Department of Anesthesiology, Peking University People's Hospital, Beijing, China.
CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Neural Plast. 2021 Apr 9;2021:5543974. doi: 10.1155/2021/5543974. eCollection 2021.
Even with an improved understanding of pain mechanisms and advances in perioperative pain management, inadequately controlled postoperative pain remains. Predicting acute postoperative pain based on presurgery physiological measures could provide valuable insights into individualized, effective analgesic strategies, thus helping improve the analgesic efficacy. Considering the strong correlation between pain perception and neural oscillations, we hypothesize that acute postoperative pain could be predicted by neural oscillations measured shortly before the surgery. Here, we explored the relationship between neural oscillations 2 hours before the thoracoscopic surgery and the subjective intensity of acute postoperative pain. The spectral power density of resting-state beta and gamma band oscillations at the frontocentral region was significantly different between patients with different levels of acute postoperative pain (i.e., low pain vs. moderate/high pain). A positive correlation was also observed between the spectral power density of resting-state beta and gamma band oscillations and subjective reports of postoperative pain. Then, we predicted the level of acute postoperative pain based on features of neural oscillations using machine learning techniques, which achieved a prediction accuracy of 92.54% and a correlation coefficient between the real pain intensities and the predicted pain intensities of 0.84. Altogether, the prediction of acute postoperative pain based on neural oscillations measured before the surgery is feasible and could meet the clinical needs in the future for better control of postoperative pain and other unwanted negative effects. The study was registered on the Clinical Trial Registry (https://clinicaltrials.gov/ct2/show/NCT03761576?term=NCT03761576&draw=2&rank=1) with the registration number NCT03761576.
尽管对疼痛机制的理解有所提高，围手术期疼痛管理也有所进步，但术后疼痛仍未得到充分控制。基于术前生理测量来预测急性术后疼痛，可以为个体化、有效的镇痛策略提供有价值的见解，从而有助于提高镇痛效果。鉴于疼痛感知与神经振荡之间存在很强的相关性，我们假设可以通过手术前短时间测量的神经振荡来预测急性术后疼痛。在这里，我们探讨了胸腔镜手术前 2 小时神经振荡与急性术后疼痛主观强度之间的关系。额叶中央区静息状态β和γ波段振荡的频谱功率密度在急性术后疼痛程度不同的患者之间存在显著差异（即低疼痛与中/高疼痛）。还观察到静息状态β和γ波段振荡的频谱功率密度与术后疼痛的主观报告之间存在正相关。然后，我们使用机器学习技术基于神经振荡的特征来预测急性术后疼痛水平，其预测准确率为 92.54%，真实疼痛强度与预测疼痛强度之间的相关系数为 0.84。总之，基于手术前测量的神经振荡来预测急性术后疼痛是可行的，未来可能满足更好地控制术后疼痛和其他不良副作用的临床需求。该研究已在临床试验注册中心（https://clinicaltrials.gov/ct2/show/NCT03761576?term=NCT03761576&draw=2&rank=1）注册，注册号为 NCT03761576。
