Association between upper airway computed tomography measurement parameters and mask ventilation in patients undergoing oral and maxillofacial surgery

Introduction

For both anesthesiologists and maxillofacial surgeons, airway management is one of the greatest challenges in the perioperative period, potentially leading to severe complications or even death (1). Over 50% of severe anesthesia-related complications in the perioperative period are caused by improper airway management (2-4). The overall incidence of difficult airways is around 5.8%, while the incidence of difficult airways in oral and maxillofacial surgery can be as high as 37–53% (5). Difficult mask ventilation (DMV) is one of the most dangerous conditions in difficult airway situations, with the overall incidence varying significantly across different studies, ranging from 0.4% to 38.9% (6). Several risk factors associated with DMV have been identified in previous research, such as advanced age, obesity, edentulism, beard growth, history of snoring, Mallampati class III–IV, and shorter thyromental distance (6-9). However, predictive models for DMV based on these factors remain insufficiently accurate (10-12), with many instances of mask ventilation difficulty still being unpredictable (13,14). This suggests that novel indices for assessing mask ventilation need to be developed.

In recent years, the correlation of ultrasound parameters, acoustic parameters, and three-dimensional (3D) facial images with DMV has been examined, providing new avenues for the prediction of DMV (15-18). Compared to ultrasound, computed tomography (CT) or magnetic resonance imaging (MRI) provides greater advantages in observing and measuring upper airway anatomical structure data (19), but MRI is not widely applied due to its high cost. CT and 3D CT reconstruction have often been used in studies on difficult intubation (20-22); however, there is little research on the correlation of CT parameters with mask ventilation. Patients undergoing oral and maxillofacial surgery typically receive preoperative craniofacial CT scans, and reconstructing and measuring the airway CT images of these patients may provide a more objective basis for mask ventilation assessment. Therefore, we conducted a single-center retrospective cohort study to assess the correlation between CT imaging measurement parameters and DMV in patients undergoing oral and maxillofacial surgery. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1643/rc).

Methods

Patients

A single-center retrospective cohort study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Ethics Committee of Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine (No. 2017-362-T264; Clinical Trial Registration No. ChiCTR-DDD-17013076). Informed consent was obtained from all patients. The cohort included patients who underwent oral and maxillofacial surgery under tracheal intubation with general anesthesia at Shanghai Ninth People’s Hospital, affiliated with Shanghai Jiao Tong University School of Medicine, from October 2018 to October 2021. All enrolled patients were over 18 years of age, had an American Society of Anesthesiologists (ASA) physical status classification of I–III, and had undergone preoperative CT scans of the head and neck. Cases with contraindications for mask ventilation (i.e., planned awake intubation) or more than 20% missing data were excluded from the study.

Primary outcome

The primary outcome of the study was DMV, which was diagnosed according to the criteria outlined by Langeron et al. (23) as follows: the inability to achieve adequate ventilation with a single hand holding the mask, thus requiring the placement of an oral or nasal airway and then the holding of the mask with a single hand to achieve adequate ventilation; the need to use both hands to lift the lower jaw and tightly hold the mask with simultaneous opening of the anesthesia machine’s ventilator to achieve adequate ventilation; or the inability to achieve adequate ventilation with the above methods and the need for two-person-assisted ventilation to maintain SpO₂ at or above 90%.

Measurement parameters

Demographic and medical history data included gender, age, body mass index (BMI), history of snoring, and history of neck radiation. Traditional airway assessment parameters confirmed from previous studies included mouth opening, thyromental distance, neck circumference, neck mobility, Mallampati score (24,25), and lip bite test (26).

CT measurement parameters were the primary indicators in our study. All CT images were stored on compact discs (CDs) in Digital Imaging and Communications in Medicine (DICOM) format. The orientation module in the Dolphin 11.8 3D imaging-processing system (Patterson Companies, St. Paul, MN, USA) was used to correct the head position. The left and right infraorbital points and left ear point were marked on the image to form the Frankfort horizontal plane (FH plane), with the FH plane parallel to the ground and the midsagittal plane perpendicular to the ground. The airway module in the software was used for 3D airway reconstruction. The data of the midsagittal plane were measured and recorded with the software’s built-in tools.

