Clinical trial NCT05176184: A Deep Learning Method to Predict Difficult Laryngoscopy Using Cervical Spine X-ray Image
Official title: A Deep Learning Method to Predict Difficult Laryngoscopy Using Cervical Spine X-ray Image: Prospective Validation Study
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Thyroid
Surgery
Intubation; Difficult or Failed
Keywords: deep learning
cervical spine X-ray
difficult laryngoscopy
Lead sponsor: Seoul National University Hospital
Sponsor class: OTHER
First posted: 2022-01-04
Enrollment: 367
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients who undergoing thyroid surgery under general anesthesia

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.

Eligibility criteria: 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

Primary outcomes:
measure: The area under the receiver operating characteristic curve of deep learning model and airway evaluations for predicting a difficult laryngoscopy.
timeFrame: during induction of anesthesia
Secondary outcomes:
measure: The area under the receiver operating characteristic curve of deep learning model and airway evaluations for predicting a difficult intubation.
timeFrame: during induction of anesthesia
measure: Other Performances for predicting a difficult laryngoscopy of deep learning model.
timeFrame: during induction of anesthesia
Other outcomes:
Locations:
city: Seoul
country: Korea, Republic of
facility: Seoul National University Hospital
state: Select A State Or Province
Study officials:
affiliation: Seoul National University Hospital
name: Hyung-Chul Lee
role: STUDY_CHAIR
References:
citation: Cook TM, MacDougall-Davis SR. Complications and failure of airway management. Br J Anaesth. 2012 Dec;109 Suppl 1:i68-i85. doi: 10.1093/bja/aes393.
citation: Lundstrom LH, Vester-Andersen M, Moller AM, Charuluxananan S, L'hermite J, Wetterslev J; Danish Anaesthesia Database. Poor prognostic value of the modified Mallampati score: a meta-analysis involving 177 088 patients. Br J Anaesth. 2011 Nov;107(5):659-67. doi: 10.1093/bja/aer292. Epub 2011 Sep 26.
citation: De Cassai A, Boscolo A, Rose K, Carron M, Navalesi P. Predictive parameters of difficult intubation in thyroid surgery: a meta-analysis. Minerva Anestesiol. 2020 Mar;86(3):317-326. doi: 10.23736/S0375-9393.19.14127-2. Epub 2020 Jan 8.