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Predictive factors for difficult mask ventilation in the obese surgical population [v1; ref status: indexed, http://f1000r.es/4i9]

Authors :
Davide Cattano
Anastasia Katsiampoura
Ruggero M. Corso
Peter V. Killoran
Chunyan Cai
Carin A. Hagberg
Source :
F1000Research, Vol 3 (2014)
Publication Year :
2014
Publisher :
F1000 Research Ltd, 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/m2) 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.

Details

Language :
English
ISSN :
20461402
Volume :
3
Database :
Directory of Open Access Journals
Journal :
F1000Research
Publication Type :
Academic Journal
Accession number :
edsdoj.78abfa067de947f88c237c8f350c9a6c
Document Type :
article
Full Text :
https://doi.org/10.12688/f1000research.5471.1