In this study, 33 anatomical landmarks were identified on the midsagittal plane based on images adjusted to the appropriate window width and level settings (Table S1 and Figure S1), covering a region extending from the nasopharyngeal roof to the glottic area. According to these landmarks, 21 measurement lines were established (Table 1 and Figure 1) to characterize morphological features of structures, including the uvula, tongue body, epiglottis, cricoid cartilage, hyoid bone, and thyrohyoid membrane. All measurements were conducted by two anesthesiologists with more than 5 years of experience. Both had extensive experience in managing anesthesia for oral and maxillofacial surgeries and conducting research on difficult airways, and both were proficient in interpreting CT imaging of the airway and surrounding structures. The two anesthesiologists worked independently, and the interrater agreement was assessed via the intraclass correlation coefficient (ICC). The final recorded results represent the average of their measurements.

Figure 1 Measurement reference lines. From Wang et al. [2025] (27). Copyright 2025, with permission from Elsevier (License No. 6138160976304). dAEH, depth of the midpoint of the epiglottis; dAP, anteroposterior diameter of the neck; dAT, depth of the posterior wall of the cricoid cartilage; dBEH, depth of the lower edge of the epiglottis; dH, distance from the mentum to the hyoid bone; dME, distance between the thyrohyoid membrane and the tip of the epiglottis; dPT, distance from the mid-mentum to the posterior edge of the tongue; dSE, distance between the skin and the tip of the epiglottis; dSV, distance between the skin and the root of the epiglottis; dT, distance between the thyroid cartilage and the hyoid bone; EpB_Ph, distance between the base of the epiglottic vallecula and the posterior pharyngeal wall; LE, length of the epiglottis; LT, length of the tongue body; LU, uvula length; Snp_Nph, distance between the posterior nasal spine and the nasopharynx; tSR, thickness of the submental region; TT, thickness of the tongue body; tTC, thickness of the pre-thyroid cartilage fat at the vocal cord level; TU, thickness of the uvula; U_Ph, distance between the uvula and the pharyngeal wall.

Data and statistical analysis

Baseline data are represented with divisions into easy mask ventilation (EMV) and DMV groups. Continuous variables that were normally distributed are described as the mean ± standard deviation and were analyzed for group differences via the t-test. Continuous variables that did not conform to a normal distribution are described as the median with interquartile range and were compared between groups via the Mann-Whitney test. Categorical variables are described as the frequency (percentage) and were compared between groups via the Fisher’s exact test or Chi-squared test. Before data analysis, missing values were handled using multiple imputation methods. Subsequently, binary logistic regression was used to test whether each CT measurement parameter was associated with DMV, with progressive adjustments being made for demographic, medical history parameters, and traditional airway assessment parameters. Finally, generalized linear models were applied for possible subgroup analyses. All statistical analyses were conducted with R v.4.2.0 (The R Foundation for Statistical Computing, Vienna, Austria), and P<0.05 was considered statistically significant.

Due to the retrospective design of study, we enrolled all eligible patients during the study period to maximize the sample size. A post hoc power analysis was subsequently performed on the final cohort. In the final multivariable binary logistic regression model, we included six covariates—age, gender, BMI, history of snoring, Mallampati score, and neck circumference—resulting in a total of seven independent variables in the model. Based on the estimation that the events per variable for each independent variable should be at least 10, the study was deemed to require at least 70 cases of EMV and 70 cases of DMV to ensure the robustness of the results.

Results

A total of 457 patients were enrolled in this study. After the exclusion of 6 patients who underwent awake tracheal intubation and 2 patients with missing data exceeding 20%, 449 patients were ultimately included in the analysis (Figure 2).

Figure 2 Patient enrollment flowchart.

Among the 449 patients, 78 cases of DMV were observed, representing an incidence rate of 17.3%. The baseline characteristics of the groups are presented in Table 2. The EMV and DMV groups differed significantly in age (P<0.001), gender (P<0.001), BMI (P<0.001), history of snoring (P<0.001), Mallampati score (P=0.041), and neck circumference (P<0.001) but not in thyromental distance (P=0.281), lip bite test (P=0.077), or mouth opening (P=0.260).

Interrater reliability analysis for all measurements demonstrated excellent agreement, with ICC values exceeding 0.75 for all 21 CT parameters and exceeding 0.90 for several measures (Table S2). Following Bonferroni correction for multiple comparisons, no statistically significant differences between the two groups were observed in the distance between the thyroid cartilage and the hyoid bone (dT), distance between the skin and the root of the epiglottis (dSV), thickness of the tongue body (TT), distance between the uvula and the pharyngeal wall (U_Ph), or distance between the posterior nasal spine and the nasopharynx (Snp_Nph) (P≥0.05; Table 3). All other measured parameters showed significant differences between the two groups (P<0.05; Table 3).

