Armstrong Bonnie A, Nemrodov Dan, Tung Arthur, Graham Simon J, Grantcharov Teodor
International Centre for Surgical Safety, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.
Institute of Health Policy, Management and Evaluation, University of Toronto, 155 College St 4th Floor, Toronto, ON, M5T 3M6, Canada.
Surg Endosc. 2023 Apr;37(4):2817-2825. doi: 10.1007/s00464-022-09799-2. Epub 2022 Dec 7.
Intraoperative adverse events lead to patient injury and death, and are increasing. Early warning systems (EWSs) have been used to detect patient deterioration and save lives. However, few studies have used EWSs to monitor surgical performance and caution about imminent technical errors. Previous (non-surgical) research has investigated neural activity to predict future motor errors using electroencephalography (EEG). The present proof-of-concept cohort study investigates whether EEG could predict technical errors in surgery.
In a large academic hospital, three surgical fellows performed 12 elective laparoscopic general surgeries. Audiovisual data of the operating room and the surgeon's neural activity were recorded. Technical errors and epochs of good surgical performance were coded into events. Neural activity was observed 40 s prior and 10 s after errors and good events to determine how far in advance errors were detected. A hierarchical regression model was used to account for possible clustering within surgeons. This prospective, proof-of-concept, cohort study was conducted from July to November 2021, with a pilot period from February to March 2020 used to optimize the technique of data capture and included participants who were blinded from study hypotheses.
Forty-five technical errors, mainly due to too little force or distance (n = 39), and 27 good surgical events were coded during grasping and dissection. Neural activity representing error monitoring (p = .008) and motor uncertainty (p = .034) was detected 17 s prior to errors, but not prior to good surgical performance.
These results show that distinct neural signatures are predictive of technical error in laparoscopic surgery. If replicated with low false-alarm rates, an EEG-based EWS of technical errors could be used to improve individualized surgical training by flagging imminent unsafe actions-before errors occur and cause patient harm.
术中不良事件会导致患者受伤甚至死亡,且此类事件正在增加。早期预警系统(EWS)已被用于检测患者病情恶化并挽救生命。然而,很少有研究使用EWS来监测手术操作情况并对即将发生的技术失误发出警示。此前(非手术领域的)研究已利用脑电图(EEG)研究神经活动以预测未来的运动失误。本概念验证队列研究旨在探究EEG能否预测手术中的技术失误。
在一家大型学术医院,三名外科住院医师进行了12例择期腹腔镜普通外科手术。记录手术室的视听数据以及外科医生的神经活动。将技术失误和良好手术操作阶段编码为事件。在失误和良好事件发生前40秒及发生后10秒观察神经活动,以确定能提前多久检测到失误。使用分层回归模型来考虑外科医生内部可能存在的聚类情况。这项前瞻性、概念验证队列研究于2021年7月至11月进行,2020年2月至3月为试点期,用于优化数据采集技术,研究对象对研究假设不知情。
在抓取和解剖过程中,共编码了45次技术失误,主要原因是用力过小或距离过短(n = 39),以及27次良好手术事件。在失误发生前17秒检测到代表错误监测(p = 0.008)和运动不确定性(p = 0.034)的神经活动,但在良好手术操作之前未检测到。
这些结果表明,独特的神经信号可预测腹腔镜手术中的技术失误。如果能以低误报率重复验证,基于EEG的技术失误早期预警系统可通过在不安全操作即将发生时(即在失误发生并对患者造成伤害之前)发出警示,用于改进个性化手术培训。