|Year : 2016 | Volume
| Issue : 1 | Page : 54-58
Diagnostic accuracy of bedside tests for predicting difficult intubation in Indian population: An observational study
Sangeeta Dhanger1, Suman Lata Gupta2, Stalin Vinayagam2, Prasanna Udupi Bidkar2, Lenin Babu Elakkumanan2, Ashok Shankar Badhe2
1 Department of Anaesthesiology and Critical Care, Indira Gandhi Medical College and Research Institute, Puducherry, India
2 Department of Anaesthesiology and Critical Care, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
|Date of Web Publication||12-Jan-2016|
FR4, Sri Anbalaya Apartments, 17th Cross Street, Krishna Nagar, Puducherry - 605 008
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Unanticipated difficult intubation can be challenging to anesthesiologists, and various bedside tests have been tried to predict difficult intubation.
Aims: The aim of this study was to determine the incidence of difficult intubation in the Indian population and also to determine the diagnostic accuracy of bedside tests in predicting difficult intubation.
Settings and Design: In this study, 200 patients belonging to age group 18–60 years of American Society of Anesthesiologists I and II, scheduled for surgery under general anesthesia requiring endotracheal intubation were enrolled. Patients with upper airway pathology, neck mass, and cervical spine injury were excluded from the study.
Materials and Methods: An attending anesthesiologist conducted preoperative assessment and recorded parameters such as body mass index, modified Mallampati grading, inter-incisor distance, neck circumference, and thyromental distance (NC/TMD). After standard anesthetic induction, laryngoscopy was performed, and intubation difficulty assessed using intubation difficulty scale on the basis of seven variables.
Statistical Analysis: The Chi-square test or student t-test was performed when appropriate. The binary multivariate logistic regression (forward-Wald) model was used to determine the independent risk factors.
Results: Among the 200 patients, 26 patients had difficult intubation with an incidence of 13%. Among different variables, the Mallampati score and NC/TMD were independently associated with difficult intubation. Receiver operating characteristic curve showed a cut-off point of 3 or 4 for Mallampati score and 5.62 for NC/TMD to predict difficult intubation.
Conclusion: The diagnostic accuracy of NC/TM ratio and Mallampatti score were better compared to other bedside tests to predict difficult intubation in Indian population.
Keywords: Difficult airway, intubation difficulty scale, Mallampatti score, neck circumference
|How to cite this article:|
Dhanger S, Gupta SL, Vinayagam S, Bidkar PU, Elakkumanan LB, Badhe AS. Diagnostic accuracy of bedside tests for predicting difficult intubation in Indian population: An observational study. Anesth Essays Res 2016;10:54-8
|How to cite this URL:|
Dhanger S, Gupta SL, Vinayagam S, Bidkar PU, Elakkumanan LB, Badhe AS. Diagnostic accuracy of bedside tests for predicting difficult intubation in Indian population: An observational study. Anesth Essays Res [serial online] 2016 [cited 2020 Aug 5];10:54-8. Available from: http://www.aeronline.org/text.asp?2016/10/1/54/165503
| Introduction|| |
Difficulty in airway management is an important cause of morbidity and mortality in anesthetic practice. Unanticipated difficult intubation can be challenging to anesthesiologists, and numerous investigators have attempted to predict difficult intubation by using various bedside tests. Though there are various screening tests available for assessing the difficult intubation, they have poor discriminative power when used alone as compared to a combination of tests. Mallampati score, thyromental distance (TMD), sternomental (SM) distance, and Wilson's risk sum score were widely recognized as tools for predicting difficult intubation.,, The diagnostic accuracy of these screening tests has varied from trial to trial, probably because of differences in the incidence of difficult intubation, inadequate statistical power, different test thresholds, or differences in patient characteristics.
Differences in patient characteristics due to race or ethnicity may influence the incidence of difficult laryngoscopy and difficult intubation. The majority of studies of difficult laryngoscopy and intubation have been performed in the Western population.,,, The results of these studies cannot be extrapolated to the Indian population as Indians were anthropometrically different compared to the Western population. This prospective study was designed to determine the incidence of difficult intubation in the Indian population and also to determine the diagnostic accuracy of various bedside tests for predicting difficult intubation in patients without any airway pathology.
| Materials and Methods|| |
After obtaining Institutional Ethics Committee approval and written informed consent, 200 patients belonging to the age group 18–60 years of American Society of Anesthesiologists I and II, scheduled for surgery under general anesthesia and requiring endotracheal intubation were enrolled in this prospective study. Patients with upper airway pathology, neck mass, and cervical spine injury were excluded from the study.
