Multiscale Models for Predicting Short and Long-term Outcome of Cardiac Resynchronization Therapy

项目摘要 心力衰竭与每年30万美国人的死亡率有关，而超过100万人患有心力衰竭 心肌梗死。许多同时患有心力衰竭和心肌梗死的患者也会发展为室性心衰。 不同步，这会加剧心脏功能障碍，加重症状。心脏再同步化 治疗(CRT)已经成为治疗心力衰竭和不同步患者的一种有效方法， 如左束支传导阻滞(LBBB)。当CRT成功时，它通过停止和均匀 逆转心力衰竭的进程。CRT立即恢复电子和机械的同步 健康的心肌，随着时间的推移，它逆转左心室(LV)的扩张。然而，35%-50%的患者失败了 对CRT做出反应。CRT的一个主要优点是能够为患有以下疾病的患者量身定做治疗- 具体的导联位置、时间和/或起搏方案，有望改善结果。然而，它也 出现了一个两难境地：在植入手术期间，有太多可能的策略需要测试。 考虑到复杂的相互作用和患者之间在解剖、电生理、脑梗塞等方面的差异 定位、心肌重塑和药物方案，个性化的计算模型有可能 通过启用虚拟治疗计划和指导来改善CRT结果。虽然计算模型中的 CRT对电或机械同步性的严重影响存在，没有一种能够预测患者特有的 结果和CRT后的长期心脏重构，而且大多数对于常规的计算来说都太昂贵了 临床应用。因此，本提案的具体目标是开发一种快速的多尺度建模方法，用于 可整合的缺血型和非缺血型LBBB患者CRT结局的患者特异性预测 变成现有的、标准的护理程序。这一目标将通过三个具体目标来实现。在目标1中， 我们将开发和验证一个快速电生理模型来识别患者特定的CRT起搏方案 这导致了基于术前测量的改善的LV同步性。在目标2中，我们将发展和 验证快速应变驱动生长模型预测CRT患者的长期(6个月)结局 缺血性和非缺血性LBBB患者。在目标3中，我们将测试包含特定于患者的假设 通过心肌细胞肥大信号多尺度模型的药物数据改善CRT重构 预测。 所有模型预测都将与我们从以下网站收集的CRT前、后临床数据进行验证 在我们的中心治疗了100名患者，包括全面的MRI检查、心电图、血压和血液测试。 总之，拟议的研究将使研究人员和临床医生能够理解为什么CRT在许多患者中失败 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%的应答率。

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.