difficult mask ventilation
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2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ron O. Abrons ◽  
Patrick Ten Eyck ◽  
Isaac D. Sheffield

Abstract Background Oropharyngeal airways are used both to facilitate airway patency during mask ventilation as well as conduits for flexible scope intubation, though none excel at both. A novel device, the Articulated Oral Airway (AOA), is designed to facilitate flexible scope intubation by active displacement of the tongue. Whether this active tongue displacement also facilitates mask ventilation, thus adding dual functionality, is unknown. This study compared the AOA to the Guedel Oral Airway (GOA) in regards to efficacy of mask ventilation of patients with factors predictive of difficult mask ventilation. The hypothesis was that the AOA would be non-inferior to the GOA in terms of expiratory tidal volumes by a margin of 1 ml/kg, thus demonstrating dual functionality. Methods In this randomized controlled clinical trial, fifty-eight patients with factors predictive of difficult mask ventilation were mask ventilated with both the GOA and the AOA. Video of the anesthetic monitors were evaluated by a blinded member of the research team, noting inspiratory and expiratory tidal volumes and expiratory CO2 waveforms. Results The AOA was found to be non-inferior to the GOA at a margin of 1 ml/kg with a mean weight-standardized expiratory tidal measurement 0.45 ml/kg lower (CI: 0.34–0.57) and inspiratory tidal measurement 0.109 lower (CI: − 0.26-0.04). There was no significant difference in expiratory waveforms (p = 0.2639). Conclusions The AOA was non-inferior to the GOA for mask ventilation of patients with predictors of difficult mask ventilation and there was no significant difference in EtCO2 waveforms between the groups. These results were consistent in the subset of patients who were initially difficult to mask ventilate. Trial registration ClinicalTrials.gov, NCT03144089, May 2017.


Author(s):  
Jiayi Wang ◽  
Jingjie Li ◽  
Pengcheng Zhao ◽  
Xuan Pu ◽  
Rong Hu ◽  
...  

Abstract Purpose Difficult mask ventilation (DMV) is a potentially life-threatening situation that can arise during anesthesia. However, most clinical predictors of DMV are based on European and US populations. On the other hand, most predictive models consist of multiple factors and complicated assessments. Since obstructive sleep apnea (OSA) is among the most important risk factors associated with DMV, the apnea-hypopnea index (AHI) may play an important role in determining patient risk.The purpose of this study was to investigate the relationship between DMV and AHI, and to determine preoperative risk factors for DMV in Chinese patients. Methods A prospective cohort trial enrolled patients scheduled for elective surgery. After obtaining informed consent, patient demographic information was collected, and patients were tested with pre-operative polysomnography. The anesthesiologist who managed the airway graded the mask ventilation. The difficult mask ventilation was defined as the mask ventilation provided by an unassisted anesthesiologist without oral airway or other adjuvant. A logistic regression model was used to analyze the association between AHI and DMV. Results A total of 159 patients were analyzed. For both primary and secondary outcomes, the unadjusted and adjusted odds ratio for DMV showed significant increases by 5 AHI units. AHI, age, and the Mallampati classification were found to be independent predictive factors for DMV. Conclusions AHI is associated with DMV as a novel independent risk factor in Chinese patients. Along with age and Mallampati classification, AHI should be included in establishing a superior predictive strategy for DMV screening. Trial registration Chinese Clinical Trial Registry ChiCTR-DDD-17013076


2021 ◽  
Vol 0 (0) ◽  
pp. 1740-1740
Author(s):  
Shuang Cao ◽  
Ming Xia ◽  
Ren Zhou ◽  
Jie Wang ◽  
Chen-Yu Jin ◽  
...  

Author(s):  
Saqer M. Althunayyan ◽  
Abdullah M. Mubarak ◽  
Raied N. Alotaibi ◽  
Musab Z. Alharthi ◽  
Mohammed A. Aljanoubi ◽  
...  

2020 ◽  
Author(s):  
Jia-Yi Wang ◽  
Jing-Jie Li ◽  
Peng-Cheng Zhao ◽  
Jia-Li Peng ◽  
Rong Hu ◽  
...  

Abstract Background: Difficult Mask Ventilation (DMV) is a potentially life-threatening situation that can arise during anesthesia. Accordingly, the majority of current airway management guidelines include risk assessments for DMV. Although Obstructive Sleep Apnea (OSA) is among the most important risk factors associated with DMV, other measurements such as the Apnea-Hypopnea Index (AHI) may play an important role in determining patient risk.This study investigated the relationship between DMV and AHI, and determined preoperative risk factors for DMV in Chinese patients.Methods: A prospective cohort trial enrolled patients scheduled for elective surgery. After obtaining informed consent, patient demographic information was collected, and patients were tested with pre-operative polysomnography. Inclusion criteria: Patients >18 years of age, American Society of Anesthesiologists Physical Status Classification (ASA) I-III, and planned elective surgery with general anesthesia. Exclusion criteria: malformations of the airway, patients undergoing regional anesthesia, and patients with contraindications to mask ventilation (i.e. planned awake intubation). A logistic regression model was used to analyze the association between AHI and DMV. Results: A total of 159 patients were analyzed. For both primary and secondary outcomes, the unadjusted and adjusted odds ratio for DMV showed significant increases of 5 AHI units. AHI, age, and the Mallampati classification were found to be independent predictive factors for DMV.Conclusions AHI is associated with DMV as a novel independent risk factor in Chinese patients. Along with age and Mallampati classification, AHI should be included in establishing a superior predictive strategy DMV screening.Trial registration: Chinese Clinical Trial Registry (Registration number # ChiCTR17013076; Date of Registration on October 22nd, 2017).


Author(s):  
Sameer Mohan Agarwal ◽  
Mohit Kumar ◽  
Amanta Lucy Ittoop ◽  
Bhawna Saini ◽  
Nishu Jha

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