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Radiological indicators to predict the application of assistant intubation techniques for patients undergoing cervical surgery

BACKGROUND: We aimed to distinguish the preoperative radiological indicators to predict the application of assistant techniques during intubation for patients undergoing selective cervical surgery. METHODS: A total of 104 patients were enrolled in this study. According to whether intubation was succ...

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Autores principales: Liu, Bingchuan, Song, Yanan, Liu, Kaixi, Zhou, Fang, Ji, Hongquan, Tian, Yun, Han, Yong Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7499909/
https://www.ncbi.nlm.nih.gov/pubmed/32943014
http://dx.doi.org/10.1186/s12871-020-01153-0
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author Liu, Bingchuan
Song, Yanan
Liu, Kaixi
Zhou, Fang
Ji, Hongquan
Tian, Yun
Han, Yong Zheng
author_facet Liu, Bingchuan
Song, Yanan
Liu, Kaixi
Zhou, Fang
Ji, Hongquan
Tian, Yun
Han, Yong Zheng
author_sort Liu, Bingchuan
collection PubMed
description BACKGROUND: We aimed to distinguish the preoperative radiological indicators to predict the application of assistant techniques during intubation for patients undergoing selective cervical surgery. METHODS: A total of 104 patients were enrolled in this study. According to whether intubation was successfully accomplished by simple Macintosh laryngoscopy, patients were divided into Macintosh laryngoscopy group (n = 78) and Assistant technique group (n = 26). We measured patients’ radiographical data via their preoperative X-ray and MRI images, and compared the differences between two groups. Binary logistic regression model was applied to distinguish the meaningful predictors. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to describe the discrimination ability of indicators. The highest Youden’s index corresponded to an optimal cut-off value. RESULTS: Ten variables exhibited significant statistical differences between two groups (P <  0.05). Based on logistic regression model, four further showed correlation with the application of assistant techniques, namely, perpendicular distance from hard palate to tip of upper incisor (X2), atlanto-occipital gap (X9), angle between a line passing through posterior-superior point of hard palate and the lowest point of the occipital bone and a line passing through the anterior-inferior point and the posterior-inferior point of the second cervical vertebral body (Angle E), and distance from skin to hyoid bone (MRI 7). Angle E owned the largest AUC (0.929), and its optimal cut-off value was 19.9° (sensitivity = 88.5%, specificity = 91.0%). the optimal cut-off value, sensitivity and specificity of other three variables were X2 (30.1 mm, 76.9, 76.9%), MRI7 (16.3 mm, 69.2, 87.2%), and X9 (7.3 mm, 73.1, 56.4%). CONCLUSIONS: Four radiological variables possessed potential ability to predict the application of assistant intubation techniques. Anaesthesiologists are recommended to apply assistant techniques more positively once encountering the mentioned cut-off values.
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spelling pubmed-74999092020-09-21 Radiological indicators to predict the application of assistant intubation techniques for patients undergoing cervical surgery Liu, Bingchuan Song, Yanan Liu, Kaixi Zhou, Fang Ji, Hongquan Tian, Yun Han, Yong Zheng BMC Anesthesiol Research Article BACKGROUND: We aimed to distinguish the preoperative radiological indicators to predict the application of assistant techniques during intubation for patients undergoing selective cervical surgery. METHODS: A total of 104 patients were enrolled in this study. According to whether intubation was successfully accomplished by simple Macintosh laryngoscopy, patients were divided into Macintosh laryngoscopy group (n = 78) and Assistant technique group (n = 26). We measured patients’ radiographical data via their preoperative X-ray and MRI images, and compared the differences between two groups. Binary logistic regression model was applied to distinguish the meaningful predictors. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to describe the discrimination ability of indicators. The highest Youden’s index corresponded to an optimal cut-off value. RESULTS: Ten variables exhibited significant statistical differences between two groups (P <  0.05). Based on logistic regression model, four further showed correlation with the application of assistant techniques, namely, perpendicular distance from hard palate to tip of upper incisor (X2), atlanto-occipital gap (X9), angle between a line passing through posterior-superior point of hard palate and the lowest point of the occipital bone and a line passing through the anterior-inferior point and the posterior-inferior point of the second cervical vertebral body (Angle E), and distance from skin to hyoid bone (MRI 7). Angle E owned the largest AUC (0.929), and its optimal cut-off value was 19.9° (sensitivity = 88.5%, specificity = 91.0%). the optimal cut-off value, sensitivity and specificity of other three variables were X2 (30.1 mm, 76.9, 76.9%), MRI7 (16.3 mm, 69.2, 87.2%), and X9 (7.3 mm, 73.1, 56.4%). CONCLUSIONS: Four radiological variables possessed potential ability to predict the application of assistant intubation techniques. Anaesthesiologists are recommended to apply assistant techniques more positively once encountering the mentioned cut-off values. BioMed Central 2020-09-17 /pmc/articles/PMC7499909/ /pubmed/32943014 http://dx.doi.org/10.1186/s12871-020-01153-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Liu, Bingchuan
Song, Yanan
Liu, Kaixi
Zhou, Fang
Ji, Hongquan
Tian, Yun
Han, Yong Zheng
Radiological indicators to predict the application of assistant intubation techniques for patients undergoing cervical surgery
title Radiological indicators to predict the application of assistant intubation techniques for patients undergoing cervical surgery
title_full Radiological indicators to predict the application of assistant intubation techniques for patients undergoing cervical surgery
title_fullStr Radiological indicators to predict the application of assistant intubation techniques for patients undergoing cervical surgery
title_full_unstemmed Radiological indicators to predict the application of assistant intubation techniques for patients undergoing cervical surgery
title_short Radiological indicators to predict the application of assistant intubation techniques for patients undergoing cervical surgery
title_sort radiological indicators to predict the application of assistant intubation techniques for patients undergoing cervical surgery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7499909/
https://www.ncbi.nlm.nih.gov/pubmed/32943014
http://dx.doi.org/10.1186/s12871-020-01153-0
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