Intubation; Difficult or Failed, Surgery, Thyroid
Conditions
Keywords
deep learning, cervical spine X-ray, difficult laryngoscopy
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.
Interventions
DIAGNOSTIC_TESTA deep learning model for predicting a difficult laryngoscopy based on a cervical spine lateral X-ray image
The deep learning model uses the input of preprocessed C-spine lateral X-ray images and outputs the level of difficulty of a laryngoscopy. The easy laryngoscopy is defined as a combination of the Cormack-Lehane grades 1-2 and the difficult laryngoscopy is defined as a combination of grades 3-4. In addition, before general anesthesia, airway evaluations related to the difficulty of laryngoscopy are performed and the results are compared with the actual level of difficulty.
Sponsors
Seoul National University Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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) |
| 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. |
Countries
South Korea
Contacts
Primary ContactHye-yeon Cho, MD
Backup ContactHyung-Chul Lee, MD, PhD
Outcome results
None listed