Mathematical Model-Based Optimization of CRT Response in Ischemia

项目总结/摘要 应用多尺度计算机建模来帮助指导和阐明心脏病的治疗正在出现。 然而，计算建模尚未用于优化心脏起搏治疗 （CRT）。CRT已成为治疗心力衰竭（HF）的有效方法，可恢复正常的激活模式， 在心脏方面，大约30%的患者在治疗后仍然没有改善（无应答者）。应答器的改进 因此，心率仍然是一个关键的临床挑战和CRT的圣杯。我们相信计算 建模可以帮助优化CRT并提高应答率。同样重要的是， 考虑心脏关键物理学的多尺度计算框架可以帮助理解几个 新颖的起搏治疗（例如，传导系统起搏（CSP），包括HIS束起搏和左分支 束（LBB）起搏），最近已经开发，以提高响应率。具体地说， 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， 这些起搏治疗用于不同心室内传导延迟和/或LV瘢痕/缺血的患者。在这里， 这里的总体目标是开发结合联合收割机机器学习算法和 基于物理的建模，从根本上了解CRT（包括CSP）的短期和长期影响， 优化CRT，并阐明CSP相对于标准CRT的优点和缺点。以下 为实现这一目标制定了具体目标。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-灌注（EMP）计算框架 CRT和CSP在治疗LBBB +缺血的机械不同步中的作用。第二，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，以优化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，以阐明CSP在缺血反应中的作用和影响因素。拟议 方法和手段是创新的。更重要的是，成功的完成将直接转化为 临床研究结果用于优化CRT治疗，以降低无应答率以及患者 识别不同的起搏治疗。这将对改善治疗产生重大影响， 降低了HF流行的成本。

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.