An attending anesthesiologist conducted preoperative assessment and recorded the following parameters: (a) Modified Mallampati grading with patient in sitting position without phonation, (b) inter-incisor distance, (c) neck circumference (NC) at the level of cricoid cartilage, and (d) TMD measured by straight distance from thyroid notch to inner mentum with neck in extended position. Height and weight were measured to calculate body mass index (BMI). Any history of snoring and difficult intubation in the previous surgery was also noted. All patients were premedicated with tablet diazepam 5 mg the night before and on the morning of surgery. In the operating room, standard monitoring was established (electrocardiogram, noninvasive blood pressure, pulse oximetry, and capnography) and patients were positioned in sniffing position. Difficult airway cart was kept ready. After preoxygenation with 100% O2 for 3 min, anesthesia was induced with injection fentanyl 2 μg/kg and injection thiopentone sodium 3–5 mg/kg, and intubation was facilitated by injection vecuronium 0.1 mg/kg and isoflurane 1% with N2O and O2 for 3 min. Intubation was performed by the experienced anesthesiologists (>5 years) who were unaware of the airway measurements using Macintosh 3 blade. The laryngoscopic view was graded by Cormack and Lehane grading without external laryngeal pressure.
Intubation difficulty was assessed by intubation difficulty scale (IDS) developed by Adnet et al. on the basis of seven variables associated with difficult intubation. They were as follows: N1, number of additional intubation attempts; N2, number of additional operators; N3, number of alternative intubation techniques used; N4, glottic exposure as defined by Cormack and Lehane (grade 1 – N4 = 0; grade 2 – N4 = 1; grade 3 – N4 = 2; and grade 4 – N4 = 3); N5 – lifting force applied during laryngoscopy (N5 = 0 if inconsiderable and N5 = 1 if considerable, as assessed subjectively); N6 – need to apply external laryngeal pressure to improve glottic pressure (N6 = 0 if no external pressure or only the Sellick maneuver was applied and N6 = 1 if external laryngeal pressure was used); and N7 – position of the vocal cords at intubation (N7 = 0 if abducted or not visible and N7 = 1 if adducted). The IDS score is the sum of N1 through N7. A score of 0 to 5 indicates no or slight difficulty and >5 indicates moderate to major difficulty.
On the basis of previous study that reported an 8% incidence of difficult laryngoscopy in the Indian population, a power calculation showed that 184 patients will be required to demonstrate significant difference between patients with easy and difficult intubation with use of α = 0.05 and β = 0.20. We included 200 patients to compensate for any dropouts. Measured data were analyzed using SPSS software (IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp). Data were presented as mean or percentage. The Chi-square test or Student t-test was performed when appropriate.
Differences between the difficult and easy groups were analyzed using a binary univariate logistic regression model to determine the significant risk factors for difficult intubation. Then all the significant variables were entered into a binary multivariate logistic regression (forward-Wald) model to determine the independent risk factors for difficult intubation. The diagnostic performance of the significant risk factors was also assessed using the receiver operating characteristic (ROC) curves. After identifying the adequate cut-off points by selecting the maximum specificity while sensitivity ≥80%, the continuous variables were transformed into binary variables to compare the accuracy of the tests. A value of P < 0.05 was considered significant.
| Results|| |
In this study, data from 200 adult patients scheduled for surgery under general anesthesia with tracheal intubation were analyzed. Demographic profile and airway characteristics of the study population were presented in [Table 1]. Among the 200 patients, 26 patients had difficult intubation according to IDS scale with an overall incidence of 13%.
According to IDS score, patients were divided into two groups: Easy (IDS <5) and difficult (IDS >5). The demographic variables and various airway parameters were compared between the two groups using binary univariate logistic regression analysis as shown in [Table 2]. Variables such as NC, TMD, NC/TMD, BMI, Mallampati score, and the Cormack grade were related to an IDS score of ≥5.
|Table 2: Binary univariate logistic regression comparing patients with an IDS score <5 and patients with an IDS score ≥5|
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Among these variables, the Mallampati score and NC/TMD were independently associated with a difficult intubation revealed by binary multivariate logistic regression (forward-Wald) analysis as shown in [Table 3]. NC/TMD ratio is of more statistical significance as an independent risk factor for difficult intubation than Mallampatti score. [Figure 1] shows the ROC curves for NC/TMD and Mallampati score. The cut-off points for difficult intubation were the Mallampati score of 3 or 4 and NC/TMD ≥5.6. NC/TMD showed the larger area under the curve (AUC = 0.710) on the ROC curve than the Mallampati score (AUC = 0.615).
|Table 3: Binary multivariate logistic regression (forward-Wald) analysis performed in each patient group to determine the independent risk factors for difficult intubation in each population|
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|Figure 1: Receiver operating characteristic curve analysis of ratio of neck circumference to thyromental distance (a) and Mallampatti grade (b). AUC = Area under the curve|
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| Discussion|| |
The incidence of difficult intubation in our study was 13% as assessed by IDS scale. We found that ratio of NC/TMD and Mallampati score were better predictors of difficult intubation compared to other predictors. Patients with NC/TMD ratio of >5.6 and Mallampati score of ≥3 were more prone for difficult intubation.
