An observational study in Obesity Difficult Airway Airway Management, sponsored by Elazıg Fethi Sekin Sehir Hastanesi. Recruiting at 1 site in Turkey (Türkiye). Open to participants aged 18 Years to 65 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-06-24.
Sponsored by Elazıg Fethi Sekin Sehir Hastanesi · Observational
The aim of this prospective study is to evaluate the accuracy of artificial intelligence (AI) and machine learning algorithms in predicting difficult airways in patients undergoing bariatric surgery. Preoperative airway assessments, including the Upper Lip Bite Test (UBLT), Mallampati score, Body Mass Index (BMI), thyromental distance (TMD), and sternomental distance (SMD), will be recorded. The study investigates whether AI models can provide higher sensitivity and specificity in predicting difficult intubation compared to traditional clinical scoring systems in the obese patient population.
6,296 studies on the registry are indexed under Obesity; 1,692 are open to participants now.
This study's planned enrollment of 340 is above the median of 135 across 1,283 observational studies indexed under Obesity.
Browse Obesity studies →Elazıg Fethi Sekin Sehir Hastanesi is the lead sponsor of 29 studies on the registry; 7 are open to participants now.
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The study population consists of adult obese patients (BMI ≥ 35 kg/m²) undergoing elective bariatric surgery under general anesthesia in a tertiary academic medical center. This population represents individuals at a higher baseline risk for difficult airway management and intubation.
Exclusion Criteria:
Patients scheduled for elective bariatric surgery under general anesthesia who undergo preoperative airway assessment using clinical and morphometric predictors.
Diagnostic Test: Preoperative Airway Assessment and Direct Laryngoscopy
Measurement of preoperative airway parameters including Upper Lip Bite Test (UBLT), Mallampati score, Body Mass Index (BMI), thyromental distance, and sternomental distance. Intraoperative airway view is graded using the Cormack-Lehane classification during standard direct laryngoscopy.
Also known as: Upper Lip Bite Test, Modified Mallampati Score, Thyromental Distance, Sternomental Distance, Cormack-Lehane Grading
Diagnostic Accuracy of the Artificial Intelligence Model in Predicting Difficult Intubation
The predictive performance of the AI model will be evaluated by comparing its preoperative difficult airway prediction against the actual intraoperative direct laryngoscopy view. The intraoperative view is graded using the Cormack-Lehane classification system. Grades 3 and 4 are clinically defined as difficult intubation, while Grades 1 and 2 are defined as easy intubation. The primary metric of diagnostic accuracy will be the Area Under the Receiver Operating Characteristic (AUC-ROC) curve.
Time frame: Intraoperative (assessed during the primary intubation attempt)
Number of Intubation Attempts
Total number of direct laryngoscopy attempts required to achieve successful tracheal intubation.
Time frame: Intraoperative
Need for Alternative Airway Management Techniques
The frequency of requiring alternative airway devices or strategies (e.g., video laryngoscope, bougie, or fiberoptic bronchoscope) to secure the airway after a primary direct laryngoscopy.
Time frame: Intraoperative
Plan to share: Yes — De-identified individual participant data (including preoperative clinical/morphometric airway measurements and intraoperative Cormack-Lehane grades) underlying the results reported in the final publication will be shared to promote transparency and reproducibility in machine learning models.
Supporting information: Study protocol, Sap
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Elazıg Fethi Sekin Sehir Hastanesi