Gastroesophageal reflux disease causing a difficult airway

Identification of difficult laryngoscopy using an optimized hybrid architecture

BACKGROUND

Identification of difficult laryngoscopy is a frequent demand in cervical spondylosis clinical surgery. This work aims to develop a hybrid architecture for identifying difficult laryngoscopy based on new indexes.

METHODS

Initially, two new indexes for identifying difficult laryngoscopy are proposed, and their efficacy for predicting difficult laryngoscopy is compared to that of two conventional indexes. Second, a hybrid adaptive architecture with convolutional layers, spatial extraction, and a vision transformer is proposed for predicting difficult laryngoscopy. The proposed adaptive hybrid architecture is then optimized by determining the optimal location for extracting spatial information.

RESULTS

The test accuracy of four indexes using simple model is 0.8320. The test accuracy of optimized hybrid architecture using four indexes is 0.8482.

CONCLUSION

The newly proposed two indexes, the angle between the lower margins of the second and sixth cervical spines and the vertical direction, are validated to be effective for recognizing difficult laryngoscopy. In addition, the optimized hybrid architecture employing four indexes demonstrates improved efficacy in detecting difficult laryngoscopy.

TRIAL REGISTRATION

Ethics permission for this research was obtained from the Medical Scientific Research Ethics Committee of Peking University Third Hospital (IRB00006761-2015021) on 30 March 2015. A well-informed agreement has been received from all participants. Patients were enrolled in this research at the Chinese Clinical Trial Registry ( http://www.chictr.org.cn , identifier: ChiCTR-ROC-16008598) on 6 June 2016.

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 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.