Effectiveness of four ultrasonographic parameters as predictors of difficult intubation in patients without anticipated difficult airway

四项超声参数作为预测无预期困难气道患者插管困难的指标的有效性

阅读:1

Abstract

BACKGROUND: Predicting difficult intubation (DI) is a key challenge, as no single clinical predictor is sufficiently valid to predict the outcome. We evaluated the effectiveness of four upper airway ultrasonographic parameters in predicting DI. The validity of the models using combinations of ultrasonography-based parameters was also investigated. METHODS: This prospective, observational, double-blinded cohort trial enrolled 1,043 surgical patients classified as American Society of Anesthesiologists physical status I-III without anticipated difficult airway. Preoperatively, their tongue thickness (TT), invisibility of hyoid bone (VH), and anterior neck soft tissue thickness from the skin to thyrohyoid membrane (ST) and hyoid bone (SH) were measured by sublingual and submandibular ultrasonography. The logistic regression, Youden index, and receiver operator characteristic analysis results were reported. RESULTS: Overall, 58 (5.6%) patients were classified as DI. The TT, SH, ST, and VH had accuracies of 78.4%, 85.0%, 84.7%, and 84.9%, respectively. The optimal values of TT, SH, and ST for predicting DI were > 5.8 cm (sensitivity: 84.5%, specificity: 78.1%, AUC: 0.880), > 1.4 cm (sensitivity: 81%, specificity: 85.2%, AUC: 0.898) and > 2.4 cm (sensitivity: 75.9%, specificity: 85.2%, AUC: 0.885) respectively. VH had a sensitivity and specificity of 72.4% and 85.6% (AUC: 0.790). The AUC values of the five models (with combinations of three or four parameters) ranged from 0.975-0.992. ST and VH had a significant impact on the individual models. CONCLUSIONS: SH had the best accuracy. Individual parameters showed limited validity. The model including all four parameters offered the best diagnostic value.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。