Predicting Difficult Videolaryngoscopic Intubation in Patients with Obesity Using Ultrasound: An Observational, Prospective Study

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METHODSː This observational prospective study included 140 patients with obesity who were scheduled for elective laparoscopic bariatric surgery under general anesthesia. Following tracheal intubation during anesthesia induction, patients were classified into either the non-difficult or difficult intubation group based on the Videolaryngoscopic Intubation and Difficult Airway Classification scale. Clinical and ultrasonographic airway parameters were recorded during the pre-anesthetic evaluation. Receiver operating characteristic curves were generated to assess the diagnostic performance of the airway measurements. RESULTSː Of the 140 enrolled patients, 128 were analysed, with 101 classified as non-difficult and 27 as difficult intubations. Skin-to-tongue thickness and the distance from the skin to the epiglottis (DSE) were the two most reliable predictors of difficult intubation, with area under the curve (AUC) values of 0.776 [95% confidence interval (CI): 0.672–0.879] and 0.774 (95% CI: 0.678–0.869), respectively. The LEMON (Look-Evaluate-Mallampati-Obstruction-Neck mobility) score, skin-to-tongue thickness, and DSE were identified as independent risk factors for predicting difficult intubation. When these three parameters were combined, predictive performance improved, with an AUC of 0.845 (95% CI: 0.760–0.929). CONCLUSIONSː The combination of the LEMON score, skin-to-tongue thickness, and DSE demonstrated superior predictive accuracy compared to any single parameter for identifying difficult videolaryngoscopic intubation in obese patients. Airway management Ultrasonography Obesity Intubation intratracheal Figures Figure 1 WHAT IS KNOWN Airway ultrasound is recommended by clinical guidelines for pre-intubation assessment to evaluate the likelihood of a difficult airway. Ultrasonographic airway parameters generally demonstrate superior predictive performance compared to traditional airway assessment methods for identifying difficult intubation. WHAT IS NEW The VIDIAC scale was utilised as the diagnostic criterion for difficult intubation, allowing for the early identification of difficult airway alerts issued by anesthetists following videolaryngoscopy. Skin-to-tongue thickness was the most accurate predictor of difficult videolaryngoscopic intubation in obese patients, with an AUC of 0.776 (95% CI: 0.672–0.879) and an optimal cut-off value of 7.09 cm. The combination of LEMON score, skin-to-tongue thickness, and DSE demonstrated superior predictive performance (AUC = 0.845; 95% CI: 0.760–0.929) compared to any single parameter for identifying difficult intubation during videolaryngoscopy in obese patients. Introduction Despite its relatively low incidence, difficult airway management remains a critical contributor to anesthesia-related mortality. 1 When complications arise, including death, brain damage, the need for an emergency surgical airway, or unanticipated intensive care unit admission, the consequences can be catastrophic. 2 According to the 2022 guidelines of the American Society of Anesthesiologists (ASA), 3 a difficult airway encompasses a range of clinical challenges, including difficult laryngoscopy, mask ventilation, supraglottic airway ventilation, tracheal intubation, invasive airway management, and extubation. The focus of this study is difficult tracheal intubation, defined as a condition requiring multiple attempts at successful tracheal intubation or failure to achieve intubation despite repeated efforts. 3 A large-scale cross-sectional real-world study involving 15.8 million adults reported that 34.8% of the study population was overweight, while 14.1% were classified as obese according to the Chinese body mass index (BMI) classification: overweight (24–28 kg/m²) and obesity (≥ 28 kg/m²). 4 Obesity-related comorbidities affect nearly all organ systems, with significant implications for the respiratory system. 5 In obese patients, excessive fat deposition in the neck and abdomen results in anatomical and physiological alterations, including upper airway narrowing, tongue hypertrophy, diaphragmatic elevation, and reduced lung volume. 6 These changes increase the risk of difficult airway management, thereby complicating both mask ventilation and intubation. 7 Therefore, accurate preoperative prediction and appropriate management of difficult airways in patients with obesity are crucial for anesthetic safety. Several physical examination findings have been proposed to identify patients at risk of difficult intubation. 8 However, no single parameter reliably reflects the upper airway function or accurately predicts airway difficulty. Thus, the development of more precise and objective tools is necessary to improve sensitivity and specificity while minimising intra- and inter-observer variability. Ultrasound is increasingly used in airway management due to its simplicity, rapid ap-plication, noninvasive nature, and portability. 9 This approach allows for direct visualisation of upper airway structures and objective measurement of airway parameters, providing real-time dynamic imaging to guide and optimise airway interventions. 10 Current guidelines recommend ultrasound as a pre-intubation assessment tool for evaluating the likelihood of difficult laryngoscopy. 3 Additionally, ultrasound-based airway assessment has been clinically valuable for confirming endotracheal tube placement, predicting post-extubation laryngeal oedema, and identifying the cricothyroid membrane in emergencies. 11 Although videolaryngoscopy has been increasingly adopted in clinical practice, research evaluating the role of ultrasound in predicting difficult videolaryngoscopic intubation remains limited. This study hypothesised that ultrasound could serve as a reliable preoperative tool for predicting difficult intubation during videolaryngoscopy. The primary objective of this study was to assess the diagnostic accuracy of ultrasound-derived airway parameters, particularly the distance from the skin to the epiglottis (DSE), in predicting difficult videolaryngoscopic intubation in obese patients. Materials and methods This study was approved by the Ethics Committee of XX Hospital of XX University (Approval No.: XXX, Date: XXX; Chairperson: XXX). The trial was registered in the XXX (Registration No.:XXX, Date:XXX, Principal Investigator: XXX). Written informed consent was obtained from all participants prior to their inclusion in the study. The study adhered to the principles outlined in the Declaration of Helsinki. Patients The required sample size was determined using the PASS 15.0 software based on the preliminary data. The DSE was selected as the primary outcome variable (area under the curve, AUC = 0.646). A power analysis was conducted with a type I error (α) of 0.05, power (1 − β) of 90%, and an allocation ratio of 1:1, yielding a minimum sample size of 112 patients ( Table Ⅰ ). To ensure study robustness and account for potential dropouts or incomplete data, 140 patients were ultimately enrolled. This observational prospective study included 140 consecutive patients with obesity who were scheduled for elective laparoscopic bariatric surgery under general anesthesia at the study institution between December 2024 and January 2025. The inclusion criteria follow: (1) age ≥ 18 years; (2) body mass index (BMI) ≥ 28 kg/m²; (3) ASA physical status classification II or III; (4) scheduled for elective general anesthesia with endotracheal intubation; (5) no consciousness disorder and able to cooperate with the examination; and (6) voluntary informed consent. The exclusion criteria follow: (1) surgery cancellation; (2) patient or family request for withdrawal; (3) presence of subglottic stenosis (cricoid cartilage diameter < 1 cm on neck CT scan); (4) missing characteristic data or poor ultrasound image quality; and (5) known history of difficult intubation. Traditional airway assessments Demographic characteristics (sex, age, BMI, and ASA classification), neck circumference (NC), interincisor gap (IIG), thyromental distance (TMD), sternomental distance (SMD), modified Mallampati test (MMT), upper lip bite test (ULBT), and LEMON (Look-Evaluate-Mallampati-Obstruction-Neck mobility) method scores were recorded during the pre-anesthetic evaluation. In addition, the following comorbid conditions were documented: smoking and alcohol consumption history, hypertension, glucose tolerance abnormalities, hepatic dysfunction, combined hyperlipidaemia, obstructive sleep apnoea syndrome (OSAS), hypoxaemia, and hypercapnia. The MMT score was used to assess oropharyngeal visibility, 12 classifying airway structures into four grades: class Ⅰ, soft palate, fauces, uvula, and pillars visible; class Ⅱ, soft palate, fauces, and uvula visible; class Ⅲ, soft palate and base of the uvula visible; and class Ⅳ, soft palate not visible at all. 13 The LEMON method, a widely used airway assessment tool, was applied, with a score of ≥ 2 indicating a high risk of difficult intubation ( eTable Ⅰ in the supplement). 14 , 15 Hypoxaemia was defined as PaO₂ 45 mmHg, based on arterial blood gas analysis. Although the Cormack-Lehane (C-L) classification and its modification by Yentis and Cook 16 , 17 are commonly used to assess laryngeal views during direct laryngoscopy, these methods may not be suitable for videolaryngoscopy. To address this limitation, Kohse et al . 18 developed the Videolaryngoscopic Intubation and Difficult Airway Classification (VIDIAC) scale ( eTable Ⅱ in the supplement), which incorporates intubation-related variables to predict difficult airway alerts issued by anesthesiologists following videolaryngoscopy. The VIDIAC scale demonstrated superior discriminatory ability compared to the C-L classification. Based on the calculated probabilities of difficult airway alerts, Kohse et al . 