University Medical Center Hamburg-Eppendorf
Hamburg, 20246, Germany
Completed
NCT Number: NCT05411406
Prediction of Difficult Mask Ventilation Using 3D-Facescan and Machine Learning
The aim of this study is to prove feasibility and assess the diagnostic performance of a machine learning algorithm that relies on data from 3D-face scans with predefined motion-sequences and scenes (MASCAN algorithm), together with patient-specific meta-data for the prediction of difficult mask ventilation. A secondary aim of the study is to verify whether voice and breathing scans improve the performance of the algorithm. From the clinical point of view, we believe that an automated assessment would be beneficial, as it preserves time and health-care resources while acting observer-independent, thus providing a rational, reproducible risk estimation.
Completed
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Notify MeKey information
Conditions
Age range
18 year and older
Sex eligibility
All sexes
Study type
Observational
Primary location
Who can participate
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
- Patients scheduling for ENT or OMS surgery in general anaesthesia, who require facemask ventilation and tracheal intubation after induction of anesthesia
- Patients aged at least 18 years
- Ability to understand the patient information and to personally sign and date the informed consent to participate in the study
- The patient is co-operative and available for the entire study
- Provided informed consent/patient representative
Exclusion criteria
- Pregnant or breastfeeding woman
- Rapid sequence induction or other contraindications for facemask ventilation
- Planned awake tracheal intubation
Treatment and study plan
Primary outcomes
-
Difficult facemask ventilation
Time frame: 1 hour
Observed difficult facemask ventilation after induction of anesthesia
Secondary outcomes
-
Difficult tracheal intubation
Time frame: 1 hour
Observed difficult intubation after induction of anesthesia
-
Difficult laryngoscopy
Time frame: 1 hour
Observed difficult laryngoscopy after induction of anesthesia
-
Number of attempts
Time frame: 1 hour
Observed during tracheal intubation
-
Failed direct laryngoscopy
Time frame: 1 hour
Observed during airwaymanagement
-
Cormack Lehane grade
Time frame: 1 hour
Grading of the best view obtained during laryngoscopy (I-IV)
-
Difficult mask ventilation alert
Time frame: 1 hour
Noted by the responsible anaesthesiologist after airway management
-
Difficult intubation alert
Time frame: 1 hour
Noted by the responsible anaesthesiologist after airway management
-
Intubation time
Time frame: 1 hour
Recorded during airwaymanagement
-
Time to sufficient mask ventilation
Time frame: 1 hour
Recorded during airwaymanagement
-
Classification of intubation difficulty
Time frame: 1 hour
VIDIAC score rating between -1 and 5 points
-
Percentage of glottis opening (POGO)
Time frame: 1 hour
Grading of the best view obtained during laryngoscopy (%)
-
Impossible facemask ventilation
Time frame: 1 hour
Observed impossible facemask ventilation after induction of anesthesia
-
Successful first attempt intubation
Time frame: 1 hour
Observed during airway management
-
Airway-related adverse events
Time frame: 1 hour
Laryngospasm, bronchospasm, larynx trauma, airway trauma, soft tissue trauma, oral bleeding, edema, dental damage, corticosteroid application, accidental esophageal intubation, aspiration, hypotension or hypoxia
-
Post-intubation recommendation for an intubation method
Time frame: 1 hour
Recommendation of the responsible anaesthesiologist after airwaymanagement
-
Minimal peripheral oxygen saturation (SpO2)
Time frame: 1 hour
Observed after induction of anesthesia
Sponsors and collaborators
Lead sponsor
Universitätsklinikum Hamburg-Eppendorf
Other
Collaborators
- Institute of Medical Technology and Intelligent Systems at Hamburg University of Technology
Registry information
Official study title
Proof-of-principle Study for the Prediction of Difficult Mask Ventilation Using 3D-Facescan and Machine Learning
Acronym: MASCAN
Important dates
- Study start
- 2022
- Primary completion
- 2023
- Study completion
- 2023
- First posted
- Jun 9, 2022
- Registry last updated
- Sep 26, 2023
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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