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Nomograms for predicting difficult airway based on ultrasound assessment

BACKGROUND: Accurate prediction of the difficult airway (DA) could help to prevent catastrophic consequences in emergency resuscitation, intensive care, and general anesthesia. Until now, there is no nomogram prediction model for DA based on ultrasound assessment. In this study, we aimed to develop...

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Autores principales: Wang, Bin, Yao, Weidong, Xue, Qi, Wang, Mingfang, Xu, Jianling, Chen, Yongquan, Zhang, Ye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756724/
https://www.ncbi.nlm.nih.gov/pubmed/35026991
http://dx.doi.org/10.1186/s12871-022-01567-y
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author Wang, Bin
Yao, Weidong
Xue, Qi
Wang, Mingfang
Xu, Jianling
Chen, Yongquan
Zhang, Ye
author_facet Wang, Bin
Yao, Weidong
Xue, Qi
Wang, Mingfang
Xu, Jianling
Chen, Yongquan
Zhang, Ye
author_sort Wang, Bin
collection PubMed
description BACKGROUND: Accurate prediction of the difficult airway (DA) could help to prevent catastrophic consequences in emergency resuscitation, intensive care, and general anesthesia. Until now, there is no nomogram prediction model for DA based on ultrasound assessment. In this study, we aimed to develop a predictive model for difficult tracheal intubation (DTI) and difficult laryngoscopy (DL) using nomogram based on ultrasound measurement. We hypothesized that nomogram could utilize multivariate data to predict DTI and DL. METHODS: A prospective observational DA study was designed. This study included 2254 patients underwent tracheal intubation. Common and airway ultrasound indicators were used for the prediction, including thyromental distance (TMD), modified Mallampati test (MMT) score, upper lip bite test (ULBT) score temporomandibular joint (TMJ) mobility and tongue thickness (TT). Univariate and the Akaike information criterion (AIC) stepwise logistic regression were used to identify independent predictors of DTI and DL. Nomograms were constructed to predict DL and DTL based on the AIC stepwise analysis results. Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the nomograms. RESULTS: Among the 2254 patients enrolled in this study, 142 (6.30%) patients had DL and 51 (2.26%) patients had DTI. After AIC stepwise analysis, ULBT, MMT, sex, TMJ, age, BMI, TMD, IID, and TT were integrated for DL nomogram; ULBT, TMJ, age, IID, TT were integrated for DTI nomogram. The areas under the ROC curves were 0.933 [95% confidence interval (CI), 0.912–0.954] and 0.974 (95% CI, 0.954–0.995) for DL and DTI, respectively. CONCLUSION: Nomograms based on airway ultrasonography could be a reliable tool in predicting DA. TRIAL REGISTRATION: Chinese Clinical Trial Registry (No. ChiCTR-RCS-14004539), registered on 13th April 2014. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12871-022-01567-y.
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spelling pubmed-87567242022-01-18 Nomograms for predicting difficult airway based on ultrasound assessment Wang, Bin Yao, Weidong Xue, Qi Wang, Mingfang Xu, Jianling Chen, Yongquan Zhang, Ye BMC Anesthesiol Research BACKGROUND: Accurate prediction of the difficult airway (DA) could help to prevent catastrophic consequences in emergency resuscitation, intensive care, and general anesthesia. Until now, there is no nomogram prediction model for DA based on ultrasound assessment. In this study, we aimed to develop a predictive model for difficult tracheal intubation (DTI) and difficult laryngoscopy (DL) using nomogram based on ultrasound measurement. We hypothesized that nomogram could utilize multivariate data to predict DTI and DL. METHODS: A prospective observational DA study was designed. This study included 2254 patients underwent tracheal intubation. Common and airway ultrasound indicators were used for the prediction, including thyromental distance (TMD), modified Mallampati test (MMT) score, upper lip bite test (ULBT) score temporomandibular joint (TMJ) mobility and tongue thickness (TT). Univariate and the Akaike information criterion (AIC) stepwise logistic regression were used to identify independent predictors of DTI and DL. Nomograms were constructed to predict DL and DTL based on the AIC stepwise analysis results. Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the nomograms. RESULTS: Among the 2254 patients enrolled in this study, 142 (6.30%) patients had DL and 51 (2.26%) patients had DTI. After AIC stepwise analysis, ULBT, MMT, sex, TMJ, age, BMI, TMD, IID, and TT were integrated for DL nomogram; ULBT, TMJ, age, IID, TT were integrated for DTI nomogram. The areas under the ROC curves were 0.933 [95% confidence interval (CI), 0.912–0.954] and 0.974 (95% CI, 0.954–0.995) for DL and DTI, respectively. CONCLUSION: Nomograms based on airway ultrasonography could be a reliable tool in predicting DA. TRIAL REGISTRATION: Chinese Clinical Trial Registry (No. ChiCTR-RCS-14004539), registered on 13th April 2014. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12871-022-01567-y. BioMed Central 2022-01-13 /pmc/articles/PMC8756724/ /pubmed/35026991 http://dx.doi.org/10.1186/s12871-022-01567-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Wang, Bin
Yao, Weidong
Xue, Qi
Wang, Mingfang
Xu, Jianling
Chen, Yongquan
Zhang, Ye
Nomograms for predicting difficult airway based on ultrasound assessment
title Nomograms for predicting difficult airway based on ultrasound assessment
title_full Nomograms for predicting difficult airway based on ultrasound assessment
title_fullStr Nomograms for predicting difficult airway based on ultrasound assessment
title_full_unstemmed Nomograms for predicting difficult airway based on ultrasound assessment
title_short Nomograms for predicting difficult airway based on ultrasound assessment
title_sort nomograms for predicting difficult airway based on ultrasound assessment
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756724/
https://www.ncbi.nlm.nih.gov/pubmed/35026991
http://dx.doi.org/10.1186/s12871-022-01567-y
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