Prediction of Difficult Mask Ventilation in Thai Adult Patients undergoing Elective Surgery using Ultrasound of Distance from Skin to Hyoid Bone, and from Skin to Thyroid Isthmus: A Prospective Observational Study

Prediction of Difficult Mask Ventilation in Thai Adult Patients undergoing Elective Surgery using Ultrasound of Distance from Skin to Hyoid Bone, and from Skin to Thyroid Isthmus: A Prospective Observational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prediction of Difficult Mask Ventilation in Thai Adult Patients undergoing Elective Surgery using Ultrasound of Distance from Skin to Hyoid Bone, and from Skin to Thyroid Isthmus: A Prospective Observational Study Santi Anchalee, Kanatawan Wasoontrarak, Pannawit Benjhawaleemas, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4214333/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2025 Read the published version in BMC Anesthesiology → Version 1 posted 5 You are reading this latest preprint version Abstract Background A previous study showed that airway ultrasound, specifically the distance from the skin to the hyoid bone (DSHB), may be correlated with a higher risk of difficult mask ventilation (DMV). However, the study was conducted in Italy and lacks data for the Asian and Thai populations. This study aimed to predict DMV using pre-operative ultrasonography to measure the DSHB and from the skin to the thyroid isthmus (DSTI) in Thai patients undergoing elective surgery under general anesthesia. Methods In total, 189 patients who underwent general anesthesia during elective surgery were enrolled in this prospective, observational study. Pre-operative physical examinations and airway evaluations were performed as usual. Airway ultrasound was performed to measure DSHB and DSTI before the anesthetic procedure. Anesthesiologists and nurse anesthetists performed bag-and-mask ventilation using the one-hand technique. DMV was assessed and recorded according to Han’s mask ventilation classification in which DMV-0 indicates no attempt at mask ventilation; DMV-I indicates successful ventilation by mask; DMV-II indicates ventilation by mask with oral airway/adjuvant ventilation; DMV-III indicates that ventilation required two providers; and DMV-IV indicates the patient’s inability to undergo mask ventilation. Results Thirty (17%) patients were classified as having DMV-0, and DMV-I, II, and III classifications were observed in 126(67%), 18(10%), and 12(6%) patients, respectively. None of the patients were classified as DMV-IV. The DSHB medians were 0.4(0.3–0.6), 0.7(0.5–1), 0.7(0.6–0.8), and 0.6(0.3–0.9) cm in DMV-0, I, II, and III, respectively (p < 0.001). The DSTI medians were 0.9(0.8–1.1), 0.8(0.7–1.1), 0.7(0.6–0.9), and 0.8(0.8–1.4) cm for DMV-0, I, II, and III, respectively (p = 0.041). Multivariate logistic regression indicated that the following factors were associated with difficult mask ventilation (DMV-III): male sex, modified Mallampati classification III, edentulousness, DSHB, and DSTI, with an area under the curve of 0.89. Conclusions This study showed that airway ultrasonography to determine DSHB and DSTI during patients’ routine physical examinations significantly improved the prediction of DMV. Patients classified as having DMV-III require prompt management for airway difficulties. However, the individual factors DSHB and DSTI alone are insufficient to predict DMV. Trial registration: Registration number: TCTR2020093002 Airway management Facemask ventilation Distance from skin to hyoid bone Distance from skin to thyroid isthmus Difficult mask ventilation Ultrasonography Figures Figure 1 Figure 2 Background Bag-and-mask ventilation (BMV) is a fundamental skill in basic airway management that includes airway opening maneuvers and positive-pressure ventilation via a facemask [ 1 ]. Effective BMV is an important strategy used to save many patients as part of the cardiopulmonary resuscitation [ 2 ] and difficult airway management guidelines [ 3 – 5 ]. Difficult bag-and-mask ventilation (DMV) is reported in 8.9% of patients [ 6 – 8 ]. However, the incidence varies depending on the definition of DMV [ 9 ]. Several risk factors for DMV, including age, edentulousness, body mass index, presence of a beard, and history of snoring/obstructive sleep apnea, have been identified in previous studies [ 6 , 7 ]. Difficult airway management generally includes difficult laryngoscopy, difficult intubation, and sometimes difficult mask ventilation (DMV). Difficult mask ventilation can be predictable or unpredictable and may occur after intubation failure [ 9 ]. Management of a difficult airway involves using facemask ventilation, a supraglottic airway, or intubation before the conclusion is reached that the patient cannot be intubated or oxygenated (“Cannot Intubate, Cannot Oxygenate” (CICO) scenario). Tracheostomy or cricothyroidotomy is an invasive procedure used to manage the CICO scenario. Delayed and unresolved airway management can lead to death or brain death [ 10 , 11 ]. Effective diagnostic tools may predict the possibility of patients experiencing DMV and should be considered as additional pre-operative clinical assessments during routine procedures. Ultrasound provides fast, easy, and accurate details, with diagnostic and therapeutic relevance [ 12 , 13 ]. Pre-operative ultrasound measurement of the anterior neck soft tissue thickness at different levels, combined with regularly used screening tests, and risk factor assessments for difficult laryngoscopy, might enhance our ability to predict difficult laryngoscopy. The previous study showed that airway ultrasound, and specifically the measurement of the distance from the skin to the hyoid bone, was correlated with an increased risk of DMV [ 17 ]. However, the study was conducted in Italy and lacks data for the Asian and Thai populations. The aim of our study was to assess the validity of pre-operative ultrasound evaluation of the anterior anatomy of the neck for predicting DMV in Thai patients undergoing general anesthesia for elective surgery. Methods This prospective observational study was conducted at Songklanagarind Hospital, Thailand, from June 1/6/2021– 10/3/2023. Patients aged ≥18 years undergoing general anesthesia for elective surgery, were enrolled prospectively. After obtaining approval from the Institutional Ethical Review Board (Prince of Songkla University, Thailand, REC.63-266-8-1), informed consent was obtained from 189 Thai patients. The exclusion criteria were as follows: cancer or trauma to the face, neck, pharynx, or epiglottis; pregnancy; and history of thyroid tracheotomy or surgery. The airway was assessed based on the modified Mallampati classification, thyromental distance, neck movement, inter-incisor gap, upper lip bite test, presence of a beard, and edentulousness. The results of the evaluation were then recorded in the patient’s file. Before initiation of this study, the airway ultrasounds of 10 patients were verified by Santi Anchalee (SA), Kanatawan Wasoontrarak (KW), and Sumidtra Prathep (SP) to reduce inter-rater variability. In the pre-operative room, with the patient lying supine with the head and neck in the neutral position, ultrasound was performed using a Phillips Lumify portable ultrasound machine (a linear 12 to 4 MHz transducer) (Phillips Healthcare; Amsterdam, Netherlands) to measure anterior neck soft tissue thickness. Following a craniocaudal sagittal scan of the neck with the probe placed on the transverse axis, ultrasound distances were measured as the distance from the skin to the thyroid isthmus (DSTI) and from the skin to the hyoid bone (DSHB) [15]. Induction of anesthesia was performed using propofol (2 mg/kg) and fentanyl (1–3 mcg/kg), and bag mask ventilation was conducted using a clear plastic mask. Han’s scale was used to evaluate the DMV grade before administration of a neuromuscular blocking agent. The DMV grade is classified as follows: DMV-0, ventilation by mask not attempted; DMV-I, ventilation by mask; DMV-II, ventilation by mask with oral airway/adjuvant ventilation, with or without a neuromuscular blocking agent; DMV-III, difficult ventilation (inadequate, unstable, or