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

NCT05411406 | Completed

• 1 other identifier: 2022-100811-BO-ff
• Study Type: observational
• Target: 423
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Mask Ventilation; General Anesthesia

Keywords

Risk prediction; Airway management

Outcome Measures

Primary Outcomes (1)

• Difficult facemask ventilation

  Observed difficult facemask ventilation after induction of anesthesia

  1 hour

Secondary Outcomes (16)

• Difficult tracheal intubation

  1 hour

• Difficult laryngoscopy

  1 hour

• Number of attempts

  1 hour

• Failed direct laryngoscopy

  1 hour

• Cormack Lehane grade

  1 hour

Study Arms (1)

Study cohort

Patients undergoing ENT or OMS surgery with general anesthesia with facemask ventilation and tracheal intubation (observational)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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

You may not qualify if:

• Pregnant or breastfeeding woman
• Rapid sequence induction or other contraindications for facemask ventilation
• Planned awake tracheal intubation

Sponsors & Collaborators

• Universitätsklinikum Hamburg-Eppendorf: lead
• Institute of Medical Technology and Intelligent Systems at Hamburg University of Technology: collaborator

Study Sites (1)

University Medical Center Hamburg-Eppendorf

Hamburg, 20246, Germany

Location

Study Officials

• Martin Petzoldt, MD

  Universitätsklinikum Hamburg-Eppendorf

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 6, 2022
• First Posted: June 9, 2022
• Study Start: November 7, 2022
• Primary Completion: May 15, 2023
• Study Completion: May 15, 2023
• Last Updated: September 26, 2023

Record last verified: 2023-09

Locations

DE (1)