Mathematical Model-Based Optimization of CRT Response in Ischemia

基于数学模型的缺血 CRT 反应优化

基本信息

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
项目类别：
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

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

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项目摘要

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.

项目摘要/摘要多尺度计算机建模的应用以帮助指导和阐明心脏病治疗。但是，尚未利用计算建模来优化心脏重新同步治疗（CRT）。虽然CRT已成为对心力衰竭（HF）的强大治疗方法，以恢复正常激活模式在心脏中，大约30％的患者在治疗后仍无法改善（无反应者）。响应者的改进因此，速率仍然是至关重要的临床挑战和CRT的圣杯。我们相信计算建模可以帮助优化CRT并提高响应率。同样重要的是，发展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颖的起搏疗法（例如，传导系统起搏（CSP），包括他的束起搏和左分支最近开发的捆绑包（LBB）起搏）以提高响应率。具体来说，计算建模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在具有不同脑室内传导延迟和/或LV疤痕/缺血的患者中。这里，这里的总体目标是开发结合机器学习算法和的计算方法基于物理学的建模，从根本上了解包括CSP在内的CRT的短期和长期影响，优化CRT，并阐明CSP比标准CRT的优势和缺点。下列构建了具体目标以实现这一目标。首先，我们将开发实验验证的多尺度心脏电力力学 - 灌注（EMP）计算框架，以模拟慢性效应 CRT和CSP在LBBB +缺血中治疗机械性异质的方面。第二，我们将整合具有高效的机器学习和优化算法以优化CRT的计算建模框架缺血中的LV心外膜和心内膜节奏。第三，我们将使用经过验证的多尺度计算 EMP框架以阐明影响CSP在缺血中的反应的影响和因素。提议方法和方法是创新的。更重要的是，成功完成将直接转化诊所的发现，以优化CRT治疗以降低无响应率和患者不同起搏疗法的识别。这将对改善治疗和降低HF流行的成本。