requiring two providers), with or without a neuromuscular blocking agent; and DMV-IV, unable to undergo mask ventilation, with or without a neuromuscular blocking agent [14,16]. A grade of at least DMV-III was considered DVM. The criteria for successful mask ventilation included chest elevation, five consecutive ET-CO 2 readings of >20 mmHg, maintaining oxygen saturation, ensuring a fresh gas flow rate <6 LPM, and applying an adjustable pressure limiting valve (APL) with a pressure <20 cmH2O.The principal outcome of this study was to assess whether DSHB and DSTI could predict DMV. Sample size We hypothesized that DSTI and DSHB could aid in the prediction of the DMV grade. The sample size was 189 patients, calculated using the probability of expected sensitivity and expected specificity with allowable error. Data from a prospective study of 1399 mask ventilation attempts using a DMV grading scale reported an incidence of 8.9% [8]. A dropout rate of 10% was expected. Sensitivity Psen = Probability of expected sensitivity = 0.8, d = Allowable error = 0.20 22 , nsen = 15.3 Specificity Pspec = Probability of expected specificity = 0.8, d = Allowable error = 0.20 22 , n spec = 15.3 Statistical and data analysis Based on the principal outcome and assuming a correlation of 0.2, the inclusion of 189 patients was essential to guarantee a power of 80%, with a significance level of 5%. Continuous data were expressed as means (standard deviation (SD)), while categorical data were presented as frequencies (percentages). Continuous variables were analyzed using a t-test or Wilcoxon rank-sum test. Categorical variables were compared using Fisher’s exact test. Two or more groups of independent variables and continuous or ordinal dependent variables were analyzed using the Kruskal–Wallis test. Logistic regression analysis was used to identify the parameters associated with DMV. Receiver operating characteristic (ROC) curves were used to verify the factors associated with the sensitivity and specificity of DMV. Results The study was conducted on Thai patients. A total of 189 patients (138 women, 51 men) were eligible for inclusion in this study (Figure 1). The clinical characteristics of the patients are summarized in Table 1. [Insert Table 1 here] In this study, 33 (17%) patients presented with DMV-0, 126 (67%) with DMV-I, 18 (10%) with DMV-II, and 12 (6%) with DMV-III. None of the patients had DMV-IV. The DSHB medians were 0.4 (0.3–0.6), 0.7 (0.5–1), 0.7 (0.6–0.8), and 0.6 (0.3–0.9) cm in DMV-0, I, II, and III, respectively (p < 0.001). The DSTI medians were 0.9 (0.8–1.1), 0.8 (0.7–1.1), 0.7 (0.6–0.9), and 0.8 (0.8–1.4) cm in DMV-0, I, II, and III respectively (p = 0.041). Summary statistics of the ultrasound distance, DSHB, and DSTI for each DMV grade are presented in Table 2. [Insert Table 2 here] Univariate logistic regression analysis showed that age (52 (15) vs 63 (15) years (p = 0.01)), weight (59 (52–67) vs 68 (60–76) kg (p = 0.042)), modified Mallampati classification (p = 0.002), and lack of teeth (25 (14%) vs 7 (58%) (p = 0.001)) correlated with DMV with statistical significance. Results of the univariate logistic regression analysis are shown in Table 3. [Insert Table 3 here] Multivariate logistic regression analysis of factors associated with DMV included male sex, modified Mallampati classification III, edentulousness, DSHB, and DSTI. The multivariate logistic regression data are shown in Table 4. [Insert Table 4 here] Scores predicting difficult mask ventilation were calculated as follows: (1.8 × Male) + (-0.6 × Mallampati class II) + (2.1 × Mallampati class III) + (2.2 × lack of teeth) + (-2.4 × DSHB) + (2.8 × DSTI). If the patients were male, the factor was one; for Mallampati class II, the factor was one; for Mallampati class III, the factor was one; for lack of teeth or being edentulous, the factor was one; DSHB was measured in centimeters; and DSTI was measured in centimeters. The cutoff value for the prediction of DMV was 3.6, with a sensitivity of 83%, specificity of 86%, positive predictive value of 0.29, and negative predictive value of 0.99. The receiver operating characteristic (ROC) curve (Figure 2) shows the probability of DMV, with an area under the curve of 0.89. Discussion In our study, the incidence of DMV was 6.35%, whereas in the previous study, it was 8.9% [ 8 ]. This observational study of 189 patients showed an association between ultrasound distance evaluation of the anterior neck soft tissues and DMV. Our statistical results were consistent with previous evidence that the positive relationship between greater DSTI and DSHB thickness and the incidence of DMV was statistically significant [ 17 ]. However, the results might not be clinically significant, as there was only a 1 mm difference between each grade of Han’s mask ventilation classification. Several risk factors for DMV, including age, edentulousness, body mass index, presence of a beard, and history of snoring/obstructive sleep apnea, have been identified in previous studies [ 6 , 7 ]. Our univariate logistic regression analysis showed that the factors associated with DMV were age, weight, modified Mallampati classification, and edentulousness. The addition of airway ultrasonography to measure DSHB and DSTI during routine patient physical examinations in conjunction with factors such as edentulousness and modified Mallampati classification III can improve the prediction of DMV, with an area under the curve of 0.89. The study was conducted in an elective surgery setting, which helped us identify DMV in patients without clinically predictable difficult results. Ultrasound distance measurements were examined independently of difficult airway prediction assessments. Patients with airway abnormalities were excluded because our objective was to provide an additional tool for identifying unexpected DMV. Adhikari et al. reported that DSHB would be the most stable distance [ 18 ]. DSHB and the distance from the skin to the epiglottis midpoint (DSEM) were assessed and used to evaluate the prediction of difficult airway management. However, DSEM is highly dependent on the length of the epiglottis. Our study is the first to determine novel ultrasound parameters for improving the sensitivity and specificity of anthropometric parameters for the pre-operative assessment of the upper airway in Thai patients. Although magnetic resonance imaging (MRI), computed tomography (CT), and other imaging procedures can be used to measure neck soft tissue thickness, they are expensive and impractical for use in the operating room. Additionally, radiation is a risk factor for routine workups in patients with normal airways. Ultrasound can be performed at the bedside, is cheap, fast, has no radiation hazard, and is as accurate as MRI [ 19 , 20 ]. Limitations This study had some limitations. First, we excluded patients with predicted difficult airway management because the study objective was to explore the use of neck ultrasound in patients with possible DMV that was unpredictable pre-operatively. Second, this study did not use the anthropometric parameters that were presently appraised as a reference. Thus, further studies are required to examine the relationship between the ultrasound distance and anthropometric parameters. Evaluation of the correlation between DMV and neck circumference would be very interesting [ 21 ]. Third, our research was conducted on patients from the Thai population, which is Asian. Therefore, the results might differ from those of other races or global populations. Finally, the airway assessors in our study were three individuals who were only able to perform airway assessments on some of the patients undergoing general anesthesia each day (approximately 100 patients per day). This limitation may have introduced selection bias. Conclusions This prospective observational study showed that the addition of airway ultrasonography to measure DSHB and DSTI during routine patient physical examinations in conjunction with factors such as edentulousness and modified Mallampati classification III can improve the prediction of the degree of DMV. However, DSHB and DSTI alone are insufficient to predict DMV. Abbreviations DSHB: the distance from the skin to the hyoid bone DSTI: the distance from the skin to the thyroid isthmus BMV: bag-and-mask ventilation DMV: difficult mask ventilation CICO: cannot intubate, cannot oxygenate SD: standard deviation ROC: receiver operating characteristic DSEM: distance from the skin to the epiglottis midpoint MRI: magnetic resonance imaging CT: computed tomography Declarations We have no potential conflict of interest . Ethics approval and consent to participate This study was approved by the Office of the Human Research Ethics Committee, Faculty of Medicine, Prince of Songkla University, Thailand (REC.63-266-8-1). The date of first registration was 28/9/2020. The patients provided informed consent to participate in the study. