Sundermann Matthew L, Salcido David D, Koller Allison C, Menegazzi James J
Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA.
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA.
Acad Emerg Med. 2016 Jan;23(1):93-7. doi: 10.1111/acem.12844. Epub 2015 Dec 31.
Cardiac arrest is one of the leading causes of death in the United States and is treated by cardiopulmonary resuscitation (CPR). CPR involves both chest compressions and positive pressure ventilations when given by medical providers. Mechanical chest compression devices automate chest compressions and are beginning to be adopted by emergency medical services with the intent of providing high-quality, consistent chest compressions that are not limited by human providers who can become fatigued. Biosignals acquired from cardiac arrest patients have been characterized in their ability to track the effect of CPR on the patient. The authors investigated the feasibility and appropriate response of a biosignal-guided mechanical chest compression device in a swine model of cardiac arrest.
After a custom signal-guided chest compression device was engineered, its ability to respond to biosignal changes in a swine model of cardiac arrest was tested. In a preliminary series of six swine, two biosignals were used: mean arterial pressure (MAP) and a mathematical derivative of the electrocardiogram waveform, median slope (MS). How these biosignals changed was observed when chest compression rate and depth were adjusted by the signal-guided chest compression device, independent of the user. Chest compression rate and depth were adjusted by the signal-guided chest compression device according to a preset threshold algorithm until either of the biosignals improved to satisfy a set "threshold" or until the chest compression rate and depth achieved maximum values. Defibrillation was attempted at the end of each resuscitation in an effort to achieve return of spontaneous circulation (ROSC).
The signal-guided chest compression device responded appropriately to biosignals by changing its rate and depth. All animals exhibited positive improvements in their biosignals. During the course of the resuscitation, three of the six animals improved their MS biosignal to reach the MS threshold, while two of the six animals improved their MAP biosignal to reach the MAP threshold. In the six experiments conducted, defibrillation was attempted on five animals, and two animals achieved ROSC.
In this proof-of-concept study, a signal-guided chest compression device was demonstrated to be capable of responding to biosignal input and delivering chest compressions with a broad range of rates and depths.
心脏骤停是美国主要死因之一，通过心肺复苏（CPR）进行治疗。医疗人员进行CPR时包括胸外按压和正压通气。机械胸外按压设备可自动进行胸外按压，急救医疗服务机构开始采用此类设备，旨在提供高质量、持续的胸外按压，不受可能会疲劳的施救人员限制。从心脏骤停患者获取的生物信号已被证实具有跟踪CPR对患者影响的能力。作者在猪心脏骤停模型中研究了生物信号引导的机械胸外按压设备的可行性及适当反应。
定制信号引导胸外按压设备后，测试其在猪心脏骤停模型中对生物信号变化的反应能力。在初步的六只猪系列实验中，使用了两种生物信号：平均动脉压（MAP）和心电图波形的数学导数，即中位数斜率（MS）。当信号引导胸外按压设备独立于使用者调整胸外按压速率和深度时，观察这些生物信号如何变化。信号引导胸外按压设备根据预设阈值算法调整胸外按压速率和深度，直到任一生物信号改善至满足设定的“阈值”，或直到胸外按压速率和深度达到最大值。每次复苏结束时尝试除颤，以实现自主循环恢复（ROSC）。
信号引导胸外按压设备通过改变速率和深度对生物信号做出适当反应。所有动物的生物信号均有积极改善。在复苏过程中，六只动物中有三只改善了其MS生物信号以达到MS阈值，六只动物中有两只改善了其MAP生物信号以达到MAP阈值。在进行的六次实验中，对五只动物尝试了除颤，两只动物实现了ROSC。
在这项概念验证研究中，证明了信号引导胸外按压设备能够响应生物信号输入，并以广泛的速率和深度进行胸外按压。
