Developing Active Stimulation and Monitoring Technologies to Optimize Pain and Nociception Management During Regional Anesthesia, General Anesthesia, and Post-Operative Care

项目总结

Project Summary Surgery and acute post-operative pain are major contributors to persistent pain, chronic pain, and opioid dependence. Currently, there are no products on the market in the United States that can be used to monitor surgical nociception. Existing products use indicators that are susceptible to intraoperative influences such as blood loss, anesthetic drugs and antihypertensives. This leaves anesthesiologists to guess whether or not the analgesic therapies they are providing will be effective at managing intra-operative nociception and subsequent post-operative pain. A significant fraction of surgeries also rely on regional anesthesia techniques, and, these techniques are not foolproof, and may fail during surgery, leading to potentially uncontrolled postoperative pain that is apparent only when the patient recovers consciousness after surgery in intense pain. As it stands, anesthesiologists have no way to directly assess the ongoing efficacy of their regional blocks, leading to unreliable nociception and pain management. Therefore, improved methods to monitor surgical nociception are clearly needed. This project proposes to develop new technology that makes it possible for the first time to use event-related potentials (ERPs) to actively assess pain and analgesia during general and regional anesthesia. ERPs are generated by painful stimuli and can be measured at the scalp by averaging waveforms from repeated stimuli generated by a variety of methods including electrical stimulation. ERPs are challenging if not impossible to use for anesthesia monitoring because they are very small, between ~1 to 10 microvolts, and are overshadowed by background electroencephalogram oscillations that are 10- to 100-fold larger in amplitude during general anesthesia or sedation. Recently, we developed a novel technology for processing ERPs that increases their precision ~150-fold even in the presence of orders-of-magnitude larger background oscillations. Using this knowledge, we propose to develop this technology for commercial use in a “Fast-Track” STTR project.