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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.
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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.
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280 participants in 1 patient group
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Data sourced from clinicaltrials.gov
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