General Anesthesia, Mask Ventilation
Conditions
Keywords
Risk prediction, Airway management
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.
Interventions
None listed
Sponsors
Institute of Medical Technology and Intelligent Systems at Hamburg University of Technology
Universitätsklinikum Hamburg-Eppendorf
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Difficult facemask ventilation | 1 hour | Observed difficult facemask ventilation after induction of anesthesia |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Difficult laryngoscopy | 1 hour | Observed difficult laryngoscopy after induction of anesthesia |
| Number of attempts | 1 hour | Observed during tracheal intubation |
| Failed direct laryngoscopy | 1 hour | Observed during airwaymanagement |
| Cormack Lehane grade | 1 hour | Grading of the best view obtained during laryngoscopy (I-IV) |
| Difficult mask ventilation alert | 1 hour | Noted by the responsible anaesthesiologist after airway management |
| Difficult intubation alert | 1 hour | Noted by the responsible anaesthesiologist after airway management |
| Intubation time | 1 hour | Recorded during airwaymanagement |
| Difficult tracheal intubation | 1 hour | Observed difficult intubation after induction of anesthesia |
| Classification of intubation difficulty | 1 hour | VIDIAC score rating between -1 and 5 points |
| Percentage of glottis opening (POGO) | 1 hour | Grading of the best view obtained during laryngoscopy (%) |
| Impossible facemask ventilation | 1 hour | Observed impossible facemask ventilation after induction of anesthesia |
| Successful first attempt intubation | 1 hour | Observed during airway management |
| Airway-related adverse events | 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 | 1 hour | Recommendation of the responsible anaesthesiologist after airwaymanagement |
| Minimal peripheral oxygen saturation (SpO2) | 1 hour | Observed after induction of anesthesia |
| Time to sufficient mask ventilation | 1 hour | Recorded during airwaymanagement |
Countries
Germany
Outcome results
None listed