Difficult Airway Incidence in Cardiovascular Surgery and a Prediction Model Development

NCT ID: NCT06986187

Last Updated: 2026-02-05

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 2000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-06-01
• Study Completion Date: 2027-12-01

Brief Summary

A 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. Data concerning incidence of difficult airway in patients undergoing cardiovascular surgery is controversial. Unwanted hemodynamic changes that may occur in patients undergoing cardiovascular surgery, combined with hemodynamic changes caused by underlying cardiac pathologies, may also lead to a physiologically difficult airway situation. Since all these interactions, combined with the hemodynamic changes caused by difficult airway interventions, may lead to catastrophic outcomes, it is vital to predict difficult airway in this patient population.

Detailed Description

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.

Conditions

Difficult Airway Intubation; Difficult Airway; Difficult Intubation; Cardiac Surgery; Cardiac Surgery in Adult Patient

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• Undergoing cardiovascular surgery
• ASA I-IV physical status
• Over 18 years of age

Exclusion Criteria

• Known difficult airway
• Head-neck and upper airway pathology
• Patients at risk of aspiration

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Diskapi Teaching and Research Hospital

OTHER

Sponsor Role: lead

Responsible Party

DILEK YAZICIOGLU

Professor of Anesthesiology

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Dilek Unal, Prof.

Role: PRINCIPAL_INVESTIGATOR

Ankara Etlik City Hospital

Locations

Etlik City Hospital

Ankara, , Turkey (Türkiye)

Site Status: RECRUITING

Countries

Turkey (Türkiye)

Central Contacts

Dilek Unal, Prof.

Role: CONTACT

dilekunalmd@gmail.com

+90 533 695 78 55

Facility Contacts

Julide Ergil, Profesor

Role: primary

julideergil@hotmail.com

+905323469216

Murat Sayın, Profesor

Role: backup

muratsayin@hotmail.com

+905326147862

References

Kohse EK, Siebert HK, Sasu PB, Loock K, Dohrmann T, Breitfeld P, Barclay-Steuart A, Stark M, Sehner S, Zollner C, Petzoldt M. A model to predict difficult airway alerts after videolaryngoscopy in adults with anticipated difficult airways - the VIDIAC score. Anaesthesia. 2022 Oct;77(10):1089-1096. doi: 10.1111/anae.15841. Epub 2022 Aug 25.

Reference Type: BACKGROUND

PMID: 36006056 (View on PubMed)

Cormack RS, Lehane J. Difficult tracheal intubation in obstetrics. Anaesthesia. 1984 Nov;39(11):1105-11.

Reference Type: BACKGROUND

PMID: 6507827 (View on PubMed)

doi: https://doi.org/10.1097/ALN.0000000000004002

Reference Type: BACKGROUND

Other Identifiers

AEŞH-EK-2025-087

Identifier Type: -

Identifier Source: org_study_id