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ORIGINAL ARTICLE Table of Contents  
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Correlation between predictors of difficult mask ventilation and its grading using a risk score


 Department of Anaesthesiology, Bharati Vidyapeeth (DTU) Medical College, Pune, Maharashtra, India

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Date of Submission10-Apr-2022
Date of Acceptance27-Sep-2022
Date of Web Publication25-Nov-2022
 

  Abstract 


Background: Maintenance of airway patency and oxygenation are the main objectives of face mask ventilation. Preoperative prediction can reduce the incidence of unanticipated difficult mask ventilation (DMV). Aim of Study: The aim of this study was to evaluate the correlation between predictors of DMV and its grading using a risk score. Study Setting and Design: This was an observational study approved by the Institutional Ethics Committee in a tertiary care hospital between 2020 and 2021. Patients and Methods: A total of 110 adult patients scheduled for elective surgery under general anaesthesia were studied. A detailed preoperative airway assessment was done to identify and risk score for seven standard predictors of DMV (male gender, age >55 years, body mass index ≥30 kg/m2, obstructive sleep apnoea [STOP-BANG score], edentulous state, modified Mallampati class and presence of beard). The risk score was correlated with the grading of mask ventilation in the operation theatre performed using the four-point scale as described by Han et al. Results: A statistically significant association was found with standard predictors such as male gender, obstructive sleep apnoea (STOP-BANG score) and the total preoperative risk score with the grading of mask ventilation (P < 0.05 for all). Additional risk factors found statistically significant were interincisor distance, thyromental distance, neck circumference, receding mandible, mandibular jaw protrusion, restricted neck movements, buck teeth and submucosal fibrosis. Conclusion: Prediction of DMV with preoperative risk score can lead to better anticipation of difficult airway management. Appropriate anticipatory airway management could potentially decrease the incidence of failed ventilation and resultant hypoxia.

Keywords: Predictors of difficult airway, predictors of difficult mask ventilation, predictor risk score for difficult mask ventilation


How to cite this URL:
Manikonda IS, Aphale SS. Correlation between predictors of difficult mask ventilation and its grading using a risk score. Airway [Epub ahead of print] [cited 2022 Nov 28]. Available from: https://www.arwy.org/preprintarticle.asp?id=361985





  Introduction Top


Face mask ventilation is the principal mode of ventilation before the placement of a definitive airway device in the majority of general anaesthetics.[1] In difficult or failed intubation, face mask ventilation is also an essential backup technique for preserving oxygenation.[2] Difficult mask ventilation (DMV) forms a component of difficult airway. In the presence of DMV, subsequent difficult laryngoscopy or difficult intubation can have serious consequences. Therefore, identifying DMV could lower morbidity and mortality amongst patients at risk.[3]

The aim of our study was to correlate the predictive ability of the DMV predictors[4] with a risk score to the grading of mask ventilation.[5]


  Patients and Methods Top


The study was carried out at a tertiary care hospital located in Western India after obtaining approval from the Institutional Ethics Committee. It was an observational cross-sectional study conducted between February 2020 and September 2021. Assuming an alpha error of 0.05 and a power of test of 80%, a sample size of 110 was calculated using a qualitative formula based on the prevalence (7.8%) from an earlier study.[6] The study was conducted on 110 consenting adult patients of either gender scheduled to undergo elective surgery under general anaesthesia. Patients requiring elective fibreoptic intubation, emergency surgery requiring rapid sequence induction or those with oromaxillary pathologies were excluded from the study.