Difficult tracheal intubation remains a relatively constant and significant source of morbidity and mortality in anesthetic practice. Preoperative detection of patients at risk for difficult intubation is the first step in airway management. Though there are various screening tests to predict difficult intubation, many studies proved their poor diagnostic accuracy when they were used alone. Therefore, combinations of individual tests or risk factors may add diagnostic value in comparison with the value of each test alone. Several studies have combined risk factors, such as the el-Ganzouri or Wilson scores,, which are a multivariate risk index systems. However, as these scores contain multiple risk factors, they are more time-consuming to perform. Thus, combining two of the most valuable risk factors may increase the diagnostic value at the same time not increasing the burden of the test significantly.
Though there are various studies to predict the diagnostic accuracy of screening tests for predicting difficult intubation, there is a significant difference between the trials. This variation could be because of the difference in the patient characteristics as most of these studies were conducted in Western population. There is a significant difference in the anthropometry of Indian and the Western population that is also translated into the anatomical indices used to predict difficult laryngoscopy. Thus, it is important to analyze whether the same parameters and cut-off values can be applied in the Indian population to predict difficult airway.
Previous studies reported that NC at the thyroid cartilage is a valuable predictor of difficult laryngoscopy in both obese and nonobese patients., However, NC does not indicate the amount of soft tissue at various topographic regions within the neck. Distribution of fat in specific neck areas, especially the anterior neck, may provide a better indication of difficult intubation than NC. TMD is considered to be an indicator of mandibular space. This test also reflects whether the displacement of the tongue by the laryngoscope blade will be easy or difficult. The diagnostic value of TMD as an individual predictor proved unsatisfactory in a meta-analysis conducted by Shiga et al. NC/TMD might represent the distribution of fat in the neck better than either NC or TMD alone.
Intubation difficulty index is the ratio between the NC/TMD developed by Kim et al. with an assumption that patients with both a large NC and a short neck might be more difficult to intubate than patients with a large NC or a short neck alone. They concluded that NC/TMD is a better indicator than either the NC or TMD alone. This finding correlates with our study. In a previous study, other variables using NC, the NC/BMI, and NC/SM distance were also assessed. However, multivariate analysis revealed that the NC/BMI and NC/SM did not show a definite connection with difficult intubation. In our study, multivariate analysis showed that NC/BMI also got the positive relationship with difficult intubation in the Indian population. This can be explained by the difference in the body habitus compared to the Western population.
The Mallampati score may estimate the size of the tongue relative to the oral cavity and may possibly indicate whether the displacement of the tongue by the laryngoscope blade is likely to be easy or difficult. Also, it assesses whether the mouth can be opened adequately to permit intubation. The Mallampati test evaluates not only the pharyngeal structure but also head and neck mobility. In our study also Mallampati score of 3 or 4 was found to be a good predictor of difficult intubation. We found that a combination of the Mallampati test and a ratio of NC/TMD most accurately predicted difficult intubation. This correlates with the findings of the meta-analysis by Shiga et al.
We used IDS scores to define difficult intubation that reflects all courses of intubation. IDS was proposed in 1997 to characterize and standardize the complexity of endotracheal intubation, and with the objective in mind to “provide a uniform approach to compare studies related to difficult intubation, and with the aim of determining the relative values of risk factors of intubation difficulty.” Since then, IDS more than 5 has been used as the definition of difficult intubation in different populations, in particular by Combes and Dhonneur  to determine predictive factors of a difficult airway in the prehospital setting, by Amathieu et al. to assess risk factors for difficult intubation in thyroid surgery, and recently by Gonzalez et al. to evaluate risk factors for difficult intubation in obese patients.
Thus, we conclude that currently available screening tests for difficult intubation have only poor to moderate discriminative power when used alone. Combinations of individual tests or risk factors add some incremental diagnostic value in comparison to the value of each test alone. A combination of NC/TMD ratio and Mallampati score can predict difficult intubation in the Indian population when compared to other parameters.
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Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3]