Prevalence and bedside predictors of difficult direct laryngoscopy in a Cambodian tertiary center: a retrospective cohort study of 3,080 adult elective surgeries

Prevalence and bedside predictors of difficult direct laryngoscopy in a Cambodian tertiary center: a retrospective cohort study of 3,080 adult elective surgeries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence and bedside predictors of difficult direct laryngoscopy in a Cambodian tertiary center: a retrospective cohort study of 3,080 adult elective surgeries NASIN PA, LEABHENG BUNLY, VIBOPHA SREY, SALY SAINT, MAKARA CHIN, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8750154/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2026 Read the published version in BMC Anesthesiology → Version 1 posted 16 You are reading this latest preprint version Abstract Background Failure to anticipate difficult direct laryngoscopy (DDL) leads to catastrophic airway events. While Western algorithms exist, evidence from Southeast Asia—particularly in settings with high volumes of head-and-neck pathology—is limited. We investigated DDL prevalence and validated a simplified cumulative risk score in a Cambodian tertiary center. Methods We conducted a retrospective cohort study of 3,080 adults undergoing elective surgery with planned Macintosh laryngoscopy (January–June 2023) at Preah Ang Duong Hospital, Phnom Penh. DDL was defined as Cormack–Lehane grade III/IV or ≥ 3 attempts. Seven bedside predictors were analyzed using multivariable logistic regression. A composite risk score (range 0–3) was derived from the strongest independent predictors. Results DDL prevalence was 9.0% (278/3,080), rising to 13.5% in maxillofacial and 11.3% in ENT procedures 1 . Independent predictors included Mallampati class III–IV (Adjusted Odds Ratio [AOR] 4.14), limited neck mobility (AOR 2.18), Thyromental Distance (TMD) ≤ 6.5 cm (AOR 1.95), and obesity (BMI ≥ 27.5 kg/m²; AOR 1.86) 2 . The Upper Lip Bite Test was not predictive (p = 0.21) 3 . A simplified composite score (Mallampati + BMI + TMD) demonstrated superior discrimination (AUC 0.76) compared to single predictors. At a cutoff of ≥ 1, the score yielded a sensitivity of 71% and specificity of 87% Conclusions DDL affects nearly 1 in 11 elective surgical patients in this cohort, driven by a complex case-mix. A simple, three-point composite score offers a zero-cost tool to enhance preoperative risk stratification in resource-limited settings where advanced airway equipment may be scarce. Airway management Intubation Intratracheal Laryngoscopy Mallampati test Asian Continental Ancestry Group Cambodia Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Securing the airway is the most time-critical step in anesthetic management. Failure can precipitate hypoxemia, aspiration, brain injury, or death within minutes, making difficult direct laryngoscopy (DDL)—defined here as Cormack–Lehane grade III/IV or the need for ≥ 3 laryngoscopic attempts—a major patient-safety concern [ 1 ]. Unexpected difficulties during endotracheal intubation remain a primary concern in general anesthesia and can lead to catastrophic outcomes [ 2 – 4 ]. The prevalence of DDL varies widely (1.5–18%) across studies and settings [ 5 ]. For example, the DIFFICAIR trial reported a 1.86% prevalence of unanticipated difficult intubation, whereas studies from Ethiopia and India reported 12.2% and 2.6–2.9%, respectively—underscoring the influence of patient demographics and practice patterns [ 5 – 7 ]. Since no single predictor reliably identifies all DDL cases, combining anatomical, physiological, and demographic factors is essential to improve prediction accuracy [ 9 , 10 ]. In some cohorts, use of the Laryngoscopic Exam Test (LET) yielded a 6.1% prevalence, further highlighting heterogeneity and the need for better tools [ 8 ]. Preoperative airway assessment allows tailored strategies and equipment selection. Common bedside tools include the Mallampati classification, thyromental distance (TMD), neck mobility, Upper Lip Bite Test (ULBT), and neck circumference (NC). However, their sensitivity is often limited; while Mallampati and ULBT can show high specificity (up to 92%), inadequate sensitivity contributes to missed cases [ 9 – 11 ]. Notably, one large study reported that 93% of difficult