All patients underwent a detailed preanaesthetic evaluation of general patient characteristics such as age, gender and body mass index (BMI) (kg/m2), history of snoring or obstructive sleep apnoea (STOP-BANG score), and assessment of airway for modified Mallampati class (MMC), interincisor distance (or intergingival distance measured with the mouth fully opened), thyromental distance (from the thyroid notch to the lower border of the mandibular mentum), neck circumference (at the level of the upper border of the cricoid cartilage), mandibular jaw protrusion class (indication of the ability to approximate inferior to superior incisors due to the extent of temporomandibular joint mobility, divided into Classes A, B and C), cervical range of motion and any physical features that may affect mask fit such as a beard, jaw disorders like receding mandible (malalignment of the upper maxilla and lower mandible in which the mandible recedes relative to the frontal plane of the forehead, sloping back towards the neck), macroglossia, loss of buccal pad of fat, submucosal fibrosis and dentition like buck teeth and edentulous state.

Preoperatively, we had identified seven standard predictors of DMV[4] (male gender, age >55 years, BMI ≥30 kg/m2, obstructive sleep apnoea or history of snoring [STOP-BANG score], edentulous state, MMC III or IV and the presence of a beard). Using these seven predictors, a risk score was devised which was applied to predict the risk of DMV [Table 1].
Table 1: Predictor risk score

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Patients were kept nil per oral for 6 h before surgery. In the operation theatre, an intravenous line was secured and basic monitoring was established that included electrocardiogram, noninvasive blood pressure and pulse oximetry. One-hand method of mask ventilation was initially performed by anaesthesia personnel with at least 2 years of clinical experience. An appropriate-sized silicone face mask was held using the left hand with the thumb and index finger forming a C around the collar of the connector, the third and fourth digits resting on the horizontal ramus of the mandible and the fifth digit over the angle of the mandible.

Inadequacy of mask ventilation was said to be present when there was no perceptible chest rise, gas leak around the mask, inadequate filling of the reservoir bag, fall in saturation below 92% and need for change of operator. If ventilation was found inadequate with one hand, an appropriate-sized Guedel oropharyngeal airway was inserted to improve mask ventilation. The two-hand method of mask ventilation with jaw thrust was considered if the above corrective steps did not improve ventilation. In the two-handed technique, the left hand was positioned similarly to the one-handed technique while the right hand was placed on the other side of the mask in a mirror-image conformation. In the operation theatre, the adequacy of mask ventilation was graded using the four-point scale (Grades 1–4) as described by Han et al.[5]

Grading of mask ventilation

  • Grade 1: Ventilated by mask
  • Grade 2: Ventilated by mask with oral airway or other adjuncts
  • Grade 3: DMV (inadequate, unstable or requiring two practitioners)
  • Grade 4: Unable to mask ventilate.


Grade 3 mask ventilation was considered DMV and Grade 4 mask ventilation was considered impossible mask ventilation. Additional parameters that could contribute to DMV were also assessed.

Statistical analysis

Categorical data are shown as n (% of cases) and continuous data are presented as mean ± standard deviation. The intergroup statistical comparison of the distribution of categorical variables was tested using the Chi-square test or Fisher's exact probability test if more than 20% of cells had an expected frequency of <5. The intergroup statistical comparison of means of normally distributed continuous variables was done using the independent sample t-test. The underlying normality assumption was tested before subjecting the study variables to the t-test.

We considered P < 0.05 to be statistically significant. All hypotheses were formulated using two-tailed alternatives against each null hypothesis (hypothesis of no difference). Statistical analysis of data was performed using IBM SPSS Statistics for Windows, version 22.0 (IBM Corp., Armonk, NY, USA).


  Results Top


The adequacy of mask ventilation was assessed in 110 patients. The number of patients with accompanying grades of mask ventilation is depicted in [Table 2].
Table 2: Distribution of grades of mask ventilation

