Dynamic models of the cardiovascular system capturing years, rather than heartbeats

心血管系统的动态模型捕捉的是岁月，而不是心跳

• 批准号: 10708040
• 负责人: Amanda E Randles
• 金额: $ 112.7万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708040
• 关键词: 3-Dimensional; Adopted; Awareness; Cardiovascular Diseases; Cardiovascular Models; Cardiovascular system; Clinic; Complex; Coupling; Data; Intervention; Machine Learning; Methodology; Methods; Modeling; Monitor; Patients; Pattern; Physics; Physiological; Stimulus; Stream; Techniques; Time; Vascular Diseases; Vascular System; base; digital twin; hemodynamics; improved; learning strategy; novel; personalized predictions; precision medicine; prevent; public health relevance; real time monitoring; screening; simulation; treatment planning; wearable device; wearable sensor technology

Predicting how a particular patient's vascular system with respond to different treatment or stimuli and adapt over long periods of time remains a grand challenge in precision medicine. The lack of real-time turn around critically limits our ability to search a wide treatment space to identify optimal intervention plans based on long-term, personalized predictions. Moreover, it prevents real-time monitoring of a patient's hemodynamics based on streaming, dynamic data such as that acquired from wearables. By moving from simulations that can capture only several heartbeats to modeling months or even years, we shift the utilization of patient-specific digital twins to provide on-demand tracking of a patient's hemodynamic state. Such data would improve screening for cardiovascular disease, improved monitoring, and finally, inform treatment planning by enabling prediction of longterm flow effects currently not attainable. The major objective of this proposal is to develop and apply a methodology coupling physics-based simulations with machine learning that, combined with wearable sensors, provides real-time, personalized predictions of 3D, complex hemodynamic patterns over months to years. A better understanding of how a patient's circulatory system and underlying hemodynamics responds under different physiological states over time is of broad relevance to treating a wide range of vascular diseases.

预测特定患者的血管系统如何应对不同的治疗或 长期刺激和适应仍然是精确医学的巨大挑战。 缺乏实时转折，严重限制了我们搜索广泛治疗空间的能力 根据长期的个性化预测确定最佳干预计划。而且，它 根据流动性数据，防止对患者血液动力学的实时监测 例如从可穿戴设备中获得的。通过从仅捕获几个的模拟移动 心跳到建模数月甚至数年的建模，我们改变了患者特定数字的利用 双胞胎可提供对患者血液动力学状态的按需跟踪。这样的数据会 改善心血管疾病的筛查，改善监测，最后告知 通过实现长期流动效应的预测，治疗计划目前无法实现。这 该建议的主要目的是开发和应用基于物理的方法论 与机器学习的模拟，结合可穿戴传感器，可提供实时 在数月到几年的时间里，对3D，复杂的血液动力学模式的个性化预测。更好 了解患者的循环系统和基础血流动力学的反应 在不同的生理状态下，随着时间的流逝，与治疗广泛的范围广泛相关 血管疾病。

项目成果

Optimizing Temporal Waveform Analysis: A Novel Pipeline for Efficient Characterization of Left Coronary Artery Velocity Profiles.

优化时间波形分析：有效表征左冠状动脉速度剖面的新颖流程。

• 发表时间: 2024
• 期刊: ArXiv
• 作者: Geddes,JustenR;Randles,Amanda
• 通讯作者: Randles,Amanda