In the binary logistic regression model, we sequentially adjusted for demographic parameters (age and gender), medical history parameters (BMI and history of snoring), and traditional airway assessment parameters (Mallampati score and neck circumference). After adjustments were made for age and gender, distance from the mid-mentum to the posterior edge of the tongue (dPT), thickness of the submental region (tSR), distance from the mentum to the hyoid bone (dH), depth of the midpoint of the epiglottis (dAEH), depth of the posterior wall of the cricoid cartilage (dAT), thickness of the prethyroid cartilage fat at the vocal cord level (tTC), anteroposterior diameter of the neck (dAP), distance between the skin and the tip of the epiglottis (dSE), length of the tongue body (LT), TT, and uvula length (LU) were significantly associated with DMV (P<0.05; Table S3, Model 1). Further adjustment for BMI and history of snoring revealed that only dAP and LU remained significantly associated with DMV (P<0.05; Table S3, Model 2). In the final adjusted model, dAP [odds ratio (OR), 1.08; 95% confidence interval (CI): 1.03–1.13; P=0.003; Table 4] and LU (OR 1.10; 95% CI: 1.03–1.18; P=0.008; Table 4) maintained a significant positive association with DMV, while Snp_Nph showed a significant negative association with DMV (OR 0.92; 95% CI: 0.85–0.99; P=0.027; Table 4).

We conducted subgroup analyses for dAP, LU, and Snp_Nph based on age, gender, BMI, history of snoring, Mallampati score, and neck circumference and compared the differences between the groups. Age was divided into two groups: <45 and ≥45 years. BMI was divided into two groups: <24 and ≥24 kg/m². Mallampati score was divided into two groups: class I–II and III–IV. Neck circumference was divided into two groups based on the median: < median and ≥ median. Figure 3 shows the results of the subgroup analyses. The association between LU and DMV appeared more robust in patients with a history of snoring (interaction P=0.039) or Mallampati class I–II grade (interaction P=0.038). Similarly, the association between Snp_Nph and DMV was stronger in patients with a BMI of <24 kg/m² (interaction P=0.050).

Figure 3 Forest plot of subgroup analysis. BMI, body mass index; dAP, anteroposterior diameter of the neck; LU, uvula length; Snp_Nph, distance between the posterior nasal spine and the nasopharynx.

Discussion

The incidence of DMV in patients undergoing oral and maxillofacial surgery in this study was 17.3%, higher than the incidence reported in previous studies using the same diagnostic criteria for general adult patients (23,28,29). This may be because the lesions in oral and maxillofacial surgery are very close to the airway, which may lead to more mask ventilation issues, and suggests that greater vigilance regarding the occurrence of DMV is warranted for patients undergoing oral and maxillofacial surgery. Our study examined the correlation between measurement parameters in oral and maxillofacial CT and DMV. The principal findings of this study are that the dAP, LU, and Snp_Nph measured on sagittal CT are significantly associated with DMV.

A novel finding of this study is that a longer anteroposterior diameter of the neck indicates a higher likelihood of DMV. An excessively long dAP usually implies excessive fat accumulation in the neck, which is commonly considered one of the important causes of DMV (30,31). Fat accumulation in the posterior neck can easily lead to restricted neck movement, thereby causing DMV (32), while fat accumulation in the anterior neck and beside the pharynx can compress the airway, leading to pharyngeal stenosis or even upper airway collapse (33). Fat accumulation in the neck is also closely related to neck circumference and obesity, and so these two may be the main confounding factors in the correlation analysis between dAP and DMV. However, in logistics regression model, after adjustments were made for confounding factors including neck circumference and BMI, the correlation between dAP and DMV remained significant, indicating that dAP is an independent risk factor for DMV and is not affected by neck circumference or BMI. This supports the observation that the side may be more important than the front in the assessment of patients’ neck conditions in clinical practice.

We further found that after adjustments were made for relevant confounding factors, there was a significant correlation between LU and DMV, which indicated that the morphology of the uvula was an independent risk factor for DMV. Previous studies have reported cases in which an elongated and slender uvula led to respiratory distress (34,35). Additionally, it has been observed that patients with obstructive sleep apnea have a longer uvula (19,36,37), suggesting that an elongated uvula is more likely to cause obstruction of the upper airway compared to a shorter uvula. These findings align with our study’s conclusion that during the induction of anesthesia, an elongated uvula increases the risk of DMV. Preoperative measurement of the maxillofacial CT can more accurately determine the length of the uvula, thereby facilitating a better assessment of the risk of DMV in patients.

