Overview
This unicentric observational study collects clinical characteristics, demographic data, and point-of-care airway ultrasound measurements in patients undergoing videolaryngoscopy. These variables are analysed using machine-learning techniques to examine their association with predefined videolaryngoscopy-related outcomes, including blade performance and adjunct requirement.
Description
Tracheal intubation is a routine procedure in anaesthesia and critical care; however, difficulties during videolaryngoscopy may still occur despite advances in airway devices. Conventional bedside airway assessments provide limited guidance for videolaryngoscopy-specific decisions, such as blade selection or anticipation of adjunct use.
This unicentric observational study collects clinical characteristics, demographic data, and point-of-care airway ultrasound measurements in patients undergoing videolaryngoscopy. These variables are analysed using machine-learning techniques to examine their association with predefined videolaryngoscopy-related outcomes, including blade performance and adjunct requirement.
The primary objective is to develop and internally evaluate a predictive model integrating multimodal data to support videolaryngoscopy strategy planning. The model is intended solely as a research and decision-support tool and does not replace clinician judgement. External validation in independent cohorts is planned.
Eligibility
Inclusion Criteria:
- Patients (male or female) ASA I-III, aged between 18 and 90 years, undergoing scheduled surgery requiring orotracheal intubation. The signature of the informed consent is required authorizing its inclusion in the study.
Exclusion Criteria:
- Obesity class II defined as a BMI greater than 35.
- Pregnant.
- Cervical tumors, goiter or patients who have required radiotherapy at the cervical level
- Abnormalities that condition anatomy alterations such as facial / cervical fractures.
- Maxillofacial abnormalities
- People who cannot give their consent