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The seven standard predictors of DMV chosen in our study along with the grade of mask ventilation and their statistical significance are presented in [Table 3]. The mean age of patients in our study was 44.59 ± 15.56 years. Of these, 76 patients were <55 years old, 27 patients were between 55 and 70 years old and 7 were over 70 years old. Two cases of DMV were in the <55-year and >70-year age group making age an insignificant predictor. There were 47 males and 63 females in our study. Of the four cases of DMV, three were males. The male gender was identified in our study as one of the significant predictors of DMV (P < 0.018). The overall mean BMI of the patients in our study was 23.24 ± 4.0 kg/m2. None of our patients had a BMI ≥35 kg/m2 and all four cases of DMV had a BMI <29.9 kg/m2. Hence, BMI was not found to be a significant factor in our study (P > 0.155). No patient had a STOP-BANG score of 6 and above. Of the four cases with DMV, two cases had a STOP-BANG score of 1 while the other two cases had STOP-BANG scores of 3 and 4, respectively. Obstructive sleep apnoea (STOP-BANG score) was identified as a significant predictor (P < 0.034) of DMV. Only one amongst the four patients with DMV was completely edentulous making this parameter statistically insignificant (P > 0.117). When MMC was evaluated, three patients of DMV had MMC II while one patient had MMC III, making MMC also statistically insignificant (P > 0.063). Of two patients who had a beard, only one had DMV making the presence of a beard statistically insignificant (P > 0.174).
Table 3: Distribution of seven standard predictors of difficult mask ventilation along with grade of mask ventilation

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Standard predictors such as male gender, obstructive sleep apnoea (STOP-BANG score) and total risk score [Table 4] showed a statistically significant association with the grade of mask ventilation (P < 0.05 for all).
Table 4: Distribution of total risk score with grade of mask ventilation

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Several measurement-based risk factors are listed in [Table 5]. The mean interincisor distance of patients who had Grade 1, Grade 2 and Grade 3 MV was 5.88 ± 0.47 cm, 5.33 ± 0.96 cm and 5.35 ± 0.46 cm respectively. Interincisor distance was significantly higher in Grade 1 MV when compared to Grade 2 and 3 MV (P < 0.008). The mean thyromental distance of patients who had Grade 1, Grade 2 and Grade 3 MV was 6.32 ± 0.39 cm, 6.12 ± 0.26 cm and 5.67 ± 0.74 cm respectively. Thyromental distance was significantly higher in Grade 1 MV compared to Grade 2 and 3 MV (P < 0.004). The mean neck circumference of patients who had Grade 1, Grade 2 and Grade 3 MV was 34.73 ± 1.23 cm, 35.85 ± 3.05 cm and 37.17 ± 2.95 cm respectively. Neck circumference was significantly lower in Grade 1 MV than in Grade 2 and 3 MV (P < 0.002).
Table 5: Interincisor distance, thyromental distance and neck circumference (mean±standard deviation) in relation to grade of mask ventilation

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Other airway parameters studied included receding mandible, mandibular jaw protrusion, restricted neck movements, buck teeth, submucosal fibrosis, macroglossia and loss of buccal pad of fat. [Table 6] lists the number of patients who had mask ventilation Grades of 1, 2 and 3 for each of these parameters. There was a statistically significant association between MV grade and the presence of receding mandible, degree of mandibular jaw protrusion, restricted neck movements, buck teeth and submucosal fibrosis (P < 0.001). Macroglossia and loss of buccal pad of fat were not found to have a statistically significant association with MV grade.
Table 6: Other airway parameters studied and grade of mask ventilation

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  Discussion Top


The main responsibility of an anaesthesiologist is optimal oxygenation and ventilation. Nearly 30% of morbidity and mortality in anaesthesia is attributed to a difficult airway. Hence, any difficulty in mask ventilation is a primary cause of concern, and anticipation of DMV is critical for improving the safety and effectiveness of airway management.

