
pmid: 32614658
Background: It has been reported that the incidence of recurrent laryngeal nerve (RLN) injury is higher in an operational procedure in the thyroid and parathyroid region. Elevating voice pitch is achieved by the cricothyroid contraction, which in turn is innervated by the external branch of the superior laryngeal nerve (EBSLN). Due to the subtle nature and clinical variability of EBSLN damage, diagnosis may be difficult. The use of intraoperative neuromonitoring (IONM) as a supplement to enhance surgical identification in thyroid and parathyroid operation appeared to have collected momentum with operators. Methods: IONM clinical usage rationale, efficiency, and safety profile are discussed in this overview in thyroid and parathyroid surgery. Results: IONM usage incurred numerous skepticisms in decreasing the frequency of EBSLN or RLN injury. However, a recent article has shown the efficacy and benefits of using IONM in thyroid and parathyroid procedures. IONM facilitates the recognition of RLN and EBSLN, verifies its functional integrity, localizes the site of a nerve injury, and gives postsurgical function feedback. This technique has been successful in reducing rates of bilateral laryngeal paralysis, with the elevated safety profile for surgical patients. Conclusions: It is recommended using IONM in all thyroidectomies and in high-risk parathyroidectomies.
Parathyroidectomy, Intraoperative Neurophysiological Monitoring, Outcome Assessment, Health Care, Recurrent Laryngeal Nerve Injuries, Thyroidectomy, Humans, Intraoperative Complications
Parathyroidectomy, Intraoperative Neurophysiological Monitoring, Outcome Assessment, Health Care, Recurrent Laryngeal Nerve Injuries, Thyroidectomy, Humans, Intraoperative Complications
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
