Predictive factors for difficult mask ventilation in the obese surgical population [v1; ref status: indexed, http://f1000r.es/4i9]
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 incid...
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doaj-78abfa067de947f88c237c8f350c9a6c2020-11-25T03:49:51ZengF1000 Research LtdF1000Research2046-14022014-10-01310.12688/f1000research.5471.15841Predictive factors for difficult mask ventilation in the obese surgical population [v1; ref status: indexed, http://f1000r.es/4i9]Davide Cattano0Anastasia Katsiampoura1Ruggero M. Corso2Peter V. Killoran3Chunyan Cai4Carin A. Hagberg5Department of Anesthesiology, University of Texas Medical School at Houston, Houston, TX 77030, USADepartment of GI Medical Oncology, MD Anderson Cancer Center Hospital, Houston, TX 77030, USAEmergency Department, Anesthesia and Intensive Care Section, GB Morgagni-L.Pierantoni Hospital, Forli, 47121, ItalyDepartment of Anesthesiology, University of Texas Medical School at Houston, Houston, TX 77030, USADivision of Clinical and Translational Sciences, Department of Internal Medicine, The University of Texas Medical School at Houston, Houston, TX 77030, USADepartment of Anesthesiology, University of Texas Medical School at Houston, Houston, TX 77030, USABackground 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.http://f1000research.com/articles/3-239/v1EpidemiologyHealth Service Delivery & Management of AnesthesiaPerioperative Critical CareRespiratory Problems in Critical Care |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Davide Cattano Anastasia Katsiampoura Ruggero M. Corso Peter V. Killoran Chunyan Cai Carin A. Hagberg |
spellingShingle |
Davide Cattano Anastasia Katsiampoura Ruggero M. Corso Peter V. Killoran Chunyan Cai Carin A. Hagberg Predictive factors for difficult mask ventilation in the obese surgical population [v1; ref status: indexed, http://f1000r.es/4i9] F1000Research Epidemiology Health Service Delivery & Management of Anesthesia Perioperative Critical Care Respiratory Problems in Critical Care |
author_facet |
Davide Cattano Anastasia Katsiampoura Ruggero M. Corso Peter V. Killoran Chunyan Cai Carin A. Hagberg |
author_sort |
Davide Cattano |
title |
Predictive factors for difficult mask ventilation in the obese surgical population [v1; ref status: indexed, http://f1000r.es/4i9] |
title_short |
Predictive factors for difficult mask ventilation in the obese surgical population [v1; ref status: indexed, http://f1000r.es/4i9] |
title_full |
Predictive factors for difficult mask ventilation in the obese surgical population [v1; ref status: indexed, http://f1000r.es/4i9] |
title_fullStr |
Predictive factors for difficult mask ventilation in the obese surgical population [v1; ref status: indexed, http://f1000r.es/4i9] |
title_full_unstemmed |
Predictive factors for difficult mask ventilation in the obese surgical population [v1; ref status: indexed, http://f1000r.es/4i9] |
title_sort |
predictive factors for difficult mask ventilation in the obese surgical population [v1; ref status: indexed, http://f1000r.es/4i9] |
publisher |
F1000 Research Ltd |
series |
F1000Research |
issn |
2046-1402 |
publishDate |
2014-10-01 |
description |
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. |
topic |
Epidemiology Health Service Delivery & Management of Anesthesia Perioperative Critical Care Respiratory Problems in Critical Care |
url |
http://f1000research.com/articles/3-239/v1 |
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