Brief Summary

The assessment and management of difficult airway is of critical importance. Unsuccessful airway management leads to serious mortality and morbidity. From the beginning of the pre-anesthesia examination, 3% to 13% of patients who are considered suitable for routine airway management may be difficult to intubate. Airway assessment issues include risk assessment and airway examination (bedside and forward) to estimate the risk of difficult airway or aspiration. Airway examination aims to determine the presence of upper airway pathologies or anatomical anomalies. Some physical characteristics are associated with difficult airways and unsuccessful intubation. Examples of these are; limited neck movement, snoring, short sternomental distance, neck circumference thickness, etc. Physical characteristics can be measured with a meter or more detailed upper airway ultrasonographic measurements. In this study, researchers aimed to evaluate the anthropometric and ultrasonographic measurement values of patients who underwent preoperative airway assessment and to see the predictability of difficult intubation with artificial intelligence-supported decision support programs.

Trial Health

On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment

329

participants targeted

Target at P75+ for all trials

Timeline

Completed

Started Mar 2024

Shorter than P25 for all trials

Geographic Reach

1 country

1 active site

Status

completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

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Study Timeline

Key milestones and dates

11 months study duration

Study Start

First participant enrolled

March 1, 2024

Completed

9 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 3, 2024

Completed

6 days until next milestone

First Submitted

Initial submission to the registry

December 9, 2024

Completed

2 months until next milestone

Study Completion

Last participant's last visit for all outcomes

January 31, 2025

Completed

2 months until next milestone

First Posted

Study publicly available on registry

April 1, 2025

Completed

2 months until next milestone

Results Posted

Study results publicly available

May 31, 2025

Completed

Last Updated

May 31, 2025

Status Verified

May 1, 2025

Enrollment Period

9 months

First QC Date

December 9, 2024

Results QC Date

April 1, 2025

Last Update Submit

May 13, 2025

Conditions

Difficult Endotracheal Intubation Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

Support Vector Machine Algorithm Percentage of Accuracy in Predicted Difficult Intubations
The dataset, labeled based on expert assessment of difficult intubation, was classified using eight widely accepted machine learning algorithms: logistic regression (LR) \[6\], support vector machine (SVM) \[7\], random forest (RF) \[8\], K-nearest neighbors (KNN) \[9\], Gaussian naive Bayes (GNB) \[10\], CatBoost \[11\], XGBoost \[12\], and decision tree (DT) \[13\]. From the original 30 parameters, the 15 most influential features were selected based on feature extraction methods and literature relevance. Preprocessing steps included handling missing values, with incomplete records excluded. The dataset was split into training (80%) and test (20%) sets. Models were trained on the training set, with hyperparameter tuning performed via 5-fold cross-validation to avoid overfitting. Final model performance was evaluated on the independent test set.
Taking ultrasonographic and anthropometric measurements of each patient took approximately 20 minutes. Machine learning estimates for each patient are approximately 1 min.

Study Arms (1)

Patients between the ages of 18 and 20 who will receive general anesthesia

ASA I-III patients over the age of 18 who meet the inclusion criteria to undergo general anesthesia

Other: Thyromental distanceOther: Neck circumferenceOther: Mouth opening distanceOther: Distance from jawbone to hyoid bone with neck in neutral positionOther: Distance from jawbone to hyoid bone with neck in extensionOther: Distance between skin and tracheaOther: Distance between skin and epiglottisOther: Distance between skin and anterior commissure of vocal cord:Other: Distance between skin and hyoid boneOther: Maximum Tongue Thickness

Interventions

Thyromental distanceOTHER

Distance between the chin and thyroid cartilage with a tape measure when the patient is in a neutral position