Incidence Of Difficult Mask Ventilation Research Articles

Geometric morphometrics and machine learning from three-dimensional facial scans for difficult mask ventilation prediction.

Unanticipated difficult mask ventilation (DMV) is a potentially life-threatening event in anesthesia. Nevertheless, predicting DMV currently remains a challenge. This study aimed to verify whether three dimensional (3D) facial scans could predict DMV in patients scheduled for general anesthesia. The 3D facial scans were taken on 669 adult patients scheduled for elective surgery under general anesthesia. Clinical variables currently used as predictors of DMV were also collected. The DMV was defined as the inability to provide adequate and stable ventilation. Spatially dense landmarks were digitized on 3D scans to describe sufficient details for facial features and then processed by 3D geometric morphometrics. Ten different machine learning (ML) algorithms, varying from simple to more advanced, were introduced. The performance of ML models for DMV prediction was compared with that of the DIFFMASK score. The area under the receiver operating characteristic curves (AUC) with its 95% confidence interval (95% CI) as well as the specificity and sensitivity were used to evaluate the predictive value of the model. The incidence of DMV was 35/669 (5.23%). The logistic regression (LR) model performed best among the 10 ML models. The AUC of the LR model was 0.825 (95% CI, 0.765-0.885). The sensitivity and specificity of the model were 0.829 (95% CI, 0.629-0.914) and 0.733 (95% CI, 0.532-0.819), respectively. The LR model demonstrated better predictive performance than the DIFFMASK score, which obtained an AUC of 0.785 (95% CI, 0.710-0.860) and a sensitivity of 0.686 (95% CI, 0.578-0.847). Notably, we identified a significant morphological difference in the mandibular region between the DMV group and the easy mask ventilation group. Our study indicated a distinct morphological difference in the mandibular region between the DMV group and the easy mask ventilation group. 3D geometric morphometrics with ML could be a rapid, efficient, and non-invasive tool for DMV prediction to improve anesthesia safety.

Incidence and Predictors of Difficult Mask Ventilation in High-Risk Adult Population Scheduled for Elective Surgery: A Prospective Observational Study.

IntroductionMask ventilation is one of the key components in the management of airway during general anaesthesia, particularly when laryngoscopy is challenging. Adequate mask ventilation provides anaesthesiologists a safe time in case of unanticipated or anticipated difficult airway situations. The aim of this study was to determine the incidence of difficult bag-mask ventilation and intubation in patients having three or more predictors for difficult mask ventilation (DMV) in adult patients scheduled for elective surgery under anaesthesia.MethodsA total of 294 patients requiring endotracheal intubation for elective surgical procedure having three or more risk factors were evaluated for the presence of difficulty in bag-mask ventilation and intubation by the anaesthesiologist. Chi-square test or Fisher's exact test and a multivariable stepwise logistic regression model were performed to identify predictors of DMV. Crude and adjusted odds ratio with 95% confidence interval were reported.ResultsIn this study, the average age of the patients was 53.59±13.32 years with a 2:1 male-to-female ratio. DMV and difficult intubation (DI) were observed in 31.6% and 3% of patients, respectively. Multivariate analysis identified history of snoring, BMI (>35 kg/m2), presence of beard and Mallampati III or IV as independent predictors for DMV. Patients with multiple factors (≥3 factors) had a threefold (OR=2.57) increased risk of difficulty in mask ventilation and a nearly fivefold (OR=4.63) increased risk of difficulty with intubation.Conclusion In our study, the incidence of DMV was observed in 93 (31.6%) patients and DI was found in 9 (3%) patients. A simple DMV risk score may help to predict DMV better, potentially improving safety during difficult airway management, decreasing morbidity and mortality associated with it.

Difficult mask ventilation in general surgical population: observation of risk factors and predictors

Background: There are few predictors of difficult mask ventilation and a simple, objective, predictive system to identify patients at risk of difficult mask ventilation does not currently exist. We present a retrospective - subgroup analysis aimed at identifying predictive factors for difficult mask ventilation (DMV) in patients undergoing pre-operative airway assessment before elective surgery at a major teaching hospital. Methods: Data for this retrospective analysis were derived from a database of airway assessments, management plans, and outcomes that were collected prospectively from August 2008 to May 2010 at a Level 1 academic trauma center. Patients were stratified into two groups based on the difficulty of mask ventilation and the cohorts were analyzed using univariate analysis and stepwise selection method. Results: A total of 1399 pre-operative assessments were completed with documentation stating that mask ventilation was attempted. Of those 1399, 124 (8.9%) patients were found to be difficult to mask ventilate. A comparison of patients with and without difficult mask ventilation identified seven risk factors for DMV: age, body mass index (BMI), neck circumference, history of difficult intubation, presence of facial hair, perceived short neck and obstructive sleep apnea. Although seven risk factors were identified, no individual subject had more than four risk factors. Conclusion: The results of this study confirm that in a real world clinical setting, the incidence of DMV is not negligible and suggest the use of a simple bedside predictive score to improve the accuracy of DMV prediction, thereby improving patient safety. Further prospective studies to validate this score would be useful.

Difficult mask ventilation in general surgical population: observation of risk factors and predictors.