intubations were not predicted by routine preoperative assessment [ 3 ]. Multivariate and ratio-based models—such as the neck-circumference-to-thyromental-distance (NC/TMD) ratio—have shown promise for improving prediction, including in obese and non-obese populations [ 9 , 12 ]. Compared with Western literature, research on difficult airway prediction in Southeast Asia is limited. To date, no study has evaluated combined bedside predictors in a Cambodian cohort, supporting the need for context-specific risk assessment [ 9 , 12 ]. This study was conducted at Preah Ang Duong Hospital, a tertiary center with a high volume of otolaryngology (ENT) and maxillofacial procedures, settings inherently associated with increased airway difficulty due to anatomical distortion. Within the eligible study cohort (January–June 2023), ENT (n = 794; 25.78%) and maxillofacial (n = 798; 25.90%) surgeries comprised over half (51.68%) of procedures. Surgeries with anatomical alteration—such as large goiters (increasing neck circumference) and extensive facial trauma—are particularly prone to difficult intubation; increased neck circumference correlates with difficult intubation, and LeFort II fractures alone account for 57% of difficult intubations in maxillofacial trauma populations [ 17 – 19 ]. Objectives Accordingly, this retrospective cohort study aims to: (i) quantify the prevalence of DDL in adult elective surgical patients; (ii) identify independent bedside predictors—including BMI, Mallampati class, TMD, ULBT, neck mobility, NC, and inter-incisor gap (IIG); and (iii) determine whether combining these predictors improves discrimination beyond individual tests, to refine preoperative risk stratification, inform institutional airway algorithms, and reduce perioperative complications in resource-constrained Southeast Asian settings. METHODS Study Design and Setting We conducted a retrospective cohort study analyzing data from 1 January to 30 June 2023 at Preah Ang Duong Hospital, Phnom Penh, Cambodia. The hospital serves as a national referral center for otorhinolaryngology and maxillofacial surgery. The study protocol was approved by the institutional ethics committee, which waived the requirement for informed consent due to the retrospective analysis of de-identified routine clinical data. Participants : The cohort included consecutive adult patients (≥ 18 years) scheduled for elective surgery under general anesthesia requiring tracheal intubation via planned Macintosh direct laryngoscopy. Exclusion Criteria : Of 5,646 eligible patients scheduled for direct laryngoscopy, 2,342 were excluded due to ineligibility (pediatric, emergency, or obstetric cases). A further 224 patients (7.3%) were excluded due to incomplete documentation of key airway predictors. The final analysis included 3,080 adult elective patients. Data Collection and Predictor Definitions Preoperative airway assessments were extracted from standardized anesthesia records. We analyzed seven bedside predictors using a priori high-risk cutoffs tailored to the population where applicable Mallampati Class: High risk if Class III or IV (modified classification). Body Mass Index (BMI): High risk if ≥ 27.5 kg/m² (WHO Asian population cutoff). Thyromental Distance (TMD): High risk if ≤ 6.5 cm. Inter-Incisor Gap (IIG): High risk if ≤ 3 cm. Neck Mobility: High risk if extension is < 80°. Neck Circumference (NC): High risk if ≥ 40 cm. Upper Lip Bite Test (ULBT): High risk if Grade III (inability to bite the upper lip). Outcome Measures Outcome Measures The primary outcome was Difficult Direct Laryngoscopy (DDL), defined as either: A modified Cormack–Lehane (C–L) glottic view of Grade III or IV; OR The requirement for three or more (≥) laryngoscopic attempts by the attending anesthesiologist. Statistical Analysis Data were analyzed using STATA (or SPSS V30) and Epi-Info. Continuous variables were summarized as mean ± SD and categorical variables as frequencies/percentages. Model Development : Univariate associations were tested using Chi-square or Fisher’s exact tests. Variables with p < 0.05 were entered into a multivariable logistic regression model to identify independent predictors. Composite Score Construction : The composite score (0–3) was derived by assigning one point for each independent predictor: Mallampati