Mask VentilationMASCANNCT05411406
Prediction of Difficult Mask Ventilation Using 3D-Facescan and Machine Learning
An observational study of Mask Ventilation and General Anesthesia, run by Universitätsklinikum Hamburg-Eppendorf at 1 site in Germany.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Mask Ventilation, General Anesthesia
- How many people
- 423 participants
- Where
- 1 siteGermany
- Study dates
- 7 Nov 2022to 15 May 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- Pregnant or breastfeeding woman
- Rapid sequence induction or other contraindications for facemask ventilation
- Planned awake tracheal intubation
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
- Conditions
- Mask Ventilation, General Anesthesia
- Keywords
- Risk prediction, Airway management
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Study cohort | Patients undergoing ENT or OMS surgery with general anesthesia with facemask ventilation and tracheal intubation (observational) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Difficult facemask ventilation
Observed difficult facemask ventilation after induction of anesthesia
Measured: 1 hour
Secondary outcomes (16)
Difficult tracheal intubation
Observed difficult intubation after induction of anesthesia
Measured: 1 hour
Difficult laryngoscopy
Observed difficult laryngoscopy after induction of anesthesia
Measured: 1 hour
Number of attempts
Observed during tracheal intubation
Measured: 1 hour
Failed direct laryngoscopy
Observed during airwaymanagement
Measured: 1 hour
Cormack Lehane grade
Grading of the best view obtained during laryngoscopy (I-IV)
Measured: 1 hour
Difficult mask ventilation alert
Noted by the responsible anaesthesiologist after airway management
Measured: 1 hour
Difficult intubation alert
Noted by the responsible anaesthesiologist after airway management
Measured: 1 hour
Intubation time
Recorded during airwaymanagement
Measured: 1 hour
Time to sufficient mask ventilation
Recorded during airwaymanagement
Measured: 1 hour
Classification of intubation difficulty
VIDIAC score rating between -1 and 5 points
Measured: 1 hour
Percentage of glottis opening (POGO)
Grading of the best view obtained during laryngoscopy (%)
Measured: 1 hour
Impossible facemask ventilation
Observed impossible facemask ventilation after induction of anesthesia
Measured: 1 hour
Successful first attempt intubation
Observed during airway management
Measured: 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
Measured: 1 hour
Post-intubation recommendation for an intubation method
Recommendation of the responsible anaesthesiologist after airwaymanagement
Measured: 1 hour
Minimal peripheral oxygen saturation (SpO2)
Observed after induction of anesthesia
Measured: 1 hour
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jun 2022
- Study start7 Nov 2022
- Primary completion15 May 2023
- Study completion15 May 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Mask VentilationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 423 participants (actual)
Sponsor and investigators
- Lead sponsor
- Universitätsklinikum Hamburg-Eppendorf
- Sponsor type
- University, hospital or other
- Collaborators
- Institute of Medical Technology and Intelligent Systems at Hamburg University of Technology
- Responsible party
- Sponsor
- Investigators
- Martin Petzoldt, MD, Principal investigator, Universitätsklinikum Hamburg-Eppendorf
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05411406
- Sponsor's protocol ID
- 2022-100811-BO-ff
- Acronym
- MASCAN
- Status last verified
- Sep 2023
- First submitted
- 6 Jun 2022
- Last updated
- 26 Sep 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 26 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Proof-of-principle Study for the Prediction of Difficult Mask Ventilation Using 3D-Facescan and Machine Learning
- ClinicalTrials.gov ID
- NCT05411406
- Acronym / sponsor ID
- MASCAN / 2022-100811-BO-ff
- Conditions
- Mask Ventilation, General Anesthesia
- Keywords
- Risk prediction, Airway management
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 423 participants (actual)
- Lead sponsor
- Universitätsklinikum Hamburg-EppendorfUniversity, hospital or other
- Collaborators
- Institute of Medical Technology and Intelligent Systems at Hamburg University of Technology
- Study start
- 7 Nov 2022 (actual)
- Primary completion
- 15 May 2023 (actual)
- Study completion
- 15 May 2023 (actual)
- First posted
- 9 Jun 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Study cohort | – | –Patients undergoing ENT or OMS surgery with general anesthesia with facemask ventilation and tracheal intubation (observational) |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (3 listed)
- Pregnant or breastfeeding woman
- Rapid sequence induction or other contraindications for facemask ventilation
- Planned awake tracheal intubation
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Difficult facemask ventilation
Time frame: 1 hour
Observed difficult facemask ventilation after induction of anesthesia
16 secondary outcomes
Difficult tracheal intubation
Time frame: 1 hour
Difficult laryngoscopy
Time frame: 1 hour
Number of attempts
Time frame: 1 hour
Failed direct laryngoscopy
Time frame: 1 hour
Cormack Lehane grade
Time frame: 1 hour
Difficult mask ventilation alert
Time frame: 1 hour
Difficult intubation alert
Time frame: 1 hour
Intubation time
Time frame: 1 hour
Time to sufficient mask ventilation
Time frame: 1 hour
Classification of intubation difficulty
Time frame: 1 hour
Percentage of glottis opening (POGO)
Time frame: 1 hour
Impossible facemask ventilation
Time frame: 1 hour
Successful first attempt intubation
Time frame: 1 hour
Airway-related adverse events
Time frame: 1 hour
Post-intubation recommendation for an intubation method
Time frame: 1 hour
Minimal peripheral oxygen saturation (SpO2)
Time frame: 1 hour
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Sep 2023.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Martin Petzoldt, MD principal investigator
Universitätsklinikum Hamburg-Eppendorf
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Mask Ventilation and General Anesthesia, run by Universitätsklinikum Hamburg-Eppendorf at 1 site in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 26 Sep 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Difficult facemask ventilation (time frame: 1 hour). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Sep 2023.