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: 10317933
• 负责人: Kenneth C Bilchick
• 金额: $ 67.74万
• 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/10317933
• 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; 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.

项目概要 心力衰竭每年导致 30 万美国人死亡，而超过 100 万人患有心力衰竭 心肌梗塞。许多同时患有心力衰竭和心肌梗塞的患者也会出现心室衰竭 不同步，加剧心脏功能障碍并使症状恶化。心脏再同步 治疗（CRT）已成为治疗心力衰竭和不同步患者的有效疗法， 例如左束支传导阻滞（LBBB）。当 CRT 成功时，它可以通过停止甚至停止来提高生存率 逆转心力衰竭的进展。 CRT 立即恢复电气和机械同步 健康的心肌，随着时间的推移，它会逆转左心室 (LV) 的扩张。然而，35-50%的患者失败 响应 CRT。 CRT 的一个主要优势是能够针对患有以下疾病的个体患者量身定制治疗方案： 具体的引导位置、时间和/或起搏协议，有望改善结果。然而，它也 出现了一个困境：在植入手术期间有太多可能的策略需要测试。 鉴于解剖学、电生理学、梗塞方面复杂的相互作用和患者之间的差异 位置、心肌重塑和药物治疗方案、个性化计算模型有可能 通过启用虚拟治疗计划和指导来改善 CRT 结果。虽然计算模型 CRT 对电气或机械同步的严重影响存在，但没有一个能够预测患者特异性 结果和长期 CRT 后心脏重塑，并且大多数对于常规来说计算成本太高 临床使用。因此，该提案的具体目标是开发一种快速的多尺度建模方法 可整合缺血性和非缺血性 LBBB 患者的 CRT 结果的患者特异性预测 纳入现有的标准护理程序。这一目标将通过三个具体目标来实现。在目标 1 中， 我们将开发并验证快速电生理学模型，以确定患者特定的 CRT 起搏方案 根据术前测量结果改善左心室同步性。在目标 2 中，我们将开发并 验证快速应变驱动生长模型，以预测 CRT 的患者特异性长期（6 个月）结果 缺血性和非缺血性 LBBB 患者。在目标 3 中，我们将检验以下假设：结合患者特异性 通过心肌细胞肥大信号传导的多尺度模型获得的药物数据可改善 CRT 重塑 预测。 所有模型预测都将根据我们收集的 CRT 前和 CRT 后临床数据进行验证 我们中心治疗了 100 名患者，包括全面的 MRI 检查、心电图、血压和血液检查。 总之，拟议的研究将使研究人员和临床医生能够了解 CRT 在许多方面失败的原因 患者，考虑到患者特定的机电、疤痕、长期重塑和药物治疗方案， 以及探索针对患者的CRT策略，以提高目前50-65%的缓解率。