CorBand: A novel wearable wrist sensor for heart failure remote monitoring

ReThink Medical，Inc.建议开发生理监测技术的流动版本， 在第一阶段，CorBand得到验证。CorBand设计用于跟踪关键生理参数， 预测心脏代偿失调，检测代偿失调的早期阶段，并将两者传递给 生理数据并向相关护理人员发出警报，最终目标是通过以下方式减少住院治疗 从而能够在心力衰竭人群中进行主动/预防性护理。心力衰竭影响580万人 在美国，相关成本每年接近400亿美元。这项技术不仅能改善 提高心力衰竭患者的生活质量，同时也有助于减轻美国医疗保健的巨大负担 系统我们的长期目标是帮助推动从被动式按服务付费的医疗保健转型 从一个范例到一个强调通过健康监测技术进行预防性护理的范例。我们的具体 目的包括：（1）制造一种坚固的器械，用于两个大型、长期、流动的 问题研究将使用植入心脏植入式电子器械（CIED）的患者进行确认。心脏 将从CorBand导出心率（HR）、心率变异性（HRV）、活动水平和液体指数， 与从Medtronic和Boston Scientific制造的CIED收集的数据进行比较，并分析 相关性(2)CorBand将在大型非卧床HF患者人群中展开。我们将收集 患者的临床记录和记录住院事件沿着来自 CorBand。我们将使用这些数据进一步建立一个预测模型，心脏失代偿使用一些 机器学习的方法。在目标（3）中，我们将对HF患者人群进行对照研究。 所有受试者都将佩戴CorBand;我们将使用一半受试者的数据来预测失代偿 事件，将该信息提供给受试者的护理人员，以提供预防性护理。另一半将 接受常规护理。然后，我们将检查两组中HF相关住院的数量， CorBand减少住院的能力。

ReThink Medical, Inc. proposes to develop an ambulatory version of the physiologic monitoring technology it validated in Phase I, the CorBand. The CorBand is designed to track key physiologic parameters that are predictive of cardiac decompensation, to detect the early stages of decompensation, and to relay both the physiologic data and generated alerts to relevant caregivers with the end goal of reducing hospitalizations by enabling proactive/preventative care in the heart failure population. Heart failure effects 5.8 million individuals in the US and the costs associated are approaching $40 billion annually. This technology will not only improve the quality of life for heart failure patients, but also help to reduce this massive burden on the US healthcare system. Our long term objective is to help drive the transformation from the reactive pay-for-service healthcare paradigm to one that puts emphasis on preventative care through health monitoring technologies. Our specific aims include (1) manufacturing a robust version of the device for use in two large, long term, ambulatory studies. It will be validated using patients who have cardiac implantable electronic devices (CIEDs). Heart rate (HR), heart rate variability (HRV), activity levels, and fluid index derived from the CorBand will be compared with data collected from CIEDs manufactured by Medtronic and Boston Scientific and analyzed for correlation. (2) The CorBand will be deployed in a large ambulatory population of HF patients. We will collect the patient's clinical notes and note hospitalization events along with remote physiologic data from the CorBand. We will use this data to further build a predictive model for cardiac decompensation using a number of machine learning approaches. In aim (3), we will perform a controlled study on a population of HF patients. All the subjects will wear the CorBand; we will use the data from half the subjects to predict decompensation events, provide that information to subject's caregiver in order to provide preventative care. The other half will receive usual care. We will then examine the number of HF related hospitalizations in both groups to evaluate the ability of the CorBand to reduce hospitalizations.