class III/IV, BMI ≥ 27.5 kg/m², and TMD ≤ 6.5 cm. Receiver Operating Characteristic (ROC) analysis determined the optimal decision threshold. A score of ≥ 1 was defined as 'High Risk,' prioritizing sensitivity to ensure difficult airways were not missed in this resource-limited context. Model Performance : Discrimination was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC). The performance of the composite score was compared against individual predictors. Calibration was evaluated using the Hosmer–Lemeshow goodness-of-fit test. A two-sided p-value < 0.05 was considered statistically significant. RESULTS Demographic and Surgical Characteristics A total of 3,080 consecutive adult patients underwent elective surgery with planned direct laryngoscopy. The mean age was 53.2 ± 20.7 years, with a balanced sex distribution (49.3% male). Using the Asian-specific BMI cutoff (≥ 27.5 kg/m²), 25.3% (n = 780) of the cohort was classified as obese. The case-mix was predominantly head-and-neck focused, with maxillofacial (25.9%) and ENT (25.8%) surgeries accounting for over half of the procedures. Prevalence of Difficult Direct Laryngoscopy The overall prevalence of DDL was 9.0% (n = 278; 95% CI: 8.0–10.0%). Analysis by surgical specialty revealed significant heterogeneity; DDL rates were highest in maxillofacial (13.5%) and ENT (11.3%) procedures, compared to gynecology (3.2%). Predictors of Difficulty In multivariable logistic regression, six factors emerged as independent predictors of DDL (Table 1). Mallampati Class III/IV was the strongest predictor (Adjusted Odds Ratio [AOR] 4.14; 95% CI: 3.03–5.64). Restricted Neck Mobility (< 80° extension) doubled the odds of difficulty (AOR 2.18; 95% CI: 1.67–2.82). Thyromental Distance (TMD) ≤ 6.5 cm (AOR 1.95) and Obesity (BMI ≥ 27.5 kg/m²; AOR 1.86) were also significant. The Upper Lip Bite Test (ULBT) was not an independent predictor in this cohort (p = 0.21). Performance of the Composite Score The simplified composite score (summing Mallampati, BMI, and TMD; range 0–3) demonstrated superior discrimination compared to any single predictor. The composite model achieved an AUC of 0.76 (95% CI: 0.73–0.79). At the optimal cutoff, the model provided a sensitivity of 71%, a specificity of 87%, and an overall accuracy of 85%. This outperformed the Mallampati test alone (AUC 0.65) and BMI alone (AUC 0.70). Table I: Bedside airway tests and association with DDL (univariate) Predictor (high-risk definition) DDL prevalence (high-risk) OR 95% CI P Mallampati III/IV 13.37% 4.03 2.95–5.51 < 0.001 Neck mobility < 80° 15.58% 2.34 1.66–2.73 < 0.001 Neck circumference ≥ 40 cm 12.34% 2.73 2.08–3.58 < 0.001 Inter-incisor gap ≤ 3 cm 14.92% 2.66 2.01–3.51 0.031 Thyromental distance ≤ 6.5 cm 11.04% 1.94 1.47–2.56 < 0.001 Upper Lip Bite Test (Grade III) was not significant (P = 0.204). Study flowchart detailing patient selection and the composition of ‘study’ and ‘direct laryngoscopy’ groups. Reasons for exclusion of various patient attendances are provided Multivariable analysis All seven predictors (including ULBT) entered a logistic model. Model fit was acceptable (Hosmer–Lemeshow p = 0.08972). Discrimination was AUC 0.78 (fair) ( Fig. 2 ) . Multicollinearity was low (all VIF < 2). 5.4.1 Independent predictors Six variables independently predicted DDL: Mallampati III/IV: AOR 4.139 (95% CI 3.032–5.644), p < 0.001 Neck mobility < 80°: AOR 2.181 (95% CI 1.671–2.822), p < 0.001 Thyromental distance ≤ 6.5 cm: AOR 1.954 (95% CI 1.468–2.566), p < 0.001 BMI ≥ 27.5 kg/m²: AOR 1.86 (95% CI 1.23–2.81), p = 0.003 Neck circumference ≥ 40 cm: AOR 1.391 (95% CI 1.074–1.802), p = 0.012 Inter-incisor gap ≤ 3 cm: AOR 1.375 (95% CI 1.038–1.784), p = 0.022 ULBT (Grade III) remained non-significant (AOR 1.162, p = 0.211). Combined predictors We observed that combining multiple predictors enhanced predictive accuracy compared to using single tests. As demonstrated in Table II and Fig. 1 , the simple composite model combining Mallampati + BMI + TMD yielded an AUC of 0.76. This was superior to the discrimination offered by Mallampati alone (AUC 0.65), BMI alone (AUC 0.70), or TMD alone (AUC 0.57). This combined model achieved an overall accuracy of 85%, with a sensitivity of 71% and a specificity of 87%. Table II:. Performance of Individual and Combined