DEVELOPMENT OF AN INTEGRATED DIFFICULT AIRWAY PREDICTION MODEL USING THE STOP-BANG QUESTIONNAIRE AND CONVENTIONAL BEDSIDE AIRWAY ASSESSMENT PARAMETERS IN ADULT SURGICAL PATIENTS: A PROSPECTIVE OBSERVATIONAL STUDY

Accurate preoperative prediction of difficult airway remains one of the greatest challenges
in anaesthetic practice. Conventional bedside airway assessment tests such as the Modified
Mallampati Classification (MMC), Thyromental Distance (TMD), Sternomental Distance
(SMD), Upper Lip Bite Test (ULBT), mouth opening, neck circumference, and neck
mobility demonstrate limited predictive accuracy when used individually. The STOP-Bang
questionnaire, originally developed for screening obstructive sleep apnoea, incorporates
clinical variables associated with difficult airway and may improve risk prediction. An
integrated prediction model combining STOP-Bang with conventional bedside airway
parameters may provide superior diagnostic performance.

Aim
To develop and validate an integrated difficult airway prediction model using the STOPBang
questionnaire and conventional bedside airway assessment parameters in adult patients
undergoing general anaesthesia Methods
A prospective observational study will be conducted among adult patients undergoing
elective surgery under general anaesthesia requiring endotracheal intubation. Preoperative
evaluation will include STOP-Bang scoring, Modified Mallampati Classification,
Thyromental distance, Sternomental distance, upper lip bite test, interincisor distance, neck circumference, and cervical spine mobility. Intraoperative airway difficulty will be
assessed using the Cormack–Lehane grading system and the Intubation Difficulty Scale
(IDS). Logistic regression analysis will be used to identify independent predictors of
difficult airway and develop an integrated prediction model. Model performance will be
evaluated using sensitivity, specificity, positive predictive value, negative predictive value,
diagnostic accuracy, receiver operating characteristic (ROC) curve analysis, area under the
curve (AUC), calibration analysis, and internal validation using bootstrapping.

Results
The integrated prediction model is expected to demonstrate superior discrimination and
calibration compared with individual bedside airway assessment methods. Combining
STOP-Bang with anatomical airway parameters is anticipated to improve sensitivity and
overall predictive accuracy for difficult airway identification.
Conclusion
Development of an integrated difficult airway prediction model may facilitate earlier
recognition of patients at risk for difficult airway, improve perioperative airway planning,
reduce airway-related complications, and enhance patient safety.
Keywords
STOP-Bang Questionnaire, Difficult Airway, Prediction Model, Machine Learning,
Diagnostic Accuracy, Airway Assessment, General Anaesthesia.