• Jianling Xu, Bin Wang, Mingfang Wang, Weidong Yao, and Yongquan Chen.
• Department of Anesthesiology, Yijishan Hospital, the First Affiliated Hospital of Wannan Medical College, 2 Zheshanxi ST, Wuhu, 241001, China.
• BMC Anesthesiol. 2022 Oct 5; 22 (1): 311.

BackgroundBased on the upper airway anatomy and joint function parameters examined by ultrasound, a multiparameter ultrasound model for difficult airway assessment (ultrasound model) was established, and we evaluated its ability to predict difficult airways.MethodsA prospective case-cohort study of difficult airway prediction in adult patients undergoing elective surgery with endotracheal intubation under general anesthesia, and ultrasound phantom examination for difficult airway assessment before anesthesia, including hyomental distance, tongue thickness, mandibular condylar mobility, mouth opening, thyromental distance, and modified Mallampati tests, was performed. Receiver operating characteristic (ROC) curve analysis was used to evaluate the effectiveness of the ultrasound model and conventional airway assessment methods in predicting difficult airways.ResultsWe successfully enrolled 1000 patients, including 51 with difficult laryngoscopy (DL) and 26 with difficult tracheal intubation (DTI). The area under the ROC curve (AUC) for the ultrasound model to predict DL was 0.84 (95% confidence interval [CI]: 0.82-0.87), and the sensitivity and specificity were 0.75 (95% CI: 0.60-0.86) and 0.82 (95% CI: 0.79-0.84), respectively. The AUC for predicting DTI was 0.89 (95% CI: 0.87-0.91), and the sensitivity and specificity were 0.85 (95% CI: 0.65-0.96) and 0.81 (95% CI: 0.78-0.83), respectively. Compared with mouth opening, thyromental distance, and modified Mallampati tests, the ultrasound model predicted a greater AUC for DL (P < 0.05). Compared with mouth opening and modified Mallampati tests, the ultrasound model predicted a greater AUC for DTI (P < 0.05).ConclusionsThe ultrasound model has good predictive performance for difficult airways.Trial RegistrationThis study is registered on chictr.org.cn (ChiCTR-ROC-17013258); principal investigator: Jianling Xu; registration date: 06/11/2017).© 2022. The Author(s).

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