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Authors Dr Mallika Balakrishnan, Dr Mathana V, Dr Bhagyalakshmi Ramesh, Dr Jagathnath Krishna Regional Cancer Centre Trivandrum, India Abstract Background: Mask Ventilation is the most fundamental skill of airway management and it is vital for the conduct of general anaesthesia 1 . Difficulties with airway management in relation to general anesthesia have been a challenge for the anesthesiologist since the birth of anesthesia. Considerable improvements have been made and general anesthesia is now regarded as a safe procedure. However, difficult airway situations still occur and it causes increased risk of morbidity and mortality especially when not anticipated 2 .In this study we intend to analyses the incidence of Difficult and Impossible Mask Ventilation (DMV) (IMV) and the factors associated with it. Objective: To evaluate the preoperative specified clinical airway assessment parameters on prediction of DMV and IMV. Subjects and Methods: This study was a prospective observational study done in our institute among patients who were posted for elective surgery under general anesthesia. Results: Two hundred patients who were mask ventilated during induction of general anesthesia were included for the study. Age >55yrs ,Body mass index(BMI) of 30kg/m2 or greater, History of Diabetes >5yrs duration ,snoring, sternomental distance (SMD)<12 cm, Edentulous status, Modified Mallampatti classification III or IV, Thyromental distance grade II & III , Upper Lip Bite Test (ULBT)class II & III were identified as significant predictors for DMV by univariate analysis. Conclusion: In our study population, Difficult Mask Ventilation was found in 11% of the patients. Independent risk factors for DMV include body mass index > 30 kg/m2, thick obese neck, SMD<12 cm and lack of teeth. The DMV prediction may lead to a better preparedness for the management of difficult airway and also potentially decreasing the morbidity & mortality resulting from hypoxia or anoxia associated with failed ventilation.

A Prospective Observational study to Identify the Incidence and Predictors of difficult and Impossible Mask Ventilation