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100万人经历了心力衰竭。 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生心室 不同步，这加剧了心脏功能障碍和心脏病症状。心脏再同步 CRT治疗（CRT）已经成为患有心力衰竭和不同步的患者的有效治疗， 例如左束支分支传导阻滞（LBBB）。当CRT成功时，它通过停止甚至 逆转心力衰竭的进程CRT立即恢复电和机械同步的 健康的心肌，随着时间的推移，它逆转了左心室（LV）的扩张。然而，35-50%的患者失败 对CRT作出反应。CRT的一个主要优势是能够根据患者的具体情况定制治疗， 具体的电极导线位置、时间和/或起搏方案，这有望改善结局。但也 提出了一个难题：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学、电生理学、梗死 位置，心肌重塑和药物治疗方案，个性化的计算模型有可能 通过实现虚拟治疗计划和指导来改善CRT结果。虽然计算模型的 CRT对电或机械同步性的急性影响存在，没有一个能够预测患者特异性 结果和长期CRT后心脏重塑，并且大多数对于常规治疗来说计算成本太高 临床应用。因此，本提案的具体目标是开发一种快速多尺度建模方法， 可整合的缺血性和非缺血性LBBB患者CRT结局的患者特异性预测 现有的常规护理标准。这一目标将通过三个具体目标来实现。在目标1中， 我们将开发并验证一种快速电生理学模型，以确定患者特定的CRT起搏方案 这导致基于术前测量的改善的LV同步。在目标2中，我们将开发和 验证快速应变驱动生长模型，以预测CRT的患者特异性长期（6个月）结局， 缺血性和非缺血性LBBB患者。在目标3中，我们将检验以下假设： 通过心肌细胞肥大信号传导的多尺度模型获得的药物数据可改善CRT重塑 预测。 所有模型预测将根据我们收集的CRT前、内和后临床数据进行验证， 100名患者在我们中心接受治疗，包括全面的MRI研究、ECG、血压和血液检查。 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在许多情况下失败， 患者，考虑到患者特定的机电、疤痕、长期重塑和药物方案， 以及探索患者特异性CRT策略，以提高目前50-65%的缓解率。

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.