18 suggested grading the VIDIAC score as follows: easy, − 1 or 0; moderate, 1; hard, 2; and severe, ≥ 3. Ultrasonographic airway assessments Preoperative airway ultrasound evaluations were performed in all enrolled patients using a Mindray ME7 portable ultrasound device (XX,XXX,XX). A single anesthesiologist with expertise in airway ultrasonography conducted all examinations to ensure consistency. Patients were placed in the supine position with maximal head and neck extension without pillow support and were instructed to maintain tongue-tip contact with the lower incisors during scanning. The following ultrasonographic measurements were recorded. (1) Skin-to-tongue thickness and midsagittal cross-sectional area (MCSA) of the tongue – measured using a low-frequency convex array probe placed under the chin in the midsagittal plane to capture the entire tongue outline ( eFigure 1A in the supplement). Skin-to-tongue thickness was defined as the maximum vertical distance from the tongue surface to the submental skin ( eFigure 1B in the supplement), while MCSA was obtained by tracing the tongue boundary on the ultrasound screen ( eFigure 1C in the supplement). (2) Tongue width – assessed using a transverse scan at the midsection of the tongue, measuring the distance between the most lateral points on the middle surface of the tongue ( eFigure 2A, B in the supplement). (3) Tongue volume (TV) – estimated by multiplying MCSA by tongue width. (4) Distance from the skin to the epiglottis (DSE) and depth of the pre-epiglottic space (Pre-E) – measured using a high-resolution linear probe placed transversely at the thyrohyoid membrane level. The DSE was defined as the distance from the skin surface to the midpoint of the epiglottis, while Pre-E was measured from the anterior edge of the strap muscles to the epiglottis midpoint. The epiglottis appeared as a hypoechoic curvilinear structure with a bright hyperechoic air-mucosal interface at its posterior borde ( eFigure 3A, B in the supplement). (5) Distance from the skin to the vocal cords (DSV) – assessed using a linear probe positioned transversely at the thyroid cartilage level. The DSV was defined as the distance from the skin surface to the anterior commissure of the vocal cords, which appeared as hyperechoic lateral V-shaped structures ( eFigure 4A, B in the supplement). Induction of general anesthesia Patients were placed in the ramped position and preoxygenated via a facemask before anesthesia induction. Continuous monitoring included electrocardiography, pulse oximetry, invasive blood pressure, temperature, end-tidal carbon dioxide (PetCO₂), bispectral index (BIS), and train-of-four (TOF) neuromuscular monitoring. Anesthesia was induced using 0.4–0.6 µg/kg sufentanil (ideal body weight, IBW), 2–2.5 mg/kg propofol (total body weight, TBW), and 0.6 mg/kg rocuronium (IBW). Once full neuromuscular blockade was confirmed (TOF = 0), tracheal intubation was performed by the same experienced anesthesiologist, who had over five years of experience, using an E.An II electronic videolaryngoscope (L-size curved blade, XX, XX, XX). After tracheal intubation was completed, the pressure-controlled ventilation-volume guaranteed (PCV-VG) mode was applied, with ventilatory adjustments to maintain PetCO₂ at 35–45 mmHg. The anesthesiologist performing intubation was blinded to the ultrasound measurements. Study endpoint The primary endpoint was difficult videolaryngoscopic intubation, which was defined using the VIDIAC scale, with scores of 2–5 indicating difficult intubation. After three failed intubation attempts, fibreoptic bronchoscopy was used to complete the procedure. Statistical analysis Statistical analyses were conducted using SPSS 26.0. Normality was assessed using the Shapiro–Wilk test. Data were reported as mean ± standard deviation (m ± SD) for normally distributed variables and as median (P25, P75) for non-normally distributed variables. Categorical data were presented as counts (n) and percentages (%). Between-group comparisons were performed using independent two-sample t-tests (normal data), Mann–Whitney U tests (non-normal data), and chi-square tests (categorical variables). Spearman rank correlation analysis was performed to assess the relationship between traditional and ultrasonographic airway parameters and the occurrence of difficult videolaryngoscopic intubation. Binary logistic regression analysis was conducted to evaluate the association between difficult videolaryngoscopic intubation (dependent variable) and independent variables, including BMI, NC, LEMON score, skin-to-tongue thickness, TV, DSE, and Pre-E. The enter method was applied for variable selection. Before conducting the regression analysis, a multicollinearity test was performed to ensure that the independent variables were not highly correlated. Receiver operating characteristic (ROC) curve analysis was used to assess the predictive performance of these parameters for difficult videolaryngoscopic intubation. The AUC was calculated to determine diagnostic accuracy, and optimal cut-off values were established using the Youden index. The sensitivity, specificity, and 95% confidence intervals (CIs) were also calculated. A P-value < 0.05 was considered significant. Results A total of 140 patients were initially included in this study. However, 12 patients were excluded due to surgery cancellation (n = 5), congenital airway stenosis (n = 1), and poor ultrasound image quality (n = 6). Ultimately, 128 patients (79 women and 49 men) were analysed. The study flowchart is presented in eFigure 5 in the supplement. Patients were stratified into two groups based on their VIDIAC scores: non-difficult intubation group (VIDIAC score < 2), 101 patients; and difficult intubation group (VIDIAC score ≥ 2), 27 patients. Significant differences were observed between the two groups regarding BMI and ASA classification, whereas no significant differences were found in age, smoking or drinking history, hypertension, glucose tolerance abnormalities, hepatic dysfunction, combined hyperlipidaemia, OSAS, hypoxaemia, or hypercapnia ( Table Ⅱ ). Patients in the difficult intubation group had significantly higher NC and LEMON scores than those in the non-difficult intubation group. However, there were no significant differences between the groups regarding IIG, TMD, SMD, MMT, or ULBT. Univariate analysis revealed significant differences in most ultrasonographic predictors of difficult intubation, including skin-to-tongue thickness, MCSA, tongue width, TV, DSE, and Pre-E. However, DSV did not differ significantly ( Table Ⅲ ). Spearman rank correlation analysis demonstrated a significant positive correlation between difficult videolaryngoscopic intubation and BMI, NC, LEMON score, skin-to-tongue thickness, MCSA, tongue width, TV, DSE, and Pre-E. The absolute values of Spearman’s correlation coefficient were > 0.3 for NC, skin-to-tongue thickness, MCSA, tongue width, TV, and DSE, indicating moderate correlations with difficult videolaryngoscopic intubation ( Table Ⅳ ). Binary logistic regression analysis identified LEMON score (OR = 1.706, 95% CI: 1.127–2.583), skin-to-tongue thickness (OR = 4.399, 95% CI: 1.674–11.564), and DSE (OR = 5.515, 95% CI: 1.690–17.998) as independent risk factors for predicting difficult videolaryngoscopic intubation ( Table Ⅴ ). The ROC analysis evaluated the predictive accuracy of traditional and ultrasonographic parameters for difficult intubation based on the VIDIAC scale ( Table Ⅵ ). A LEMON score of ≥ 2 was identified as a significant predictor, with sensitivity of 85%, specificity of 52%, and AUC of 0.699. Among the evaluated parameters, skin-to-tongue thickness was the most reliable predictor of difficult intubation, with AUC of 0.776 (95% CI: 0.672–0.879), sensitivity of 74%, and specificity of 72%. The diagnostic efficacy of DSE was slightly lower, with AUC of 0.774 (95% CI: 0.678–0.869), sensitivity of 67%, and specificity of 79%. For BMI, NC, MCSA, tongue width, TV, and Pre-E, the AUC values were 0.695, 0.719, 0.722, 0.737, 0.762, and 0.710, respectively (95% CI: 0.584–0.806, 0.610–0.828, 0.597–0.846, 0.627–0.846, 0.659–0.866, and 0.605–0.815). The optimal cut-off values for these measurements follow: BMI, 45.39 kg/m²; NC, 45.90 cm; skin-to-tongue thickness, 7.09 cm; MCSA, 28.31 cm²; tongue width, 5.08 cm; TV, 153.2 cm³; DSE, 2.42 cm; and Pre-E, 1.07 cm. A combined analysis of three independent risk factors – LEMON score, skin-to-tongue thickness, and DSE – was conducted to improve the predictive accuracy for difficult intubation. The combination of LEMON score ≥ 2, skin-to-tongue thickness > 7.09 cm, and DSE > 2.42 cm demonstrated an AUC of 0.845 (95% CI: 0.760–0.929), with sensitivity of 74% and specificity of 86% (Fig. 1 ). Discussion Existing assessment tools for predicting difficult intubation are often inadequate, highlighting the need for a more reliable method to evaluate airway anatomy and identify high-risk patients. Ultrasonography provides detailed visualisation of major airway structures and has been explored as a predictive tool for difficult intubation. 9 However, there is limited evidence regarding the most reliable ultrasound-derived parameters for this purpose. Tongue hypertrophy can narrow the oropharyngeal space, compromising laryngoscop-ic exposure and increasing the likelihood of difficult intubation. Yao et al . 19 reported that tongue thickness > 6.1 cm was an independent predictor of difficult tracheal intubation. The present study supports this finding, although the optimal cut-off value identified was 7.09 cm of skin-to-tongue thickness, with sensitivity of 74% and specificity of 72%. This discrepancy may be attributed to the exclusive inclusion of obese patients in our study. Similarly, Zheng et al . 20 demonstrated that the MCSA of the tongue, measured by ultrasonography, was an effective predictor of difficult laryngoscopy. However, the TV threshold identified in this study (153.2 cm³) was substantially higher than that reported in previous research 21 (100 cm³), likely due to the macroglossia in obese individuals. The AUC values for all tongue parameters assessed via ultrasound ranged within 0.7–0.9, indicating a moderate predictive value for difficult intubation and underscoring their significance in airway assessment. The DSE, which reflects the soft tissue thickness of the neck, partially accounts for the difficulty in aligning the oral, pharyngeal, and laryngeal axes during laryngoscopy. 22 Previous studies by Falcetta et al . 23 and Wu et al . 24 reported DSE cut-off values of 2.54 and 2.39 cm, respectively, consistent with the threshold of 2.42 cm identified in this study. Glottic exposure during tracheal intubation may be compromised when DSE exceeds this threshold. As DSE increases, lifting the epiglottis becomes more challenging, further limiting visualisation of the vocal cords. The AUC values for all epiglottic parameters exceeded 0.7, suggesting their utility as diagnostic indicators of difficult intubation. A difficult airway involves multiple interrelated factors. Reliance on a single parameter is insufficient for clinical decision making. Significant variables were incorporated into a binary logistic regression model to improve predictive accuracy, and confounding factors were eliminated. The final model included three predictors: LEMON score, skin-to-tongue thickness, and DSE. The combined predictors were analysed in a ROC curve, and the combined predictive model demonstrated superior performance compared to any individual parameter, achieving AUC of 0.845 with sensitivity of 74% and specificity of 86%. Although videolaryngoscopy is increasingly utilised in clinical practice, traditional airway assessment tools, such as the C-L classification 19 – 21 , 23 – 26 and the Intubation Difficulty Scale (IDS) 20 , 26 ,were originally designed for direct laryngoscopy. Koshe et al . 18 developed the VIDIVC scale to address this limitation in predicting difficult intubation during videolaryngoscopy. This scale was validated in adult patients undergoing otolaryngologic and maxillofacial surgeries, a population with a high prevalence of difficult airways. The findings of the present study suggest that the VIDIAC score is also applicable to obese patients, given their similarly increased risk of difficult intubation and the scale’s effectiveness in evaluating periglottic anatomy – including the epiglottis, vocal cords, and arytenoid cartilage – during videolaryngoscopy. To the best of our knowledge, this study is the first to establish a correlation between airway ultrasound assessments and the VIDIAC scale. This study has several limitations. First, the analysis was conducted at a single centre with a relatively small sample size, which may have introduced bias. Second, because all participants were of Asian ethnicity, caution is required when generalising the findings to other populations. Third, resource constraints limited the randomisation – only one ultrasound machine (Mindray ME7) and a single qualified operator were available, restricting patient enrolment. Finally, obese patients more frequently experience difficult mask ventilation than difficult intubation. Future research should investigate the relationship between airway ultrasound parameters and difficult mask ventilation to further refine airway management strategies. Conclusions Skin-to-tongue thickness and DSE were identified as the most reliable predictors of difficult videolaryngoscopic intubation in obese patients. When combined with the LEMON score, predictive performance was further enhanced, thereby providing a more comprehensive assessment approach for airway management in this high-risk population. Declarations Conflicts of interest The authors certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript. Funding. This study was supported by XXXXXX Author Contribution YW and JC made substantial contributions to the conception or design of the work,revised it critically for important intellectual content.JL DL and CB made substantial contributions to the acquisition.JL and ZM made substantial contributions to the analysis, or interpretation of data,drafted the work. Acknowledgement The authors gratefully acknowledge the colleagues in the Department of Anesthesiology, Fourth Affiliated Hospital of China Medical University, for their valuable assistance with clinical samples References Cook TM. Strategies for the prevention of airway complications - a narrative review. Anaesthesia. 