年内死亡率为15％。早期发现pH和结构的挑战 心肺系统的差异（例如，较薄的心室壁，更分布的依从性和更大的合规性 外围容器的数量）可以解释全身性临床结果的明显差异 高血压和pH。 我们目前对pH的理解主要是通过动物模型，临床研究和 计算建模。但是，手术带或慢性低氧动物模型并未完全繁殖 人ph的病因。肺血管耐药性（PVR），刚度和 心室弹性提供了对疾病进展的有限见解。计算模型已经 开发用于研究pH的心室和血液动力学中的生长和重塑（G＆R）。但是，这些 模型不完整：心室G＆R模型缺乏与不断发展的肺部血流动力学的耦合，而 肺血液动力学模型不包括心室 - 动脉耦合。鉴于互动 RV和肺脉管系统之间是pH的临床过程的关键决定因素，特别是 从补偿到代偿重塑的过渡，我们认为有迫切需要开发 一个多尺度（MS）计算模型，可以将短期（例如血液动力学）和长期G＆R融入 RV与肺循环之间的相互作用。 在这个项目中，我们建议开发心肺肺的多尺度多物理计算模型 使用在小儿肺动脉高压和 对照（例如心脏移植）受试者。该模型将是第一个此类模型，因为它将能够描述 不断发展的心室和血管生物力学与血液动力学之间的双向相互作用 人肺动脉高压数据。