Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
3500 participants
OBSERVATIONAL
2012-05-31
2026-12-31
Brief Summary
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Detailed Description
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The primary purpose of the study is to develop algorithms capable of discriminating patients who are likely to be difficult to intubate from those who are likely to be easy to intubate based on facial appearance. The primary analysis is the demonstration of statistical significance in the ability of the derived algorithms to determine successfully whether a subject was easy or difficult to intubate. A secondary analysis is the demonstration of a statistical difference in performance between the derived algorithms versus conventional airway assessment tests.
An ongoing modification of the protocol will allow anesthesia personnel to attempt intubation with a Miller (straight) laryngoscope blade, instead of a Macintosh blade. This secondary outcome was chosen because of the strong preference, and in some cases, better skill, with such a blade. It is possible that different facial features will predict difficulty with this blade than has been predicted to date with a Macintosh blade.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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easy to intubate, model derivation
easy to intubate, model derivation. photographing head and neck
photographing head and neck
Taking three photographs of head and neck-one photograph from front, one from left and one fron right. The photographs are analyzed by facial structure software to create face model.
difficult to intubate, model derivation
difficult to intubate, model derivation.photographing head and neck
photographing head and neck
Taking three photographs of head and neck-one photograph from front, one from left and one fron right. The photographs are analyzed by facial structure software to create face model.
easy to intubate, model validation
easy to intubate, model validation. photographing head and neck
photographing head and neck
Taking three photographs of head and neck-one photograph from front, one from left and one fron right. The photographs are analyzed by facial structure software to create face model.
difficult to intubate, model validation
difficult to intubate, model validation. photographing head and neck
photographing head and neck
Taking three photographs of head and neck-one photograph from front, one from left and one fron right. The photographs are analyzed by facial structure software to create face model.
Test
A group of unlabeled subjects (mix of easy and difficult intubations) to test the reproducibility of the derived and validated model(s)
photographing head and neck
Taking three photographs of head and neck-one photograph from front, one from left and one fron right. The photographs are analyzed by facial structure software to create face model.
Interventions
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photographing head and neck
Taking three photographs of head and neck-one photograph from front, one from left and one fron right. The photographs are analyzed by facial structure software to create face model.
Eligibility Criteria
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Inclusion Criteria
* Patients consenting to acquisition of photographic images of the head and neck
Exclusion Criteria
* Patients in whom central venous catheters or other interventions that prevent full view of the features of the face in frontal and profile views
* Patients who were neither easy nor difficult to intubate by our criteria
18 Years
99 Years
ALL
Yes
Sponsors
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Wake Forest University Health Sciences
OTHER
Responsible Party
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Principal Investigators
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Scott Segal, MD, MHCM
Role: PRINCIPAL_INVESTIGATOR
Wake Forest University Health Sciences
Locations
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Wake Forest Baptist Medical Center
Winston-Salem, North Carolina, United States
Countries
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Central Contacts
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Facility Contacts
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Scott Segal, MD
Role: primary
References
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Connor CW, Segal S. Accurate classification of difficult intubation by computerized facial analysis. Anesth Analg. 2011 Jan;112(1):84-93. doi: 10.1213/ANE.0b013e31820098d6. Epub 2010 Nov 16.
Tavolara TE, Gurcan MN, Segal S, Niazi MKK. Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models. Comput Biol Med. 2021 Sep;136:104737. doi: 10.1016/j.compbiomed.2021.104737. Epub 2021 Aug 4.
Other Identifiers
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IRB00036442
Identifier Type: -
Identifier Source: org_study_id
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