Background : The major responsibility of the anaesthesiologist is to provide adequate ventilation to the patient. Most vital element for this is the airway. Difficulties in optimal airway management can lead to serious adverse effects and failure can even lead to mortality. We have evaluated the feasibility of sonography as an imaging tool in identifying important airway anatomical structures on the anterior aspect of the neck and correlated the ultrasound-guided measurements of the airway parameters with the Cormack Lehane classification of the direct laryngoscopy for prediction of the difficult airway. Aim : To predict Difficult Laryngoscopy by Ultrasound guided valuation Of Anterior Neck Soft Tissue Thickness. Method : The study was a prospective observational study. For this study, n (no of cases) =100 considering power of 95% from the previous study; including patients between the age group of 18 to 65 years, ASA I to III grades, scheduled for elective surgery and requiring general anaesthesia with directlaryngoscopy and endotracheal intubation. Patient with anticipated difficult airway were excluded. Modified Mallampati score, Neck circumference at the level of the thyroid cartilage, Thyromental distance, BMI, distance from skin to hyoid bone and distance from skin to the anterior commissure of vocal cords using the USG machine followed by MCLS grade on laryngoscopy were noted. Result : With reference to ROC analysis, the optimal cutoffs of DSHB, DSAC, neck circumference and BMI measurements for the prediction of difficult Laryngoscopy is 0.81 cm, 0.92 cm, 35.75cm and 24.8 kg/m 2 respectively with the area under the curves being 0.944, 0.970, 0.801 and 0.745 respectively. Similarly, the optimal cutoff value for modifiedMallampati grades for the prediction of difficult Laryngoscopy is Grade II and above with area under the curves being 0.718. We also found that with experience the required time to measure the distances using USG was reduced with experience. Conclusion : We conclude from our study that the BMI, modified Mallampati grade and neck circumference are good predictors of
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