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding No funding was received. Authors' contributions Santi Anchalee: methodology, data collection, analysis, writing – review & editing. Kanatawan Wasoontrarak: Conceptualization, methodology, data collection, analysis and writing -original draft. Pannawit Benjhawaleemas: Conceptualization and supervision Sunisa Chatmongkolchart: Conceptualization and supervision Sumidtra Prathep: Conceptualization , methodology, data collection, analysis, writing – review & editing. Acknowledgements Not applicable References Davies JD, Costa BK, Asciutto AJ. Approaches to manual ventilation. Respir Care. 2014;59:810–22. discussion 822-4. Link MS, Berkow LC, Kudenchuk PJ, Halperin HR, Hess EP, Moitra VK, et al. Part 7: Adult advanced cardiovascular life support: 2015 American Heart Association guidelines update for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation. 2015;18(suppl 2):S444–64. Apfelbaum JL, Hagberg CA, Caplan RA, Blitt CD, Connis RT, Nickinovich DG, Hagberg CA, Caplan RA, Benumof JL, Berry FA, Blitt CD, Bode RH, Cheney FW, Connis RT, Guidry OF, Nickinovich DG, Ovassapian A. American Society of Anesthesiologists Task Force on Management of the Difficult Airway. 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Patient demographic data and pre-operative variables; data are expressed as number (%), mean ( SD), or median (IQR) Patient Characteristics Age 52 (15) Male 51 (27) Weight (kg) 59 (52–67) Height (cm) 159 (155–165) BMI (kg/m 2 ) 23 (21–26) ASA classification 1 14 (7) 2 142 (75) 3 33 (18) Modified Mallampati classification I 51 (27) II 109 (58) III 28 (15) Flexion range of motion (degree) 45 (45–45) Extension range of motion (degree) 30 (30–30) Upper lip bite test (ULBT) Grade 1 118 (63) 2 55 (29) 3 14 (8) Snoring 55 (29) History of OSA requiring CPAP 0(0) Edentulousness 33 (18) Thyromental distance (cm) 8 (6–9) Inter-incisor gap (cm) 4 (4–5) SD: standard deviation; IQR: interquartile range; BMI: body mass index; ASA: American Society of Anesthesiologists; OSA: obstructive sleep apnea; CPAP: continuous positive airway pressure Table 2. Ultrasound distance grading of DMV DMV-0 (n=33) DMV-I (n= 126) DMV-II (n=18) DMV-III (n=12) p – value DSHB 0.4 (0.3–0.6) 0.7 (0.5–1) 0.7 (0.6–0.8) 0.6 (0.3–0.9) < 0.001 DSTI 0.9 (0.8–1.1) 0.8 (0.7–1.1) 0.7 (0.6–0.9) 0.8 (0.8–1.4) 0.041 Numerical data are expressed as median (interquartile range (IQR)) cm. DMV: difficult mask ventilation; DSHB: distance from the skin surface to the hyoid bone; DSTI: distance from the skin surface to the thyroid isthmus. Table 3. Univariate logistic regression analysis of DMV Factor DMV-0, I, II (n=174) DMV-III (n=12) p – value Age 52 (15) 63 (15) 0.01 Male sex 45 (26) 6 (50) 0.093 Weight (kg) 59 (52–67) 68 (60–76) 0.042 Height (cm) 159 (7) 163 (10) 0.105 BMI (kg/m 2 ) 23.2 (21.3–25.7) 26.4 (21–29.6) 0.225 ASA classification 0.304 1 14 (8) 0 (0) 2 131 (75) 8 (67) 3 29 (17) 4 (33) Modified Mallampati classification 0.002 I 48 (28) 3 (25) II 105 (60) 3 (25) II 21 (12) 6 (50) Flexion range of motion (degrees) 45 (45–45) 45 (45–45) 0.594 Extension range of motion (degrees) 30 (30–30) 30 (30–30) 0.635 Upper lip bite test (ULBT) Grade 0.324 1 111 (64) 6 (50) 2 51 (29) 4 (33) 3 12 (7) 2 (17) Snoring 50 (29) 4 (33.) 0.747 Lack of teeth 25 (14) 7 (58) 0.001 Thyromental distance (cm) 7.5 (6–9) 7.5 (6–8.6) 0.843 Inter-incisor gap (cm) 4 (4–5) 4 (4–4.6) 0.806 DSHB 0.6 (0.4–0.9) 0.6 (0.3–0.9) 0.49 DSTI 0.8 (0.7–1.1) 0.8 (0.8–1.4) 0.259 DMV: difficult mask ventilation; BMI: body mass index; ASA: American Society of Anesthesiologists; DSHB: distance from the skin surface to the hyoid bone; DSTI: distance from the skin surface to the thyroid isthmus. Table 4. Multivariate logistic regression analysis of DMV Factor crude OR(95%CI) adj. OR(95%CI) P(Wald's test) P(LR-test) Male 2.87 (0.88–9.34) 6.2 (1.27–30.22) 0.024 0.019 Modified Mallampati classification: III 4.57 (1.04–20.04) 8.09 (1.22–53.68) 0.03 Edentulousness 8.34 (2.46–28.36) 8.75 (2.02–37.81) 0.004 0.003 DSHB 0.54 (0.07–4.15) 0.09 (0.01–1.54) 0.098 0.083 DSTI 4.34 (0.82–22.84) 16.4 (1.46–183.87) 0.023 0.012 DMV: difficult mask ventilation; OR: odds ratio; 95%CI: 95% confidence interval; LR: likelihood ratio; DSHB: distance from the skin surface to the hyoid bone; DSTI: distance from the skin surface to the thyroid isthmus. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2025 Read the published version in BMC Anesthesiology → Version 1 posted Editorial decision: Revision requested 08 May, 2024 Editor assigned by journal 18 Apr, 2024 Editor invited by journal 18 Apr, 2024 Submission checks completed at journal 18 Apr, 2024 First submitted to journal 03 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4214333","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292774013,"identity":"3a67a3b8-48be-433e-bd95-aa31c27dcf5b","order_by":0,"name":"Santi Anchalee","email":"","orcid":"","institution":"Prince of Songkla University","correspondingAuthor":false,"prefix":"","firstName":"Santi","middleName":"","lastName":"Anchalee","suffix":""},{"id":292774015,"identity":"26e91072-8b0b-47ce-83aa-c20851a10b52","order_by":1,"name":"Kanatawan Wasoontrarak","email":"","orcid":"","institution":"Prince of Songkla University","correspondingAuthor":false,"prefix":"","firstName":"Kanatawan","middleName":"","lastName":"Wasoontrarak","suffix":""},{"id":292774016,"identity":"2a1d5342-d444-4307-a242-9afd17f8cd99","order_by":2,"name":"Pannawit Benjhawaleemas","email":"","orcid":"","institution":"Prince of Songkla University","correspondingAuthor":false,"prefix":"","firstName":"Pannawit","middleName":"","lastName":"Benjhawaleemas","suffix":""},{"id":292774017,"identity":"56f05ac4-08ff-4d4d-b4c6-0f740b38cb43","order_by":3,"name":"Sunisa Chatmongkolchart","email":"","orcid":"","institution":"Prince of Songkla University","correspondingAuthor":false,"prefix":"","firstName":"Sunisa","middleName":"","lastName":"Chatmongkolchart","suffix":""},{"id":292774018,"identity":"7a28fcb3-ced9-424f-9e42-192fb89e0161","order_by":4,"name":"Sumidtra Prathep","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYJACZgjF2CABJOVAzAMP8OtgbIZrOcDAYAzWkkCcFgYGkJbEBhALnxZz6cPPHxdU3LE3uHa48fbHPXbp88MOPwTaYien24Bdi2VfmmHzjDPPEjfcTmy2OPAsOXfj7TQDoJZkY7MD2LUYnGEwbOZtO5xgcDuxTeLAAebcjbMTQFoOJG7DqYX9YzPvv8P2UC316Yaz0z8Q0MIDtKXhMOMGiJbDCfLSOfhtsezhKZzNc+xZ4kyQX84cOG64QTqn4ECCAW6/mPOwb/jMU3PHnu92+sMbFQeq5eVnp2/+8KHCTg6n9yEUkqzBASRx4rTIN+BWPQpGwSgYBSMTAAB6HG1X1mDTvwAAAABJRU5ErkJggg==","orcid":"","institution":"Prince of Songkla University","correspondingAuthor":true,"prefix":"","firstName":"Sumidtra","middleName":"","lastName":"Prathep","suffix":""}],"badges":[],"createdAt":"2024-04-03 18:13:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4214333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4214333/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12871-025-02920-7","type":"published","date":"2025-01-27T15:57:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55176052,"identity":"72621c51-08a3-49af-bb83-c9437a209b7a","added_by":"auto","created_at":"2024-04-23 16:16:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70485,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the study (BMV: bag-mask ventilation)\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4214333/v1/f94b173ddcc397a804590184.png"},{"id":55176054,"identity":"73ac1ea3-449a-4328-8320-46d662900171","added_by":"auto","created_at":"2024-04-23 