2018;73(1):93–111. PubMed PMID: 29210033. Epub 2017/12/07. eng. Cook TM, Woodall N, Frerk C. 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 1: anaesthesia. Br J Anaesth. 2011;106(5):617–31. PubMed PMID: 21447488. Epub 2011/03/31. eng. Apfelbaum 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(1):31–81. PubMed PMID: 34762729. Epub 2021/11/12. eng. Chen K, Shen Z, Gu W, Lyu Z, Qi X, Mu Y, et al. Prevalence of obesity and associated complications in China: A cross-sectional, real-world study in 15.8 million adults. Diabetes Obes Metab. 2023;25(11):3390–9. PubMed PMID: 37589256. Epub 2023/08/17. eng. Abdelaal M, le Roux CW, Docherty NG. Morbidity and mortality associated with obesity. Annals translational Med. 2017;5(7):161. PubMed PMID: 28480197. Pubmed Central PMCID: PMC5401682. Epub 2017/05/10. eng. Littleton SW. Impact of obesity on respiratory function. Respirology (Carlton. Vic). 2012;17(1):43–9. PubMed PMID: 22040049. Epub 2011/11/02. eng. Kristensen MS. Airway management and morbid obesity. Eur J Anaesthesiol. 2010;27(11):923–7. PubMed PMID: 20689440. Epub 2010/08/07. eng. Detsky ME, Jivraj N, Adhikari NK, Friedrich JO, Pinto R, Simel DL, et al. Will This Patient Be Difficult to Intubate? The Rational Clinical Examination Systematic Review. JAMA. 2019;321(5):493–503. PubMed PMID: 30721300. Epub 2019/02/06. eng. Jain K, Yadav M, Gupta N, Thulkar S, Bhatnagar S. Ultrasonographic assessment of airway. Journal of anaesthesiology, clinical pharmacology. 2020 Jan-Mar;36(1):5–12. PubMed PMID: 32174650. Pubmed Central PMCID: PMC7047677. Epub 2020/03/17. eng. Gottlieb M, Holladay D, Burns KM, Nakitende D, Bailitz J. Ultrasound for airway management: An evidence-based review for the emergency clinician. Am J Emerg Med. 2020;38(5):1007–13. PubMed PMID: 31843325. Epub 2019/12/18. eng. Lin J, Bellinger R, Shedd A, Wolfshohl J, Walker J, Healy J et al. Point-of-Care Ultrasound in Airway Evaluation and Management: A Comprehensive Review. Diagnostics (Basel Switzerland). 2023;13(9). PubMed PMID: 37174933. Pubmed Central PMCID: PMC10177245. Epub 2023/05/13. eng. Mallampati SR, Gatt SP, Gugino LD, Desai SP, Waraksa B, Freiberger D, et al. A clinical sign to predict difficult tracheal intubation: a prospective study. Can Anaesth Soc J. 1985;32(4):429–34. PubMed PMID: 4027773. Epub 1985/07/01. eng. Samsoon GL, Young JR. Difficult tracheal intubation: a retrospective study. Anaesthesia. 1987;42(5):487–90. PubMed PMID: 3592174. Epub 1987/05/01. eng. Reed MJ, Rennie LM, Dunn MJ, Gray AJ, Robertson CE, McKeown DW. Is the 'LEMON' method an easily applied emergency airway assessment tool? Eur J Emerg medicine: official J Eur Soc Emerg Med. 2004;11(3):154–7. PubMed PMID: 15167176. Epub 2004/05/29. eng. Reed MJ, Dunn MJ, McKeown DW. Can an airway assessment score predict difficulty at intubation in the emergency department? Emergency medicine journal: EMJ. 2005;22(2):99–102. PubMed PMID: 15662057. Pubmed Central PMCID: PMC1726680. Epub 2005/01/22. eng. Yentis SM, Lee DJ. Evaluation of an improved scoring system for the grading of direct laryngoscopy. Anaesthesia. 1998;53(11):1041–4. PubMed PMID: 10023271. Epub 1999/02/19. eng. Cook TM. A new practical classification of laryngeal view. Anaesthesia. 2000;55(3):274–9. PubMed PMID: 10671848. Epub 2000/02/15. eng. Kohse EK, Siebert HK, Sasu PB, Loock K, Dohrmann T, Breitfeld P, et al. A model to predict difficult airway alerts after videolaryngoscopy in adults with anticipated difficult airways - the VIDIAC score. Anaesthesia. 2022;77(10):1089–96. PubMed PMID: 36006056. Epub 2022/08/26. eng. Yao W, Wang B. Can tongue thickness measured by ultrasonography predict difficult tracheal intubation? Br J Anaesth. 2017;118(4):601–9. PubMed PMID: 28403413. Epub 2017/04/14. eng. Zheng Z, Ma W, Du R. Effectiveness and validity of midsagittal tongue cross-sectional area and width measured by ultrasound to predict difficult airways. Minerva Anestesiol. 2021;87(4):403–13. PubMed PMID: 33591134. Epub 2021/02/17. eng. Parameswari A, Govind M, Vakamudi M. Correlation between preoperative ultrasonographic airway assessment and laryngoscopic view in adult patients: A prospective study. Journal of anaesthesiology, clinical pharmacology. 2017 Jul-Sep;33(3):353–8. PubMed PMID: 29109635. Pubmed Central PMCID: PMC5672513. Epub 2017/11/08. eng. Osman A, Sum KM. Role of upper airway ultrasound in airway management. J intensive care. 2016;4:52. PubMed PMID: 27529028. Pubmed Central PMCID: PMC4983796. Epub 2016/08/17. eng. Falcetta S, Cavallo S, Gabbanelli V, Pelaia P, Sorbello M, Zdravkovic I, et al. Evaluation of two neck ultrasound measurements as predictors of difficult direct laryngoscopy: A prospective observational study. Eur J Anaesthesiol. 2018;35(8):605–12. PubMed PMID: 29889671. Epub 2018/06/12. eng. Wu J, Dong J, Ding Y, Zheng J. Role of anterior neck soft tissue quantifications by ultrasound in predicting difficult laryngoscopy. Med Sci monitor: Int Med J experimental Clin Res. 2014;20:2343–50. PubMed PMID: 25403231. Pubmed Central PMCID: PMC4247231. Epub 2014/11/19. eng. El-Tawansy A, Mohamed Salama Elnajar A, Abdel Baky Mahmoud H, Ibrahim Amin M, Abd Elmohsen Bedewy A. Validity of Airway Ultrasound in Correlation with Cormack-Lehane Grading in Obese Patients: A Cross-Sectional Study. Anesthesiology pain Med. 2024;14(2):e142701. PubMed PMID: 39411381. Pubmed Central PMCID: PMC11473998. Epub 2024/10/16. eng. Akin S, Yildirim M, Artaş H, Bolat E. Predicting difficult airway in morbidly obese patients using ultrasound. Turk J Med Sci. 2024;54(1):262–74. PubMed PMID: 38812631. Pubmed Central PMCID: PMC11031157. Epub 2024/05/30. eng. Tables Table Ⅰ.— Sample estimation based on the distance from the skin to the epiglottis (DSE) for predicting difficult videolaryngoscopy intubation. Power analysis We performed the POWER analysis Priori analysis on the primary outcome Distance from the skin to the epiglottis (DSE) based on the two-tailed statistical test Receiver operating characteristic (ROC) curve analysis and accepting the cutoff for significance (a) 0.05 and a power (1 − b) of 0.9 The variability of the primary outcome was AUC (area under the curve) = 0.646 based on data taken from Our preliminary experiment We considered as clinically relevant a difference (or a different effect, please specify) of Not clear* Consequently, the effect size was OR = 2.5 The total sample size needed was 112 *Definitive clinical data comparing the distance from the skin to the epiglottis (DSE) between obese patients with and without difficult intubation based on the VIDIAC scale are not available. Table Ⅱ.— Comparison of demographic characteristics between patients with non-difficult and difficult intubation during videolaryngoscopy . Variables non-difficult intubation （n=101） difficult intubation （n=27） P value Gender n (%) Male 29（28.7%） 20（74.1%） <0.001 Female 72（71.3%） 7（25.9%） Age (y) 32（25,38） 29（25,35） 0.155 BMI (kg/m²) 40.2（36，44） 46（39.7,51.4） 0.002 ASA classification n (%) Ⅱ 50（49.5%） 6（22.2%） 0.015 Ⅲ 51（50.5%） 21（77.8%） Smoking history n (%) 26（25.7%） 11（40.7%） 0.153 Drinking history n (%) 20（19.8%） 5（18.5%） >0.999 Hypertension n (%) 21（20.8%） 7（25.9%） 0.603 Glucose tolerance abnormality n (%) 41（40.6%） 8（29.6%） 0.375 Hepatic dysfunction n (%) 44（43.6%） 16（59.3%） 0.193 Combined hyperlipidemia n (%) 26（25.7%） 7（25.9%） >0.999 OSAS n (%) 41（40.6%） 16（59.3%） 0.126 Hypoxaemia n (%) 15（14.9%） 7（25.9%） 0.248 Hypercapnia n (%) 13（12.9%） 7（25.9%） 0.133 Abbreviations: BMI, body mass index; ASA, American society of Aneshesiologists; OSAS, obstructive sleep apnea syndrome. Table Ⅲ.— Comparison of traditional and ultrasonographic parameters between patients with non-difficult and difficult intubation during videolaryngoscopy. Variables non-difficult intubation （n=101） difficult intubation （n=27） P value Neck circumference (cm) 43.5（40.5,48.0） 48.5（44.6,51.5） <0.001 Interincisor gap (cm) 5.12±0.65 5.10±0.69 0.885 Thyromental distance (cm) 10.46±1.28 10.40±1.26 0.831 Sternomental distance (cm) 17.9（17.0,19.2） 18.2（17.0,19.8） 0.840 Modified Mallampati test n (%) Ⅰ 23（22.8%） 8（29.6%） 0.229 Ⅱ 24（23.8%） 2（7.4%） Ⅲ 39（38.6%） 11（40.8%） Ⅳ 15（14.8%） 6（22.2%） Upper lip bite test n (%) 1 42（41.6%） 7（25.9%） 0.182 2 59（58.4%） 20（74.1%） 3 0 0 LEMON score 2（2,4） 3（3,5） 0.001 skin-to-tongue thickness (cm) 6.78±0.53 7.39±0.63 <0.001 MCSA (cm²) 25.56（23.48,28.25） 30.14（26.24,32.37） <0.001 tongue width (cm) 4.82±0.54 5.27±0.56 <0.001 TV (cm³) 118.05（105.62,145.86） 158.25（130.15,183.44） <0.001 DSE (cm) 2.14（1.95,2.33） 2.54（2.26,2.76） <0.001 Pre-E (cm) 0.87（0.75,1.07） 1.10（0.94,1.28） 0.001 DSV (cm) 0.96±0.26 0.94±0.32 0.740 Abbreviations: LEMON, Look-Evaluate-Mallampatti-Obstruction-Neck; MCSA, midsagittal cross-sectional area of the tongue; TV, tongue volume; DSE, distance from the skin to the epiglottis; Pre-E, depth of the pre-epiglottic space; DSV, distance from the skin to the vocal cords. Table Ⅳ.— Spearman rank correlation analysis between traditional and ultrasonographic airway parameters and the occurrence of difficult videolaryngoscopic intubation . Variables r P value BMI 0.276 0.002 neck circumference 0.310 <0.001 LEMON score 0.289 0.001 skin-to-tongue thickness 0.390 <0.001 MCSA 0.313 <0.001 tongue width 0.334 0.001 TV 0.371 <0.001 DSE 0.387 <0.001 Pre-E 0.297 0.001 DSV -0.027 0.765 Abbreviations: r, Spearman correlation coefficient; BMI, body mass index; LEMON, Look-Evaluate-Mallampatti-Obstruction-Neck; MCSA, midsagittal cross-sectional area of the tongue; TV, tongue volume; DSE, distance from the skin to the epiglottis; Pre-E, depth of the pre-epiglottic space; DSV, distance from the skin to the vocal cords. Table Ⅴ.— Binary logistic regression analysis of factors associated with difficult videolaryngoscopic intubation. Variables OR（95%CI） P value LEMON score 1.706（1.127-2.583） 0.012 skin-to-tongue thickness 4.399（1.674-11.564） 0.003 DSE 5.515（1.690-17.998） 0.005 Abbreviations: OR, risk ratio; LEMON, Look-Evaluate-Mallampatti-Obstruction-Neck; DSE, distance from the skin to the epiglottis. Table Ⅵ.