Vocal cord paralysis predicted by neural monitoring electrophysiologic changes with recurrent laryngeal nerve compressive neuropraxic injury in a canine model

Sidharth V. Puram, Harold Chow, Che Wei Wu, James T. Heaton, Dipti Kamani, Gautham Gorti, Feng Yu Chiang, Gianlorenzo Dionigi, Marcin Barczyński, Rick Schneider, Henning Dralle, Kerstin Lorenz, Gregory W. Randolph

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Background Recurrent laryngeal nerve (RLN) injury is a known complication of thyroid/parathyroid surgery. Intraoperative nerve monitoring (IONM) has been used to gain more information regarding the functional status of the RLN intraoperatively; however, the electromyography (EMG) parameters of RLN after nontransection neuropraxic compressive injury remain unknown. Methods We developed a canine model to identify IONM EMG correlates of postoperative vocal cord paralysis (VCP) using a standardized method to simulate surgical RLN compression sufficient to cause VCP. Results Compression nerve injury decreased EMG amplitude and increased EMG latency, with a 60% increase in RLN threshold stimulation compared to preinjury values. If RLN amplitude decreases by 80% with an absolute amplitude of 300 μV or less in combination with a latency increase of 10% or more, then nerve injury and associated VCP is likely. Conclusion These results may help surgeons to prognosticate postoperative neural function and intraoperative decision-making regarding contralateral thyroid surgery.

Original languageEnglish
Pages (from-to)E1341-E1350
JournalHead and Neck
Volume38
DOIs
StatePublished - Apr 1 2016

Keywords

  • endocrine
  • injury
  • intraoperative nerve monitoring (IONM)
  • recurrent laryngeal nerve
  • thyroid surgery
  • vocal cord palsy

Fingerprint

Dive into the research topics of 'Vocal cord paralysis predicted by neural monitoring electrophysiologic changes with recurrent laryngeal nerve compressive neuropraxic injury in a canine model'. Together they form a unique fingerprint.

Cite this