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Ventilación difícil con mascarilla en obesos/Difficult mask ventilation in the obese

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Factores predictivos para ventilación difícil con mascarilla en la población obesa quirúrgica
Predictive factors for difficult mask ventilation in the obese surgical population.
Cattano D, Katsiampoura A, Corso RM, Killoran PV, Cai C, Hagberg CA.
F1000Res. 2014 Oct 9;3:239. doi: 10.12688/f1000research.5471.1. eCollection 2014.
Abstract
Background Difficult Mask Ventilation (DMV), is a situation in which it is impossible for an unassisted anesthesiologist to maintain oxygen saturation >90% using 100% oxygen and positive pressure ventilation to prevent or reverse signs of inadequate ventilation during mask ventilation. The incidence varies from 0.08 - 15%. Patient-related anatomical features are by far the most significant cause. We analyzed data from an obese surgical population (BMI> 30 kg/m (2)) to identify specific risk and predictive factors for DMV. Methods Five hundred and fifty seven obese patients were identified from a database of 1399 cases associated with preoperative airway examinations where mask ventilation was attempted. Assessment of mask ventilation in this group was stratified by a severity score (0-3), and a step-wise selection method was used to identify independent predictors. The area under the curve of the receiver-operating-characteristic was then used to evaluate the model's predictive value. Adjusted odds ratios and their 95% confidence intervals were also calculated. Results DMV was observed in 80/557 (14%) patients. Three independent predictive factors for DMV in obese patients were identified: age 49 years, short neck, and neck circumference 43 cm. In the current study th sensitivity for one factor is 0.90 with a specificity 0.35. However, the specificity increased to 0.80 with inclusion of more than one factor. Conclusion According to the current investigation, the three predictive factors are strongly associated with DMV in obese patients. Each independent risk factor alone provides a good screening for DMV and two factors substantially improve specificity. Based on our analysis, we speculate that the absence of at least 2 of the factors we identified might have a significant negative predictive value and can reasonably exclude DMV, with a negative likelihood ratio 0.81.
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