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

Universitätsklinikum Hamburg-Eppendorf423 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

423

Conditions

Mask Ventilation General Anesthesia

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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

Locations (1)

Outcomes

Primary Outcomes

Difficult facemask ventilation

Observed difficult facemask ventilation after induction of anesthesia

Time frame: 1 hour

Secondary Outcomes

Difficult tracheal intubation

Observed difficult intubation after induction of anesthesia

Time frame: 1 hour

Difficult laryngoscopy

Observed difficult laryngoscopy after induction of anesthesia

Time frame: 1 hour

Number of attempts

Observed during tracheal intubation

Time frame: 1 hour

Failed direct laryngoscopy

Observed during airwaymanagement

Time frame: 1 hour

Cormack Lehane grade

Grading of the best view obtained during laryngoscopy (I-IV)

Time frame: 1 hour

Difficult mask ventilation alert

Noted by the responsible anaesthesiologist after airway management

Time frame: 1 hour

Difficult intubation alert

Noted by the responsible anaesthesiologist after airway management

Time frame: 1 hour

Intubation time

Recorded during airwaymanagement

Time frame: 1 hour

Time to sufficient mask ventilation

Recorded during airwaymanagement

Time frame: 1 hour

Classification of intubation difficulty

VIDIAC score rating between -1 and 5 points

Time frame: 1 hour

Percentage of glottis opening (POGO)

Grading of the best view obtained during laryngoscopy (%)

Time frame: 1 hour

Impossible facemask ventilation

Observed impossible facemask ventilation after induction of anesthesia

Time frame: 1 hour

Successful first attempt intubation

Observed during airway management

Time frame: 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

Time frame: 1 hour

Post-intubation recommendation for an intubation method

Recommendation of the responsible anaesthesiologist after airwaymanagement

Time frame: 1 hour

Minimal peripheral oxygen saturation (SpO2)

Observed after induction of anesthesia

Time frame: 1 hour