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 e...