Head & neck · 2018
Diagnosis, anatomy, and electromyography profiles of 73 nonrecurrent laryngeal nerves.
СтатьяНазвание и аннотация: Перевод в очереди
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Wang Tie, Dionigi Gianlorenzo, Zhang Daqi, Bian Xuehai, Zhou Le, Fu Yantao, Zhang Guang, Liu Xiaoli, Sun Hui
Аннотация
BACKGROUND: The purpose of this work was to compare methods of detecting nonrecurrent laryngeal nerves (NRLNs).
METHODS: Specificity and sensitivity were compared in three NRLN detection methods: CT, electromyography (EMG), and A-B point comparison.
RESULTS: A total of 73 intraoperative pictures and 36 CT details of NRLNs are presented. Incidence of NRLN was 0.39%. Type I NRLN accounted for 50.7%, type IIA 45.2%, type IIB 4.1%. The NRLN median latency was 2.13 ms vs 3.00 ms median in an RLN control group (P < .001). When the threshold was set to 2.5 ms, EMG latency detection had 96.7% sensitivity and 91.6% specificity for detecting NRLN, and the A-B point comparison algrithm had 97.3% sensitivity and 92.5% specificity. Combining EMG latency detection with A-B point comparison achieved 100% sensitivity and specificity for detecting NRLN.
CONCLUSION: This is the largest series of NRLN presented in the literature. Latency shorter than 2.50 ms combined with the A-B point comparison method is the ideal algorithm procedure for early NRLN identification.
METHODS: Specificity and sensitivity were compared in three NRLN detection methods: CT, electromyography (EMG), and A-B point comparison.
RESULTS: A total of 73 intraoperative pictures and 36 CT details of NRLNs are presented. Incidence of NRLN was 0.39%. Type I NRLN accounted for 50.7%, type IIA 45.2%, type IIB 4.1%. The NRLN median latency was 2.13 ms vs 3.00 ms median in an RLN control group (P < .001). When the threshold was set to 2.5 ms, EMG latency detection had 96.7% sensitivity and 91.6% specificity for detecting NRLN, and the A-B point comparison algrithm had 97.3% sensitivity and 92.5% specificity. Combining EMG latency detection with A-B point comparison achieved 100% sensitivity and specificity for detecting NRLN.
CONCLUSION: This is the largest series of NRLN presented in the literature. Latency shorter than 2.50 ms combined with the A-B point comparison method is the ideal algorithm procedure for early NRLN identification.