PrediSuisse: Automatized Assessment of Difficult Airway

N/ANot Yet RecruitingNCT06453525

Centre Hospitalier Universitaire Vaudois1,800 enrolled

Overview

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.

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.

Study Type

OBSERVATIONAL

Enrollment

1,800

Conditions

Anesthesia Intubation; Difficult or Failed Airway Complication of Anesthesia

Eligibility

Sex: ALLMin age: 18 YearsMax age: 100 Years

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

Software creation

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.

Time frame: 18 months

Secondary Outcomes

Team Communication

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.