Wearable Cardiomechanics Monitor to Decrease Heart Failure Readmissions

 心力衰竭（HF）是当今社会面临的最主要挑战之一，每年夺去数十万美国人的生命，每年花费超过300亿医疗保险美元。这项研究的最终目标是创建一个非侵入性和非侵入性的系统，用于在家中监测HF患者，自动评估他们经历加重的风险，并向护理人员和患者本身提供反馈。这将使患者能够在家中积极主动地管理HF，接受定制的治疗，可以不断适应以满足他们不断变化的需求。假设是通过（1）测量血液动力学、活动和心血管对家中压力源（例如运动）的反应的组合，以及（2）将这些异质性测量与现代数据分析相结合，可以在即将住院前超过7天的预测窗口内实现家中HF加重的预测。为了验证这一假设，提出了以下四个具体目标：（1）收集纵向血流动力学（2）调整现有算法以基于BCG、ECG和活动时间序列数据预测即将发生的HF恶化;（3）开发基于BCG的可穿戴硬件，用于在家中连续记录老年HF患者的血液动力学和活动;以及（4）开发创新的传感策略和算法，用于提高可穿戴BCG测量的鲁棒性。一个先前演示和验证的用于质量中心（COM）BCG测量的体重秤将在项目过程中扩大并部署在200名患者的家中。同时，硬件和分析工作将建立在我们之前的数据和现有原型的基础上。对于带回家BCG研究的前25名参与者，我们还将进行直接的生理研究，以量化基于尺度（COM）和可穿戴BCG测量的潜在机制，并开发用于在两个域（COM与胸部表面振动）之间转换的计算技术。这些技术，结合迭代和实验的努力，以提高可穿戴BCG测量的鲁棒性，然后将被应用于优化可穿戴系统的BCG和活动监测。然后，该系统将扩大规模，并部署在家中的最后50名参与者（200）在带回家的研究。虽然我们预计可穿戴设备将提供最佳解决方案，但通过使用现有基于规模的系统进行验证，可以降低项目风险。该项目的成功完成可能最终减少HF相关的住院治疗，从而提高美国老年人的生活质量，并降低整体医疗保健成本。

 DESCRIPTION (provided by applicant): Heart failure (HF) is one of the most major challenges faced by society today, claiming hundreds of thousands of American lives each year and costing more than 30 billion Medicare dollars annually. The ultimate goal of this research is to create a non-invasive and unobtrusive system for monitoring HF patients at home, automatically assessing their risk of experiencing an exacerbation, and providing feedback to caregivers and the patients themselves. This will enable proactive management of HF at home, with patients receiving tailored therapies that can adapt continuously to meet their changing needs. The hypothesis is that by (1) measuring a combination of hemodynamics, activity, and cardiovascular response to stressors (e.g. exercise) at home, and (2) combining these heterogeneous measures with modern data analytics, prediction of HF exacerbation at home can be achieved with a predictive window of greater than 7 days before impending hospitalization. To examine this hypothesis, the following four specific aims are proposed: (1) to collect longitudinal hemodynamic (ballistocardiogram, BCG) and activity data unobtrusively at home for the first time in a population of elderly HF patients; (2) to adapt existing algorithms fo predicting an impending HF exacerbation based on BCG, ECG, and activity time series data; (3) to develop wearable hardware based on BCG to be used at home for continuous hemodynamic and activity recording from elderly HF patients; and (4) to develop innovative sensing strategies and algorithms for improving the robustness of wearable BCG measurements. A previously demonstrated and verified weighing scale for center-of-mass (COM) BCG measurement will be scaled-up and deployed in the home of 200 total patients over the course of the project. Simultaneously, the hardware and analytics efforts will build on our prior data and existing prototypes. For the first 25 participants in the take-home BCG study, we will also conduct a direct physiologic study to quantify the underlying mechanisms contributing to both the scale-based (COM) and wearable BCG measurements, and to develop computational techniques for converting between the two domains (COM versus surface vibrations of the chest). These techniques, in combination with iterative and experimental efforts to improve the robustness of wearable BCG measurements, will then be applied to optimizing the wearable system for BCG and activity monitoring. This system will then be scaled-up and deployed in the home for the final 50 participants (of 200) in the take-home study. While we anticipate that the wearable will provide the best solution, the project risk is mitigated through the validation efforts with the existing scale-based system. Successful completion of this project could ultimately reduce HF related hospitalizations, and thus both improve quality of life for elderly Americans, and reduce overall healthcare costs.