There are few predictors of difficult mask ventilation and a simple, objective, predictive system to identify patients at risk of difficult mask ventilation does not currently exist. We present a retrospective - subgroup analysis aimed at identifying predictive factors for difficult mask ventilation (DMV) in patients undergoing pre-operative airway assessment before elective surgery at a major teaching hospital. Data for this retrospective analysis were derived from a database of airway assessments, management plans, and outcomes that were collected prospectively from August 2008 to May 2010 at a Level 1 academic trauma center. Patients were stratified into two groups based on the difficulty of mask ventilation and the cohorts were analyzed using univariate analysis and stepwise selection method. A total of 1399 pre-operative assessments were completed with documentation stating that mask ventilation was attempted. Of those 1399, 124 (8.9%) patients were found to be difficult to mask ventilate. A comparison of patients with and without difficult mask ventilation identified seven risk factors for DMV: age, body mass index (BMI), neck circumference, history of difficult intubation, presence of facial hair, perceived short neck and obstructive sleep apnea. Although seven risk factors were identified, no individual subject had more than four risk factors. The results of this study confirm that in a real world clinical setting, the incidence of DMV is not negligible and suggest the use of a simple bedside predictive score to improve the accuracy of DMV prediction, thereby improving patient safety. Further prospective studies to validate this score would be useful.

The incidence and risk factors of difficult mask ventilation

The ability to ventilate and oxygenate a patient using a bag-mask breathing system may be lifesaving in the case of failure of the initial intubation attempt. In this study, we aimed to determine the incidence of difficult mask ventilation (DMV) and to find preoperative risk factors for this procedure. Based on methods used for overcoming some difficulties with bag-mask ventilation (MV), classification has been made into four categories: easy MV, awkward MV, difficult MV, and impossible MV. A univariate analysis was performed to identify potential risk factors predicting DMV, followed by a stepwise forward binary logistic regression, and the odds ratio and 95% confidence interval were calculated. A total of 576 patients were studied. Incidence of easy MV, awkward MV, and difficult MV were found to be 75.5% (n = 435), 16.7% (n = 96), and 7.8% (n = 45), respectively. Height, weight, age, male gender, increased Mallampati class, history of snoring, lack of teeth, and beard were found to be DMV risk factors (P < 0.05). Using a multivariate analysis, Mallampati class 4, male, history of snoring, age, and weight were found to be significantly associated with DMV. Although the incidence of DMV in general was 7.8% (n = 45), the incidence of DMV among patients with difficult intubation (n = 123) was found to be 15.5% (n = 19). Mallampati class 4, male patients, history of snoring, increasing age, and increasing weight were found to be risk factors for DMV in our study.

Large Hypopharyngeal Tongue: A Shared Anatomic Abnormality for Difficult Mask Ventilation, Difficult Intubation, and Obstructive Sleep Apnea?

Groupe Hospitalier Pitié-Salpêtrière, Paris, France. olivier.langeron@psl.ap-hop-paris.frIn Reply:—We greatly appreciate the interest expressed by Drs. Chou and Wu regarding our article. 1As reported previously, 2some subjects in whom the trachea is difficult to intubate are at increased risk of obstructive sleep apnea (OSA). Moreover, patients who have difficult intubation (DI) and OSA may share upper airway anatomical abnormalities with a difficult mask ventilation (DMV) situation. In our study, we identified five independent factors (age &gt; 55 yr, body mass index &gt; 26 kg/m2, beard, lack of teeth, and history of snoring) associated significantly with DMV. Some of these risk factors for DMV (age &gt; 55 yr, body mass index &gt; 26 kg/m2, and history of snoring) are related to malproportion of overall body size or malproportion between the oropharyngeal free space and internal structures of the oropharynx, as in OSA. Our first comment in response to Drs. Chou and Wu is that the purpose of our study was to identify factors predicting DMV and not to correlate DMV to OSA. Second, Drs. Chou and Wu reported that a longer mandible and hyoid distance (MHD) indicated an increased tongue mass in the hypopharynx, compromising the posterior airway space. 3Unfortunately, measurement of MHD may be difficult because of its wide variation among the adult population and the need for experience as mentioned in their letter. Moreover in their previous study, 3MHD was obtained from lateral cervical spine radiograph measurements and not from clinical findings. Last, in a more recent study, 2MHD was not significantly different in DI and non-DI patients and was not related to OSA. Nevertheless, we are convinced that MHD could be valuable information in predicting an OSA–DMV–DI situation if it could be validated in a clinical setting without performing measurements from radiographs. Therefore, further studies are required to determine if MHD could be a reliable clinical predictor of OSA–DMV–DI cases.We thank Dr. Takenaka et al. for the interest in our work and their valuable comments. We fully agree that the jaw-thrust maneuver can reopen the obstructed airway by lifting up the base of the tongue and the epiglottis in anesthetized patients. Moreover, Calder et al. 4reported that impaired mandibular protrusion was associated with a more difficult laryngoscopy in patients with cervical spine disease. Indeed, in patients with an occipito-atlanto-axial disease as in the study of Calder et al. , 4a poor mandibular protrusion is recognized. Consequently, we may assume that decreased mandibular protrusion in an awake patient is a good predictor of a reduced cervical spine mobility associated with impaired laryngoscopy. In addition, this test may not assess reliably a potential decreased airway patency in the anesthetized patient without spine disease. Therefore, we cannot extrapolate information related to difficult laryngoscopy and difficult intubation to those with DMV requiring specific investigation. In our opinion, the relation between a reduced mandibular protrusion in the awake state and DMV remains to be shown.We thank Dr. Gentili for his interest in our work and the comments. The purpose of our study was to identify factors predicting DMV per se and not to determine the influence of the degree of clinical experience of the anesthesiologist in a DMV situation. Consequently, as mentioned in the article, this study involved staff anesthesiologists. Nevertheless, we agree with Dr. Gentili that the annual clinical case loads for an individual anesthesiologist may influence his or her clinical practice. However, this is true for any daily clinical activity for a physician and was not the purpose of our study. Last, incidence of DMV has been assessed rarely in previous studies related to airway management, and no specific investigation has been performed to study DMV. This point may explain largely the discrepancy for the DMV incidence in our study in comparison with other studies.