Remote vital sign and opioid overdose detection via a non-invasive wearable sensing patch

项目概要/摘要 大约有200万美国人患有阿片类药物使用障碍，2017年有17，029人 过量服用处方阿片类药物阿片类药物引起呼吸抑制;然而，致命的过量通常 在1-3小时内发生，留下一个机会窗口，用廉价的药物进行干预。 纳洛酮，一种阿片类拮抗剂，可以逆转阿片类药物的作用，迅速恢复正常呼吸 rates. VivaLNK建议加强目前市场上的重要童子军健康可穿戴传感贴片， 在阿片类药物过量风险人群中使用，通过检测阿片类药物过量的生理指标（缓慢 呼吸率，低心率和低血压），以及发送潜在救生的能力 过量警报，可由紧急联系人/护理人员接收，并使用 阿片拮抗剂如纳洛酮。健康监测紧急通知系统，可以远程 监测患者的生命体征，以发现恶化迹象，并向急诊室发送可能挽救生命的警报 接触者/护理人员，有可能大大减少美国致命阿片类药物过量的数量。的 Vital Scout贴片已经战略性地定位为利用内置ECG监测过量参数 传感器，用于连续记录波形，呼吸率、心率、心率变异性，以及 活动计算。虽然目前正在开发几种用于过量警报的设备，但Vital Scout是唯一有可能捕捉到三个关键生命体征的警报， 过量，同时监测身体活动，为生命体征数据提供额外的背景。 Vital Scout贴片收集的生命体征基于机器学习算法， 我建议通过以下具体目标进一步发展：1）改进和优化重要侦察兵 通过提高灵敏度将过量算法与警报功能结合，以检测较低的 呼吸和心率显示可能用药过量2)扩展Vital的传感能力 Scout系统包括血压监测。该算法将使用三个志愿者进行优化 队列（约20例/队列）（i）健康志愿者，（ii）65岁以上的老年患者（更可能患有低血压） 由于药物引起的低血压）和（iii）低血压患者（以优秀学生运动员为代表）。数据 将为机器学习线性回归提供初始的广泛数据 算法我们将评估这些是否是提供准确血压的适当指定 测量，类似于阿片类药物人群中所见。实现所有这些目标将导致 增强的Vital Scout将成为阿片类药物过量检测的最敏感设备， 阿片类药物成瘾者和有阿片类药物过量风险者的生命体征，包括但不限于， 患有肺病或服用抗抑郁药的人。

PROJECT SUMMARY/ ABSTRACT Approximately 2 million Americans are living with an opioid use disorder, and in 2017, 17,029 people overdosed on prescription opioids. Opioids cause respiratory depression; however, fatal overdose usually occurs over a period of 1–3 hours, leaving a window of opportunity to intervene with the inexpensive drug naloxone, an opioid antagonist, that can reverse the effects of opioids, quickly restoring normal respiration rates. VivaLNK proposes to enhance the currently marketed Vital Scout wellness wearable sensing patch for use in people at risk for opioid overdose, by detecting physiologic indicators of an opioid overdose (slow respiration rate, low heart rate and low blood pressure), and the ability to send a potentially life-saving overdose alert, which could be received by an emergency contact/caregiver and hasten treatment with an opioid antagonist such as naloxone. Health monitoring emergency notification systems that can remotely monitor patient vital signs for indications of deterioration and send a potentially life-saving alert to emergency contacts/caregivers, have the potential to greatly reduce the number of fatal opioid overdoses in the US. The Vital Scout patch is already strategically positioned to monitor overdose parameters utilizing the built in ECG sensor to continuously register waveforms from which respiration rate, heart rate, heart rate variability, and activity are calculated. While several devices are currently being developed for overdose alerts, the Vital Scout is the only alert which has the potential to capture the three critical vital signs indicative of an overdose, and simultaneously monitor physical activity, providing additional context to the vital signs data. The vital signs collected by the Vital Scout patch are based on a machine-learning algorithm which we propose to further develop by the following specific aims: 1) Refinement and optimization of the Vital Scout to incorporate an overdose algorithm with an alert function by improving the sensitivity in order to detect lower respiration and heart rate indicative of a potential overdose. 2) Expand the sensing capabilities of the Vital Scout system to include blood pressure monitoring. The algorithm will be optimized using three volunteer cohorts (~20/ cohort) (i) healthy volunteers, (ii) elderly patients over the age of 65 (more likely to have low BP due to medically-induced hypotension) and (iii) low BP patients (represented by elite student-athletes). Data from these cohorts will provide the initial broad range of data for the machine-learning linear regression algorithm. We will assess whether these are appropriate designations for providing accurate blood pressure measurements, similar to what is seen within the opioid population. Meeting all of these goals will result in an enhanced Vital Scout that will be the most sensitive device for opioid overdose detection to remotely monitor the vital signs of people addicted to opioids and those at-risk for opioid overdose, including but not limited to, people with pulmonary disease or those taking antidepressants.