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

每年，美国约有570万成年人患有心力衰竭，治疗心力衰竭的相关医疗服务费用约为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”）植入术作为终点治疗，成为终末期心力衰竭的重要治疗选择。然而，移植的死亡率高得令人无法接受。例如，1年死亡率高达69%。植入的风险因患者而异，其结果高度取决于术前治疗设计和术后护理。目前的治疗方法以指南为基础，很大程度上依赖于从患者症状推断出的疾病阶段。与疾病病因和预后相关的个体因素往往被忽视。本项目开发了一种个性化的术前评估和术后控制系统，目的是：(1)对个体患者进行有效的风险评估；(2)对患者心功能进行个性化建模和评估；(3)植入左心室辅助装置的鲁棒和自适应控制。这项工作的结果可能会导致技术革新终末期心力衰竭治疗，使心力衰竭患者受益，最终将提高整个社会的健康和生活质量。对教育的广泛影响包括新的课程模块、科学推广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整合到一个统一的框架中，实现对心脏状况的高效建模和分析，并为有效监测和控制LVAD提供实用的基础设施。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，本项目将：(1)将概率风险分析与弹性网正则化相结合，预测植入风险和生存时间；(2)建立基于谱近似的替代模型，有效量化参数不确定性，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节LVAD控制器，使心输出量和压力灌注维持在不同生理活动可接受的生理范围内。该项目的完成将为终末期心力衰竭患者带来个性化风险分层、治疗计划和术后护理的新范式，而不是传统的基于指南的解决方案。这些方法适用于各种领域，包括风险评估、图像分割、计算建模和自适应控制。这些应用包括神经系统、先进制造业和民用基础设施。

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.