Schneider Rick, Randolph Gregory W, Barczynski Marcin, Dionigi Gianlorenzo, Wu Che-Wei, Chiang Feng-Yu, Machens Andreas, Kamani Dipti, Dralle Henning
Medical Faculty, Department of General, Visceral, and Vascular Surgery, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Division of Thyroid and Parathyroid Surgery Harvard Medical School, Boston, Massachusetts, USA.
Gland Surg. 2016 Dec;5(6):607-616. doi: 10.21037/gs.2016.11.10.
The continuous intraoperative neural monitoring (CIONM) technique is increasingly acknowledged as a useful tool to recognize impending nerve injury and to abort the related manoeuvre to prevent nerve injury during thyroid surgery. CIONM provides valuable real-time information constantly, which is really useful during complex thyroid surgeries especially in the settings of unusual anatomy. Thus, CIONM overcomes the key methodological limitation inherent in intermittent nerve monitoring (IINOM); which is allowing the nerve to be at risk in between the stimulations. The clinically important combined electromyographic (EMG) event, indicative of impending recurrent laryngeal nerve (RLN) injury, prevents the majority of traction related injuries to the anatomically intact RLN enabling modification of the causative surgical manoeuvre in 80% of cases. These EMG changes can progress to loss of EMG signal with postoperative vocal cord palsy (VCP) if corrective action is not taken. As a further extension, CIONM also helps to identify intraoperative functional nerve recovery with restitution of amplitude to ≥50% of initial baseline; this allows continuing of resection of contralateral side. CIONM facilitates for early corrective action before permanent damage to the nerve has been done. CIONM is a recent but rapidly evolving technique, constantly being refined by various studies focusing on improvement in its implementation and interpretation, as well as on the elimination of the technical snags.
术中连续神经监测（CIONM）技术越来越被认为是一种有用的工具，可用于识别即将发生的神经损伤，并在甲状腺手术期间中止相关操作以防止神经损伤。CIONM持续提供有价值的实时信息，这在复杂的甲状腺手术中，尤其是在解剖结构异常的情况下非常有用。因此，CIONM克服了间歇性神经监测（IINOM）固有的关键方法学局限性；即间歇性神经监测会使神经在两次刺激之间处于危险之中。临床上重要的联合肌电图（EMG）事件，表明即将发生喉返神经（RLN）损伤，可预防大多数与牵拉相关的对解剖结构完整的RLN的损伤，在80%的病例中能够修改导致损伤的手术操作。如果不采取纠正措施，这些EMG变化可进展为术后声带麻痹（VCP）导致的EMG信号消失。作为进一步的扩展，CIONM还有助于识别术中神经功能恢复，即振幅恢复到初始基线的≥50%；这允许继续切除对侧。CIONM有助于在神经受到永久性损伤之前尽早采取纠正措施。CIONM是一项最近但发展迅速的技术，各种研究不断对其进行完善，重点在于改进其实施和解读，以及消除技术障碍。
