Neuromonitoring of recurrent laryngeal nerve using continuous intraoperative neuromonitoring system during anterior cervical spine surgery: A porcine model study

Purpose: Recurrent laryngeal nerve (RLN) injury is a recognized complication of anterior cervical spine surgery (ACSS), particularly during anterior cervical discectomy and fusion (ACDF). This study explores the use of continuous intraoperative neurophysiological monitoring (CIONM) with an endotracheal electromyography (EMG) tube during ACSS in a porcine model, focusing on adverse EMG signal events during retraction and the differences in RLN injury and recovery between surgical levels and approach side (left or right). Materials and methods: All 12 pigs were intubated with an EMG-equipped endotracheal tube. All surgical procedures were performed via an anterior approach. RLN injury caused by traction was assessed in four segments (C3/4, C4/5, C5/6, C6/7) on both sides, respectively. Laryngeal EMG was continuously measured through electrical stimulation of the vagus nerve using automatic periodic stimulation with CIONM. During surgery, an alarm was set to activate if the EMG amplitude decreased by more than 50% or latency increased by more than 10%, indicating nerve injury. When the alarm was triggered, traction was stopped, and EMG changes were observed. Traction time and recovery time were recorded until the EMG returned to baseline. Results: There were no statistically significant differences in traction or recovery times between the left and right sides (p > 0.05). However, traction times significantly differed by cervical level (p < 0.001), with longer durations at C3/4 and C4/5. Recovery times also differed significantly by level (p < 0.001), with notably prolonged recovery at C6/7, where some nerves failed to return to baseline within 30 min. Conclusion: These findings suggest that lower cervical levels, particularly C6/7, are more vulnerable to nerve damage, requiring heightened attention during ACSS. The study highlights the advantages of CIONM, including real-time detection of RLN stretch and its benefits during complex surgeries.