Another significant parameter we identified is Snp_Nph, which is the distance from the posterior nasal spine (PNS) to the top of the nasopharynx. The PNS point is an important landmark in cephalometric measurements and also holds significant importance in the assessment of the upper airway (38,39). This distance reflects the degree of nasal cavity narrowing to a certain extent. In the univariate analysis, the correlation between Snp_Nph and DMV was not significant. However, as we progressively adjusted for confounding factors, a significant negative correlation between Snp_Nph and DMV emerged. This indicates that nasal ventilation is also an important pathway for mask ventilation, but the correlation between nasopharyngeal narrowing and DMV is not applicable to all populations. Therefore, further subgroup analysis is necessary.

Exploratory analyses produced several hypothesis-generating observations. The correlation between LU and DMV appeared stronger in the subgroup of patients with a history of snoring or Mallampati class I–II. Similarly, the correlation between Snp_Nph and DMV was more pronounced in patients with a BMI of <24 kg/m². However, these subgroup analyses were underpowered, and the findings require confirmation in larger, dedicated studies.

This study is the first to identify several parameters related to DMV in sagittal CT measurements, but certain limitations should be addressed. First, since we employed a single-center, retrospective cohort design, certain parameters, such as dental conditions, were excluded from the analysis due to a high number of missing values. Additionally, the history of neck radiation, neck mobility, and the presence of a beard were not included in the analysis due to a low number of positive cases. Second, a recognized limitation of using preoperative CT for airway assessment is that the examination is conducted while the patient is conscious. The tone and position of the upper airway soft tissues (e.g., the tongue, uvula, and epiglottis) may change under general anesthesia and muscle relaxation, meaning our measurements might not have fully captured the dynamic airway conditions during induction. Moreover, a sample size of 449 cases did not support the construction of a stable predictive model. Therefore, in further research, a larger sample size may be needed to build a predictive model that includes CT parameters.

Conclusions

Our study identified three preoperative midsagittal CT-derived parameters—dAP, LU, and Snp_Nph—as independent risk factors for DMV in patients undergoing oral and maxillofacial surgery. For these patients, preoperative head and neck CT imaging—routinely performed for most—can be used as a valuable tool for predicting the risk of DMV. Patients identified as at high risk for DMV based on CT measurements should be managed with preparatory mask ventilation strategies, including immediate availability of oropharyngeal or nasopharyngeal airways and the presence of an experienced anesthesiologist to mitigate the risk of induction-associated hypoxemia. Future integration with artificial intelligence could potentially establish head-and-neck CT imaging as one of the most valuable tools for predicting difficult airway in patients undergoing oral and maxillofacial surgery (40).

Acknowledgments

We thank all the patients for allowing us to use their data.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1643/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1643/dss

