Clinical trial NCT06453525: PrediSuisse: Automatized Assessment of Difficult Airway
Official title: PrediSuisse: Automatized Assessment of Difficult Airway Using Three Videolaryngoscopes with the Help of Facial Recognition Techniques and Neural Network
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Anesthesia
Intubation; Difficult or Failed
Airway Complication of Anesthesia
Keywords: Intubation
difficult airway
videolaryngoscopy
Lead sponsor: Centre Hospitalier Universitaire Vaudois
Sponsor class: OTHER
First posted: 2024-06-11
Enrollment: 1800
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients (≥ 18 years old) presenting at the pre-anesthesia consult for an elective general anesthesia necessitating a tracheal intubation at the University Hospital of Lausanne, Geneva and Cardiocentro Lugano

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Software creation
timeFrame: 18 months
Secondary outcomes:
measure: Team Communication
timeFrame: 18 months
Other outcomes:
Locations:
city: Lausanne
country: Switzerland
facility: University Hospital Lausanne CHUV
Study officials:
affiliation: Centre Hospitalier Universitaire Vaudois
name: Patrick Schoettker, PhD
role: PRINCIPAL_INVESTIGATOR
References: