Multiscale Models for Predicting Short and Long-term Outcome of Cardiac Resynchronization Therapy

用于预测心脏再同步治疗的短期和长期结果的多尺度模型

• 批准号: 10469500
• 负责人: Kenneth C Bilchick
• 金额: $ 64.67万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-13 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10469500
• 关键词: Acute; American; Anatomy; Angiotensin Receptor; Angiotensin-Converting Enzyme Inhibitors; Blood Pressure; Blood Tests; Body Surface; Brain natriuretic peptide; Bundle-Branch Block; Canis familiaris; Cardiac; Cardiac Electrophysiologic Techniques; Cardiac Myocytes; Cells; Cicatrix; Clinical; Clinical Data; Complex; Computer Models; Custom; Data; Devices; Disease; Electrocardiogram; Electrophysiology (science); Elements; Goals; Growth; Heart; Heart Hypertrophy; Heart failure; Hormonal; Human; Infarction; Lead; Left; Left ventricular structure; Life; Location; Magnetic Resonance Imaging; Maps; Measurement; Mechanics; Methods; Modeling; Myocardial Contraction; Myocardial Infarction; Myocardial dysfunction; Myocardium; Operative Surgical Procedures; Outcome; Patients; Pharmaceutical Preparations; Physiology; Procedures; Protocols documentation; Publishing; Regimen; Research Personnel; Signal Pathway; Signal Transduction; Symptoms; Testing; Time; Ventricular; Ventricular Remodeling; base; blood pump; cardiac resynchronization therapy; design; effective therapy; experience; heart circulation; hormonal signals; imaging study; implantation; improved; improved outcome; individual patient; individualized medicine; innovation; mechanical signal; mortality; multi-scale modeling; network models; predictive modeling; response; routine care; simulation; standard of care; success; therapy outcome; treatment planning; virtual

Project Summary Heart failure is associated with an annual mortality rate of 300,000 Americans, while over a million experience a myocardial infarction. Many patients with both heart failure and myocardial infarction also develop ventricular dyssynchrony, which exacerbates cardiac dysfunction and worsens symptoms. Cardiac resynchronization therapy (CRT) has emerged as an effective therapy for patients who suffer from heart failure and dyssynchrony, such as left bundle branch block (LBBB). When CRT is successful, it improves survival by stopping and even reversing the progression of heart failure. CRT immediately restores electrical and mechanical synchrony of the healthy myocardium, and over time it reverses dilation of the left ventricle (LV). However, 35-50% of patients fail to respond to CRT. A major strength of CRT is the ability to tailor the therapy to individual patients with patient- specific lead locations, timing, and/or pacing protocol, which promises to improve outcome. However, it also presents a dilemma: there are far too many possible strategies to test during the implantation surgery. Given the complex interactions and patient-to-patient differences in anatomy, electrophysiology, infarct location, myocardial remodeling, and drug regimens, individualized computational models have the potential to improve CRT outcome by enabling virtual treatment planning and guidance. While computational models of the acute impact of CRT on electrical or mechanical synchrony exist, none are capable of predicting patient-specific outcomes and long-term post-CRT cardiac remodeling, and most are too computationally expensive for routine clinical use. Thus, the specific objective of this proposal is to develop a fast multiscale modeling approach for patient-specific prediction of CRT outcome in ischemic and non-ischemic LBBB patients that can be integrated into the existing, standard of care routine. This objective will be accomplished in three specific aims. In Aim 1, we will develop and validate a rapid electrophysiology model to identify patient-specific CRT pacing protocols that lead to improved LV synchrony based on pre-procedure measurements. In Aim 2, we will develop and validate a rapid strain-driven growth model to predict patient-specific long-term (6 months) outcomes of CRT in ischemic and non-ischemic LBBB patients. In Aim 3, we will test the hypothesis that incorporating patient-specific drug data through a multiscale model of cardiomyocyte hypertrophic signaling improves CRT remodeling predictions. All model predictions will be validated against pre- intra- and post-CRT clinical data we collected from 100 patients treated in our center, including comprehensive MRI studies, ECGs, blood pressure, and blood tests. Together, the proposed studies will enable researchers and clinicians to understand why CRT fails in many patients, taking into account patient-specific electromechanics, scar, long-term remodeling, and drug regimen, as well as explore patient-specific CRT strategies in order to improve the current 50-65% response rate.

项目摘要 心力衰竭与30万美国人的年死亡率有关，而超过一百万的经验 心肌梗塞。许多心力衰竭和心肌梗塞的患者也会出现心室 异议障碍，会加剧心脏功能障碍并恶化症状。心脏重新同步 治疗（CRT）已成为患有心力衰竭和不良障碍的患者的有效疗法， 例如左束分支块（LBBB）。当CRT成功时，它可以通过停止甚至可以提高生存 扭转心力衰竭的进展。 CRT立即恢复电气和机械同步 健康的心肌，随着时间的流逝，它会逆转左心室（LV）的扩张。但是，35-50％的患者失败 回应CRT。 CRT的主要优势是能够为患有患者的个体患者调整治疗 - 特定的潜在客户位置，时机和/或起搏方案有望改善结果。但是，也是如此 呈现一个困境：在植入手术期间，有太多可能的测试策略。 鉴于解剖学，电生理学，梗死的复杂相互作用和患者对患者的差异 位置，心肌重塑和药物方案，个性化计算模型有可能 通过实现虚拟治疗计划和指导来改善CRT结果。而计算模型 CRT对电气或机械同步的急性影响，没有一个能够预测患者特异性的 结果和长期CRT心脏重塑，大多数在计算上对于常规而言太昂贵了 临床用途。因此，该提案的具体目标是为 可以整合的缺血性和非缺血性LBBB患者中CRT结局的患者特异性预测 进入现有的护理标准常规。这个目标将以三个具体目标来实现。在AIM 1中， 我们将开发和验证快速的电生理模型，以识别患者特异性的CRT起搏方案 这导致基于预处理测量值改善LV同步。在AIM 2中，我们将发展和 验证快速应变驱动的生长模型，以预测患者特异性的长期（6个月）CRT的结果 缺血性和非缺血性LBBB患者。在AIM 3中，我们将检验以下假设，即合并患者特异性 通过心肌细胞肥厚信号的多尺度模型的药物数据改善CRT重塑 预测。 所有模型预测将均可根据我们从我们从中收集的临床数据进行验证 在我们中心接受治疗的100名患者，包括全面的MRI研究，心电图，血压和血液检查。 拟议的研究一起将使研究人员和临床医生能够理解为什么CRT在许多人中失败 患者考虑到患者特异性机电，疤痕，长期重塑和药物方案， 以及探索特定于患者的CRT策略，以提高当前的50-65％的缓解率。