Data-driven optimization of therapy for heart failure

数据驱动的心力衰竭治疗优化

• 批准号: 10467277
• 负责人: Kenneth S Campbell
• 金额: $ 57.93万
• 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/10467277
• 关键词: Algorithm Design; Algorithms; Area; Artificial Intelligence; Atrial Fibrillation; Baroreflex; Biophysics; Blood Circulation; Cardiac; Cardiology; Cardiovascular system; Caring; Clinical; Clinical Data; Computer Models; Computer software; Data; Data Analyses; Development; Devices; Dimensions; Doctor of Philosophy; EFRAC; 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; Randomized Clinical Trials; 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; algorithm development; algorithm training; blood pressure control; blood pump; clinical care; computer program; gradient boosting; improved; innovation; laptop; novel therapeutic intervention; optimal treatments; outcome prediction; pressure; random forest; research vision; response; simulation; standard care; success; therapeutic evaluation; treatment optimization; ventricular assist device; wireless

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.

摘要