— ROC curve analysis of traditional and ultrasonographic airway parameters for predicting difficult videolaryngoscopic intubation. Variables AUC （95%CI） Cut-off value Sensitivity （95%CI） Specificity （95%CI） P value BMI (kg/m²) 0.695 （0.584-0.806） >45.39 0.56 （0.37-0.72） 0.80 （0.71-0.87） 0.002 NC (cm) 0.719 （0.610-0.828） >45.90 0.74 （0.55-0.87） 0.66 （0.57-0.75） 0.001 LEMON score 0.699 （0.594-0.805） >2 0.85 （0.68-0.94） 0.52 （0.42-0.61） 0.002 skin-to-tongue thickness (cm) 0.776 （0.672-0.879） >7.09 0.74 （0.55-0.87） 0.72 （0.63-0.80） 28.31 0.67 （0.48-0.81） 0.77 （0.68-0.84） 5.08 0.74 （0.55-0.87） 0.65 （0.56-0.74） 153.2 0.59 （0.41-0.75） 0.83 （0.75-0.89） 2.42 0.67 （0.48-0.81） 0.79 （0.70-0.86） 1.07 0.63 （0.44-0.78） 0.75 （0.66-0.83） 0.001 Abbreviations: ROC, receiver operating characteristic; BMI, body mass index; NC, neck circumference; LEMON, Look-Evaluate-Mallampatti-Obstruction-Neck; MCSA, midsagittal cross-sectional area of the tongue; TV, tongue volume; DSE, distance from the skin to the epiglottis; Pre-E, depth of the pre-epiglottic space; AUC, area under the curve. Additional Declarations No competing interests reported. Supplementary Files SupplementaryDigitalMaterial1eTable.docx SupplementaryDigitalMaterial2eTable.docx SupplementaryDigitalMaterial3eFigure14.docx SupplementaryDigitalMaterial4eFigure5.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviews received at journal 17 Sep, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers invited by journal 16 Aug, 2025 Editor assigned by journal 17 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 15 Jul, 2025 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. 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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-7131036","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504077807,"identity":"026bcba5-5f8a-4895-a346-1fb7917220b2","order_by":0,"name":"Jing Li","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":504077808,"identity":"3e6c9438-0963-4f1a-a2a3-8750b71f0d57","order_by":1,"name":"Zijing Meng","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zijing","middleName":"","lastName":"Meng","suffix":""},{"id":504077809,"identity":"48a748b1-2058-4faa-9ebe-64f36d825147","order_by":2,"name":"Deming Li","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Deming","middleName":"","lastName":"Li","suffix":""},{"id":504077810,"identity":"779986be-8f70-4516-b58e-691fe8ae2ec2","order_by":3,"name":"Chuhan Bian","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chuhan","middleName":"","lastName":"Bian","suffix":""},{"id":504077811,"identity":"20d5108b-cdc4-44eb-9f88-dc43d1ac420c","order_by":4,"name":"Jing Cai","email":"","orcid":"","institution":"China Medical 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13:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7131036/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7131036/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89980012,"identity":"985f2710-8ee7-4d1c-8365-3c6c90562535","added_by":"auto","created_at":"2025-08-27 06:21:16","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":479623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eROC curves of traditional\u003c/em\u003e \u003cem\u003eparameters, ultrasonographic parameters and the combined predictors for predicting difficult videolaryngoscopic intubation.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7131036/v1/a43621e528d77795ccea0849.jpeg"},{"id":89982446,"identity":"1b8f66c9-08c2-4d5c-b8ec-7ad800581c5b","added_by":"auto","created_at":"2025-08-27 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06:13:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16745,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDigitalMaterial2eTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7131036/v1/66584726763e086bc384607c.docx"},{"id":89978020,"identity":"3d59a690-74a9-4e74-922c-3c2408d28b3c","added_by":"auto","created_at":"2025-08-27 06:13:17","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":91896,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDigitalMaterial3eFigure14.docx","url":"https://assets-eu.researchsquare.com/files/rs-7131036/v1/5c601cb176a2dc80d1f2ee50.docx"},{"id":89978026,"identity":"8a5fce2b-dee6-416f-8b03-3f74516fd90c","added_by":"auto","created_at":"2025-08-27 06:13:17","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":73338,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDigitalMaterial4eFigure5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7131036/v1/e930df1ab26fe2d159a70412.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Difficult Videolaryngoscopic Intubation in Patients with Obesity Using Ultrasound: An Observational, Prospective Study","fulltext":[{"header":"WHAT IS KNOWN","content":"\u003cp\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e Airway ultrasound is recommended by clinical guidelines for pre-intubation assessment to evaluate the likelihood of a difficult airway.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUltrasonographic airway parameters generally demonstrate superior predictive performance compared to traditional airway assessment methods for identifying difficult intubation.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eWHAT IS NEW\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe VIDIAC scale was utilised as the diagnostic criterion for difficult intubation, allowing for the early identification of difficult airway alerts issued by anesthetists following videolaryngoscopy.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSkin-to-tongue thickness was the most accurate predictor of difficult videolaryngoscopic intubation in obese patients, with an AUC of 0.776 (95% CI: 0.672–0.879) and an optimal cut-off value of 7.09 cm.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe combination of LEMON score, skin-to-tongue thickness, and DSE demonstrated superior predictive performance (AUC = 0.845; 95% CI: 0.760–0.929) compared to any single parameter for identifying difficult intubation during videolaryngoscopy in obese patients.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eDespite its relatively low incidence, difficult airway management remains a critical contributor to anesthesia-related mortality.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e When complications arise, including death, brain damage, the need for an emergency surgical airway, or unanticipated intensive care unit admission, the consequences can be catastrophic.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e According to the 2022 guidelines of the American Society of Anesthesiologists (ASA),\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e a difficult airway encompasses a range of clinical challenges, including difficult laryngoscopy, mask ventilation, supraglottic airway ventilation, tracheal intubation, invasive airway management, and extubation. The focus of this study is difficult tracheal intubation, defined as a condition requiring multiple attempts at successful tracheal intubation or failure to achieve intubation despite repeated efforts.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA large-scale cross-sectional real-world study involving 15.8\u0026nbsp;million adults reported that 34.8% of the study population was overweight, while 14.1% were classified as obese according to the Chinese body mass index (BMI) classification: overweight (24\u0026ndash;28 kg/m\u0026sup2;) and obesity (\u0026ge;\u0026thinsp;28 kg/m\u0026sup2;).\u003csup\u003e4\u003c/sup\u003e Obesity-related comorbidities affect nearly all organ systems, with significant implications for the respiratory system.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e In obese patients, excessive fat deposition in the neck and abdomen results in anatomical and physiological alterations, including upper airway narrowing, tongue hypertrophy, diaphragmatic elevation, and reduced lung volume.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e These changes increase the risk of difficult airway management, thereby complicating both mask ventilation and intubation.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Therefore, accurate preoperative prediction and appropriate management of difficult airways in patients with obesity are crucial for anesthetic safety.\u003c/p\u003e\u003cp\u003eSeveral physical examination findings have been proposed to identify patients at risk of difficult intubation.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e However, no single parameter reliably reflects the upper airway function or accurately predicts airway difficulty. Thus, the development of more precise and objective tools is necessary to improve sensitivity and specificity while minimising intra- and inter-observer variability.\u003c/p\u003e\u003cp\u003eUltrasound is increasingly used in airway management due to its simplicity, rapid ap-plication, noninvasive nature, and portability.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e This approach allows for direct visualisation of upper airway structures and objective measurement of airway parameters, providing real-time dynamic imaging to guide and optimise airway interventions.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Current guidelines recommend ultrasound as a pre-intubation assessment tool for evaluating the likelihood of difficult laryngoscopy.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Additionally, ultrasound-based airway assessment has been clinically valuable for confirming endotracheal tube placement, predicting post-extubation laryngeal oedema, and identifying the cricothyroid membrane in emergencies. \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAlthough videolaryngoscopy has been increasingly adopted in clinical practice, research evaluating the role of ultrasound in predicting difficult videolaryngoscopic intubation remains limited. This study hypothesised that ultrasound could serve as a reliable preoperative tool for predicting difficult intubation during videolaryngoscopy. The primary objective of this study was to assess the diagnostic accuracy of ultrasound-derived airway parameters, particularly the distance from the skin to the epiglottis (DSE), in predicting difficult videolaryngoscopic intubation in obese patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThis study was approved by the Ethics Committee of XX Hospital of XX University (Approval No.: XXX, Date: XXX; Chairperson: XXX). The trial was registered in the XXX (Registration No.:XXX, Date:XXX, Principal Investigator: XXX). Written informed consent was obtained from all participants prior to their inclusion in the study. The study adhered to the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePatients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe required sample size was determined using the PASS 15.0 software based on the preliminary data. The DSE was selected as the primary outcome variable (area under the curve, AUC\u0026thinsp;=\u0026thinsp;0.646). A power analysis was conducted with a type I error (α) of 0.05, power (1\u0026thinsp;\u0026minus;\u0026thinsp;β) of 90%, and an allocation ratio of 1:1, yielding a minimum sample size of 112 patients (\u003cb\u003eTable Ⅰ\u003c/b\u003e). To ensure study robustness and account for potential dropouts or incomplete data, 140 patients were ultimately enrolled.