Facial Analysis to Classify Difficult Intubation

This study is currently recruiting participants. (see Contacts and Locations)
Verified September 2014 by Tufts Medical Center
Sponsor:
Information provided by (Responsible Party):
Tufts Medical Center
ClinicalTrials.gov Identifier:
NCT01612949
First received: June 4, 2012
Last updated: September 11, 2014
Last verified: September 2014

June 4, 2012
September 11, 2014
May 2012
June 2015   (final data collection date for primary outcome measure)
Computer algorithm to predict difficulty of endotracheal intubation [ Time Frame: Approximately 1 year, based on current enrollment pattern ] [ Designated as safety issue: Yes ]
The outcome will be a computer algorithm that can accurately predict how easy or difficult it is to intubate a patient based upon digital photographs from three different perspectives. Such an application can provide a consistent, quantitative measure of intubation difficulty by analyzing facial features in captured photographs-features which have previously been shown to correlate with how easy or how hard it would be to perform the intubation procedure. A digital application has the potential to decrease complications related to intubation difficulty and increase patient safety.
Same as current
Complete list of historical versions of study NCT01612949 on ClinicalTrials.gov Archive Site
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Facial Analysis to Classify Difficult Intubation
Comparison of a Computerized Image Analysis to Conventional Airway Examination Techniques to Predict Difficult Endotracheal Intubation

The aim of this project is to develop a computer algorithm that can accurately predict how easy or difficult it is to intubate a patient based upon digital photographs from three different perspectives. Such an application can provide a consistent, quantitative measure of intubation difficulty by analyzing facial features in captured photographs - features which have previously been shown to correlate with how easy or how hard it would be to perform the intubation procedure. This is in contrast to established subjective protocols that also serve to predict intubation difficulty, albeit with lower accuracy. A digital application has the potential to decrease potential complications related to intubation difficulty and increase patient safety.

Both control and experimental cohorts will be recruited in this study. In order to drive clinical acceptance of this technique, the investigators will need to study and then demonstrate applicability to all patients regardless of race or gender. This will require the recruitment of a control population of patients who have been demonstrated at surgery to be easy to intubate. Such patients are in relative abundance. The experimental group will consist of patients who are found at surgery to be difficult to intubate. Patients are defined as easy to intubate if their anesthetic record described a single attempt with a Macintosh 3 blade resulting in a grade 1 laryngoscopic view (full exposure of the vocal cords). Difficult intubation was defined by at least one of: more than one attempt by an operator with at least one year of anesthesia experience, grade 3 or 4 laryngoscopic view on a 4 point scale, 5 need for a second operator, or non-elective use of an alternative airway device such as a bougie, fiberoptic bronchoscope or intubating laryngeal mask airway.

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.

Observational
Observational Model: Cohort
Time Perspective: Prospective
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Non-Probability Sample

Patients undergoing surgical procedures requiring general anesthesia with endotracheal intubation; patients from all ethnic groups

Difficult Intubation
Other: 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 derivation
    Intervention: Other: photographing head and neck
  • difficult to intubate, model derivation
    Intervention: Other: photographing head and neck
  • easy to intubate, model validation
    Intervention: Other: photographing head and neck
  • difficult to intubate, model validation
    Intervention: Other: photographing head and neck
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.

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruiting
310
June 2015
June 2015   (final data collection date for primary outcome measure)

Inclusion Criteria:

  • Patients requiring endotracheal intubation
  • Patients consenting to acquisition of photographic images of the head and neck

Exclusion Criteria:

  • Patients who had undergone head or neck surgery
  • 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
Both
18 Years to 64 Years
No
Contact: Scott Segal, MD, MHCM 617-636-6044 ssegal@tuftsmedicalcenter.org
Contact: Iwona Bonney, PhD 617-636-9322 ibonney@tuftsmedicalcenter.org
United States
 
NCT01612949
IRB#9929
No
Tufts Medical Center
Tufts Medical Center
Not Provided
Principal Investigator: Scott Segal, MD, MHCM Tufts Medical Center
Tufts Medical Center
September 2014

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP