A Deep Learning Method to Predict Difficult Laryngoscopy Using Cervical Spine X-ray Image

• Conditions: Surgery; Thyroid; Intubation; Difficult or Failed

• Registration Number: NCT05176184
• Lead Sponsor: Seoul National University Hospital

Brief Summary

An unanticipated difficult laryngoscopy is associated with serious airway-related complications. The investigators developed a deep learning-based model that predicts a difficult laryngoscopy (Cormack-Lehane grade 3-4) from a cervical spine lateral X-ray using data from 14,135 patients undergoing thyroid surgery. This model showed excellent predictive performance, which was higher than that of other deep learning architectures. In this study, the investigators prospectively validate the model for predicting a difficult laryngoscopy and compare predictive power with clinical airway evaluation.

Detailed Description

Predicting a difficulty of a laryngoscopy is important for patient safety, as an unanticipated difficult laryngoscopy is associated with serious airway-related complications, such as brain damage, cardiopulmonary arrest, or death. Although clinical predictors, such as the modified Mallampati classification, thyromental distance, inter-incisor gap, and the upper lip bite test, are used for airway evaluation in clinical practice, these indicators have low sensitivity and large inter-assessor variability and require patient cooperation.

The investigators developed a deep learning-based model that predicts a difficult laryngoscopy from a cervical spine lateral X-ray using data from 14,135 patients undergoing thyroid surgery. And this study is under submission.

This deep learning model showed the highest performance in predicting difficult laryngoscopy compared to other deep learning models (VGG-Net, ResNet, Xception, ResNext, DenseNet, and SENet) with a sensitivity of 95.6%, a specificity of 91.2%, and an area under ROC curve (AUROC) of 0.972.

However, as the model was a retrospective design using existing medical records, the presence or absence of cricoid pressure to obtain the optimal laryngoscopy was not evaluated, and not compared with airway evaluations.

In this study, the investigators prospectively validate the model for predicting a difficult laryngoscopy and compare predictive power with clinical airway evaluation. If this study prospective confirm our results, this approach can be helpful in improving patient safety and preventing airway-related complications through objective and accurate airway evaluation.

Study Timeline

• Status Verified: 2021-12
• Study Start: 2021-12-01
• Study First Submitted: 2021-12-12
• Study First Submitted QC: 2022-01-03
• Last Update Submitted: 2022-01-03
• Study First Posted: 2022-01-04
• Last Update Posted: 2022-01-04
• Primary Completion: 2022-11-25
• Study Completion: 2022-11-25

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 367

Inclusion Criteria

• elective thyroid surgery under general anesthesia

Exclusion Criteria

• age < 18 years
• no C-spine lateral X-ray image obtained within 3 months before surgery
• Patient who safety is not guaranteed when using a direct laryngoscope. (poor dental condition, risk of neck extension)
• Patients who not cooperate with the physical examination for airway evaluation

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The area under the receiver operating characteristic curve of deep learning model and airway evaluations for predicting a difficult laryngoscopy.	during induction of anesthesia	Difficult laryngoscopy definition: Cormack-Lehane grade 3 or 4 . Airway evaluations: Inter-incisor gap (millimeter), thyromental distance (millimeter), thyromental height (millimeter), sternomental distance (millimeter), and modified Mallampati class

Secondary Outcome Measures

Name	Time	Method
Other Performances for predicting a difficult laryngoscopy of deep learning model.	during induction of anesthesia	sensitivity (percent), specificity(percent), Positive predictive value(percent), Negative predictive value (percent), F1-score, and balanced accuracy.
The area under the receiver operating characteristic curve of deep learning model and airway evaluations for predicting a difficult intubation.	during induction of anesthesia	Difficult intubation: Intubation Difficulty Scale (score)

Trial Locations

Locations (1)

Seoul National University Hospital; 🇰🇷 Seoul, Select A State Or Province, Korea, Republic of