DMV is described as a situation in which it is not possible for the anaesthesiologist to provide adequate ventilation because of one or more of the following problems – inadequate mask seal, excessive gas leak or excessive resistance to the ingress or egress of gas – with oxygen saturation of <90%.[1]

The incidence of DMV in our study was 3.6% and no patient had impossible mask ventilation [Table 2]. The incidence of DMV in our study is higher than that reported in studies by Asai et al. (1.4%),[7] Rose and Cohen (0.9%),[8] El-Ganzouri et al. (0.07%),[9] Kheterpal et al. (1.4%)[10] and Lundstrøm et al. (1.0%)[3] but lower than that reported by Shah and Sundaram (7.8%).[6] This wide disparity of reported incidence of DMV can be due to its variable definition and the DMV grading method utilised, different predictive factors, population size and possible racial anatomical variations.

While relating age to DMV, we had four cases of DMV, of which two were in the <55-year age group and two in the >70-year age group. We found that age did not influence the incidence of DMV significantly (P > 0.179) which was in contrast to the studies by Moon et al. (>46 years),[11] Cattano et al. (≥47 years),[12] Khan and Ahmed (>50 years)[13] and Langeron et al. (>55 years),[14] all of whom concluded that age was an independent risk factor.

While there were more females in our study group (57.3%), three of the four cases of DMV were male making the male gender a significant predictor (P < 0.018) of DMV amongst the standard seven predictors studied. This outcome is supported by studies of Moon et al.,[11] Leoni et al.,[15] Khan and Ahmed[13] and Yildiz et al.[16]

The four cases of DMV had a BMI <29.9 kg/m2. Hence, the standard predictor of BMI was found to be not significant in our study (P > 0.155). This could be related to the varied distribution and less population size and ethnic group as was proven by Yildiz et al.[16]

No patient had a STOP-BANG score of 6 and above. Of the four cases with DMV, two cases had a score of 1 while one case each had a score of 3 and 4. Amongst the seven standard predictors chosen, obstructive sleep apnoea (STOP-BANG score) (P < 0.034) was a significant predictor of DMV. This was similar to that reported by Khan and Ahmed,[13] Cattano et al.,[12] Shah and Sundaram,[6] Kheterpal et al.,[10] Langeron et al.,[14] Yildiz et al.[16] and Moon et al.[11]

While assessing the association of edentulousness with DMV, our study revealed that amongst the four patients with DMV, only one was completely edentulous. Edentulousness was, therefore, not significant (P > 0.117) which is contrary to studies of Shah and Sundaram, and Langeron et al.[6],[14]

With reference to MMC, only three patients of DMV in our study had MMC II while one case of DMV had MMC III. MMC was not found to be statistically significant amongst the seven standard predictors. This outcome is contrary to that reported by Moon et al.,[11] Shah and Sundaram,[6] Kheterpal et al.[10] and Yildiz et al.[16] Only one of the two patients with a beard had DMV making the presence of a beard not important amongst the seven standard predictors. This is contrary to that reported by Langeron et al.,[14] Kheterpal et al.[10] and Cattano et al.[12]

Amongst the seven standard predictors used in creating our risk score, only male gender (P < 0.018) and obstructive sleep apnoea (STOP-BANG score) (P < 0.034) were found to be statistically significant.

The risk score calculated using the seven standard predictors had a potential range of 0–11. The highest and lowest risk scores in our study were a score of 6 in one case (0.9%) and a score of 0 in 36 cases (30.9%). Four patients with an MV Grade of 3 had varied total risk scores of 1, 2, 4 and 6, respectively. A significantly higher proportion of cases with lower total preoperative risk scores had lower grades of mask ventilation (1 and 2) and vice versa. Total risk score distribution differs significantly with MV grades (P < 0.001).

The optimal cut-off score calculated for the predictive risk score was ≥3. This has an accuracy of 83.6% with a high specificity of 84.9% and sensitivity of 50%. This has a high negative predictive value (NPV) of 97.8% with a low positive predictive value of 11.1%. With this cut-off score, we had anticipated DMV in 16.36% of cases, but only 50% of DMV cases were predicted successfully. With such a high NPV, we can conclude that the cases with less than optimal cut-off scores can exclude the likelihood of DMV.

