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Erschienen in: Journal of Clinical Monitoring and Computing 3/2019

11.08.2018 | Original Research

Tracheal sounds accurately detect apnea in patients recovering from anesthesia

verfasst von: Jing Liu, Chunyu Ai, Bowen Zhang, Yun Wang, Lara M. Brewer, Chien-Kun Ting, Desheng Huang, Lu Yu

Erschienen in: Journal of Clinical Monitoring and Computing | Ausgabe 3/2019

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Abstract

Apnea should be monitored continuously in the post anesthesia care unit (PACU) to avoid serious complications. It has been confirmed that tracheal sounds can be used to detect apnea during sedation in healthy subjects, but the performance of this acoustic method has not been evaluated in patients with frequent apnea events in the PACU. Tracheal sounds were acquired from the patients in the PACU using a microphone encased in a plastic bell. Concurrently, a processed nasal pressure signal was used as a reference standard to identify real respiratory events. The logarithm of the tracheal sound variance (log-var) was used to detect apnea, and the results were compared to the reference method. Sensitivity, specificity, positive likelihood ratios (PLR), and negative likelihood ratios (NLR) were calculated. One hundred and twenty-one patients aged 55.5 ± 13.2 years (mean ± SD) with a body mass index of 24.6 ± 3.7 kg/m2 were included in data analysis. The total monitoring time was 52.6 h. Thirty-four patients experienced 236 events of apnea lasting for a total of 122.2 min. The log-var apnea detection algorithm detected apnea with 92% sensitivity, 98% specificity, 46 PLR and 0.08 NLR. The performance of apnea detection in the PACU using the log-var tracheal sounds method proved to be reliable and accurate. Tracheal sounds could be used to minimize the potential risks from apnea in PACU patients.
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Metadaten
Titel
Tracheal sounds accurately detect apnea in patients recovering from anesthesia
verfasst von
Jing Liu
Chunyu Ai
Bowen Zhang
Yun Wang
Lara M. Brewer
Chien-Kun Ting
Desheng Huang
Lu Yu
Publikationsdatum
11.08.2018
Verlag
Springer Netherlands
Erschienen in
Journal of Clinical Monitoring and Computing / Ausgabe 3/2019
Print ISSN: 1387-1307
Elektronische ISSN: 1573-2614
DOI
https://doi.org/10.1007/s10877-018-0192-6

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