16:16:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25679,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve showing the probability of difficult mask ventilation\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4214333/v1/8725ee46562045ea31217c65.png"},{"id":75351246,"identity":"923d36d3-0be4-478d-8a6b-ddcf3254fac1","added_by":"auto","created_at":"2025-02-03 16:08:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":765308,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4214333/v1/e87d3b21-8ac0-49cf-b478-490ca8f1a9a2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of Difficult Mask Ventilation in Thai Adult Patients undergoing Elective Surgery using Ultrasound of Distance from Skin to Hyoid Bone, and from Skin to Thyroid Isthmus: A Prospective Observational Study","fulltext":[{"header":"Background","content":"\u003cp\u003eBag-and-mask ventilation (BMV) is a fundamental skill in basic airway management that includes airway opening maneuvers and positive-pressure ventilation via a facemask [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Effective BMV is an important strategy used to save many patients as part of the cardiopulmonary resuscitation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and difficult airway management guidelines [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifficult bag-and-mask ventilation (DMV) is reported in 8.9% of patients [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the incidence varies depending on the definition of DMV [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Several risk factors for DMV, including age, edentulousness, body mass index, presence of a beard, and history of snoring/obstructive sleep apnea, have been identified in previous studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifficult airway management generally includes difficult laryngoscopy, difficult intubation, and sometimes difficult mask ventilation (DMV). Difficult mask ventilation can be predictable or unpredictable and may occur after intubation failure [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Management of a difficult airway involves using facemask ventilation, a supraglottic airway, or intubation before the conclusion is reached that the patient cannot be intubated or oxygenated (\u0026ldquo;Cannot Intubate, Cannot Oxygenate\u0026rdquo; (CICO) scenario). Tracheostomy or cricothyroidotomy is an invasive procedure used to manage the CICO scenario. Delayed and unresolved airway management can lead to death or brain death [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEffective diagnostic tools may predict the possibility of patients experiencing DMV and should be considered as additional pre-operative clinical assessments during routine procedures. Ultrasound provides fast, easy, and accurate details, with diagnostic and therapeutic relevance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Pre-operative ultrasound measurement of the anterior neck soft tissue thickness at different levels, combined with regularly used screening tests, and risk factor assessments for difficult laryngoscopy, might enhance our ability to predict difficult laryngoscopy.\u003c/p\u003e \u003cp\u003eThe previous study showed that airway ultrasound, and specifically the measurement of the distance from the skin to the hyoid bone, was correlated with an increased risk of DMV [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the study was conducted in Italy and lacks data for the Asian and Thai populations. The aim of our study was to assess the validity of pre-operative ultrasound evaluation of the anterior anatomy of the neck for predicting DMV in Thai patients undergoing general anesthesia for elective surgery.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis prospective observational study was conducted at Songklanagarind Hospital, Thailand, from June 1/6/2021\u0026ndash; 10/3/2023. Patients aged \u0026ge;18 years undergoing general anesthesia for elective surgery, were enrolled prospectively. After obtaining approval from the Institutional Ethical Review Board (Prince of Songkla University, Thailand, REC.63-266-8-1), informed consent was obtained from 189 Thai patients.\u003c/p\u003e\n\u003cp\u003eThe exclusion criteria were as follows: cancer or trauma to the face, neck, pharynx, or epiglottis; pregnancy; and history of thyroid tracheotomy or surgery. The airway was assessed based on the modified Mallampati classification, thyromental distance, neck movement, inter-incisor gap, upper lip bite test, presence of a beard, and edentulousness. The results of the evaluation were then recorded in the patient\u0026rsquo;s file.\u003c/p\u003e\n\u003cp\u003eBefore initiation of this study, the airway ultrasounds of 10 patients were verified by Santi Anchalee (SA), Kanatawan Wasoontrarak (KW), and Sumidtra Prathep (SP) to reduce inter-rater variability.\u003c/p\u003e\n\u003cp\u003eIn the pre-operative room, with the patient lying supine with the head and neck in the neutral position, ultrasound was performed using a Phillips Lumify portable ultrasound machine (a linear 12 to 4 MHz transducer)\u0026nbsp;(Phillips Healthcare; Amsterdam, Netherlands) to measure anterior neck soft tissue thickness. Following a craniocaudal sagittal scan of the neck with the probe placed on the transverse axis, ultrasound distances were measured as the distance from the skin to the thyroid isthmus (DSTI) and from the skin to the hyoid bone (DSHB) [15].\u003c/p\u003e\n\u003cp\u003eInduction of anesthesia was performed using propofol (2 mg/kg) and fentanyl (1\u0026ndash;3 mcg/kg), and bag mask ventilation was conducted using a clear plastic mask. Han\u0026rsquo;s scale was used to evaluate the DMV grade before administration of a neuromuscular blocking agent. The DMV grade is classified as follows: DMV-0, ventilation by mask not attempted; DMV-I, ventilation by mask; DMV-II, ventilation by mask with oral airway/adjuvant ventilation, with or without a neuromuscular blocking agent; DMV-III, difficult ventilation (inadequate, unstable, or requiring two providers), with or without a neuromuscular blocking agent; and DMV-IV, unable to undergo mask ventilation, with or without a neuromuscular blocking agent [14,16]. A grade of at least DMV-III was considered DVM. The criteria for successful mask ventilation included chest elevation, five consecutive ET-CO\u003csub\u003e2\u003c/sub\u003e readings of \u0026gt;20 mmHg, maintaining oxygen saturation, ensuring a fresh gas flow rate \u0026lt;6 LPM, and applying an adjustable pressure limiting valve (APL) with a pressure \u0026lt;20 cmH2O.The principal outcome of this study was to assess whether DSHB and DSTI could predict DMV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe hypothesized that DSTI and DSHB could aid in the prediction of the DMV grade. The sample size was 189 patients, calculated using the probability of expected sensitivity and expected specificity with allowable error. Data from a prospective study of 1399 mask ventilation attempts using a DMV grading scale reported an incidence of 8.9% [8]. A dropout rate of 10% was expected.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"450\" height=\"317\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSensitivity\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003ePsen = Probability of expected sensitivity = 0.8,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ed = Allowable error = 0.20\u003csup\u003e22\u003c/sup\u003e,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ensen = 15.3\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSpecificity\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePspec = Probability of expected specificity = 0.8,\u003c/p\u003e\n\u003cp\u003ed = Allowable error = 0.20\u003csup\u003e22\u003c/sup\u003e,\u003c/p\u003e\n\u003cp\u003en spec = 15.3 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical and data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the principal outcome and assuming a correlation of 0.2, the inclusion of 189 patients was essential to guarantee a power of 80%, with a significance level of 5%. Continuous data were expressed as means (standard deviation (SD)), while categorical data were presented as frequencies (percentages). Continuous variables were analyzed using a t-test or Wilcoxon rank-sum test. Categorical variables were compared using Fisher\u0026rsquo;s exact test. Two or more groups of independent variables and continuous or ordinal dependent variables were analyzed using the Kruskal\u0026ndash;Wallis test. Logistic regression analysis was used to identify the parameters associated with DMV. Receiver operating characteristic (ROC) curves were used to verify the factors associated with the sensitivity and specificity of DMV.