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Indian Journal of Anesthesia and Analgesia

Volume  12, Issue 3, July - Sep 2025, Pages 183-197
 

Original Article

Preoperative Clinical and Sonographic Assessment of Airway and its Correlation with Cormack–Lehane Grading During Intubation in Predicting Difficult Airway

Bijitha V.K.1, Biju M.L.2, Sajil M.S.3

1 Final Year Postgraduate Resident, Department of Anesthesia, Travancore Medical College, Kollam, Kerala, India.  2 Professor, Department of Anesthesia, Travancore Medical College, Kollam, Kerala, India.  3 Associate Professor, Department of Anesthesia, Travancore Medical College, Kollam, Kerala, India 

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DOI: 10.21088/ijaa.2349.8471.12325.4

Abstract

Background and Objectives: Unexpected difficult airway is a major challenge in general anesthesia, associated with significant morbidity and mortality. Ineffective airway management can lead to rapid deterioration of oxygenation and ventilation, resulting in severe consequences such as brain injury and death. The preanesthetic evaluation of the airway is typically conducted using a variety of assessment techniques. However, these techniques often lack discriminative value when used in isolation and perform better when combined with other tests. Ultrasound, when used alongside other risk assessment methods at different levels of the neck, may help identify predictors of a difficult airway, including challenging laryngoscopy, thus enabling faster and more accurate airway assessments in both routine and emergency situations. Aim:  • To assess the effectiveness of ultrasonography in preoperative airway evaluation for predicting difficult airway.  Objectives:  • To evaluate the correlation between preoperative sonographic airway assessment and laryngoscopic CL grading. • To identify the sonographic parameter with better predictability of difficult airway. Methods: This prospective observational study was conducted with 68 patients presenting to Travancore Medical College, Kollam, for elective surgery under general anesthesia with endotracheal intubation. The three parameters anterior  neck soft tissue thickness at the level of the hyoid, anterior neck soft tissue thickness at the level of the vocal cords, and the hyomental distance ratio, which is calculated by dividing the hyomental distance in the extended position by the hyomental distance in the neutral position (HMDe/HMDn) and three physical criteria the Modified Mallampati (MPC) class, thyromental distance (TMD), and sternomental distance (SMD) were assessed and correlated with the Cormack-Lehane grading. Results and Discussion: The optimal cutoff value for ANS-Hyoid to predict difficult laryngoscopy was >0.89 cm, with 22.2% sensitivity and 96% specificity (AUC = 0.6489). For ANS-VC, the best cutoff value was >0.87 cm, showing 22.2% sensitivity and 98% specificity (AUC = 0.695). The ideal cutoff value for HMDR was <1.15, with 38.89% sensitivity and 96% specificity (AUC = 0.75). Our findings suggest that while ANS-Hyoid has high specificity, its low sensitivity indicates it may not alone be sufficient for identifying difficult intubation. The correlation between greater anterior neck soft tissue thickness at the vocal cord level and higher CL grades supports the use of sonographic measurements in predicting difficult laryngoscopy. HMDR demonstrated the best combination of sensitivity and specificity, highlighting its potential utility in improving the accuracy of pre-operative airway assessments.  Conclusion: Our study found that among the USG parameters evaluated, the Hyomental Distance Ratio (HMDr) provided the most reliable prediction of difficult intubation. In summary, integrating ultrasonography (USG) with both traditional and novel assessment tools is likely to enhance preoperative evaluations and lead to more effective management of difficult airway.


Keywords : • Difficult airway • General anesthesia • Ultrasonography • Preoperative assessment • Cormack-Lehane grading • Sonographic parameters • Airway management Hyomental distance ratio • Anterior neck soft tissue thickness • Laryngoscopy
Corresponding Author : Bijitha V.K.,