Data-driven optimization of therapy for heart failure

摘要 这个合作项目整合了工程，人工智能，计算机建模， 生理学和临床心脏病学，以探索心力衰竭患者的新治疗策略。的 moonshot goal是一个模拟框架，可以预测患者的心脏在治疗过程中将如何生长和重塑。 潜在的治疗干预。一旦该框架通过了患者数据的验证， 来比较不同治疗的预期结果。然后，临床医生可以使用这些预测来指导 他们的治疗选择 该项目旨在推进计算心脏病学，并使该领域更接近随机临床试验 测试接受模型优化疗法治疗的患者是否比接受模型优化疗法治疗的患者有更好的结果。 接受标准的临床治疗 多学科研究团队由3名科学家组成（Ken坎贝尔博士; Jonathan Wenk博士; Lik博士）。 Chuan Lee，PhD）和2名心脏病专家（Emma Birks，MD/PhD; Gaurang Vaidya，MD）。他们的技能组合 范围从分子生物物理学，通过计算机建模和工程，到临床护理和心室 辅助设备。 该计划有三个目标： 1)开发PyMyoVent作为实施压力反射控制和心肌生长的试验平台。 2)使用MyoFE创建并验证患者特定的双心室有限元模型， 生长和功能重塑。 3)部署个性化的MyoFE模型来预测心力衰竭患者的最佳治疗方案。 该计划是高度创新的奖励，并明智地利用作为正常护理的一部分收集的临床数据 来自肯塔基州大学注册研究的100名患者。这些数据将包括 压力信号从患者无线传输，这些患者的周围插入了MEMS装置， 肺动脉基本的贡献包括创建有限元模型， 压力感受器反射并响应于生理信号而生长和适应，所述生理信号包括肌丝应激和细胞应激。 能源使用。

ABSTRACT This collaborative project integrates concepts from engineering, artificial intelligence, computer modeling, physiology, and clinical cardiology to explore new therapeutic strategies for patients who have heart failure. The moonshot goal is a simulation framework that can predict how a patient's heart will grow and remodel during a potential therapeutic intervention. Once the framework has been validated with patient data, it could be deployed to compare the outcomes predicted for different treatments. A clinician could then use the predictions to guide their choice of therapy. This project seeks to advance computational cardiology and move the field closer to a randomized clinical trial that tests whether patients treated with model-optimized therapies have better outcomes than patients who received standard clinical care. The multidisciplinary research team consists of 3 scientists (Ken Campbell, PhD; Jonathan Wenk, PhD; Lik- Chuan Lee, PhD) and 2 cardiologists (Emma Birks, MD/PhD; Gaurang Vaidya, MD). Together, their skillsets range from molecular biophysics, through computer modeling and engineering, to clinical care and Ventricular Assist Devices. The plan has 3 Aims: 1) Develop PyMyoVent as a testbed for implementing baroreflex control and myocardial growth. 2) Use MyoFE to create and validate patient-specific biventricular finite element models that incorporate growth and functional remodeling. 3) Deploy personalized MyoFE models to predict optimal therapies for patients who have heart failure. The plan is highly innovative reward and makes intelligent use of clinical data collected as part of normal care from 100 patients who are enrolled in a research registry at the University of Kentucky. These data will include pressure signals transmitted wirelessly from patients who have had a CardioMEMS device inserted around their pulmonary artery. Fundamental contributions include the creation of finite element models that are controlled by a baroreflex and grow and adapt in response to physiological signals including myofilament stress and cellular energy use.