The upper lip bite test (ULBT) is considered an effective method for predicting difficult airways, but data on the ULBT for predicting difficult tracheal intubation are lacking. This study aimed to examine the clinical utility of the ULBT in predicting difficult endotracheal intubation. We conducted an observational case-cohort study of adult patients undergoing elective surgery and requiring endotracheal intubation for general anesthesia. Difficult airway assessment was performed on the recruited patients before the operation, including the ULBT, mouth opening, thyromental distance, modified Mallampati test, and body mass index. The primary outcome was the incidence of difficult tracheal intubation. The receiver operating characteristic curve analysis was used to compare the performance of variables in predicting difficult tracheal intubation. We successfully recruited 2522 patients for analysis and observed 64 patients with difficult tracheal intubation. When predicting difficult tracheal intubation, grade 2 ULBT had a sensitivity of 0.75 and a specificity of 0.54, and grade 3 had a sensitivity of 0.28 and a specificity of 0.75. Compared with mouth opening, the area under the receiver operating characteristic curve of the ULBT was lower in predicting difficult tracheal intubation (0.69 [95% confidence interval: 0.67–0.71] vs. 0.84 [95% confidence interval: 0.82–0.87], P < 0.05). Clinical Trials Registry : ChiCTR-ROC-16009050, principal investigator: Weidong Yao.

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Background: The accurate prediction of difficult airway (DA) is important in ICU and general anaesthesia. Our hypothesis is that machine learning models can predict difficult tracheal intubation (DTI) and difficult laryngoscopy (DL).Methods: We performed a secondary analysis of two prospective observational DA research programmes. DTI and DL prediction models were established by machine learning based on multivariate data. Machine learning algorithms, such as logistic regression, support vector machine, and random forest, were used. Five times repeated 5-fold cross-validation were used to compare parameters such as the area under the receiver operating characteristic (ROC) curve (AUC), recall rate, accuracy and the F1 score. The feature importance of the indicators were analysed by the random forest and AdaBoost models.Results: 3958 tracheal intubation patients were included in this study. Among the five machine learning algorithms, the best AUCs were obtained by the Bayes model for DTI (0.956, 95% CI 0.950–0.961) and the random forest model for DL (0.903, 95% CI 0.895–0.911). The random forest model had the best accuracy for DTI (0.966, 95% CI 0.964–0.968) and DL (0.926, 95% CI 0.923–0.929). The random forest model also had the highest F1 score for DTI (0.361, 95% CI 0.327–0.394) and DL (0.530, 95% CI 0.492–0.569). The naïve Bayes model had the highest recall rate for DTI (0.902, 95% CI 0.877–0.927) and DL (0.809, 95% CI 0.787–0.830).Conclusions: Machine learning algorithms based on multivariate indicators were effective in predicting DA.

BackgroundThe anatomical characteristics of difficult airways can be analysed geometrically. This study aims to develop and validate a geometry-assisted difficult airway screening method (GADAS method) for difficult tracheal intubation.MethodsIn the GADAS method, a geometric simulated model was established based on computer graphics. According to the law of deformation of the upper airway on laryngoscopy, the expected visibility of the glottis was calculated to simulate the real visibility on laryngoscopy. Validation of the new method: Approved by the Ethics Committee of Yijishan Hospital of Wannan Medical College. Adult patients who needed tracheal intubation under general anaesthesia for elective surgery were enrolled. The data of patients were input into the computer software to calculate the expected visibility of the glottis. The results of tracheal intubation were recorded by anaesthesiologists. The primary observation outcome was the screening performance of the expected visibility of the glottis for difficult tracheal intubation.ResultsThe geometric model and software of the GADAS method were successfully developed and are available for use. We successfully observed 2068 patients, of whom 56 patients had difficult intubation. The area under the receiver operating characteristic curve of low expected glottis visibility for predicting difficult laryngoscopy was 0.96 (95% confidence interval [CI]: 0.95-0.96). The sensitivity and specificity were 89.3% (95% CI: 78.1-96.0%) and 94.3% (95% CI: 93.2%-95.3), respectively.ConclusionsIt is feasible to screen difficult-airway patients by applying computer techniques to simulate geometric changes in the upper airway.

Background: Hyomental distance (HMD), an anatomical feature of the upper airway, can be measured precisely by ultrasonography. But the sensitivity and specificity of HMD compared to thyromental distance (TMD) to predict difficult airways is still unknown. Methods: A case-cohort study was conducted. The written informed consent was obtained. Elective surgery adult patients undergoing general anaesthesia and tracheal intubation were recruited. The other inclusion criteria were: no maxillofacial deformity, trauma, airway stenosis, known difficult airway. The exclusion criteria were: tracheal intubations or operations were canceled, or patients' data were missing. HMD ultrasound measurements of patients in a sniffing position and other usual airway evaluations were performed before general anaesthesia induction. The primary outcome was the intubation difficulty level. Predictive values were calculated. Results: We successfully enrolled 2357 patients (62 difficult intubation patients) in the cohort study for analysis. The area under the receiver operating characteristic curve (AUC) of the HMD and TMD for predicting difficult intubation was 0.86 (95% CI, 0.84-0.87) and 0.77 (95% CI, 0.75-0.78) respectively (comparison: P < 0.001). With an optimal cut-off value of HMD < 4.9 cm, we observed a sensitivity and specificity of 0.90 (95% CI, 0.80-0.96) and 0.73 (95% CI, 0.71-0.75). Meanwhile, with TMD < 7.0 cm, the sensitivity and specificity were 0.77 (95% CI, 0.65-0.87) and 0.65 (95% CI, 0.63-0.67) respectively.Conclusion: In comparison to TMD, HMD measured by ultrasound was more sensitive in predicting difficult intubation. ?(C) 2022 Societefranc , aise d'anesthesie et de reanimation (Sfar). Published by Elsevier Masson SAS. All rights reserved.

