Mathematical Model-Based Optimization of CRT Response in Ischemia

项目摘要/摘要 多尺度计算机建模在帮助指导和阐明心脏病治疗方面的应用正在兴起。 然而，计算模型还没有被用于优化心脏再同步治疗 (CRT)。虽然CRT已经成为治疗心力衰竭(HF)的一种恢复正常激活模式的强大疗法 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)。提高应答者的素质 因此，Rate仍然是CRT的一个关键的临床挑战和圣杯。我们相信计算能力 建模可以帮助优化CRT，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以下几个 新的起搏疗法(例如，传导系统起搏(CSP)，包括希氏束起搏和左支起搏 束(LBB)起搏)，这是最近为提高应答率而开发的。具体来说， 计算机模拟可以帮助阐明影响长期和短期效果的关键因素 这些起搏疗法适用于有不同室内传导延迟和/或左心室瘢痕/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，以从根本上了解CRT的短期和长期影响， 优化CRT，阐明CSP相对于标准CRT的优缺点。以下是 具体目标是为实现这一目标而构建的。首先，我们将开发一种经过实验验证的 模拟慢性效应的多尺度心脏电力学-灌流(EMP)计算框架 CRT和CSP治疗LBBB+缺血的机械不同步性第二，我们将整合 采用高效机器学习和优化算法优化CRT的计算建模框架 缺血时采用左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CSP在脑缺血中的作用及影响因素。建议数 方法和方法都是创新的。更重要的是，成功完成将直接将 对临床优化CRT治疗以降低无应答率和患者的研究结果 不同起搏疗法的识别。这将对改善治疗和 降低出血热疫情的成本。

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.