Data-driven optimization of therapy for heart failure

数据驱动的心力衰竭治疗优化

• 批准号: 10615143
• 负责人: Kenneth S Campbell
• 金额: $ 56.6万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10615143
• 关键词: Algorithm Design; Algorithms; Area; Artificial Intelligence; Atrial Fibrillation; Baroreflex; Biophysics; Cardiac; Cardiology; Cardiovascular system; Caring; Circulation; Clinical; Clinical Data; Computer Models; Computer software; Data; Development; Devices; Dimensions; Doctor of Philosophy; EFRAC; Echocardiography; Elements; Engineering; Enrollment; Foundations; Future; Geometry; Goals; Growth; Heart; Heart failure; Intelligence; Interdisciplinary Study; Intervention; Kentucky; Left ventricular structure; Machine Learning; Magnetic Resonance Imaging; Manuscripts; Microfilaments; Modeling; Molecular; Myocardial; Myosin ATPase; Paper; Patient-Focused Outcomes; Patients; Physiological; Physiology; Positioning Attribute; Pulmonary artery structure; Recommendation; Reflex action; Registries; Research; Research Personnel; Rewards; Running; Science; Scientist; Signal Transduction; Stress; Structure; Swarm intelligence; System; Techniques; Testing; Therapeutic; Therapeutic Intervention; Thick; Time; Universities; Ventricular; algorithm development; algorithm training; blood pressure control; blood pump; clinical care; computer program; constriction; gradient boosting; improved; innovation; laptop; novel therapeutic intervention; optimal treatments; outcome prediction; pressure; random forest; randomized, clinical trials; research vision; response; simulation; skills; standard care; success; therapeutic evaluation; treatment optimization; ventricular assist device; wireless transmission

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.

抽象的 这个协作项目整合了工程，人工智能，计算机建模， 生理学和临床心脏病学探索心力衰竭患者的新治疗策略。这 Moonshot目标是一个模拟框架，可以预测患者的心脏在 潜在的治疗干预。一旦框架已通过患者数据验证，就可以部署它 比较不同治疗方法预测的结果。然后，临床医生可以使用预测来指导 他们选择治疗。 该项目旨在提高计算心脏病学，并将现场移至更近的临床试验 测试与模型优化疗法治疗的患者是否比患者的结局更好 接受了标准的临床护理。 多学科研究团队由3位科学家（Ken Campbell博士； Jonathan Wenk，PhD； Lik- Chuan Lee，PhD）和2位心脏病专家（MD/PhD Emma Birks； Gaurang Vaidya，MD）。在一起，他们的技能 范围从分子生物物理学，到计算机建模和工程，到临床护理和心室 协助设备。 该计划有3个目标： 1）开发伴随的伴侣作为实施压力反射控制和心肌生长的测试床。 2）使用Myofe创建和验证合并的患者特异性双脑室有限元模型 生长和功能重塑。 3）部署个性化的Myofe模型，以预测患有心力衰竭的患者的最佳疗法。 该计划是高度创新的奖励，并巧妙地使用了作为正常护理的一部分收集的临床数据 来自肯塔基大学研究注册中心的100名患者。这些数据将包括 压力信号是从插入心脏组设备的患者的无线传输的 肺动脉。基本贡献包括创建由有限元模型控制的 压力反射并根据生理信号（包括肌丝应力和细胞）生长和适应 能源使用。