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study was conducted on Thai patients. A total of 189 patients (138 women, 51 men) were eligible for inclusion in this study (Figure 1). The clinical characteristics of the patients are summarized in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Insert Table 1 here]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, 33 (17%) patients presented with DMV-0, 126 (67%) with DMV-I, 18 (10%) with DMV-II, and 12 (6%) with DMV-III. None of the patients had DMV-IV. The DSHB medians were 0.4 (0.3\u0026ndash;0.6), 0.7 (0.5\u0026ndash;1), 0.7 (0.6\u0026ndash;0.8), and 0.6 (0.3\u0026ndash;0.9) cm in DMV-0, I, II, and III, respectively (p \u0026lt; 0.001). The DSTI medians were 0.9 (0.8\u0026ndash;1.1), 0.8 (0.7\u0026ndash;1.1), 0.7 (0.6\u0026ndash;0.9), and 0.8 (0.8\u0026ndash;1.4) cm in DMV-0, I, II, and III respectively (p = 0.041). Summary statistics of the ultrasound distance, DSHB, and DSTI for each DMV grade are presented in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Insert Table 2 here]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnivariate logistic regression analysis showed that age (52 (15) vs 63 (15) years (p = 0.01)), weight (59 (52\u0026ndash;67) vs 68 (60\u0026ndash;76) kg (p = 0.042)), modified Mallampati classification (p = 0.002), and lack of teeth (25 (14%) vs 7 (58%) (p = 0.001)) correlated with DMV with statistical significance. Results of the univariate logistic regression analysis are shown in Table 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Insert Table 3 here]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression analysis of factors associated with DMV included male sex, modified Mallampati classification III, edentulousness, DSHB, and DSTI. The multivariate logistic regression data are shown in Table 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Insert Table 4 here]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eScores predicting difficult mask ventilation were calculated as follows: (1.8 \u0026times; Male) + (-0.6 \u0026times; Mallampati class II) + (2.1 \u0026times; Mallampati class III) + (2.2 \u0026times; lack of teeth) + (-2.4 \u0026times; DSHB) + (2.8 \u0026times; DSTI). If the patients were male, the factor was one; for Mallampati class II, the factor was one; for Mallampati class III, the factor was one; for lack of teeth or being edentulous, the factor was one; DSHB was measured in centimeters; and DSTI was measured in centimeters. The cutoff value for the prediction of DMV was 3.6, with a sensitivity of 83%, specificity of 86%, positive predictive value of 0.29, and negative predictive value of 0.99. The receiver operating characteristic (ROC) curve (Figure 2) shows the probability of DMV, with an area under the curve of 0.89.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, the incidence of DMV was 6.35%, whereas in the previous study, it was 8.9% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This observational study of 189 patients showed an association between ultrasound distance evaluation of the anterior neck soft tissues and DMV. Our statistical results were consistent with previous evidence that the positive relationship between greater DSTI and DSHB thickness and the incidence of DMV was statistically significant [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the results might not be clinically significant, as there was only a 1 mm difference between each grade of Han\u0026rsquo;s mask ventilation classification. Several risk factors for DMV, including age, edentulousness, body mass index, presence of a beard, and history of snoring/obstructive sleep apnea, have been identified in previous studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Our univariate logistic regression analysis showed that the factors associated with DMV were age, weight, modified Mallampati classification, and edentulousness. The addition of airway ultrasonography to measure DSHB and DSTI during routine patient physical examinations in conjunction with factors such as edentulousness and modified Mallampati classification III can improve the prediction of DMV, with an area under the curve of 0.89.\u003c/p\u003e \u003cp\u003eThe study was conducted in an elective surgery setting, which helped us identify DMV in patients without clinically predictable difficult results. Ultrasound distance measurements were examined independently of difficult airway prediction assessments. Patients with airway abnormalities were excluded because our objective was to provide an additional tool for identifying unexpected DMV. Adhikari et al. reported that DSHB would be the most stable distance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. DSHB and the distance from the skin to the epiglottis midpoint (DSEM) were assessed and used to evaluate the prediction of difficult airway management. However, DSEM is highly dependent on the length of the epiglottis. Our study is the first to determine novel ultrasound parameters for improving the sensitivity and specificity of anthropometric parameters for the pre-operative assessment of the upper airway in Thai patients. Although magnetic resonance imaging (MRI), computed tomography (CT), and other imaging procedures can be used to measure neck soft tissue thickness, they are expensive and impractical for use in the operating room. Additionally, radiation is a risk factor for routine workups in patients with normal airways. Ultrasound can be performed at the bedside, is cheap, fast, has no radiation hazard, and is as accurate as MRI [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study had some limitations. First, we excluded patients with predicted difficult airway management because the study objective was to explore the use of neck ultrasound in patients with possible DMV that was unpredictable pre-operatively. Second, this study did not use the anthropometric parameters that were presently appraised as a reference. Thus, further studies are required to examine the relationship between the ultrasound distance and anthropometric parameters. Evaluation of the correlation between DMV and neck circumference would be very interesting [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Third, our research was conducted on patients from the Thai population, which is Asian. Therefore, the results might differ from those of other races or global populations. Finally, the airway assessors in our study were three individuals who were only able to perform airway assessments on some of the patients undergoing general anesthesia each day (approximately 100 patients per day). This limitation may have introduced selection bias.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis prospective observational study showed that the addition of airway ultrasonography to measure DSHB and DSTI during routine patient physical examinations in conjunction with factors such as edentulousness and modified Mallampati classification III can improve the prediction of the degree of DMV. However, DSHB and DSTI alone are insufficient to predict DMV.