The valid prediction of unanticipated difficult tracheal intubation (DTI) is very important in clinic anesthesia. The purpose of this study was to develop a machine learning model for predicting unanticipated DTI. A comprehensive analysis of two prospective observational difficult airway research programs was performed. In total, 3958 patients who underwent tracheal intubation were included in this study. Data were split into a training set and a test set according to 70%:30% randomly. XGBoost machine learning was used to develop a machine learning model for predicting unanticipated DTI. The F1 score was used as the main performance metric because of data imbalance. The model parameter tuning was performed in the training set via pipeline grid search with the aim of optimizing the F1 score. Then, the tuning model were used for unanticipated DTI prediction in the test set. The indicators feature importance and decision rule were analyzed. With the XGBoost machine learning model for unanticipated DTI prediction, the best F1 score of 0.500 ± 0.102 was obtained on the training set with ten-fold cross-validation. The XGBoost model had the area under the precision recall curve (AUPRC) 0.600 and the area under the receiver operating characteristic curve (AUROC) 0.924 with an F1 value of 0.57 in the test set. XGBoost was an effective machine learning model for unanticipated DTI prediction.

Abstract Background Based 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. Methods A 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. Results We 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). Conclusions The ultrasound model has good predictive performance for difficult airways. Trial registration This study is registered on chictr.org.cn (ChiCTR-ROC-17013258); principal investigator: Jianling Xu; registration date: 06/11/2017).

Abstract 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.

Objective We investigated the “BURP” maneuver’s effect on the association between difficult laryngoscopy and difficult intubation, and predictors of a difficult airway. Methods Adult patients who underwent general anesthesia and tracheal intubation from September 2016 to May 2018 were included. The “BURP” maneuver was performed when glottic exposure was classified as Cormack–Lehane grade 3 or 4, suggesting difficult laryngoscopy. The thyromental distance, modified Mallampati score, and interincisor distance were assessed before anesthesia. Results Among this study’s 2028 patients, the “BURP” maneuver decreased difficult laryngoscopies from 428 (21.1%) to 124 (6.1%) cases and increased the difficult intubation to difficult laryngoscopy ratio from 53/428 (12.4%) to 52/124 (41.9%). For laryngoscopies classified as difficult without the “BURP” maneuver, the area under the curve (AUC) of the thyromental distance, modified Mallampati score, and interincisor distance was 0.60, 0.57, and 0.66, respectively. In difficult laryngoscopies using the “BURP” maneuver, the AUC of the thyromental distance, modified Mallampati score, and interincisor distance was 0.71, 0.67, and 0.76, respectively. Conclusions The “BURP” maneuver improves the laryngoscopic view and assists in difficult laryngoscopies. Compared with difficult laryngoscopies without the “BURP” maneuver, those with the “BURP” maneuver are more closely associated with difficult intubations and are more predictable. Trial registration: www.chictr.org.cn identifier: ChiCTR-ROC- 16009050.

BACKGROUND: Compared with men, women often have a shorter interincisor distance and a shorter thyromental distance but are less likely to have difficult airway. The hypothesis is that the prediction criteria of difficult airway differ between men and women. The aim of this study was to investigate differences in the prediction criteria of anatomic predictors for difficult airways in men and women. METHODS: We enrolled adult patients who underwent general anesthesia and tracheal intubation. The interincisor distance, thyromental distance, modified Mallampati test results, upper lip bite test results, and tongue thickness of each patient were evaluated prior to the initiation of anesthesia. The primary outcome was difficult tracheal intubation. Receiver operating characteristic (ROC) curve analysis and Youden's index were used to determine the criteria for predictors in men and women. RESULTS: In total, 1059 men and 1195 women were examined. Compared with women, men had a higher incidence of difficult tracheal intubation (P<0.001). The cut-off values for predicting difficult tracheal intubation of the interincisor distance, thyromental distance. modified Mallampati test results, upper lip bite test results, and tongue thickness determined by Youden's index were <= 38 mm, <= 70 mm, >3, >2, and >62 mm, respectively, for men, and <= 33 mm, <= 65 mm, >2, >1, and >60 mm, respectively, for women. CONCLUSIONS: The optimal cut-off values of predictors of difficult airway differ between males and females.