Prevalence and Predictors of Difficult Mask Ventilation in Neurosurgical Patients

Résumé

Background: Many predictive tools are proposed to identify preoperative risk in patients for difficult mask ventilation (DMV) and intubation (DI). Outcome of good prediction is derived from multiple factors build into predictive model to get adequate diagnostic accuracy, stratify treatment and improve outcome.

Objectives: Study prevalence and predictors of DMV in neurosurgical patients; establish accuracy of cervical spine limitation (CSL) /simplified airway risk index (SARI) in predicting DMV; establish correlation between DMV and DI; observe effect of neuromuscular blocking drugs (NMBD) and experience of anaesthetist on mask ventilation efficiency.

Study Design: Prospective, Observational.

Participants: Electively posted 200 neurosurgical patients.

Methods: Preoperative airway assessment included information about age, BMI, snoring, OSA, beard, macroglossia, mandibulodental abnormalities, mouth opening, neck circumference, thyromental distance, atlanto-occipital extension grading. Predictive model SARI used to assess diagnostic accuracy of DMV and DI. Information about ventilation and intubation were collected. Effect of NMBDs and experience of anaesthetist on efficiency of mask ventilation observed. All variables found were analysed to identify independent predictors.

Results: DMV observed in 96 patients. 31 patients had both DMV and DI reflecting their strong correlation. Snoring, OSA, beard, Mallampati III and IV, limited jaw protrusion, BMI ≥30 kg/m2 and neck circumference ≥ 40cms were independent risk factors for DMV. 60% patients with CSL had DMV. SARI had sensitivity of 94.7% and specificity of 99% respectively and the incidence of unanticipated DMV and DI using SARI was 2.8% and 3.4%. NMBD and increasing experience of anaesthetist improved efficiency of mask ventilation parameters were statistically significant.

Conclusion: Several pre-operative risk factors for DMV combined with DL in neurosurgical patients have been identified, yet none have convincing diagnostic accuracy as stand lone test.

Implications: Combining several risk factors increase the predictive value of the DMV using multivariable risk models, that would perhaps lead to decrease in incidence of unanticipated DMV and DI.