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDSHB: the distance from the skin to the hyoid bone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDSTI:\u0026nbsp;the distance\u0026nbsp;from the skin to the thyroid isthmus\u003c/p\u003e\n\u003cp\u003eBMV: bag-and-mask ventilation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDMV: difficult mask ventilation\u003c/p\u003e\n\u003cp\u003eCICO: cannot intubate, cannot oxygenate\u003c/p\u003e\n\u003cp\u003eSD: standard deviation\u003c/p\u003e\n\u003cp\u003eROC:\u0026nbsp;receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eDSEM: distance from the skin to the epiglottis midpoint\u003c/p\u003e\n\u003cp\u003eMRI: magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003eCT: computed tomography\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eWe have no potential conflict of interest\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Office of the Human Research Ethics Committee, Faculty of Medicine, Prince of Songkla University, Thailand (REC.63-266-8-1). The date of first registration was 28/9/2020. The patients provided informed consent to participate in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSanti Anchalee: methodology, data collection, analysis, writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eKanatawan Wasoontrarak: Conceptualization,\u0026nbsp;methodology, data collection,\u0026nbsp;analysis\u0026nbsp;and writing -original draft.\u003c/p\u003e\n\u003cp\u003ePannawit\u0026nbsp;Benjhawaleemas: Conceptualization and supervision\u003c/p\u003e\n\u003cp\u003eSunisa Chatmongkolchart: Conceptualization and supervision\u003c/p\u003e\n\u003cp\u003eSumidtra Prathep:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eConceptualization\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003emethodology, data collection, analysis, writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDavies JD, Costa BK, Asciutto AJ. Approaches to manual ventilation. Respir Care. 2014;59:810\u0026ndash;22. discussion 822-4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLink MS, Berkow LC, Kudenchuk PJ, Halperin HR, Hess EP, Moitra VK, et al. Part 7: Adult advanced cardiovascular life support: 2015 American Heart Association guidelines update for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation. 2015;18(suppl 2):S444\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApfelbaum JL, Hagberg CA, Caplan RA, Blitt CD, Connis RT, Nickinovich DG, Hagberg CA, Caplan RA, Benumof JL, Berry FA, Blitt CD, Bode RH, Cheney FW, Connis RT, Guidry OF, Nickinovich DG, Ovassapian A. American Society of Anesthesiologists Task Force on Management of the Difficult Airway. Practice guidelines for management of the difficult airway: an updated report by the American Society of Anesthesiologists Task Force on Management of the Difficult Airway. Anesthesiology. 2013;118:251\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApfelbaum JL, Hagberg CA, Connis RT, Abdelmalak BB, Agarkar M, Dutton RP, et al. 2022 American Society of Anesthesiologists practice guidelines for management of the difficult airway. Anesthesiology. 2022;136:31\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrerk C, Mitchell VS, McNarry AF, Mendonca C, Bhagrath R, Patel A, et al. Difficult Airway Society 2015 guidelines for management of unanticipated difficult intubation in adults. Br J Anaesth. 2015;115:827\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Orbany M, Woehlck HJ. Difficult mask ventilation. Anesth Analg. 2009;109:1870\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangeron O, Masso E, Huraux C, Guggiari M, Bianchi A, Coriat P, et al. Prediction of difficult mask ventilation. Anesthesiology. 2000;92:1229\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCattano 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.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdnet F. Difficult mask ventilation: an underestimated aspect of the problem of the difficult airway? Anesthesiology. 2000;92:1217\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaw JA, Broemling N, Cooper RM, Drolet P, Duggan LV, Griesdale DE, et al. The difficult airway with recommendations for management \u0026ndash; Part 1 \u0026ndash; Difficult tracheal intubation encountered in an unconscious/induced patient. Can J Anaesth. 2013;60:1089\u0026ndash;118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook TM, Woodall N, Harper J, Benger J, Fourth National Audit Project. Major complications of airway management in the UK: results of the fourth national audit project of the royal college of anaesthetists and the difficult airway society. Part 2: Intensive care and emergency departments. Br J Anaesth. 2011;106:632\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManno E, Navarra M, Faccio L, Motevallian M, Bertolaccini L, Mfochiv\u0026egrave; A, et al. Deep impact of ultrasound in the intensive care unit: the \u0026lsquo;ICU-sound\u0026rsquo; protocol. Anesthesiology. 2012;117:801\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePelosi P, Corradi F. Ultrasonography in the intensive care unit: looking at the world through colored glasses. Anesthesiology. 2012;117:696\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreen L. Can\u0026rsquo;t intubate, can\u0026rsquo;t ventilate! A survey of knowledge and skills in a large teaching hospital. Eur J Anaesthesiol. 2009;26:480\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Dong J, Ding Y, Zheng J. Role of anterior neck soft tissue quantifications by ultrasound in predicting difficult laryngoscopy. Med Sci Monit. 2014;20:2343\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan R, Tremper KK, Kheterpal S, O'Reilly M. Grading scale for mask ventilation (letter). Anesthesiology. 2004;101:267.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlessandri F, Antenucci G, Piervincenzi E, Buonopane C, Bellucci R, Andreoli C, et al. Ultrasound as a new tool in the assessment of airway difficulties: an observational study. Eur J Anaesthesiol. 2019;36:509\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdhikari S, Zeger W, Schmier C, Crum T, Craven A, Frrokaj I, et al. Pilot study to determine the utility of point-of-care ultrasound in the assessment of difficult laryngoscopy. Acad Emerg Med. 2011;18:754\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrasad A, Yu E, Wong DT, Karkhanis R, Gullane P, Chan VW. Comparison of sonography and computed tomography as imaging tools for assessment of airway structures. J Ultrasound Med. 2011;30:965\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbe T, Kawakami Y, Sugita M, Yoshikawa K, Fukunaga T. Use of B-mode ultrasound for visceral fat mass evaluation: comparisons with odds ratio imaging. Appl Hum Sci. 1995;14:133\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiad W, Vaez MN, Raveendran R, Tam AD, Quereshy FA, Chung F, et al. Neck circumference as a predictor of difficult intubation and difficult mask ventilation in morbidly obese patients: a prospective observational study. Eur J Anaesthesiol. 2016;33:244\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression. Source Code Biol Med. 2008;3:17.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Patient demographic data and pre-operative variables; data are expressed as number (%), mean\u003cu\u003e\u0026nbsp;(\u003c/u\u003eSD), or median (IQR)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ePatient Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e52 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e51 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e59 (52\u0026ndash;67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e159 (155\u0026ndash;165)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e23 (21\u0026ndash;26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eASA classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e14 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e142 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e33 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModified Mallampati classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e51 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e109 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e28 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Flexion range of motion (degree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e45 (45\u0026ndash;45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eExtension range of motion (degree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e30 (30\u0026ndash;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUpper