Prediction of Difficult Mask Ventilation Using 3D-Facesca... | NCT05411406

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

NCT05411406

Sponsor

Universitätsklinikum Hamburg-Eppendorf

Status

Completed

Last Update Posted

Sep 26, 2023Actual

Enrollment

423Actual

Phase

Not provided

Conditions

Mask Ventilation
General Anesthesia

Interventions

Not provided

Countries

Germany

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Sep 2023

Overall Recruitment Status or Expanded Access Status

Completed

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Nov 7, 2022Actual

Primary Completion Date

May 15, 2023Actual

Completion Date

May 15, 2023Actual

First Submitted Date

Jun 6, 2022

First Submission Date that Met QC Criteria

Jun 6, 2022

First Posted Date

Jun 9, 2022Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Sep 24, 2023

Last Update Posted Date

Sep 26, 2023Actual

Name	Class
Institute of Medical Technology and Intelligent Systems at Hamburg University of Technology	UNKNOWN

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

423Actual

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
Study cohort		Patients undergoing ENT or OMS surgery with general anesthesia with facemask ventilation and tracheal intubation (observational)

Interventions

Not provided

Outcomes Module

Primary Outcomes

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

Secondary Outcomes

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

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

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

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder 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.

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Name	Affiliation	Role
Martin Petzoldt, MD	Universitätsklinikum Hamburg-Eppendorf	Principal Investigator

Locations

Facility	Status	City	State	ZIP	Country	Contacts
University Medical Center Hamburg-Eppendorf		Hamburg		20246	Germany