ThyroidNCT05176184
A Deep Learning Method to Predict Difficult Laryngoscopy Using Cervical Spine X-ray Image
An observational study of Thyroid and Surgery, run by Seoul National University Hospital at 1 site in South Korea.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Thyroid, Surgery, Intubation; Difficult or Failed
- What is being tested?
- A deep learning model for predicting a difficult laryngoscopy based on a cervical spine lateral X-ray image
- How many people
- 367 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Dec 2021to 25 Nov 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients who undergoing thyroid surgery under general anesthesia
Who may be able to join
- elective thyroid surgery under general anesthesia
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Hyung-Chul Lee, MD, PhD contact
South Korea1 site, 1 recruiting
- Recruiting
Seoul National University Hospital
Seoul, Select A State Or Province, 03080
Hye-yeon Cho, MD, bdbd7799@gmail.com
About this study
Official title: A Deep Learning Method to Predict Difficult Laryngoscopy Using Cervical Spine X-ray Image: Prospective Validation Study
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.
Read the 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.
- Conditions
- Thyroid, Surgery, Intubation; Difficult or Failed
- Being tested
- A deep learning model for predicting a difficult laryngoscopy based on a cervical spine lateral X-ray image
- Keywords
- deep learning, cervical spine X-ray, difficult laryngoscopy
- Medical subject headings
- Thyroid Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
A deep learning model for predicting a difficult laryngoscopy based on a cervical spine lateral X-ray imageDiagnostic test
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
The area under the receiver operating characteristic curve of deep learning model and airway evaluations for predicting a difficult laryngoscopy.
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
Measured: during induction of anesthesia
Secondary outcomes (2)
The area under the receiver operating characteristic curve of deep learning model and airway evaluations for predicting a difficult intubation.
Difficult intubation: Intubation Difficulty Scale (score)
Measured: during induction of anesthesia
Other Performances for predicting a difficult laryngoscopy of deep learning model.
sensitivity (percent), specificity(percent), Positive predictive value(percent), Negative predictive value (percent), F1-score, and balanced accuracy.
Measured: during induction of anesthesia
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jan 2022
- Study start1 Dec 2021
- Primary completion25 Nov 2022 (estimated)
- Study completion25 Nov 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for ThyroidStudies of the same condition, open and under way.
- More trials run by Seoul National University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 367 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Seoul National University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Hyung-Chul Lee, Study chair, Seoul National University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05176184
- Sponsor's protocol ID
- 2111-111-1272
- Status last verified
- Dec 2021
- First submitted
- 12 Dec 2021
- Last updated
- 4 Jan 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 23242753Lundstrom 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.
PubMed 21948956De 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.
PubMed 31922378
Study at a glance
From the registry record, last updated by the sponsor on 4 Jan 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Deep Learning Method to Predict Difficult Laryngoscopy Using Cervical Spine X-ray Image: Prospective Validation Study
- ClinicalTrials.gov ID
- NCT05176184
- Acronym / sponsor ID
- 2111-111-1272
- Conditions
- Thyroid, Surgery, Intubation; Difficult or Failed
- Keywords
- deep learning, cervical spine X-ray, difficult laryngoscopy
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 367 participants (estimated)
- Lead sponsor
- Seoul National University HospitalUniversity, hospital or other
- Study start
- 1 Dec 2021 (actual)
- Primary completion
- 25 Nov 2022 (estimated)
- Study completion
- 25 Nov 2022 (estimated)
- First posted
- 4 Jan 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who undergoing thyroid surgery under general anesthesia
Key inclusion criteria (1 listed)
- elective thyroid surgery under general anesthesia
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| A deep learning model for predicting a difficult laryngoscopy based on a cervical spine lateral X-ray imageThe 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. | Diagnostic test | – |
Primary outcome
The area under the receiver operating characteristic curve of deep learning model and airway evaluations for predicting a difficult laryngoscopy.
Time frame: 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
2 secondary outcomes
The area under the receiver operating characteristic curve of deep learning model and airway evaluations for predicting a difficult intubation.
Time frame: during induction of anesthesia
Other Performances for predicting a difficult laryngoscopy of deep learning model.
Time frame: during induction of anesthesia
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Jan 2022.
- Background: PubMed 23242753Cook 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.
- Background: PubMed 21948956Lundstrom 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.
- Background: PubMed 31922378De 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.
- VGG-NetLink provided by the sponsor.
- ResNetLink provided by the sponsor.
- XceptionLink provided by the sponsor.
- ResNextLink provided by the sponsor.
- DenseNetLink provided by the sponsor.
- SENetLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Hyung-Chul Lee, MD, PhD contact
Hyung-Chul Lee study chair
Seoul National University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Thyroid and Surgery, run by Seoul National University Hospital at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 4 Jan 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Hye-yeon Cho, MD (+82-10-3808-7110, bdbd7799@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The area under the receiver operating characteristic curve of deep learning model and airway evaluations for predicting a difficult laryngoscopy. (time frame: during induction of anesthesia). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05176184, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Jan 2022.