lip bite test (ULBT) Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e118 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e55 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e14 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eSnoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e55 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eHistory of OSA requiring CPAP \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eEdentulousness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e33 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eThyromental distance (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e8 (6\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eInter-incisor gap (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e4 (4\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD: standard deviation; IQR: interquartile range; BMI: body mass index; ASA: American Society of Anesthesiologists; OSA: obstructive sleep apnea; CPAP: continuous positive airway pressure \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Ultrasound distance grading of DMV\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.943521594684384%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.282392026578073%\" valign=\"top\"\u003e\n \u003cp\u003eDMV-0\u003c/p\u003e\n \u003cp\u003e(n=33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.109634551495017%\" valign=\"top\"\u003e\n \u003cp\u003eDMV-I\u003c/p\u003e\n \u003cp\u003e(n=\u0026nbsp;126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.109634551495017%\" valign=\"top\"\u003e\n \u003cp\u003eDMV-II\u003c/p\u003e\n \u003cp\u003e(n=18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.777408637873755%\" valign=\"top\"\u003e\n \u003cp\u003eDMV-III\u003c/p\u003e\n \u003cp\u003e(n=12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.777408637873755%\" valign=\"top\"\u003e\n \u003cp\u003ep \u0026ndash; value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.943521594684384%\" valign=\"top\"\u003e\n \u003cp\u003eDSHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.282392026578073%\" valign=\"top\"\u003e\n \u003cp\u003e0.4 (0.3\u0026ndash;0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.109634551495017%\" valign=\"top\"\u003e\n \u003cp\u003e0.7 (0.5\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.109634551495017%\" valign=\"top\"\u003e\n \u003cp\u003e0.7 (0.6\u0026ndash;0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.777408637873755%\" valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.3\u0026ndash;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.777408637873755%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.943521594684384%\" valign=\"top\"\u003e\n \u003cp\u003eDSTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.282392026578073%\" valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.8\u0026ndash;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.109634551495017%\" valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.7\u0026ndash;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.109634551495017%\" valign=\"top\"\u003e\n \u003cp\u003e0.7 (0.6\u0026ndash;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.777408637873755%\" valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.8\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.777408637873755%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNumerical data are expressed as median (interquartile range (IQR)) cm. DMV: difficult mask ventilation; DSHB: distance from the skin surface to the hyoid bone; DSTI: distance from the skin surface to the thyroid isthmus. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Univariate logistic regression analysis of DMV\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eFactor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003eDMV-0, I, II\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003eDMV-III\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003ep \u0026ndash; value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e52 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e63 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e45 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e6 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e59 (52\u0026ndash;67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e68 (60\u0026ndash;76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e159 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e163 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e23.2 (21.3\u0026ndash;25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e26.4 (21\u0026ndash;29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.98338870431893%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eASA classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.777408637873755%\" valign=\"top\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e14 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e131 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e8 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e29 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e4 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.98338870431893%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModified Mallampati classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.777408637873755%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e48 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e3 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e105 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e3 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e21 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e6 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eFlexion range of motion (degrees)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e45 (45\u0026ndash;45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e45 (45\u0026ndash;45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eExtension range of motion (degrees)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e30 (30\u0026ndash;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e30 (30\u0026ndash;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.98338870431893%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUpper lip bite test (ULBT) Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.777408637873755%\" valign=\"top\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e111 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e6 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e51 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e4 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e12 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e2 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eSnoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e50 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e4 (33.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eLack of teeth\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e25 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e7 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eThyromental distance (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e7.5 (6\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e7.5 (6\u0026ndash;8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eInter-incisor gap (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e4 (4\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e4 (4\u0026ndash;4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eDSHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.4\u0026ndash;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.3\u0026ndash;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.958402662229616%\" valign=\"top\"\u003e\n \u003cp\u003eDSTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.7\u0026ndash;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.8\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.80532445923461%\" valign=\"top\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDMV: difficult mask ventilation; BMI: body mass index; ASA: American Society of Anesthesiologists; DSHB: distance from the skin surface to the hyoid bone; DSTI: distance from the skin surface to the thyroid isthmus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Multivariate logistic regression analysis of DMV\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.30716723549488%\" valign=\"top\"\u003e\n \u003cp\u003eFactor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.426621160409557%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ecrude OR(95%CI)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.283276450511945%\" valign=\"top\"\u003e\n \u003cp\u003eadj. OR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\" valign=\"top\"\u003e\n \u003cp\u003eP(Wald\u0026apos;s test)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.406143344709896%\" valign=\"top\"\u003e\n \u003cp\u003eP(LR-test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.30716723549488%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.35494880546075%\" valign=\"top\"\u003e\n \u003cp\u003e2.87 (0.88\u0026ndash;9.