\u003c/p\u003e\u003cp\u003eThis observational prospective study included 140 consecutive patients with obesity who were scheduled for elective laparoscopic bariatric surgery under general anesthesia at the study institution between December 2024 and January 2025. The inclusion criteria follow: (1) age \u0026ge; 18 years; (2) body mass index (BMI) \u0026ge; 28 kg/m\u0026sup2;; (3) ASA physical status classification II or III; (4) scheduled for elective general anesthesia with endotracheal intubation; (5) no consciousness disorder and able to cooperate with the examination; and (6) voluntary informed consent. The exclusion criteria follow: (1) surgery cancellation; (2) patient or family request for withdrawal; (3) presence of subglottic stenosis (cricoid cartilage diameter\u0026thinsp;\u0026lt;\u0026thinsp;1 cm on neck CT scan); (4) missing characteristic data or poor ultrasound image quality; and (5) known history of difficult intubation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTraditional airway assessments\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDemographic characteristics (sex, age, BMI, and ASA classification), neck circumference (NC), interincisor gap (IIG), thyromental distance (TMD), sternomental distance (SMD), modified Mallampati test (MMT), upper lip bite test (ULBT), and LEMON (Look-Evaluate-Mallampati-Obstruction-Neck mobility) method scores were recorded during the pre-anesthetic evaluation. In addition, the following comorbid conditions were documented: smoking and alcohol consumption history, hypertension, glucose tolerance abnormalities, hepatic dysfunction, combined hyperlipidaemia, obstructive sleep apnoea syndrome (OSAS), hypoxaemia, and hypercapnia.\u003c/p\u003e\u003cp\u003eThe MMT score was used to assess oropharyngeal visibility,\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e classifying airway structures into four grades: class Ⅰ, soft palate, fauces, uvula, and pillars visible; class Ⅱ, soft palate, fauces, and uvula visible; class Ⅲ, soft palate and base of the uvula visible; and class Ⅳ, soft palate not visible at all.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e The LEMON method, a widely used airway assessment tool, was applied, with a score of \u0026ge;\u0026thinsp;2 indicating a high risk of difficult intubation (\u003cb\u003eeTable Ⅰ\u003c/b\u003e in the supplement).\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Hypoxaemia was defined as PaO₂ \u0026lt; 80 mmHg, and hypercapnia as PaCO₂ \u0026gt;45 mmHg, based on arterial blood gas analysis.\u003c/p\u003e\u003cp\u003eAlthough the Cormack-Lehane (C-L) classification and its modification by Yentis and Cook\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e are commonly used to assess laryngeal views during direct laryngoscopy, these methods may not be suitable for videolaryngoscopy. To address this limitation, Kohse \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e developed the Videolaryngoscopic Intubation and Difficult Airway Classification (VIDIAC) scale (\u003cb\u003eeTable Ⅱ\u003c/b\u003e in the supplement), which incorporates intubation-related variables to predict difficult airway alerts issued by anesthesiologists following videolaryngoscopy. The VIDIAC scale demonstrated superior discriminatory ability compared to the C-L classification. Based on the calculated probabilities of difficult airway alerts, Kohse \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e suggested grading the VIDIAC score as follows: easy, \u0026minus;\u0026thinsp;1 or 0; moderate, 1; hard, 2; and severe, \u0026ge;\u0026thinsp;3.\u003c/p\u003e\u003cp\u003e\u003cb\u003eUltrasonographic airway assessments\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePreoperative airway ultrasound evaluations were performed in all enrolled patients using a Mindray ME7 portable ultrasound device (XX,XXX,XX). A single anesthesiologist with expertise in airway ultrasonography conducted all examinations to ensure consistency. Patients were placed in the supine position with maximal head and neck extension without pillow support and were instructed to maintain tongue-tip contact with the lower incisors during scanning.\u003c/p\u003e\u003cp\u003eThe following ultrasonographic measurements were recorded. (1) Skin-to-tongue thickness and midsagittal cross-sectional area (MCSA) of the tongue \u0026ndash; measured using a low-frequency convex array probe placed under the chin in the midsagittal plane to capture the entire tongue outline (\u003cb\u003eeFigure 1A\u003c/b\u003e in the supplement). Skin-to-tongue thickness was defined as the maximum vertical distance from the tongue surface to the submental skin (\u003cb\u003eeFigure 1B\u003c/b\u003e in the supplement), while MCSA was obtained by tracing the tongue boundary on the ultrasound screen (\u003cb\u003eeFigure 1C\u003c/b\u003e in the supplement). (2) Tongue width \u0026ndash; assessed using a transverse scan at the midsection of the tongue, measuring the distance between the most lateral points on the middle surface of the tongue (\u003cb\u003eeFigure 2A, B\u003c/b\u003e in the supplement). (3) Tongue volume (TV) \u0026ndash; estimated by multiplying MCSA by tongue width. (4) Distance from the skin to the epiglottis (DSE) and depth of the pre-epiglottic space (Pre-E) \u0026ndash; measured using a high-resolution linear probe placed transversely at the thyrohyoid membrane level. The DSE was defined as the distance from the skin surface to the midpoint of the epiglottis, while Pre-E was measured from the anterior edge of the strap muscles to the epiglottis midpoint. The epiglottis appeared as a hypoechoic curvilinear structure with a bright hyperechoic air-mucosal interface at its posterior borde (\u003cb\u003eeFigure 3A, B\u003c/b\u003e in the supplement). (5) Distance from the skin to the vocal cords (DSV) \u0026ndash; assessed using a linear probe positioned transversely at the thyroid cartilage level. The DSV was defined as the distance from the skin surface to the anterior commissure of the vocal cords, which appeared as hyperechoic lateral V-shaped structures (\u003cb\u003eeFigure 4A, B\u003c/b\u003e in the supplement).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInduction of general anesthesia\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients were placed in the ramped position and preoxygenated via a facemask before anesthesia induction. Continuous monitoring included electrocardiography, pulse oximetry, invasive blood pressure, temperature, end-tidal carbon dioxide (PetCO₂), bispectral index (BIS), and train-of-four (TOF) neuromuscular monitoring. Anesthesia was induced using 0.4\u0026ndash;0.6 \u0026micro;g/kg sufentanil (ideal body weight, IBW), 2\u0026ndash;2.5 mg/kg propofol (total body weight, TBW), and 0.6 mg/kg rocuronium (IBW). Once full neuromuscular blockade was confirmed (TOF\u0026thinsp;=\u0026thinsp;0), tracheal intubation was performed by the same experienced anesthesiologist, who had over five years of experience, using an E.An II electronic videolaryngoscope (L-size curved blade, XX, XX, XX). After tracheal intubation was completed, the pressure-controlled ventilation-volume guaranteed (PCV-VG) mode was applied, with ventilatory adjustments to maintain PetCO₂ at 35\u0026ndash;45 mmHg. The anesthesiologist performing intubation was blinded to the ultrasound measurements.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy endpoint\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary endpoint was difficult videolaryngoscopic intubation, which was defined using the VIDIAC scale, with scores of 2\u0026ndash;5 indicating difficult intubation. After three failed intubation attempts, fibreoptic bronchoscopy was used to complete the procedure.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using SPSS 26.0. Normality was assessed using the Shapiro\u0026ndash;Wilk test. Data were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (m\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) for normally distributed variables and as median (P25, P75) for non-normally distributed variables. Categorical data were presented as counts (n) and percentages (%). Between-group comparisons were performed using independent two-sample t-tests (normal data), Mann\u0026ndash;Whitney U tests (non-normal data), and chi-square tests (categorical variables). Spearman rank correlation analysis was performed to assess the relationship between traditional and ultrasonographic airway parameters and the occurrence of difficult videolaryngoscopic intubation. Binary logistic regression analysis was conducted to evaluate the association between difficult videolaryngoscopic intubation (dependent variable) and independent variables, including BMI, NC, LEMON score, skin-to-tongue thickness, TV, DSE, and Pre-E. The enter method was applied for variable selection. Before conducting the regression analysis, a multicollinearity test was performed to ensure that the independent variables were not highly correlated. Receiver operating characteristic (ROC) curve analysis was used to assess the predictive performance of these parameters for difficult videolaryngoscopic intubation. The AUC was calculated to determine diagnostic accuracy, and optimal cut-off values were established using the Youden index. The sensitivity, specificity, and 95% confidence intervals (CIs) were also calculated. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 140 patients were initially included in this study. However, 12 patients were excluded due to surgery cancellation (n\u0026thinsp;=\u0026thinsp;5), congenital airway stenosis (n\u0026thinsp;=\u0026thinsp;1), and poor ultrasound image quality (n\u0026thinsp;=\u0026thinsp;6). Ultimately, 128 patients (79 women and 49 men) were analysed. The study flowchart is presented in \u003cb\u003eeFigure 5\u003c/b\u003e in the supplement.\u003c/p\u003e\u003cp\u003ePatients were stratified into two groups based on their VIDIAC scores: non-difficult intubation group (VIDIAC score\u0026thinsp;\u0026lt;\u0026thinsp;2), 101 patients; and difficult intubation group (VIDIAC score\u0026thinsp;\u0026ge;\u0026thinsp;2), 27 patients. Significant differences were observed between the two groups regarding BMI and ASA classification, whereas no significant differences were found in age, smoking or drinking history, hypertension, glucose tolerance abnormalities, hepatic dysfunction, combined hyperlipidaemia, OSAS, hypoxaemia, or hypercapnia (\u003cb\u003eTable Ⅱ\u003c/b\u003e).\u003c/p\u003e\u003cp\u003ePatients in the difficult intubation group had significantly higher NC and LEMON scores than those in the non-difficult intubation group. However, there were no significant differences between the groups regarding IIG, TMD, SMD, MMT, or ULBT. Univariate analysis revealed significant differences in most ultrasonographic predictors of difficult intubation, including skin-to-tongue thickness, MCSA, tongue width, TV, DSE, and Pre-E. However, DSV did not differ significantly (\u003cb\u003eTable Ⅲ\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eSpearman rank correlation analysis demonstrated a significant positive correlation between difficult videolaryngoscopic intubation and BMI, NC, LEMON score, skin-to-tongue thickness, MCSA, tongue width, TV, DSE, and Pre-E. The absolute values of Spearman\u0026rsquo;s correlation coefficient were \u0026gt;\u0026thinsp;0.3 for NC, skin-to-tongue thickness, MCSA, tongue width, TV, and DSE, indicating moderate correlations with difficult videolaryngoscopic intubation (\u003cb\u003eTable Ⅳ\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eBinary logistic regression analysis identified LEMON score (OR\u0026thinsp;=\u0026thinsp;1.706, 95% CI: 1.127\u0026ndash;2.583), skin-to-tongue thickness (OR\u0026thinsp;=\u0026thinsp;4.399, 95% CI: 1.674\u0026ndash;11.564), and DSE (OR\u0026thinsp;=\u0026thinsp;5.515, 95% CI: 1.690\u0026ndash;17.998) as independent risk factors for predicting difficult videolaryngoscopic intubation (\u003cb\u003eTable Ⅴ\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThe ROC analysis evaluated the predictive accuracy of traditional and ultrasonographic parameters for difficult intubation based on the VIDIAC scale (\u003cb\u003eTable Ⅵ\u003c/b\u003e). A LEMON score of \u0026ge;\u0026thinsp;2 was identified as a significant predictor, with sensitivity of 85%, specificity of 52%, and AUC of 0.699. Among the evaluated parameters, skin-to-tongue thickness was the most reliable predictor of difficult intubation, with AUC of 0.776 (95% CI: 0.672\u0026ndash;0.879), sensitivity of 74%, and specificity of 72%. The diagnostic efficacy of DSE was slightly lower, with AUC of 0.774 (95% CI: 0.678\u0026ndash;0.869), sensitivity of 67%, and specificity of 79%. For BMI, NC, MCSA, tongue width, TV, and Pre-E, the AUC values were 0.695, 0.719, 0.722, 0.737, 0.762, and 0.710, respectively (95% CI: 0.584\u0026ndash;0.806, 0.610\u0026ndash;0.828, 0.597\u0026ndash;0.846, 0.627\u0026ndash;0.846, 0.659\u0026ndash;0.866, and 0.605\u0026ndash;0.815). The optimal cut-off values for these measurements follow: BMI, 45.39 kg/m\u0026sup2;; NC, 45.90 cm; skin-to-tongue thickness, 7.09 cm; MCSA, 28.31 cm\u0026sup2;; tongue width, 5.08 cm; TV, 153.2 cm\u0026sup3;; DSE, 2.42 cm; and Pre-E, 1.07 cm. A combined analysis of three independent risk factors \u0026ndash; LEMON score, skin-to-tongue thickness, and DSE \u0026ndash; was conducted to improve the predictive accuracy for difficult intubation. The combination of LEMON score\u0026thinsp;\u0026ge;\u0026thinsp;2, skin-to-tongue thickness\u0026thinsp;\u0026gt;\u0026thinsp;7.09 cm, and DSE\u0026thinsp;\u0026gt;\u0026thinsp;2.42 cm demonstrated an AUC of 0.845 (95% CI: 0.760\u0026ndash;0.929), with sensitivity of 74% and specificity of 86% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eExisting assessment tools for predicting difficult intubation are often inadequate, highlighting the need for a more reliable method to evaluate airway anatomy and identify high-risk patients. Ultrasonography provides detailed visualisation of major airway structures and has been explored as a predictive tool for difficult intubation.