Machine learning models based on ultrasound and physical examination for airway assessment.

Purpose: To demonstrate the utility of machine learning models for predicting difficult airways using clinical and ultrasound parameters.

Methods: This is a prospective non-consecutive cohort of patients undergoing elective surgery. We collected as predictor variables age, sex, BMI, OSA, Mallampatti, thyromental distance, bite test, cervical circumference, cervical ultrasound measurements, and Cormack-Lehanne class after laryngoscopy.

Infectious complications in paediatric patients treated with extracorporeal membrane oxygenation.

Introduction: The aim of this study was to analyse the incidence, treatment and evolution of infections in children treated with ECMO.

Methods: A retrospective study based on a prospective database was performed. Children under the age of 18 years treated with ECMO from September 2006 to November 2015 were included.