Severity of Recurrent Laryngeal Nerve Injuries in Thyroid Surgery

Abstract

Background: Few studies in the literature have reported recovery data for different types of recurrent laryngeal nerve injuries (RLNIs). This study is the first attempt to classify RLNIs and rank them by severity.

Methods: This prospective clinical study analyzed 281 RLNIs in which a true loss of signal was identified by intraoperative neuromonitoring (IONM), and vocal cord palsy (VCP) was confirmed by a postoperative laryngoscope. For each injury type, the prevalence of VCP, the time of VCP recovery, and physical changes on nerves were analyzed. Additionally, different RLNI types were experimentally induced in a porcine model to compare morphological change.

Results: The overall VCP rate in at-risk patients/nerves was 8.9/4.6 %, respectively. The distribution of RLNI types, in order of frequency, was traction (71 %), thermal (17 %), compression (4.2 %), clamping (3.4 %), ligature entrapment (1.6 %), suction (1.4 %), and nerve transection (1.4 %). Complete recovery from VCP was documented in 91 % of RLNIs. Recovery time was significantly faster in the traction group compared to the other groups (p < 0.001). The rates of temporary and permanent VCP were 98.6 and 1.4 % for traction lesion, 72 and 28 % for thermal injury, 100 and 0 % for compression injury, 50 and 50 % for clamping injury, 100 and 0 % for ligature entrapment, 100 and 0 % for suction injury, and 0 and 100 % for nerve transection, respectively. Physical changes were noted in 14 % of RLNIs in which 56 % of VCP was permanent. However, among the remaining 86 % IONM-detectable RLNIs without physical changes, only 1.2 % of VCP was permanent. A porcine model of traction lesion showed only distorted outer nerve structure, whereas the thermal lesion showed severe damage in the inner endoneurium.

Conclusions: Different RNLIs induce different morphological alterations and have different recovery outcomes. Permanent VCP is rare in lesions that are visually undetectable but detectable by IONM. By enabling early detection of RLNI and prediction of outcome, IONM can help clinicians plan intra- and postoperative treatment.