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e However, there is limited evidence regarding the most reliable ultrasound-derived parameters for this purpose.\u003c/p\u003e\u003cp\u003eTongue hypertrophy can narrow the oropharyngeal space, compromising laryngoscop-ic exposure and increasing the likelihood of difficult intubation. Yao \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e reported that tongue thickness\u0026thinsp;\u0026gt;\u0026thinsp;6.1 cm was an independent predictor of difficult tracheal intubation. The present study supports this finding, although the optimal cut-off value identified was 7.09 cm of skin-to-tongue thickness, with sensitivity of 74% and specificity of 72%. This discrepancy may be attributed to the exclusive inclusion of obese patients in our study. Similarly, Zheng \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e demonstrated that the MCSA of the tongue, measured by ultrasonography, was an effective predictor of difficult laryngoscopy. However, the TV threshold identified in this study (153.2 cm\u0026sup3;) was substantially higher than that reported in previous research\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (100 cm\u0026sup3;), likely due to the macroglossia in obese individuals. The AUC values for all tongue parameters assessed via ultrasound ranged within 0.7\u0026ndash;0.9, indicating a moderate predictive value for difficult intubation and underscoring their significance in airway assessment. The DSE, which reflects the soft tissue thickness of the neck, partially accounts for the difficulty in aligning the oral, pharyngeal, and laryngeal axes during laryngoscopy.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Previous studies by Falcetta \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and Wu \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e reported DSE cut-off values of 2.54 and 2.39 cm, respectively, consistent with the threshold of 2.42 cm identified in this study. Glottic exposure during tracheal intubation may be compromised when DSE exceeds this threshold. As DSE increases, lifting the epiglottis becomes more challenging, further limiting visualisation of the vocal cords. The AUC values for all epiglottic parameters exceeded 0.7, suggesting their utility as diagnostic indicators of difficult intubation.\u003c/p\u003e\u003cp\u003eA difficult airway involves multiple interrelated factors. Reliance on a single parameter is insufficient for clinical decision making. Significant variables were incorporated into a binary logistic regression model to improve predictive accuracy, and confounding factors were eliminated. The final model included three predictors: LEMON score, skin-to-tongue thickness, and DSE. The combined predictors were analysed in a ROC curve, and the combined predictive model demonstrated superior performance compared to any individual parameter, achieving AUC of 0.845 with sensitivity of 74% and specificity of 86%.\u003c/p\u003e\u003cp\u003eAlthough videolaryngoscopy is increasingly utilised in clinical practice, traditional airway assessment tools, such as the C-L classification\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and the Intubation Difficulty Scale (IDS)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e ,were originally designed for direct laryngoscopy. Koshe \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e developed the VIDIVC scale to address this limitation in predicting difficult intubation during videolaryngoscopy. This scale was validated in adult patients undergoing otolaryngologic and maxillofacial surgeries, a population with a high prevalence of difficult airways. The findings of the present study suggest that the VIDIAC score is also applicable to obese patients, given their similarly increased risk of difficult intubation and the scale\u0026rsquo;s effectiveness in evaluating periglottic anatomy \u0026ndash; including the epiglottis, vocal cords, and arytenoid cartilage \u0026ndash; during videolaryngoscopy. To the best of our knowledge, this study is the first to establish a correlation between airway ultrasound assessments and the VIDIAC scale.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the analysis was conducted at a single centre with a relatively small sample size, which may have introduced bias. Second, because all participants were of Asian ethnicity, caution is required when generalising the findings to other populations. Third, resource constraints limited the randomisation \u0026ndash; only one ultrasound machine (Mindray ME7) and a single qualified operator were available, restricting patient enrolment. Finally, obese patients more frequently experience difficult mask ventilation than difficult intubation. Future research should investigate the relationship between airway ultrasound parameters and difficult mask ventilation to further refine airway management strategies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSkin-to-tongue thickness and DSE were identified as the most reliable predictors of difficult videolaryngoscopic intubation in obese patients. When combined with the LEMON score, predictive performance was further enhanced, thereby providing a more comprehensive assessment approach for airway management in this high-risk population.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of interest\u003c/h2\u003e\n\u003cp\u003eThe authors certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding.\u003c/h2\u003e\n\u003cp\u003eThis study was supported by XXXXXX\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eYW and JC made substantial contributions to the conception or design of the work,revised it critically for important intellectual content.JL DL and CB made substantial contributions to the acquisition.JL and ZM made substantial contributions to the analysis, or interpretation of data,drafted the work.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors gratefully acknowledge the colleagues in the Department of Anesthesiology, Fourth Affiliated Hospital of China Medical University, for their valuable assistance with clinical samples\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCook TM. Strategies for the prevention of airway complications - a narrative review. Anaesthesia. 2018;73(1):93\u0026ndash;111. PubMed PMID: 29210033. Epub 2017/12/07. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook TM, Woodall N, Frerk C. 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 1: anaesthesia. Br J Anaesth. 2011;106(5):617\u0026ndash;31. PubMed PMID: 21447488. Epub 2011/03/31. eng.\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(1):31\u0026ndash;81. PubMed PMID: 34762729. Epub 2021/11/12. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen K, Shen Z, Gu W, Lyu Z, Qi X, Mu Y, et al. Prevalence of obesity and associated complications in China: A cross-sectional, real-world study in 15.8 million adults. Diabetes Obes Metab. 2023;25(11):3390\u0026ndash;9. PubMed PMID: 37589256. Epub 2023/08/17. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdelaal M, le Roux CW, Docherty NG. Morbidity and mortality associated with obesity. Annals translational Med. 2017;5(7):161. PubMed PMID: 28480197. Pubmed Central PMCID: PMC5401682. Epub 2017/05/10. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLittleton SW. Impact of obesity on respiratory function. Respirology (Carlton. Vic). 2012;17(1):43\u0026ndash;9. PubMed PMID: 22040049. Epub 2011/11/02. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKristensen MS. Airway management and morbid obesity. Eur J Anaesthesiol. 2010;27(11):923\u0026ndash;7. PubMed PMID: 20689440. Epub 2010/08/07. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDetsky ME, Jivraj N, Adhikari NK, Friedrich JO, Pinto R, Simel DL, et al. Will This Patient Be Difficult to Intubate? The Rational Clinical Examination Systematic Review. JAMA. 2019;321(5):493\u0026ndash;503. PubMed PMID: 30721300. Epub 2019/02/06. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJain K, Yadav M, Gupta N, Thulkar S, Bhatnagar S. Ultrasonographic assessment of airway. Journal of anaesthesiology, clinical pharmacology. 2020 Jan-Mar;36(1):5\u0026ndash;12. PubMed PMID: 32174650. Pubmed Central PMCID: PMC7047677. Epub 2020/03/17. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGottlieb M, Holladay D, Burns KM, Nakitende D, Bailitz J. Ultrasound for airway management: An evidence-based review for the emergency clinician. Am J Emerg Med. 2020;38(5):1007\u0026ndash;13. PubMed PMID: 31843325. Epub 2019/12/18. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin J, Bellinger R, Shedd A, Wolfshohl J, Walker J, Healy J et al. Point-of-Care Ultrasound in Airway Evaluation and Management: A Comprehensive Review. Diagnostics (Basel Switzerland). 2023;13(9). PubMed PMID: 37174933. Pubmed Central PMCID: PMC10177245. Epub 2023/05/13. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMallampati SR, Gatt SP, Gugino LD, Desai SP, Waraksa B, Freiberger D, et al. A clinical sign to predict difficult tracheal intubation: a prospective study. Can Anaesth Soc J. 1985;32(4):429\u0026ndash;34. PubMed PMID: 4027773. Epub 1985/07/01. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSamsoon GL, Young JR. Difficult tracheal intubation: a retrospective study. Anaesthesia. 1987;42(5):487\u0026ndash;90. PubMed PMID: 3592174. Epub 1987/05/01. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReed MJ, Rennie LM, Dunn MJ, Gray AJ, Robertson CE, McKeown DW. Is the 'LEMON' method an easily applied emergency airway assessment tool? Eur J Emerg medicine: official J Eur Soc Emerg Med. 2004;11(3):154\u0026ndash;7. PubMed PMID: 15167176. Epub 2004/05/29. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReed MJ, Dunn MJ, McKeown DW. Can an airway assessment score predict difficulty at intubation in the emergency department? Emergency medicine journal: EMJ. 2005;22(2):99\u0026ndash;102. PubMed PMID: 15662057. Pubmed Central PMCID: PMC1726680. Epub 2005/01/22. