Obesity Difficult Airway Airway Management
Conditions
Keywords
Artificial Intelligence, Airway Management, Obesity, Machine Learning
Brief summary
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.
Interventions
DIAGNOSTIC_TESTPreoperative 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.
Sponsors
Elazıg Fethi Sekin Sehir Hastanesi
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
Yes
Inclusion criteria
1. Adult patients aged 18 to 65 years. 2. Scheduled for elective bariatric surgery under general anesthesia. 3. Body Mass Index (BMI) ≥ 35 kg/m². 4. Consenting to participate in the study.
Exclusion criteria
1. Patients with known upper airway anatomical deformities, head and neck tumors, or a history of head/neck radiotherapy. 2. History of maxillofacial, airway, or cervical spine surgery. 3. Emergency surgeries. 4. Patients requiring planned awake fiberoptic intubation based on obvious preoperative clinical indicators.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic Accuracy of the Artificial Intelligence Model in Predicting Difficult Intubation | Intraoperative (assessed during the primary intubation attempt) | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Number of Intubation Attempts | Intraoperative | Total number of direct laryngoscopy attempts required to achieve successful tracheal intubation. |
| Need for Alternative Airway Management Techniques | Intraoperative | 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. |
Countries
Turkey (Türkiye)
Contacts
CONTACTMuhammed Başpınar, M.D.
Outcome results
None listed