Models for Predicting DDL. Model AUC Sensitivity (%) Specificity (%) Accuracy (%) Mallampati + BMI + TMD 0.76 71 87 85 Mallampati alone 0.65 65 80 76 BMI alone 0.70 60 70 67 TMD alone 0.57 50 65 60 DISCUSSION Overview of key findings We examined 3,080 adult elective cases requiring planned direct laryngoscopy and quantified both the prevalence of difficult direct laryngoscopy (DDL) and its independent bedside predictors in a high-volume Cambodian tertiary center. We also evaluated a simple composite model that improves preoperative risk stratification in this setting. Prevalence of DDL DDL occurred in 9.03% (278/3,080; 95% CI, 8.01–10.04%). This lies within the global range (1.5–18%) yet exceeds rates in several Western series (e.g., DIFFICAIR ~ 1.87%) [ 5 ]. The difference reflects case-mix: 51.68% of cases were ENT (25.78%) or maxillofacial (25.90%), specialties with frequent anatomic distortion and intrinsically higher airway risk. Comparisons with Germany (4.9%) and Thailand (3.2%) support this interpretation [ 21 , 23 ], while higher rates in Ethiopia (12.2%) illustrate the impact of setting and resources [ 6 ]. A meta-analysis reported a global prevalence of 5.51% [ 27 ]. Independent predictors of DDL Six bedside factors were independently associated with DDL: Mallampati III–IV: AOR 4.139 (95% CI 3.032–5.644; p < 0.001). Neck mobility < 80°: AOR 2.181 (95% CI 1.671–2.822; p < 0.001). Thyromental distance ≤ 6.5 cm: AOR 1.954 (95% CI 1.468–2.566; p < 0.001). BMI ≥ 27.5 kg/m² (Asian cutoff): AOR 1.86 (95% CI 1.23–2.81; p = 0.003). Neck circumference ≥ 40 cm: AOR 1.391 (95% CI 1.074–1.802; p = 0.012). Inter-incisor gap ≤ 3 cm: AOR 1.375 (95% CI 1.038–1.784; p = 0.022). The upper lip bite test was not significant (p = 0.211). Predictive challenges and model performance Single tools show strong associations yet modest sensitivity. Large-scale work reported that up to 93% of difficult intubations were missed when relying on single predictors [ 3 , 5 , 20 ]. Internal validation supported a multidimensional model (AUC 0.78 ; Hosmer–Lemeshow p = 0.09). Combined predictors improve accuracy Composite assessment outperformed single tests. A simple index combining Mallampati, BMI, and TMD yielded AUC 0.76, sensitivity 71%, specificity 87%, and overall accuracy 85%, consistent with the existing literature emphasizing combined approaches such as SARI/LEMON and the DIFFICAIR program [ 3 , 5 , 31 , 41 ]. Comparison with existing literature Demographics Our cohort (mean age ~ 53 years; near-equal sex distribution; mean BMI 25.3 kg/m² with 25.3% obese by Asian cut-offs) broadly aligns with regional reports, though the female proportion is higher than in some series. Neither age nor sex was an independent predictor in our final model. Surgical specialties The high proportions of ENT and maxillofacial surgery distinguish this population and elevate expected DDL risk. The observed specialty-specific rates align with reports linking thyroid enlargement, tracheal deviation, and facial fractures to difficult laryngoscopy [ 3 , 41 ]. DDL prevalence across settings The 9.03% prevalence exceeds many high-resource datasets (e.g., Denmark ~ 1.87%; Germany ~ 4.9%) but is similar to several low- and middle-income settings (e.g., Ethiopia ~ 12.2%) and higher than Thailand (~ 3.2%). Differences likely reflect case-mix and context, reinforcing the need for region-specific tools. ([ 5 ], [ 21 ], [ 6 ], [ 23 ]). Limitations Several limitations merit consideration. First, the retrospective design depended on routine documentation, inviting missing data and measurement variability (e.g., Mallampati, TMD, neck circumference). Second, this is a single-center study with a head-and-neck–heavy case mix, limiting generalizability. Third, residual confounding may persist despite adjustment (e.g., comorbidities, operator experience). These constraints support future prospective, multicenter validation. Predictive factors Findings corroborate with prior evidence: Mallampati III–IV remained the strongest predictor (AOR 4.139); specificity is high, sensitivity limited, favoring composite use[ 11 , 31 ]. TMD ≤ 6.5 cm indicates restricted mandibular space and poorer glottic view [ 20 ]. BMI ≥ 27.5 kg/m² and NC ≥ 40 cm capture soft-tissue load and landmark obscuration[ 21 ]. Limited neck mobility