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYentis SM, Lee DJ. Evaluation of an improved scoring system for the grading of direct laryngoscopy. Anaesthesia. 1998;53(11):1041\u0026ndash;4. PubMed PMID: 10023271. Epub 1999/02/19. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook TM. A new practical classification of laryngeal view. Anaesthesia. 2000;55(3):274\u0026ndash;9. PubMed PMID: 10671848. Epub 2000/02/15. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKohse EK, Siebert HK, Sasu PB, Loock K, Dohrmann T, Breitfeld P, et al. A model to predict difficult airway alerts after videolaryngoscopy in adults with anticipated difficult airways - the VIDIAC score. Anaesthesia. 2022;77(10):1089\u0026ndash;96. PubMed PMID: 36006056. Epub 2022/08/26. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao W, Wang B. Can tongue thickness measured by ultrasonography predict difficult tracheal intubation? Br J Anaesth. 2017;118(4):601\u0026ndash;9. PubMed PMID: 28403413. Epub 2017/04/14. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng Z, Ma W, Du R. Effectiveness and validity of midsagittal tongue cross-sectional area and width measured by ultrasound to predict difficult airways. Minerva Anestesiol. 2021;87(4):403\u0026ndash;13. PubMed PMID: 33591134. Epub 2021/02/17. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParameswari A, Govind M, Vakamudi M. Correlation between preoperative ultrasonographic airway assessment and laryngoscopic view in adult patients: A prospective study. Journal of anaesthesiology, clinical pharmacology. 2017 Jul-Sep;33(3):353\u0026ndash;8. PubMed PMID: 29109635. Pubmed Central PMCID: PMC5672513. Epub 2017/11/08. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOsman A, Sum KM. Role of upper airway ultrasound in airway management. J intensive care. 2016;4:52. PubMed PMID: 27529028. Pubmed Central PMCID: PMC4983796. Epub 2016/08/17. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFalcetta S, Cavallo S, Gabbanelli V, Pelaia P, Sorbello M, Zdravkovic I, et al. Evaluation of two neck ultrasound measurements as predictors of difficult direct laryngoscopy: A prospective observational study. Eur J Anaesthesiol. 2018;35(8):605\u0026ndash;12. PubMed PMID: 29889671. Epub 2018/06/12. eng.\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 monitor: Int Med J experimental Clin Res. 2014;20:2343\u0026ndash;50. PubMed PMID: 25403231. Pubmed Central PMCID: PMC4247231. Epub 2014/11/19. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEl-Tawansy A, Mohamed Salama Elnajar A, Abdel Baky Mahmoud H, Ibrahim Amin M, Abd Elmohsen Bedewy A. Validity of Airway Ultrasound in Correlation with Cormack-Lehane Grading in Obese Patients: A Cross-Sectional Study. Anesthesiology pain Med. 2024;14(2):e142701. PubMed PMID: 39411381. Pubmed Central PMCID: PMC11473998. Epub 2024/10/16. eng.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkin S, Yildirim M, Artaş H, Bolat E. Predicting difficult airway in morbidly obese patients using ultrasound. Turk J Med Sci. 2024;54(1):262\u0026ndash;74. PubMed PMID: 38812631. Pubmed Central PMCID: PMC11031157. Epub 2024/05/30. eng.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable Ⅰ.\u0026mdash;\u003cem\u003eSample estimation based on the distance from the skin to the epiglottis (DSE) for predicting difficult\u003c/em\u003e \u003cem\u003evideolaryngoscopy intubation.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5556%;\"\u003e\n \u003cp\u003ePower analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4444%;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5556%;\"\u003e\n \u003cp\u003eWe performed the POWER analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003ePriori analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5556%;\"\u003e\n \u003cp\u003eon the primary outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eDistance from the skin to the epiglottis (DSE)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5556%;\"\u003e\n \u003cp\u003ebased on the two-tailed statistical test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eReceiver operating characteristic (ROC) curve analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5556%;\"\u003e\n \u003cp\u003eand accepting the cutoff for significance (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5556%;\"\u003e\n \u003cp\u003eand a power (1 \u0026minus; b) of\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5556%;\"\u003e\n \u003cp\u003eThe variability of the primary outcome was\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eAUC (area under the curve) = 0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5556%;\"\u003e\n \u003cp\u003ebased on data taken from\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eOur preliminary experiment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5556%;\"\u003e\n \u003cp\u003eWe considered as clinically relevant a difference (or a different effect, please specify) of\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eNot clear*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5556%;\"\u003e\n \u003cp\u003eConsequently, the effect size was\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003eOR = 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.5556%;\"\u003e\n \u003cp\u003eThe total sample size needed was\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.4444%;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Definitive clinical data comparing the distance from the skin to the epiglottis (DSE) between obese patients with and without difficult intubation based on the VIDIAC scale are not available.\u003c/p\u003e\n\u003cp\u003eTable Ⅱ.\u0026mdash; \u003cem\u003eComparison of demographic characteristics between patients with non-difficult and difficult intubation during\u003c/em\u003e \u003cem\u003evideolaryngoscopy\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003enon-difficult\u003c/p\u003e\n \u003cp\u003eintubation\u003c/p\u003e\n \u003cp\u003e（n=101）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003edifficult\u003c/p\u003e\n \u003cp\u003eintubation\u003c/p\u003e\n \u003cp\u003e（n=27）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eGender n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e29（28.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e20（74.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e72（71.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e7（25.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eAge (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e32（25,38）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e29（25,35）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e40.2（36，44）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e46（39.7,51.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eASA classification n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e50（49.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e6（22.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e51（50.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e21（77.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eSmoking history n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e26（25.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e11（40.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eDrinking history n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e20（19.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e5（18.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026gt;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eHypertension n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e21（20.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e7（25.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eGlucose tolerance abnormality n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e41（40.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e8（29.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eHepatic dysfunction n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e44（43.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e16（59.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eCombined hyperlipidemia n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e26（25.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e7（25.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026gt;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eOSAS n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e41（40.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e16（59.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eHypoxaemia n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e15（14.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e7（25.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eHypercapnia n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e13（12.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e7（25.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; ASA, American society of Aneshesiologists; OSAS, obstructive sleep apnea syndrome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable Ⅲ.\u0026mdash; \u003cem\u003eComparison of traditional and ultrasonographic parameters between patients with non-difficult and difficult intubation during videolaryngoscopy.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003enon-difficult\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eintubation\u003c/p\u003e\n \u003cp\u003e（n=101）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003edifficult\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eintubation\u003c/p\u003e\n \u003cp\u003e（n=27）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNeck circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e43.5（40.5,48.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e48.5（44.6,51.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eInterincisor gap (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e5.12\u0026plusmn;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e5.10\u0026plusmn;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eThyromental distance (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e10.46\u0026plusmn;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e10.40\u0026plusmn;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eSternomental distance (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e17.9（17.0,19.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e18.2（17.0,19.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eModified Mallampati test n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e23（22.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e8（29.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e24（23.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e2（7.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e39（38.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e11（40.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e15（14.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e6（22.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eUpper lip bite test n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e42（41.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e7（25.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e59（58.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e20（74.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eLEMON score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e2（2,4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e3（3,5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eskin-to-tongue thickness (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e6.78\u0026plusmn;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e7.39\u0026plusmn;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eMCSA (cm\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e25.56（23.48,28.