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

• 批准号: 1728338
• 负责人: Dongping Du
• 金额: $ 27.6万
• 依托单位: Texas Tech 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=1728338&HistoricalAwards=false
• 关键词: Collaborative; Research; Personalized; Modeling; Monitoring

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.

美国每年约有570万成年人患有心力衰竭，治疗心力衰竭的相关医疗保健服务费用约为307亿美元。据估计，每年有15万名新患者被诊断为终末期心力衰竭。左心室辅助装置（又称“起搏器”）植入术作为终末期心力衰竭的最终治疗手段，已成为重要的治疗选择。然而，植入具有不可接受的高死亡率。例如，1年死亡率高达69%。植入的风险因患者而异，结果高度依赖于术前治疗设计和术后护理。目前的治疗是基于指南的，并且极大地依赖于从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往往被忽视。本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）个体患者的有效风险评估;（2）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鲁棒和自适应控制。这项工作的成果可以导致能够彻底改变终末期心力衰竭治疗的技术，并使心力衰竭患者的整体人群受益，这将最终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课程模块、科学外展活动以及积极招募和参与代表性不足的群体。该项目将把统计推断、个性化心脏建模和自适应控制理论纳入统一框架，以实现心脏状况的高效建模和分析，以及有效监测和控制LVAD的实用基础设施。它将在LVAD植入的个性化诊断和治疗设计中利用建模、监测、控制和优化方法。具体而言，该项目将：（1）将概率风险分析与弹性网络正则化相结合，以预测植入风险和生存时间;（2）开发基于谱近似的代理模型，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）通过二次优化过程自适应地调整LVAD控制器，以将心输出量和压力灌注维持在关于不同生理活动的可接受生理范围内。该项目的完成将为终末期心力衰竭患者的个性化风险分层、治疗计划和术后护理带来新的范例，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具有变革性。这些应用包括神经系统、先进制造和民用基础设施。

项目成果

Risk Prediction Model for Survival of Wait-List Patients on Axial CF-LVAD: A UNOS Database Analysis

轴流 CF-LVAD 等候名单患者生存的风险预测模型：UNOS 数据库分析

• DOI: 10.1016/j.transproceed.2022.04.029
• 发表时间: 2022
• 期刊: Transplantation Proceedings
• 影响因子: 0.9
• 作者: Nair, Nandini;Du, Dongping;Hu, Zhiyong;Gongora, Enrique
• 通讯作者: Gongora, Enrique

Deep spatio-temporal sparse decomposition for trend prediction and anomaly detection in cardiac electrical conduction

• DOI: 10.1080/24725579.2021.1982081
• 发表时间: 2021-09
• 期刊: IISE Transactions on Healthcare Systems Engineering
• 作者: Xinyu Zhao;Hao Yan;Zhiyong Hu;D. Du

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.
• 作者: Yuncheng Du;D. Du

Propagation of Parametric Uncertainty in Aliev-Panfilov Model of Cardiac Excitation

心脏兴奋 Aliev-Panfilov 模型中参数不确定性的传播

• 发表时间: 2018
• 期刊: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC
• 作者: Son, J.

Modelling and control of a failing heart managed by a left ventricular assist device

由左心室辅助装置管理的衰竭心脏的建模和控制

• DOI: 10.1016/j.bbe.2020.01.014
• 发表时间: 2020
• 期刊: Biocybernetics and Biomedical Engineering
• 影响因子: 6.4
• 作者: Son, Jeongeun;Du, Dongping;Du, Yuncheng
• 通讯作者: Du, Yuncheng