remains critical to align axes for direct laryngoscopy[ 7 ]. The superiority of the Mallampati + BMI + TMD index (AUC 0.76) mirrors multifactor approaches (e.g., SARI, LEMON) [ 3 , 5 , 31 , 41 ]. CONCLUSION This retrospective cohort from Preah Ang Duong Hospital quantified the burden of difficult direct laryngoscopy (DDL) and identified independent predictors in adult elective surgical patients. DDL occurred in 9.03% (95% CI 8.01–10.04), driven by a head-and-neck–heavy case-mix; maxillofacial (13.53%) and ENT (11.34%) procedures had the highest rates. Six bedside factors were independently associated with DDL: Mallampati III–IV (AOR 4.139, 95% CI 3.032–5.644), limited neck mobility < 80° (AOR 2.181, 95% CI 1.671–2.822), TMD ≤ 6.5 cm (AOR 1.954, 95% CI 1.468–2.566), BMI ≥ 27.5 kg/m² (AOR 1.86, 95% CI 1.23–2.81), NC ≥ 40 cm (AOR 1.391, 95% CI 1.074–1.802), and IIG ≤ 3 cm (AOR 1.375, 95% CI 1.038–1.784). The ULBT was not significant (p = 0.211). A simple composite (Mallampati + BMI + TMD) showed superior discrimination (AUC 0.76, 71% sensitivity, 87% specificity, 85% accuracy). Age and sex were not independent predictors. These data support routine, multifactor screening to anticipate DDL and guide preparation in resource-constrained Southeast-Asian settings. Recommendation We recommend embedding the composite screening test into routine pre-operative checklists and documentation to proactively flag high-risk patients. For those identified as high risk, conduct a focused pre-induction team briefing, prepare advanced airway devices (e.g., video laryngoscope and flexible fiberoptic scope), and ensure experienced personnel are present at induction. Optimize equipment and processes by standardizing difficult-airway carts, maintaining rapid access to video laryngoscopes and emergency cricothyrotomy sets, and performing regular readiness checks. Target high-risk subgroups—particularly maxillofacial and ENT cases—by considering adjunctive evaluation when anatomical distortion is suspected and allocating senior airway support. Strengthen training through regular simulation-based education emphasizing multifactor assessment, device proficiency, escalation algorithms, and crisis resource management. Finally, pursue prospective, multi-center validation of the Mallampati + BMI + TMD model and explore objective adjuncts such as ultrasound-based airway evaluation to further refine prediction. Declarations Ethics approval and consent to participate This study was reviewed and approved by the National Ethics Committee for Health Research (NECHR), Cambodia (Reference Number: 478) and the Preah Ang Duong Hospital Research Ethics Committee (Approval Date: 11 December 2024 ). The requirement for informed consent was waived by these committees due to the retrospective design of the study and the use of de-identified data. All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication Not applicable: the manuscript contains no individual person’s data in any form (including images). Competing Interest The authors declare that they have no competing interests. Clinical trial number not applicable. Funding This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution **PN** : conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft; **BL** : investigation, data curation, validation, writing—review & editing; **SV** : investigation, data curation, validation, writing—review & editing; **SS** : investigation, data curation, validation, writing—review & editing; **CM** : writing—review & editing; **PS** : writing—review & editing; **HMR** : writing—review & editing; **LS** : writing—review & editing. All authors read and approved the final manuscript. Acknowledgement The authors thank the anesthesia and surgical teams at Preah Ang Duong Hospital for their support and collaboration throughout data collection and manuscript preparation. Data Availability De-identified dataset, analysis code, and the data dictionary are available from the corresponding author on reasonable request, in accordance with institutional policy and ethics approval. References 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. Committee on Standards and Practice Parameters, Apfelbaum JL, Connis RT, Nickinovich DG, American Society of Anesthesiologists Task Force on Preanesthesia Evaluation, Pasternak LR, et al. Practice advisory for preanesthesia evaluation: an updated report by the American Society of Anesthesiologists Task Force on Preanesthesia Evaluation. Anesthesiology. 