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e30.14（26.24,32.37）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003etongue width (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e4.82\u0026plusmn;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e5.27\u0026plusmn;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eTV (cm\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e118.05（105.62,145.86）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e158.25（130.15,183.44）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eDSE (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e2.14（1.95,2.33）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e2.54（2.26,2.76）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ePre-E (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e0.87（0.75,1.07）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1.10（0.94,1.28）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eDSV (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.26\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.94\u0026plusmn;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: LEMON, Look-Evaluate-Mallampatti-Obstruction-Neck; MCSA, midsagittal cross-sectional area of the tongue; TV, tongue volume; DSE, distance from the skin to the epiglottis; Pre-E, depth of the pre-epiglottic space; DSV, distance from the skin to the vocal cords.\u003c/p\u003e\n\u003cp\u003eTable Ⅳ.\u0026mdash; \u003cem\u003eSpearman rank correlation analysis between traditional and ultrasonographic airway parameters and the occurrence of difficult\u003c/em\u003e \u003cem\u003evideolaryngoscopic\u003c/em\u003e\u003cem\u003e\u0026nbsp;intubation\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eneck circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eLEMON score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eskin-to-tongue thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eMCSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003etongue width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eTV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eDSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003ePre-E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eDSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: r, Spearman correlation coefficient; BMI, body mass index; LEMON, Look-Evaluate-Mallampatti-Obstruction-Neck; MCSA, midsagittal cross-sectional area of the tongue; TV, tongue volume; DSE, distance from the skin to the epiglottis; Pre-E, depth of the pre-epiglottic space; DSV, distance from the skin to the vocal cords. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable Ⅴ.\u0026mdash; \u003cem\u003eBinary logistic regression analysis of factors associated with difficult videolaryngoscopic intubation.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eOR（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eLEMON score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e1.706（1.127-2.583）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eskin-to-tongue thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e4.399（1.674-11.564）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eDSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e5.515（1.690-17.998）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: OR, risk ratio; LEMON, Look-Evaluate-Mallampatti-Obstruction-Neck; DSE, distance from the skin to the epiglottis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable Ⅵ.\u0026mdash; \u003cem\u003eROC curve analysis of traditional and ultrasonographic airway parameters for predicting difficult videolaryngoscopic intubation.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003cp\u003e（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eCut-off value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003cp\u003e（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003cp\u003e（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003cp\u003e（0.584-0.806）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026gt;45.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003cp\u003e（0.37-0.72）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003cp\u003e（0.71-0.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eNC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003cp\u003e（0.610-0.828）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026gt;45.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003cp\u003e（0.55-0.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003cp\u003e（0.57-0.75）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eLEMON score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003cp\u003e（0.594-0.805）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026gt;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003cp\u003e（0.68-0.94）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003cp\u003e（0.42-0.61）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eskin-to-tongue thickness (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003cp\u003e（0.672-0.879）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026gt;7.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003cp\u003e（0.55-0.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003cp\u003e（0.63-0.80）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMCSA (cm\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003cp\u003e（0.597-0.846）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026gt;28.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003cp\u003e（0.48-0.81）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003cp\u003e（0.68-0.84）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003etongue width (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003cp\u003e（0.627-0.846）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026gt;5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003cp\u003e（0.55-0.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003cp\u003e（0.56-0.74）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eTV (cm\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003cp\u003e（0.659-0.866）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026gt;153.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003cp\u003e（0.41-0.75）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e（0.75-0.89）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eDSE (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003cp\u003e（0.678-0.869）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026gt;2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003cp\u003e（0.48-0.81）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003cp\u003e（0.70-0.86）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003ePre-E (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003cp\u003e（0.605-0.815）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026gt;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003cp\u003e（0.44-0.78）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e（0.66-0.83）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: ROC, receiver operating characteristic; BMI, body mass index; NC, neck circumference; LEMON, Look-Evaluate-Mallampatti-Obstruction-Neck; MCSA, midsagittal cross-sectional area of the tongue; TV, tongue volume; DSE, distance from the skin to the epiglottis; Pre-E, depth of the pre-epiglottic space; AUC, area under the curve.\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":"journal-of-clinical-monitoring-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Clinical Monitoring and Computing](https://www.springer.com/journal/10877)","snPcode":"10877","submissionUrl":"https://submission.nature.com/new-submission/10877/3","title":"Journal of Clinical Monitoring and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Airway management, Ultrasonography, Obesity, Intubation, intratracheal","lastPublishedDoi":"10.21203/rs.3.rs-7131036/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7131036/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBACKGROUNDː This study aimed to assess the effectiveness and validity of ultrasonographic measurements in predicting difficult videolaryngoscopic intubation in patients with obesity.\u003c/p\u003e\u003cp\u003eMETHODSː This observational prospective study included 140 patients with obesity who were scheduled for elective laparoscopic bariatric surgery under general anesthesia. Following tracheal intubation during anesthesia induction, patients were classified into either the non-difficult or difficult intubation group based on the Videolaryngoscopic Intubation and Difficult Airway Classification scale. Clinical and ultrasonographic airway parameters were recorded during the pre-anesthetic evaluation. Receiver operating characteristic curves were generated to assess the diagnostic performance of the airway measurements.\u003c/p\u003e\u003cp\u003eRESULTSː Of the 140 enrolled patients, 128 were analysed, with 101 classified as non-difficult and 27 as difficult intubations. Skin-to-tongue thickness and the distance from the skin to the epiglottis (DSE) were the two most reliable predictors of difficult intubation, with area under the curve (AUC) values of 0.776 [95% confidence interval (CI): 0.672\u0026ndash;0.879] and 0.774 (95% CI: 0.678\u0026ndash;0.869), respectively. The LEMON (Look-Evaluate-Mallampati-Obstruction-Neck mobility) score, skin-to-tongue thickness, and DSE were identified as independent risk factors for predicting difficult intubation. When these three parameters were combined, predictive performance improved, with an AUC of 0.845 (95% CI: 0.760\u0026ndash;0.929).\u003c/p\u003e\u003cp\u003eCONCLUSIONSː The combination of the LEMON score, skin-to-tongue thickness, and DSE demonstrated superior predictive accuracy compared to any single parameter for identifying difficult videolaryngoscopic intubation in obese patients.\u003c/p\u003e","manuscriptTitle":"Predicting Difficult Videolaryngoscopic Intubation in Patients with Obesity Using Ultrasound: An Observational, Prospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 06:13:12","doi":"10.21203/rs.3.rs-7131036/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-09T17:51:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T09:50:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147103527377267461928458635339020550121","date":"2025-11-09T09:17:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T08:19:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305965621098818282434564120356285782236","date":"2025-08-22T08:17:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-16T14:57:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T06:35:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-17T06:34:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Clinical Monitoring and Computing","date":"2025-07-15T13:18:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-clinical-monitoring-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Clinical Monitoring and Computing](https://www.springer.com/journal/10877)","snPcode":"10877","submissionUrl":"https://submission.nature.com/new-submission/10877/3","title":"Journal of Clinical Monitoring and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"34cd44fa-790b-49f2-a6a7-7c8b6858dd5d","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T11:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 06:13:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7131036","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7131036","identity":"rs-7131036","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}