Airway Complication of Anesthesia, Anesthesia, Intubation; Difficult or Failed
Conditions
Keywords
Intubation, difficult airway, videolaryngoscopy
Brief summary
In the "PrediSuisse" research project, the investigators aim to create a reliable, reproducible, ultra-portable and radiation-free automatized software, able to identify automatically collected features, facial characteristics, and range of movements, to predict intubation difficulty. The software will generate a difficulty intubation score tailored to three commercially available videolaryngoscopes with different type of blades, corresponding to the predicted endotracheal intubation difficulty while providing the anaesthesiologist a reliable and non-subjective tool to assess individual patient's risks with regards to airway management.
Detailed description
The Swiss multi-institutional research project "PrediSuisse" aims to automatically predict and classify the difficulty of intubation and airway management using three commercially available videolaryngoscopes (VL) by acquiring face/profiles photos and sequences on a training set of 900 patients during the pre-anaesthesia consultation. For each patient, with the help of recently developed Machine Learning (ML), Artificial Intelligence (AI) and Convolutional Neural Network (CNN) techniques, a specially developed software will be trained to provide a predicted airway management difficulty index. This will be performed by correlating those photos/sequences and the real difficulty level of intubation, determined by three experts by reviewing the recordings of the intubations of the training set patients. The software will then be used in routine on a set of 900 other patients to validate the prediction performance.
Interventions
OTHERintubation
Tracheal intubation using one of the three existing videolaryngoscopes
Sponsors
Centre Hospitalier Universitaire Vaudois
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No
Inclusion criteria
* Adult patients (≥ 18 years old) presenting at the pre-anesthesia consult for an elective general anesthesia necessitating a tracheal intubation * Signed informed consent.
Exclusion criteria
* Patients not speaking French (in Geneva and Lausanne) or Italian (in Lugano). * Patients previously operated on the airway with anatomical modifications (ENT Flaps, tracheotomies). * Patients unable to follow procedures or to give consent will also be excluded.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Software creation | 18 months | The primary outcome is to create a reliable, reproducible, ultra-portable and radiation-free automated software, capable of identifying automatically collected features such as facial characteristics, mouth opening, range of motion while moving the neck and thyromental distance to predict intubation difficulty. The identification of the difficult intubation score will be compared by the one goven independantly by three airway experts. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Team Communication | 18 months | The secondary outcome is to evaluate the impact of streaming images of the intubations acquired by the videolaryngoscopy directly to the screens located in the operating room (OR) on communication between healthcare professionals in the OR with the help of a dedicated questionnaire. |
Countries
Switzerland
Contacts
CONTACTPatrick Schoettker, PhD
PRINCIPAL_INVESTIGATORPatrick Schoettker, PhD
Centre Hospitalier Universitaire Vaudois
Outcome results
None listed