2012;116(3):522–38. 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(3):272–81. Epub 2014 Dec 16. Law JA, Broemling N, Cooper RM, Drolet P, Duggan LV, Griesdale DE, et al. The difficult airway with recommendations for management-part 2-the anticipated difficult airway. Can J Anaesth. 2013;60(11):1119–38. Epub 2013 Oct 17. Nørskov AK, Rosenstock CV, Wetterslev J, Lundstrøm LH. Incidence of unanticipated difficult airway using an objective airway score versus a standard clinical airway assessment: the DIFFICAIR trial - trial protocol for a cluster randomized clinical trial. Trials. 2013;14:347. Alemayehu T, Sitot M, Zemedkun A, Tesfaye S, Angasa D, Abebe F. Assessment of predictors for difficult intubation and laryngoscopy in adult elective surgical patients at Tikur Anbessa Specialized Hospital, Ethiopia: A cross-sectional study. Ann Med Surg (Lond). 2022;77:103682. Narkhede HH, Patel RD, Narkhede HR. A prospective observational study of predictors of difficult intubation in Indian patients. J Anaesthesiol Clin Pharmacol 2019 Jan-Mar;35(1):119–23. Akhlaghi M, Abedinzadeh M, Ahmadi A, Heidari Z. Predicting Difficult Laryngoscopy and Intubation With Laryngoscopic Exam Test: A New Method. Acta Med Iran. 2017;55(7):453–8. Pradeep S, Bhar Kundu S, Nivetha C. Evaluation of neck-circumference- thyromental-distance ratio as a predictor of difficult intubation: A prospective, observational study. Indian J Anaesth. 2023;67(5):445–51. Epub 2023 May 11. El-Radaideh K, Dheeb E, Shbool H, Garaibeh S, Bataineh A, Khraise W, et al. Evaluation of different airway tests to determine difficult intubation in apparently normal adult patients: undergoing surgical procedures. Patient Saf Surg. 2020;14(1):43. Shiga T, Wajima Z, Inoue T, Sakamoto A. Predicting difficult intubation in apparently normal patients: a meta-analysis of bedside screening test performance. Anesthesiology. 2005;103(2):429–37. Ahmed AM, Zaky MN, El-Mekawy NM, Ollaek MA, Sami WM, Mohamed DM. Evaluation of thyromental height test in prediction of difficult airway in obese surgical patients: An observational study. Indian J Anaesth. 2021;65(12):880–5. Epub 2021 Dec 22. Oria MS, Halimi SA, Negin F, Asady A. Predisposing Factors of Difficult Tracheal Intubation Among Adult Patients in Aliabad Teaching Hospital in Kabul, Afghanistan - A Prospective Observational Study. Int J Gen Med. 2022;15:1161–9. Han YZ, Tian Y, Xu M, Ni C, Li M, Wang J, et al. Neck circumference to inter-incisor gap ratio: a new predictor of difficult laryngoscopy in cervical spondylosis patients. BMC Anesthesiol. 2017;17(1):55. Bhiwal AK, Sharma C, Tripathi A, BK A, Choudhary V, Gupta S. Evaluation of thyromental height test as a single anatomical measure for prediction of difficult laryngoscopy: a prospective observational study. Ain-Shams J Anesthesiology. 2023;15(1). Marković D, Šurbatović M, Milisavljević D, Marjanović V, Stošić B, Stanković M. Prediction of a Difficult Airway Using the ARNE Score and Flexible Laryngoscopy in Patients with Laryngeal Pathology. Med (Kaunas). 2024;60(4):619. Olusomi BB, Aliyu SZ, Babajide AM, Sulaiman AO, Adegboyega OS, Gbenga HO, et al. Goitre-Related Factors for Predicting Difficult Intubation in Patients Scheduled for Thyroidectomy in a Resource-Challenged Health Institution in North Central Nigeria. Ethiop J Health Sci. 2018;28(2):169–76. Yang J, Trivedi A, Alvarez Z, Bhattacharyya R, Sartorato F, Gargano F, et al. Predicting Difficult Airway Intubation Based on Maxillofacial Trauma: A Retrospective Study. Cureus. 2022;14(5):e24844. Tuzuner-Oncul AM, Kucukyavuz Z. Prevalence and prediction of difficult intubation in maxillofacial surgery patients. J Oral Maxillofac Surg. 2008;66(8):1652–8. Naithani U, Gupta G, Keerti, Gupta M, Meena K, Sharma CP, et al. Predicting difficult intubation in surgical patients scheduled for general anaesthesia: a prospective study of 435 patients. J Evol Med Dent Sci. 2013;2(14):2270–86. Heinrich S, Birkholz T, Irouschek A, Ackermann A, Schmidt J. Incidences and predictors of difficult laryngoscopy in adult patients undergoing general anesthesia: a single-center analysis of 102,305 cases. J Anesth. 