Prediction of Difficult Mask Ventilation Using Ultrasound Evaluation of the Palatoglossal Space: An Observational Study - Samanta, Mirambica; A. Namasivayam, S.; G. Thirumaaran, U.; S, Sangavi (Journal of Neonatal Surgery, 2025)
ABSTRACT Introduction:Difficult mask ventilation (DMV) continues to be a major concern in airway management. Traditional methods, such as Modified Mallampati Grading (MMG), have limitations, particularly in anaesthetized patients. Ultrasonographic assessment of the palatoglossal space (PGS) and tongue thickness (TT) has emerged as a promising technique for airway evaluation. This study aimed to determine the diagnostic accuracy of PGS and TT in predicting DMV. Materials & Methods:This prospective observational study was conducted at the Department of Anaesthesia, Meenakshi Medical College Hospital & Research Institute, from January 2024 to July 2024. A total of 100 adult patients undergoing elective surgery under general anaesthesia were enrolled. PGS and TT were measured using submandibular ultrasonography with a curvilinear probe. DMV was defined as inadequate face mask ventilation despite standard airway maneuvers. Statistical analysis included ROC curve generation and calculation of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results:Among 100 patients, 56% were male and 44% female, with a mean age of 42 years and BMI of 24 kg/m². The incidence of DMV was 28.1%. Patients with DMV had significantly lower PGS values (0.68 ± 2.01 mm) compared to those with easy mask ventilation (7.01 ± 1.59 mm, P = 0.000). TT was significantly higher in DMV patients (43.01 ± 2.99 mm) versus the easy mask ventilation group (41.3 ± 2.68 mm, P = 0.002). ROC analysis revealed an AUC of 0.999 for PGS and 0.876 for TT in predicting DMV. A PGS cut-off of ≤6.5 mm showed 94.4% sensitivity and 93% specificity. Conclusion:Ultrasonographic assessment of the palatoglossal space is a highly effective tool for predicting DMV. PGS outperforms TT in terms of diagnostic accuracy, and its simplicity supports its integration into routine preoperative airway assessment.