Collaborative Research: Personalized Modeling, Monitoring and Control for Advancing Ventricular Assist Device Therapy in End-stage Heart Failure

合作研究：个性化建模、监测和控制，以推进心室辅助装置治疗终末期心力衰竭

基本信息

批准号：
1727487
负责人：
Yuncheng Du
金额：
$ 24.06万
依托单位：
Clarkson University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1727487&HistoricalAwards=false
关键词：
Collaborative Research Personalized Modeling Monitoring

项目摘要

Annually, about 5.7 million adults in U.S. have heart failure, and the associated cost of health care services to treat heart failure is approximately $30.7 billion. An estimated 150,000 new patients are diagnosed with end-stage heart failure annually. Left Ventricular Assist Device (known as "pacemaker") implantation, as the destination therapy, becomes an important treatment option for end-stage heart failure. However, the implantation has unacceptably high mortality rate. For instance, the 1-year mortality rate is as high as 69%. The risk of implantation varies among patients, and the outcome highly depends on preoperative treatment design and postoperative care. Current therapies are guideline-based and greatly rely on the stage of the disease inferred from patients' symptoms. Individual factors associated to disease etiology and prognosis are often neglected. This project develops a personalized preoperative-assessment and postoperative-control system for: (1) efficient risk evaluation of individual patient; (2) personalized modeling and estimation of a patient's heart function; (3) robust and adaptive control of implanted Left Ventricular Assist Devices. The outcomes from this work can lead to technologies that can revolutionize the end-stage heart failure therapy and benefit the overall population of heart failure patients, which will ultimately advance the health and life quality of the whole society. Broader impact on education includes new curriculum modules, science outreach activities, and active recruitment and involvement of underrepresented groups.This project will bring statistical inference, personalized cardiac modeling, and adaptive control theory into a unified framework for efficient modeling and analysis of heart condition, as well as a practical infrastructure for effective monitoring and control of LVAD. It will leverage modeling, monitoring, control, and optimization methodologies in personalized diagnosis and therapeutic design of LVAD implantation. In particular, this project will: (1) integrate the probabilistic risk analysis with elastic net regularization to predict implantation risk and survival time; (2) develop a spectral approximation-based surrogate model to efficiently quantify parametric uncertainties and accurately estimate model parameters for personalized cardiac modeling; (3) adaptively tune the LVAD controller through a quadratic optimization procedure to maintain the cardiac output and pressure perfusion within acceptable physiological ranges concerning different physiological activities. The accomplishment of this project will give rise to a new paradigm of personalized risk stratification, treatment planning, and postoperative care for end-stage heart failure patients, as opposed to traditional guideline-based solutions. The methodologies are transformative to various fields that involve risk assessment, image segmentation, computational modeling, and adaptive control. These applications include neural systems, advanced manufacturing and civil infrastructure.

每年，美国约有570万成年人患有心力衰竭，治疗心力衰竭的医疗保健服务的相关成本约为307亿美元。估计每年有15万名新患者被诊断出患有终阶段的心力衰竭。左心室辅助装置（称为“起搏器”）植入作为目的地治疗，成为终末期心力衰竭的重要治疗选择。但是，植入的死亡率高得不可接受。例如，1年的死亡率高达69％。植入风险在患者之间有所不同，结果很大程度上取决于术前治疗设计和术后护理。当前的疗法是基于指南的，很大程度上依赖于患者症状推断的疾病阶段。与疾病病因和预后相关的个体因素经常被忽略。该项目开发了一个个性化的术前评估和术后控制系统，用于：（1）对个体患者的有效风险评估；（2）对患者心脏功能的个性化建模和估计；（3）植入左心室辅助装置的稳健和自适应控制。这项工作的结果可能会导致可以彻底改变末期心力衰竭疗法的技术，并使心力衰竭患者的整体人群受益，这最终将提高整个社会的健康和生活质量。对教育的更广泛影响包括新的课程模块，科学宣传活动以及积极招募和人数不足的群体的参与。该项目将把统计推理，个性化心脏建模和自适应控制理论带入有效的框架，以有效地建模和分析心脏病，以对心脏病结构进行分析，以进行实际的基础结构，以有效监控LVAD。它将利用个性化诊断和LVAD植入治疗设计中的建模，监测，控制和优化方法。特别是，该项目将：（1）将概率的风险分析与弹性净正规化整合在一起，以预测植入风险和生存时间；（2）开发基于光谱近似的替代模型，以有效量化参数不确定性并准确估计个性化心脏建模的模型参数；（3）通过二次优化程序自适应调整LVAD控制器，以维持有关不同生理活动的可接受生理范围内的心脏输出和压力灌注。该项目的完成将产生新的范式的风险分层，治疗计划和术后心力衰竭患者的术后护理，而不是传统的基于指南的解决方案。这些方法对涉及风险评估，图像分割，计算建模和自适应控制的各个领域进行了变革。这些应用包括神经系统，高级制造和民用基础设施。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Fault detection and diagnosis using empirical mode decomposition based principal component analysis

DOI：
10.1016/j.compchemeng.2018.03.022
发表时间：
2018-07
期刊：
Comput. Chem. Eng.
影响因子：
0
作者：
Yuncheng Du;D. Du
通讯作者：
Yuncheng Du;D. Du

Model Identification and Physical Exercise Control Using Nonlinear Heart Rate Model and Particle Filter

DOI：
10.1109/coase.2019.8843217
发表时间：
2019-01-01
期刊：
2019 IEEE 15TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE)
影响因子：
0
作者：
Du, Dongping;Hu, Zhiyong;Du, Yuncheng
通讯作者：
Du, Yuncheng

A Two-Stage Model Identification Method for Simulation of Electrical Wave Propagation in Heart Tissue

模拟心脏组织中电波传播的两阶段模型辨识方法