使用定制设计的光电体积描记仪在小鼠模型中检测阿片类药物过量

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2023-10-01 DOI:10.1016/j.irbm.2023.100792
Orlando S. Hoilett , Jason D. Ummel , Luke E. Schepers , Arvin H. Soepriatna , Jessica L. Ma , Akio K. Fujita , Alyson S. Pickering , Benjamin D. Walters , Craig J. Goergen , Jacqueline C. Linnes
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引用次数: 1

摘要

背景和目的仅在2020年,美国就发生了超过68000例与阿片类药物过量相关的死亡,这表明需要开发技术来帮助遏制这种日益严重的流行病。实时检测呼吸频率(RR)抑郁的能力有可能通过提醒紧急医疗服务或愿意的旁观者注意服药过量事件来减少不良后果。本研究的目的是设计、构建和测试一种新型的基于光体积描记术(PPG)的测量设备,该设备能够监测RR并识别呼吸抑制。材料和方法我们开发了一种新的阿片类药物诱导的呼吸抑制(OIRD)小鼠模型,以证明PPG设备的能力。我们使用异氟烷和阿片类药物过量诱导小鼠呼吸抑制,并通过注射纳洛酮启动恢复事件,同时通过PPG和实验室参考系统监测呼吸。结果与讨论该装置准确识别了所有麻醉诱导的呼吸抑制(n=5)和OIRD事件(n=3)。我们基于PPG的监测仪显示出与参考呼吸测量系统的显著相关性(p<;0.01)。在异氟烷试验中测得的偏差为每分钟0.6次呼吸(BrPM),而在羟考酮试验中测到的偏差为-1.0次BrPM,平均绝对误差分别为1.5和3.6次BrPM,表明我们的设备能够在小鼠模型中准确地测量RR。结论这些初步实验表明,我们的设备可以检测OIRD,并且通过对固件的修改和在人类受试者中进行更广泛的验证,有可能适用于人类。我们目前的研究是检测OIRD并实时提醒旁观者和卫生专业人员的概念验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Opioid Overdose Detection in a Murine Model Using a Custom-Designed Photoplethysmography Device

Opioid Overdose Detection in a Murine Model Using a Custom-Designed Photoplethysmography Device

Background and Objective

Over 68,000 opioid-overdose related deaths occurred within the United States in 2020 alone, indicating a need to develop technologies to help curb this growing epidemic. The ability to detect respiratory rate (RR) depression in real-time has the potential to decrease adverse outcomes by alerting emergency medical services or willing bystanders to an overdose event. The aim of this investigation was to design, build, and test a novel photoplethysmography (PPG)-based measurement device capable of monitoring RR and identifying respiratory depression.

Materials and Methods

We developed a novel murine model for opioid-induced respiratory depression (OIRD) to demonstrate the PPG device's capabilities. We induced respiratory depression in mice using both isoflurane and opioid-overdose and initiated recovery events with injections of naloxone while monitoring respiration via PPG and a laboratory reference system.

Results and Discussion

The device accurately identified all anesthesia-induced respiratory depression (n = 5) and OIRD events (n = 3). Our PPG-based monitor showed significant correlation with a reference respiratory measurement system (p<0.01). The bias measured across the isoflurane trials was 0.6 breaths per minute (BrPM), while the bias measured across the oxycodone trials was −1.0 BrPM, with mean absolute errors of 1.5 and 3.6 BrPM, respectively, indicating that our device was able to accurately measure RR in a murine model.

Conclusions

These preliminary experiments suggest that our device could detect OIRD and could potentially be adaptable to humans with modifications to firmware and more extensive validation in human subjects. Our present study is a proof-of-concept for detecting OIRD and alerting bystanders and health professionals in real-time.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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