Mathematical Model-Based Optimization of CRT Response in Ischemia

基于数学模型的缺血 CRT 反应优化

• 批准号: 10734486
• 负责人: GHASSAN S KASSAB
• 金额: $ 81.2万
• 依托单位: CALIFORNIA MEDICAL INNOVATIONS INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10734486
• 关键词: 3-Dimensional; Acute; Address; Affect; Algorithms; Animal Model; Animals; Blood Vessels; Bundle-Branch Block; Cardiac; Cardiovascular system; Chronic; Cicatrix; Clinic; Clinical; Computer Models; Coronary; Coronary sinus structure; Coupling; Development; Disadvantaged; Disease; Effectiveness; Electrophysiology (science); Epidemic; Geometry; Goals; Growth; Health Care Costs; Heart; Heart Diseases; Heart failure; Histology; Infarction; Intraventricular; Ischemia; Knowledge; Left; Location; Long-Term Effects; Machine Learning; Maps; Measures; Mechanics; Methodology; Modeling; Morphology; Outcome; Patients; Pattern; Perfusion; Physics; Physiological; Positioning Attribute; Property; Purkinje Cells; Recommendation; Reperfusion Therapy; Role; Severities; Spatial Distribution; Structure of purkinje fibers; Subendocardial Layer; Surface; System; Time; Translating; Ventricular; Work; cardiac resynchronization therapy; clinically relevant; computational platform; computer framework; coronary perfusion; cost; hemodynamics; improved; innovation; machine learning algorithm; mathematical model; mortality; novel; novel strategies; premature; preservation; response; success; treatment optimization; treatment response

PROJECT SUMMARY/ABSTRACT Application of multiscale computer modeling to help guide and elucidate heart disease treatments is emerging. Computational modeling, however, has not been exploited for optimizing cardiac resynchronization therapy (CRT). While CRT has emerged as a powerful treatment for heart failure (HF) to restore normal activation pattern in the heart, about 30% of patients still do not improve after therapy (non-responders). Improvement of responder rate therefore remains a crucial clinical challenge and the holy grail of CRT. We believe that computational modeling can help optimize CRT and improve the responder rate. Equally important, the development of a multiscale computational framework that considers the key physics of the heart can help understand several novel pacing therapies (e.g., conduction system pacing (CSP) including HIS bundle pacing and left branch bundle (LBB) pacing) that have been developed recently to improve the responder rate. Specifically, computational modeling can help elucidate the key factors affecting the long and short-term effectiveness of these pacing therapies in patients with different intraventricular conduction delay and/or LV scar/ischemia. Here, the overall goal here is to develop computational approaches that combine machine learning algorithms and physics-based modeling to fundamentally understand the short and long-term effects of CRT that includes CSP, optimize CRT, and to elucidate the advantages and disadvantages of CSP over standard CRT. The following specific aims are constructed to accomplish this goal. First, we will develop an experimentally-validated multiscale cardiac electro-mechanics-perfusion (EMP) computational framework to simulate the chronic effects of CRT and CSP in treating mechanical dyssynchrony in LBBB + ischemia. Second, we will integrate the computational modeling framework with efficient machine learning and optimization algorithms to optimize CRT with LV epicardial and endocardial pacing in ischemia. Third, we will use the validated multiscale computational EMP framework to elucidate the effects and factors affecting the response of CSP in ischemia. The proposed approach and methodologies are innovative. More importantly, successful completion will directly translate the findings to the clinic for optimization of CRT therapy to reduce non-responder rates as well as patient identification for different pacing therapies. This would have substantial impact on improving the treatment and reducing the cost of HF epidemic.

项目概要/摘要 多尺度计算机模型的应用正在兴起，以帮助指导和阐明心脏病的治疗。 然而，计算模型尚未被用于优化心脏再同步治疗 （显像管）。虽然 CRT 已成为心力衰竭 (HF) 恢复正常激活模式的有效治疗方法 在心脏方面，大约 30% 的患者在治疗后仍然没有改善（无反应者）。响应器的改进 因此，速率仍然是一个关键的临床挑战和 CRT 的圣杯。我们相信计算 建模有助于优化 CRT 并提高响应率。同样重要的是，开发一个 考虑心脏关键物理的多尺度计算框架可以帮助理解几个 新型起搏疗法（例如传导系统起搏 (CSP)，包括 HIS 束起搏和左支 最近开发的捆绑（LBB）起搏）可以提高应答率。具体来说， 计算模型可以帮助阐明影响长期和短期有效性的关键因素 这些起搏疗法适用于患有不同心室内传导延迟和/或左心室疤痕/缺血的患者。这里， 这里的总体目标是开发结合机器学习算法和 基于物理的建模，从根本上了解 CRT 的短期和长期影响，包括 CSP、 优化 CRT，并阐明 CSP 相对于标准 CRT 的优缺点。下列 制定具体目标是为了实现这一目标。首先，我们将开发一个经过实验验证的 用于模拟慢性影响的多尺度心脏机电灌注 (EMP) 计算框架 CRT 和 CSP 治疗 LBBB + 缺血机械不同步的效果。其次，我们将整合 具有高效机器学习和优化算法的计算建模框架，可优化 CRT 缺血时左室心外膜和心内膜起搏。第三，我们将使用经过验证的多尺度计算 EMP 框架阐明了缺血中 CSP 反应的影响和因素。拟议的 方法和方法具有创新性。更重要的是，成功完成将直接转化为 临床研究结果用于优化 CRT 治疗，以降低无反应率以及患者 识别不同的起搏治疗。这将对改善治疗和治疗产生重大影响 降低心衰流行的成本。