Funding: This study was funded by the Shanghai Hospital Development Center Foundation (No. SHDC22023219) and the Science and Technology Commission of Shanghai Municipality (STCSM) (No. 23Y11908100).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1643/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine (No. 2017-362-T264) and informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Cook TM, Woodall N, Frerk CFourth National Audit Project. Major complications of airway management in the UK: results of the Fourth National Audit Project of the Royal College of Anaesthetists and the Difficult Airway Society. Part 1: anaesthesia. Br J Anaesth 2011;106:617-31. [Crossref] [PubMed]
2. Schulz CM, Burden A, Posner KL, Mincer SL, Steadman R, Wagner KJ, Domino KB. Frequency and Type of Situational Awareness Errors Contributing to Death and Brain Damage: A Closed Claims Analysis. Anesthesiology 2017;127:326-37. [Crossref] [PubMed]
3. Crosby ET, Cooper RM, Douglas MJ, Doyle DJ, Hung OR, Labrecque P, Muir H, Murphy MF, Preston RP, Rose DK, Roy L. The unanticipated difficult airway with recommendations for management. Can J Anaesth 1998;45:757-76. [Crossref] [PubMed]
4. Todd DW. A comparison of endotracheal intubation and use of the laryngeal mask airway for ambulatory oral surgery patients. J Oral Maxillofac Surg 2002;60:2-4; discussion 4-5. [Crossref] [PubMed]
5. Connelly NR, Ghandour K, Robbins L, Dunn S, Gibson C. Management of unexpected difficult airway at a teaching institution over a 7-year period. J Clin Anesth 2006;18:198-204. [Crossref] [PubMed]
6. Hung KC, Chuang MH, Kang FC, Chang YJ, Lin CM, Yu CH, Chen IW, Sun CK. Prevalence and risk factors of difficult mask ventilation: A systematic review and meta-analysis. J Clin Anesth 2023;90:111197. [Crossref] [PubMed]
7. Khan M, Siddiqui AS, Raza SA, Samad K. Incidence and Predictors of Difficult Mask Ventilation in High-Risk Adult Population Scheduled for Elective Surgery: A Prospective Observational Study. Cureus 2022;14:e22002. [Crossref] [PubMed]
8. Moon TS, Fox PE, Somasundaram A, Minhajuddin A, Gonzales MX, Pak TJ, Ogunnaike B. The influence of morbid obesity on difficult intubation and difficult mask ventilation. J Anesth 2019;33:96-102. [Crossref] [PubMed]
9. Liu Y, He Y, Wang X, Li J, Zhang Z, Zhuang X, Liu H, Li R, Liu H, Zhuang Y, Wang Q, Tang Z, Wang Y, Ma W. Advances in airway management in recent 10 years from 2013 to 2023. Anesthesiol Perioper Sci 2023;1:27.
10. Khan MN, Ahmed A. Accuracy of STOP-Bang Questionnaire in Predicting Difficult Mask Ventilation: An Observational Study. Cureus 2021;13:e15955. [Crossref] [PubMed]
11. Lundstrøm LH, Rosenstock CV, Wetterslev J, Nørskov AK. The DIFFMASK score for predicting difficult facemask ventilation: a cohort study of 46,804 patients. Anaesthesia 2019;74:1267-76. [Crossref] [PubMed]
12. Saito T, Asai T, Taguchi A, Sophia CTH, Liu W, Thinn KK, Ti LK. Prediction of failed facemask ventilation: new scoring system for difficult airway. J Anesth 2020;34:367-72. [Crossref] [PubMed]
13. Nørskov AK, Wetterslev J, Rosenstock CV, Afshari A, Astrup G, Jakobsen JC, Thomsen JL, Lundstrøm LH. Collaborators. Prediction of difficult mask ventilation using a systematic assessment of risk factors vs. existing practice - a cluster randomised clinical trial in 94,006 patients. Anaesthesia 2017;72:296-308. [Crossref] [PubMed]
14. Nørskov AK, Rosenstock CV, Wetterslev J, Astrup G, Afshari A, Lundstrøm LH. Diagnostic accuracy of anaesthesiologists' prediction of difficult airway management in daily clinical practice: a cohort study of 188 064 patients registered in the Danish Anaesthesia Database. Anaesthesia 2015;70:272-81. [Crossref] [PubMed]
15. Bianchini A, Nardozi L, Nardi E, Scuppa MF. Airways ultrasound in predicting difficult face mask ventilation. Minerva Anestesiol 2021;87:26-34. [Crossref] [PubMed]
16. Lin HY, Tzeng IS, Hsieh YL, Kao MC, Huang YC. Submental Ultrasound Is Effective in Predicting Difficult Mask Ventilation but Not in Difficult Laryngoscopy. Ultrasound Med Biol 2021;47:2243-9. [Crossref] [PubMed]
17. Cao S, Xia M, Zhou R, Wang J, Jin CY, Pei B, Zhou ZK, Qian YM, Jiang H. Voice parameters for difficult mask ventilation evaluation: an observational study. Ann Transl Med 2021;9:1740. [Crossref] [PubMed]
