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对不同治疗或治疗的反应 长时间的刺激和适应仍然是精准医学的一个巨大挑战。 缺乏实时周转严重限制了我们搜索广泛治疗空间以进行治疗的能力。 根据长期、个性化的预测确定最佳干预计划。而且，它 阻止基于流动态数据实时监测患者的血流动力学 例如从可穿戴设备获得的信息。通过从只能捕获几个的模拟 心跳到几个月甚至几年的建模，我们将患者特定数字的使用转变为 twins 提供对患者血流动力学状态的按需跟踪。这样的数据将 改善心血管疾病筛查、改善监测，最后告知 通过预测目前无法实现的长期血流影响来制定治疗计划。这 该提案的主要目标是开发和应用一种基于物理的耦合方法 机器学习模拟与可穿戴传感器相结合，提供实时、 对数月至数年的 3D、复杂血流动力学模式进行个性化预测。更好的 了解患者的循环系统和潜在血流动力学如何反应 随着时间的推移，在不同的生理状态下，与治疗多种疾病具有广泛的相关性。 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