2013;27(6):815–21. Epub 2013 Jun 9. De Cassai A, Boscolo A, Rose K, Carron M, Navalesi P. Predictive parameters of difficult intubation in thyroid surgery: a meta-analysis. Minerva Anestesiol. 2020;86(3):317–26. Epub 2020 Jan 8. Ittichaikulthol W, Chanpradub S, Amnoundetchakorn S, Arayajarernwong N, Wongkum W. Modified Mallampati test and thyromental distance as a predictor of difficult laryngoscopy in Thai patients. J Med Assoc Thai. 2010;93(1):84–9. Selvi O, Kahraman T, Senturk O, Tulgar S, Serifsoy E, Ozer Z. Evaluation of the reliability of preoperative descriptive airway assessment tests in prediction of the Cormack-Lehane score: A prospective randomized clinical study. J Clin Anesth. 2017;36:21–6. Epub 2016 Oct 31. Klock PA Jr, Benumof JL. Definition and incidence of the difficult airway. Benumof's Airway Management. Paris: Elsevier; 2007. pp. 215–20. Kheterpal S, Martin L, Shanks AM, Tremper KK. Prediction and outcomes of impossible mask ventilation: a review of 50,000 anesthetics. Anesthesiology. 2009;110(4):891–7. Wang Z, Jin Y, Zheng Y, Chen H, Feng J, Sun J. Evaluation of preoperative difficult airway prediction methods for adult patients without obvious airway abnormalities: a systematic review and meta-analysis. BMC Anesthesiol. 2024;24(1):242. Kharrat I, Achour I, Trabelsi JJ, Trigui M, Thabet W, Mnejja M, et al. Prediction of difficulty in direct laryngoscopy. Sci Rep. 2022;12(1):10722. Roth D, Pace NL, Lee A, Hovhannisyan K, Warenits AM, Arrich J, et al. Bedside tests for predicting difficult airways: an abridged Cochrane diagnostic test accuracy systematic review. Anaesthesia. 2019;74(7):915–28. Epub 2019 Mar 6. Kheterpal S, Healy D, Aziz MF, Shanks AM, Freundlich RE, Linton F, et al. Incidence, predictors, and outcome of difficult mask ventilation combined with difficult laryngoscopy: a report from the multicenter perioperative outcomes group. Anesthesiology. 2013;119(6):1360–9. Juvin P, Lavaut E, Dupont H, Lefevre P, Demetriou M, Dumoulin JL, et al. Difficult tracheal intubation is more common in obese than in lean patients. Anesth Analg. 2003;97(2):595–600. Wang LY, Zhang KD, Zhang ZH, Zhang DX, Wang HL, Qi F. Evaluation of the reliability of the upper lip bite test and the modified mallampati test in predicting difficult intubation under direct laryngoscopy in apparently normal patients: a prospective observational clinical study. BMC Anesthesiol. 2022;22(1):314. Saghaei M, Safavi MR. Prediction of prolonged laryngoscopy. Anaesthesia. 2001;56(12):1198–201. Kheterpal S, Healy D, Aziz MF, Shanks AM, Freundlich RE, Linton F, et al. Incidence, predictors, and outcome of difficult mask ventilation combined with difficult laryngoscopy: a report from the multicenter perioperative outcomes group. Anesthesiology. 2013;119(6):1360–9. Shah JP, Patel SG, Singh B, Wong RJ, editors. Jatin Shah's Head and Neck Surgery and Oncology. Philadelphia: Elsevier; 2020. pp. 365–439. 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. Hung OR, Murphy MF, editors. Hung's Difficult and Failed Airway Management. 3rd ed. New York: McGraw-Hill Education; 2020. Zimmerman B, Chason H, Schick A, Asselin N, Lindquist D, Musica N. Assessment of the Thyromental Height Test as an Effective Airway Evaluation Tool. Ann Emerg Med. 2021;77(3):305–14. Shehabi Y, Gatt S, Buckman T, Isert P. Effect of adrenaline, fentanyl and warming of injectate on shivering following extradural analgesia in labour. Anaesth Intensive Care. 1990;18(1):31–7. Orebaugh SL. Definition, incidence, and predictors of the difficult airway. Atlas of airway management: techniques and tools. Philadelphia: Lippincott Williams & Wilkins; 2007. p. 45. Khan ZH, Mohammadi M, Rasouli MR, Farrokhnia F, Khan RH. The Diagnostic Value of the Upper Lip Bite Test Combined with Sternomental Distance, Thyromental Distance, and Interincisor Distance for Prediction of Easy Laryngoscopy and Intubation: A Prospective Study. Anesth Analgesia. 2009;109(3):822–4. Adapted from Zimmerman B, Chason H, Schick A, Asselin N, Lindquist D, Musica N. Title of the article. Ann Emerg Med. 2021;77(3):305–14. Domínguez-Pérez M, González-Dzib RDS. Correlation between Predictive Index of Difficult Intubation and Cormack. Rev Med Inst Mex Seguro Soc. 2023;61(1):15–20. Spanish. Ben-Noun L, Laor A. Relationship of neck circumference to cardiovascular risk factors. Obes Res. 2003;11(2):226–31. Wilson ME, Spiegelhalter D, Robertson JA, Lesser P. Predicting difficult intubation. Br J Anaesth. 