Prediction of Difficult Mask Ventilation Using 3D-Facescan and Machine Learning (MASCAN)

Proof-of-principle Study for the 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.

Study Overview

• Status: Completed
• Conditions: General Anesthesia; Mask Ventilation

• Study Type: Observational
• Enrollment (Actual): 423

Contacts and Locations

Study Locations

• Germany
  • Hamburg, Germany, 20246
    • University Medical Center Hamburg-Eppendorf

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Consecutive sampling: Adult patients that undergo ENT or OMS surgery in a tertiary care hospital who require facemask ventilation and tracheal intubation after induction of anesthesia.

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Study cohort; Patients undergoing ENT or OMS surgery with general anesthesia with facemask ventilation and tracheal intubation (observational)

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Difficult facemask ventilation	Observed difficult facemask ventilation after induction of anesthesia	1 hour

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Difficult tracheal intubation	Observed difficult intubation after induction of anesthesia	1 hour
Difficult laryngoscopy	Observed difficult laryngoscopy after induction of anesthesia	1 hour
Number of attempts	Observed during tracheal intubation	1 hour
Failed direct laryngoscopy	Observed during airwaymanagement	1 hour
Cormack Lehane grade	Grading of the best view obtained during laryngoscopy (I-IV)	1 hour
Difficult mask ventilation alert	Noted by the responsible anaesthesiologist after airway management	1 hour
Difficult intubation alert	Noted by the responsible anaesthesiologist after airway management	1 hour
Intubation time	Recorded during airwaymanagement	1 hour
Time to sufficient mask ventilation	Recorded during airwaymanagement	1 hour
Classification of intubation difficulty	VIDIAC score rating between -1 and 5 points	1 hour
Percentage of glottis opening (POGO)	Grading of the best view obtained during laryngoscopy (%)	1 hour
Impossible facemask ventilation	Observed impossible facemask ventilation after induction of anesthesia	1 hour
Successful first attempt intubation	Observed during airway management	1 hour
Airway-related adverse events	Laryngospasm, bronchospasm, larynx trauma, airway trauma, soft tissue trauma, oral bleeding, edema, dental damage, corticosteroid application, accidental esophageal intubation, aspiration, hypotension or hypoxia	1 hour
Post-intubation recommendation for an intubation method	Recommendation of the responsible anaesthesiologist after airwaymanagement	1 hour
Minimal peripheral oxygen saturation (SpO2)	Observed after induction of anesthesia	1 hour

Collaborators and Investigators

• Sponsor: Universitätsklinikum Hamburg-Eppendorf
• Collaborators: Institute of Medical Technology and Intelligent Systems at Hamburg University of Technology
• Investigators: Principal Investigator: Martin Petzoldt, MD, Universitätsklinikum Hamburg-Eppendorf

Study record dates

Study Major Dates

• Study Start (Actual): November 7, 2022
• Primary Completion (Actual): May 15, 2023
• Study Completion (Actual): May 15, 2023

Study Registration Dates

• First Submitted: June 6, 2022
• First Submitted That Met QC Criteria: June 6, 2022
• First Posted (Actual): June 9, 2022

Study Record Updates

• Last Update Posted (Actual): September 26, 2023
• Last Update Submitted That Met QC Criteria: September 24, 2023
• Last Verified: September 1, 2023

More Information

Terms related to this study

• Keywords: Airway management; Risk prediction
• Other Study ID Numbers: 2022-100811-BO-ff

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No