Difficult Airway Incidence in Cardiovascular Surgery and a Prediction Model Development

Difficult airway is a clinical condition that occurs when one or more of the components of difficult mask ventilation, difficult laryngoscopy, difficult endotracheal intubation, difficult supraglottic airway device (SGA) placement, and inability to intubate-oxygenate are present.

Different diagnostic criteria for all components of difficult airway and similarly different predictive criteria for the risk of occurrence in a patient have been defined. The LEMON Score, El-Ganzouri Risk Index, and Arne Score, which are evaluated by physical examination of the upper airway structures during the pre-anesthetic examination, and the Cormack-Lehane Classification (CL) used to evaluate the laryngoscopic image during intubation, can be counted among the difficult airway prediction tests.

Difficult airway situations that occur during anesthesia application can be defined by the Han Score for mask ventilation, the Intubation Difficulty Scale (IDS) for endotracheal entubation, the videolaryngoscopic intubation and difficult airway classification (VIDIAC) in patients using videolaryngoscopy, and the difficult SGA placement score.

Previous studies have reported that the incidence of difficult airway is higher in patients undergoing cardiovascular surgery compared to other patient groups. Borde et al. reported the rate of difficult intubation in patients undergoing cardiac surgery as 24%. The rate of difficult laryngoscopy in patients undergoing coronary artery surgery was reported as 10% by Ezri et al. and 7% by Heinrich et al. However, it is seen that the predictive criteria and diagnostic criteria for the components of the difficult airway are used interchangeably and incorrectly in these studies. Therefore, the current information on the incidence of difficult airways in patients undergoing cardiovascular surgery is contradictory and open to debate.

Accurate information on the incidence of difficult airway in this patient population can contribute to anesthesiology education, equipment and personnel planning, and most importantly, patient safety. Unwanted hemodynamic changes that may occur following anesthesia induction in patients undergoing cardiovascular surgery, combined with hemodynamic changes caused by underlying cardiac pathologies, may lead to the emergence of a physiological difficult airway condition. Since all these interactions, when combined with hemodynamic changes caused by difficult airway interventions, may lead to catastrophic outcomes, predicting difficult airway in this patient population is of vital importance.

Despite its clinical importance, to our knowledge, this subject has not yet been investigated in the literature with artificial intelligence algorithms.

The aim of this study is to investigate the incidence of difficult airway and difficult intubation in patients undergoing cardiovascular surgery and the associated factors and to develop a machine learning model that can predict difficult airway using artificial intelligence algorithms.