Wearable Wireless Respiratory Monitoring System that Detects and Predicts Opioid Induced Respiratory Depression

项目总结。阿片类药物是美国医院最常用的处方药，阿片类药物引起的呼吸道 抑郁症(OIRD)是一个普遍存在的威胁生命的问题。OIRD在普通医院地板上的发作 是常见的，发生在高达46%的患者中。尽管这个问题很普遍，但税务局尚未 通过监测技术有效解决这一问题。临床上非常需要一种能够穿戴的传感器 通过可操作的警报检测OIRD的发病和进展，远在严重的 换气不足、低氧血症、心律失常和死亡。为了满足这种临床需求，RTM生命体征有限责任公司 开发一种可穿戴的、具有声学气管声的无线呼吸监测系统(RMS) 传感器(TSS)和软件算法，可以连续测量人的呼吸频率(RR)、潮汐 容量(TV)、每分钟通气量(MV)、呼吸模式、呼吸暂停持续时间、血氧饱和度、呼吸暂停程度 鼾声、心率(HR)、身体位置和活动量。RMS算法计算风险指数得分 (RIS)专门用于OIRD，每隔20秒。RTM已获得FDA的突破性设备指定 为预防阿片类药物过量和住院患者、门诊患者和 长期使用或滥用阿片类药物的美国公民。RTM计划首先将RMS商业化，用于监测 由于#年OIRD的高发病率，住院患者在普通护理楼层使用阿片类药物 这些环境和使用现有方法的不切实际，如结合脉搏血氧仪和 碳酸氢图为所有接受阿片类药物的患者提供充分的监测。已经演示过了 为了证明我们技术的可行性，我们正在努力优化RMS以处理噪音环境，并验证 临床研究中的系统。在第一阶段，我们将使用动态滤波和有源噪声来优化现有的均方根 取消软件(阶段1，目标1)，并使用优化的RMS进行人体研究，以测量 使用过滤/噪声消除软件，在嘈杂环境中准确测量RR、TV和呼吸暂停持续时间 打开，与关闭(阶段1，目标2)。在阶段2中，我们将构建并在工作台上测试功能RMS之前 到人体临床试验(第二阶段，目标1)，获得FDA研究设备豁免(IDE)，然后 对手术后使用芬太尼和其他阿片类药物的患者进行观察性临床试验 展示RMS检测和预测OIRD事件的发生和进展的能力(n=120名患者) (阶段2，目标2)。这些步骤加在一起将完成关键的开发里程碑和所需的临床测试 用于监管部门的批准和RMS设备的商业化。这个项目的成功完成将 生产商业上可行的可穿戴无线RMS，具有改善健康和安全的潜力 通过准确检测和预测OIRD的患者在接受阿片类药物治疗前的可操作警报 出现严重低通气量、低氧血症、心律失常和死亡。

PROJECT SUMMARY. Opioids are the most prescribed drug in US hospitals, and opioid induced respiratory depression (OIRD) is a widespread and life-threatening problem. Episodes of OIRD on the general hospital floor are common, occurring in up to 46% of patients. Despite the prevalence of this problem, OIRD has yet to be effectively addressed through monitoring technology. There is great clinical need for a wearable sensor capable of detecting the onset and progression of OIRD with actionable alerts, well before the onset of severe hypoventilation, hypoxemia, cardiac arrhythmia, and death. To meet this clinical need, RTM Vital Signs, LLC is developing a wearable, wireless Respiratory Monitoring System (RMS) with an acoustic Trachea Sound Sensor (TSS) and a software algorithm that continuously measures a person’s respiratory rate (RR), tidal volume (TV), minute ventilation (MV), breathing pattern, duration of apnea, oxygen saturation, degree of snoring, heart rate (HR), body position, and activity level. The RMS algorithm calculates a Risk Index Score (RIS) specifically for OIRD every 20 seconds. RTM has received Breakthrough Device Designation from the FDA to develop the RMS for the prevention of an opioid overdose and death in hospitalized patients, outpatients, and US citizens that chronically use or abuse opioids. RTM plans to commercialize the RMS first for monitoring hospitalized patients managed with opioids on the general nursing floors, due to the high incidence of OIRD in these environments and the impracticality of using existing methods such as pulse oximetry combined with capnography to provide sufficient monitoring for all patients receiving opioids. Having already demonstrated feasibility of our technology, we are working to optimize the RMS to handle noisy environments and validate the system in a clinical study. In Phase 1, we will optimize the existing RMS with dynamic filtering and active noise cancellation software (Phase 1, Aim 1) and conduct a human study using the optimized RMS to measure the accuracy of RR, TV, and apnea duration in a noisy environment with the filtering/noise cancellation software turned on, versus turned off (Phase 1, Aim 2). In Phase 2, we will construct and bench-test functional RMS prior to a human clinical trial (Phase 2, Aim 1), obtain an FDA Investigational Device Exemption (IDE), and then conduct an observational clinical trial in post-operative surgical patients managed with fentanyl and other opioids to demonstrate RMS’s ability to detect and predict the onset and progression of an OIRD event (n = 120 patients) (Phase 2, Aim 2). Together, these steps will accomplish key development milestones and clinical testing needed for regulatory approval and commercialization of the RMS device. Successful completion of this project will produce a commercially viable wearable wireless RMS with the potential to improve the health and safety of patients receiving opioids by accurately detecting and predicting OIRD with actionable alerts prior to the onset of severe hypoventilation, hypoxemia, cardiac arrhythmia, and death.