The DIFFMASK score developed by Lundstrøm et al. had a score of ≥5 as the optimal cut-off value in the range of 0–18 using 10 predictors for predicting DMV.[3] The higher sensitivity of 85% and specificity of 59% than our study could be related to the larger population size and higher number of predictors evaluated.

Amongst the additional parameters studied [Table 5] and [Table 6], interincisor distance, thyromental distance, neck circumference, receding mandible, mandibular jaw protrusion test, restricted neck movements, buck teeth and submucosal fibrosis were found to be statistically significant.

Parameters that were not found to be statistically significant were macroglossia and loss of buccal pad of fat [Table 6].

In our study, interincisor distance was significantly higher in Grade 1 MV when compared to Grade 2 and 3 MV (P < 0.008). Thyromental distance was also significantly higher in Grade 1 MV compared to Grade 2 and 3 MV (P < 0.004). Our findings were similar to that drawn by Yildiz et al.[16]

The neck circumference in cases with DMV was 37.17 ± 2.95 cm. It was significantly lower in Grade 1 MV than in Grade 2 and 3 MV (P < 0.002). Similar studies by Cattano et al.,[12] Leoni et al.[15] and Khan and Ahmed[13] identified neck circumference ≥40 cm as an independent risk factor of DMV while Özdilek et al.[17] concluded that neck circumference of ≥42 cm was not a significant predictive factor for DMV.

In our study, only one patient with a receding mandible had DMV. The presence of a receding mandible was identified as an important predictor of DMV, an outcome supported by Shah and Sundaram[6]

The distribution of MV grades in our study differed significantly between various classes of mandibular jaw protrusion. Leoni et al.[15] and Kheterpal et al.[10] also concluded that limited mandibular protrusion is a significant parameter for DMV. Restricted neck movement, buck teeth and submucosal fibrosis were found to be significant predictors of DMV.

Loss of buccal pad of fat was not a significant predictor in our study. Macroglossia was also not a significant predictor which was contrary to the findings reported by Shah and Sundaram.[6]

Several significant parameters were identified in our study which could improve the efficacy of prediction of DMV. Assessment of these parameters preoperatively and the utilisation of a preoperative predictive risk score can help us conclude that patients with a risk score of ≥3 points represent a subset of patients who require heightened attention when face mask ventilation is planned compared with those patients who are obviously at low risk of difficulties. This score can be useful in a clinical context but further validation is necessary.

Our study has the following limitations. The definition of DMV is subjective and observer dependent, possibly resulting in a disparity with the incidence quoted in other studies. As our study was conducted in the general adult population, our outcomes cannot be extrapolated to the paediatric age group. The variables for the DMV assessed were different from the previous studies due to the different social or ethnic groups and the different population sizes. Initially, the difficulty of mask ventilation alone was assessed in our study. Progressive difficulty in mask ventilation due to repeated attempts for difficult intubation due to other consequential factors was not included in our study setting. The number of patients with DMV was very low in our study, resulting in lower power of analysis. Hence, further study with a larger population size is needed for accurate prediction.


  Conclusion Top


Preoperative parameters such as male gender, obstructive sleep apnoea, reduced interincisor distance and thyromental distance, large neck circumference, limited mandibular jaw protrusion, restricted neck movements, the presence of buck teeth, receding mandible and submucosal fibrosis can predict DMV preoperatively. This could lead to better preparedness and anticipation of difficulties in airway management including unanticipated emergencies, potentially decreasing adverse events of failed ventilation leading to hypoxia.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

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Holland J, Donaldson W. Difficult mask ventilation. Anaesth Tutorial of the Week 2015;321:1-7.  Back to cited text no. 1
    
2.
El-Orbany M, Woehlck HJ. Difficult mask ventilation. Anesth Analg 2009;109:1870-80.  Back to cited text no. 2
    
3.
Lundstrøm LH, Rosenstock CV, Wetterslev J, Nørskov AK. The DIFFMASK score for predicting difficult facemask ventilation: A cohort study of 46,804 patients. Anaesthesia 2019;74:1267-76.  Back to cited text no. 3
    