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.35494880546075%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e6.2 (1.27\u0026ndash;30.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\" valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.406143344709896%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.30716723549488%\" valign=\"top\"\u003e\n \u003cp\u003eModified Mallampati classification: III\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.35494880546075%\" valign=\"top\"\u003e\n \u003cp\u003e4.57 (1.04\u0026ndash;20.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.35494880546075%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e8.09 (1.22\u0026ndash;53.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.406143344709896%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.30716723549488%\" valign=\"top\"\u003e\n \u003cp\u003eEdentulousness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.35494880546075%\" valign=\"top\"\u003e\n \u003cp\u003e8.34 (2.46\u0026ndash;28.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.35494880546075%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e8.75 (2.02\u0026ndash;37.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.406143344709896%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.30716723549488%\" valign=\"top\"\u003e\n \u003cp\u003eDSHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.35494880546075%\" valign=\"top\"\u003e\n \u003cp\u003e0.54 (0.07\u0026ndash;4.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.35494880546075%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.09 (0.01\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\" valign=\"top\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.406143344709896%\" valign=\"top\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.30716723549488%\" valign=\"top\"\u003e\n \u003cp\u003eDSTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.35494880546075%\" valign=\"top\"\u003e\n \u003cp\u003e4.34 (0.82\u0026ndash;22.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.35494880546075%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e16.4 (1.46\u0026ndash;183.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.576791808873722%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.406143344709896%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDMV: difficult mask ventilation; OR: odds ratio; 95%CI: 95% confidence interval; LR: likelihood ratio; DSHB: distance from the skin surface to the hyoid bone; DSTI: distance from the skin surface to the thyroid isthmus.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Airway management, Facemask ventilation, Distance from skin to hyoid bone, Distance from skin to thyroid isthmus, Difficult mask ventilation, Ultrasonography","lastPublishedDoi":"10.21203/rs.3.rs-4214333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4214333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA previous study showed that airway ultrasound, specifically the distance from the skin to the hyoid bone (DSHB), may be correlated with a higher risk of difficult mask ventilation (DMV). However, the study was conducted in Italy and lacks data for the Asian and Thai populations. This study aimed to predict DMV using pre-operative ultrasonography to measure the DSHB and from the skin to the thyroid isthmus (DSTI) in Thai patients undergoing elective surgery under general anesthesia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn total, 189 patients who underwent general anesthesia during elective surgery were enrolled in this prospective, observational study. Pre-operative physical examinations and airway evaluations were performed as usual. Airway ultrasound was performed to measure DSHB and DSTI before the anesthetic procedure. Anesthesiologists and nurse anesthetists performed bag-and-mask ventilation using the one-hand technique. DMV was assessed and recorded according to Han\u0026rsquo;s mask ventilation classification in which DMV-0 indicates no attempt at mask ventilation; DMV-I indicates successful ventilation by mask; DMV-II indicates ventilation by mask with oral airway/adjuvant ventilation; DMV-III indicates that ventilation required two providers; and DMV-IV indicates the patient\u0026rsquo;s inability to undergo mask ventilation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThirty (17%) patients were classified as having DMV-0, and DMV-I, II, and III classifications were observed in 126(67%), 18(10%), and 12(6%) patients, respectively. None of the patients were classified as DMV-IV. The DSHB medians were 0.4(0.3\u0026ndash;0.6), 0.7(0.5\u0026ndash;1), 0.7(0.6\u0026ndash;0.8), and 0.6(0.3\u0026ndash;0.9) cm in DMV-0, I, II, and III, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The DSTI medians were 0.9(0.8\u0026ndash;1.1), 0.8(0.7\u0026ndash;1.1), 0.7(0.6\u0026ndash;0.9), and 0.8(0.8\u0026ndash;1.4) cm for DMV-0, I, II, and III, respectively (p\u0026thinsp;=\u0026thinsp;0.041). Multivariate logistic regression indicated that the following factors were associated with difficult mask ventilation (DMV-III): male sex, modified Mallampati classification III, edentulousness, DSHB, and DSTI, with an area under the curve of 0.89.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study showed that airway ultrasonography to determine DSHB and DSTI during patients\u0026rsquo; routine physical examinations significantly improved the prediction of DMV. Patients classified as having DMV-III require prompt management for airway difficulties. However, the individual factors DSHB and DSTI alone are insufficient to predict DMV.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eRegistration number: TCTR2020093002\u003c/p\u003e","manuscriptTitle":"Prediction of Difficult Mask Ventilation in Thai Adult Patients undergoing Elective Surgery using Ultrasound of Distance from Skin to Hyoid Bone, and from Skin to Thyroid Isthmus: A Prospective Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-23 16:16:17","doi":"10.21203/rs.3.rs-4214333/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-08T06:56:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-18T11:06:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-18T07:10:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-18T07:09:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2024-04-03T18:12:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03edc0c3-369e-4f43-8d38-fa121ac62314","owner":[],"postedDate":"April 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:01:33+00:00","versionOfRecord":{"articleIdentity":"rs-4214333","link":"https://doi.org/10.1186/s12871-025-02920-7","journal":{"identity":"bmc-anesthesiology","isVorOnly":false,"title":"BMC Anesthesiology"},"publishedOn":"2025-01-27 15:57:22","publishedOnDateReadable":"January 27th, 2025"},"versionCreatedAt":"2024-04-23 16:16:17","video":"","vorDoi":"10.1186/s12871-025-02920-7","vorDoiUrl":"https://doi.org/10.1186/s12871-025-02920-7","workflowStages":[]},"version":"v1","identity":"rs-4214333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4214333","identity":"rs-4214333","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}