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

Sponsor
Seoul National University Hospital (Other)
Overall Status
Recruiting
CT.gov ID
NCT05176184
Collaborator
(none)
367
1
11.8
31.1

Study Details

Study Description

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.

Condition or Disease Intervention/Treatment Phase
  • Diagnostic Test: A deep learning model for predicting a difficult laryngoscopy based on a cervical spine lateral X-ray image

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 Design

Study Type:
Observational
Anticipated Enrollment :
367 participants
Observational Model:
Cohort
Time Perspective:
Prospective
Official Title:
A Deep Learning Method to Predict Difficult Laryngoscopy Using Cervical Spine X-ray Image: Prospective Validation Study
Actual Study Start Date :
Dec 1, 2021
Anticipated Primary Completion Date :
Nov 25, 2022
Anticipated Study Completion Date :
Nov 25, 2022

Outcome Measures

Primary Outcome Measures

  1. 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

  1. 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)

  2. 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.

Eligibility Criteria

Criteria

Ages Eligible for Study:
18 Years and Older
Sexes Eligible for Study:
All
Accepts 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

Contacts and Locations

Locations

Site City State Country Postal Code
1 Seoul National University Hospital Seoul Select A State Or Province Korea, Republic of 03080

Sponsors and Collaborators

  • Seoul National University Hospital

Investigators

  • Study Chair: Hyung-Chul Lee, Seoul National University Hospital

Study Documents (Full-Text)

None provided.

More Information

Additional Information:

Publications

Responsible Party:
Seoul National University Hospital
ClinicalTrials.gov Identifier:
NCT05176184
Other Study ID Numbers:
  • 2111-111-1272
First Posted:
Jan 4, 2022
Last Update Posted:
Jan 4, 2022
Last Verified:
Dec 1, 2021
Individual Participant Data (IPD) Sharing Statement:
No
Plan to Share IPD:
No
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Keywords provided by Seoul National University Hospital

Study Results

No Results Posted as of Jan 4, 2022