18. Pei B, Jin C, Cao S, Ji N, Xia M, Jiang H. Geometric morphometrics and machine learning from three-dimensional facial scans for difficult mask ventilation prediction. Front Med (Lausanne) 2023;10:1203023. [Crossref] [PubMed]
19. Whyte A, Gibson D. Imaging of adult obstructive sleep apnoea. Eur J Radiol 2018;102:176-87. [Crossref] [PubMed]
20. Naguib M, Malabarey T, AlSatli RA, Al Damegh S, Samarkandi AH. Predictive models for difficult laryngoscopy and intubation. A clinical, radiologic and three-dimensional computer imaging study. Can J Anaesth 1999;46:748-59. [Crossref] [PubMed]
21. Geng QS, Zhu YS. The feasibility of CT rebuilding three-dimensional images of upper airway to predict difficult airway. China J Oral Maxillofac Surg 2006. Available online: https://www.semanticscholar.org/paper/The-feasibility-of-CT-rebuilding-three-dimensional-Ye-sen/cde208cf338f7608f025a12c6395945913db28e9
22. Ji C, Ni Q, Chen W. Diagnostic accuracy of radiology (CT, X-ray, US) for predicting difficult intubation in adults: A meta-analysis. J Clin Anesth 2018;45:79-87. [Crossref] [PubMed]
23. Langeron O, Masso E, Huraux C, Guggiari M, Bianchi A, Coriat P, Riou B. Prediction of difficult mask ventilation. Anesthesiology 2000;92:1229-36. [Crossref] [PubMed]
24. Mallampati SR, Gatt SP, Gugino LD, Desai SP, Waraksa B, Freiberger D, Liu PL. A clinical sign to predict difficult tracheal intubation: a prospective study. Can Anaesth Soc J 1985;32:429-34. [Crossref] [PubMed]
25. Lee A, Fan LT, Gin T, Karmakar MK, Ngan Kee WD. A systematic review (meta-analysis) of the accuracy of the Mallampati tests to predict the difficult airway. Anesth Analg 2006;102:1867-78. [Crossref] [PubMed]
26. Faramarzi E, Soleimanpour H, Khan ZH, Mahmoodpoor A, Sanaie S. Upper lip bite test for prediction of difficult airway: A systematic review. Pak J Med Sci 2018;34:1019-23. [Crossref] [PubMed]
27. Wang J, Zhao P, Zhou R, Yan J, Jiang H. Computed Tomography-Based Predictors of Difficult Intubation in Oral and Maxillofacial Surgery Patients. J Oral Maxillofac Surg 2025;83:937-49. [Crossref] [PubMed]
28. Yildiz TS, Solak M, Toker K. The incidence and risk factors of difficult mask ventilation. J Anesth 2005;19:7-11. [Crossref] [PubMed]
29. Shah PN, Sundaram V. Incidence and predictors of difficult mask ventilation and intubation. J Anaesthesiol Clin Pharmacol 2012;28:451-5. [Crossref] [PubMed]
30. Kristensen MS. Airway management and morbid obesity. Eur J Anaesthesiol 2010;27:923-7. [Crossref] [PubMed]
31. Selvi A, Ozayar E, Turksal E, Kurtay A, Kucuk O. Can chin–nape circumference and the ratio of neck circumference to chin–nape circumference predict difficult mask ventilation or difficult intubation in obese patients? Medicine (Baltimore) 2023;102:e36614. [Crossref] [PubMed]
32. Mashour GA, Stallmer ML, Kheterpal S, Shanks A. Predictors of difficult intubation in patients with cervical spine limitations. J Neurosurg Anesthesiol 2008;20:110-5. [Crossref] [PubMed]
33. Anderson MR, Shashaty MGS. Impact of Obesity in Critical Illness. Chest 2021;160:2135-45. [Crossref] [PubMed]
34. Shott SR, Cunningham MJ. Apnea and the elongated uvula. Int J Pediatr Otorhinolaryngol 1992;24:183-9. [Crossref] [PubMed]
35. Paliwal R, Patel S, Patel P, Soni H. Elongated uvula and diagnostic utility of spirometry in upper airway obstruction. Lung India 2010;27:30-2. [Crossref] [PubMed]
36. Yucel A, Unlu M, Haktanir A, Acar M, Fidan F. Evaluation of the upper airway cross-sectional area changes in different degrees of severity of obstructive sleep apnea syndrome: cephalometric and dynamic CT study. AJNR Am J Neuroradiol 2005;26:2624-9.
37. Araz O, Karaman A, Esdur V, Ucar EY, Subasi ID, Alper F, Akgun M. The association of OSAS and uvula: the role of MRI in this egg-chicken problem in OSAS. Sleep Breath 2020;24:465-70. [Crossref] [PubMed]
38. Fastuca R, Zecca PA, Caprioglio A. Role of mandibular displacement and airway size in improving breathing after rapid maxillary expansion. Prog Orthod 2014;15:40. [Crossref] [PubMed]
39. Meisgeier A, Dürrschnabel F, Pienkohs S, Weiser A, Neff A. Cephalometric Screening Assessment for Superior Airway Space Narrowing—Added Value of Three-Dimensional Imaging. J Clin Med 2024;13:2685. [Crossref] [PubMed]
40. Zhu Y, Liu X, Li Y, Yi B. The applications and prospects of big data in perioperative anesthetic management. Anesthesiol Perioper Sci 2024;2:30.