4.
Hagberg CA, Artime CA. Airway management in the adult. In: Miller RD, Eriksson LI, Fleisher LA, Wiener-Kronish JP, Cohen NH, Young WL, editors. Miller's Anesthesia. 8th ed. Philadelphia: Elsevier; 2014. p. 1647-83.  Back to cited text no. 4
    
5.
Han R, Tremper KK, Kheterpal S, O'Reilly M. Grading scale for mask ventilation. Anesthesiology 2004;101:267.  Back to cited text no. 5
    
6.
Shah PN, Sundaram V. Incidence and predictors of difficult mask ventilation and intubation. J Anaesthesiol Clin Pharmacol 2012;28:451-5.  Back to cited text no. 6
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7.
Asai T, Koga K, Vaughan RS. Respiratory complications associated with tracheal intubation and extubation. Br J Anaesth 1998;80:767-75.  Back to cited text no. 7
    
8.
Rose DK, Cohen MM. The airway: Problems and predictions in 18,500 patients. Can J Anaesth 1994;41:372-83.  Back to cited text no. 8
    
9.
el-Ganzouri AR, McCarthy RJ, Tuman KJ, Tanck EN, Ivankovich AD. Preoperative airway assessment: Predictive value of a multivariate risk index. Anesth Analg 1996;82:1197-204.  Back to cited text no. 9
    
10.
Kheterpal S, Han R, Tremper KK, Shanks A, Tait AR, O'Reilly M, et al. Incidence and predictors of difficult and impossible mask ventilation. Anesthesiology 2006;105:885-91.  Back to cited text no. 10
    
11.
Moon TS, Fox PE, Somasundaram A, Minhajuddin A, Gonzales MX, Pak TJ, et al. The influence of morbid obesity on difficult intubation and difficult mask ventilation. J Anesth 2019;33:96-102.  Back to cited text no. 11
    
12.
Cattano D, Killoran PV, Cai C, Katsiampoura AD, Corso RM, Hagberg CA. Difficult mask ventilation in general surgical population: Observation of risk factors and predictors. F1000Res 2014;3:204.  Back to cited text no. 12
    
13.
Khan MN, Ahmed A. Accuracy of STOP-Bang questionnaire in predicting difficult mask ventilation: An observational study. Cureus 2021;13:e15955.  Back to cited text no. 13
    
14.
Langeron O, Masso E, Huraux C, Guggiari M, Bianchi A, Coriat P, et al. Prediction of difficult mask ventilation. Anesthesiology 2000;92:1229-36.  Back to cited text no. 14
    
15.
Leoni A, Arlati S, Ghisi D, Verwej M, Lugani D, Ghisi P, et al. Difficult mask ventilation in obese patients: Analysis of predictive factors. Minerva Anestesiol 2014;80:149-57.  Back to cited text no. 15
    
16.
Yildiz TS, Solak M, Toker K. The incidence and risk factors of difficult mask ventilation. J Anesth 2005;19:7-11.  Back to cited text no. 16
    
17.
Özdilek A, Beyoglu CA, Erbabacan ŞE, Ekici B, Altındaş F, Vehid S, et al. Correlation of neck circumference with difficult mask ventilation and difficult laryngoscopy in morbidly obese patients: An observational study. Obes Surg 2018;28:2860-7.  Back to cited text no. 17
    

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Correspondence Address:
Indu Sowbhagya Manikonda,
Post Graduate Student, Department of Anaesthesiology, Bharati Vidyapeeth (DTU) Medical College, Pune - 411 043, Maharashtra
India
Shubhada S Aphale,
Professor, Department of Anaesthesiology, Bharati Vidyapeeth (DTU) Medical College, Pune - 411 043, Maharashtra
India
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Source of Support: None, Conflict of Interest: None




 
 
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