1988;61(2):211–6. Li WX, Wang DD, Li X, Li WX, Han Y, Cai YR. Risk factors for difficult mask ventilation and difficult intubation among patients undergoing pharyngeal and laryngeal surgery. Heliyon. 2023;9(3):e14408. Adnet F, Borron SW, Racine SX, Clemessy JL, Fournier JL, Plaisance P, et al. The intubation difficulty scale (IDS): proposal and evaluation of a new score characterizing the complexity of endotracheal intubation. Anesthesiology. 1997;87(6):1290–7. Reed MJ, Dunn MJG, McKeown DW. Can an airway assessment score predict difficulty at intubation in the emergency department? Emerg Med J. 2005;22(2):99–102. Shaha AR. Difficult airway and intubation in thyroid surgery. Ann Otol Rhinol Laryngol. 2015;124(4):334–5. Amathieu R, Combes X, Abdi W, Housseini LE, Rezzoug A, Dinca A, et al. An algorithm for difficult airway management, modified for modern optical devices (Airtraq laryngoscope; LMA CTrach™): a 2-year prospective validation in patients for elective abdominal, gynecologic, and thyroid surgery. Anesthesiology. 2011;114(1):25–33. Liu DX, Ye Y, Zhu YH, Li J, He HY, Dong L, et al. Intubation of non-difficult airways using video laryngoscope versus direct laryngoscope: a randomized, parallel-group study. BMC Anesthesiol. 2019;19(1):75. Tasche KK, Dorneden AM, Swift WM, Boyd NH, Shonka DC, Pagedar NA. Airway Management in Substernal Goiter Surgery. Ann Otol Rhinol Laryngol. 2022;131(2):225–8. Epub 2021 May 25. Jayaraj AK, Siddiqui N, Abdelghany SMO, Balki M. Management of difficult and failed intubation in the general surgical population: a historical cohort study in a tertiary care centre. Can J Anaesth. 2022;69(4):427–37. English. Epub 2021 Dec 14. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2026 Read the published version in BMC Anesthesiology → Version 1 posted Editorial decision: Revision requested 25 Mar, 2026 Reviews received at journal 15 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviews received at journal 08 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor invited by journal 04 Feb, 2026 Editor assigned by journal 04 Feb, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 03 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8750154","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587767241,"identity":"0fa1d13f-2db0-4c94-bc19-1efb8e4c3415","order_by":0,"name":"NASIN PA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYJCCAwxsQJK9AUgYWJCihecASIsEsfaAtEgkgFhEaDFnP/7wcEGZjbz8zOdXN/wokGDgb+9OwKvFsifH4PCMc2mGG27nlN3sATpM4szZDXi1GBzIYTjM23Y4wUA6J+0GD1CLgUQuAS3nnz8Aa5GfeSbt5h+itNxIMABrYbjBfuw2cbbceGNwmAfklzM5bLdlDCR4CPvlfPrjzzygEGs//uzmmz82cvztvfi1IAEeAzBJrHIQYH9AiupRMApGwSgYQQAAyn1JxEO7SgQAAAAASUVORK5CYII=","orcid":"","institution":"Preah Ang Duong Hospital","correspondingAuthor":true,"prefix":"","firstName":"NASIN","middleName":"","lastName":"PA","suffix":""},{"id":587767242,"identity":"412483da-6a57-406d-a1a5-24e201818388","order_by":1,"name":"LEABHENG BUNLY","email":"","orcid":"","institution":"Preah Ang Duong Hospital","correspondingAuthor":false,"prefix":"","firstName":"LEABHENG","middleName":"","lastName":"BUNLY","suffix":""},{"id":587767243,"identity":"3f9de5f1-0249-404e-8def-d5b450d96c33","order_by":2,"name":"VIBOPHA SREY","email":"","orcid":"","institution":"Calmette Hospital","correspondingAuthor":false,"prefix":"","firstName":"VIBOPHA","middleName":"","lastName":"SREY","suffix":""},{"id":587767244,"identity":"3f51f24a-511c-4f0e-a383-e8d628c73387","order_by":3,"name":"SALY SAINT","email":"","orcid":"","institution":"University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"SALY","middleName":"","lastName":"SAINT","suffix":""},{"id":587767245,"identity":"4bd828b6-f7b2-4c77-8cca-3c0c358b12ac","order_by":4,"name":"MAKARA CHIN","email":"","orcid":"","institution":"Preah Ang Duong Hospital","correspondingAuthor":false,"prefix":"","firstName":"MAKARA","middleName":"","lastName":"CHIN","suffix":""},{"id":587767246,"identity":"7d4296af-426e-4432-b27d-e81e9c4d8c89","order_by":5,"name":"SARATH PHOEUN","email":"","orcid":"","institution":"Calmette Hospital","correspondingAuthor":false,"prefix":"","firstName":"SARATH","middleName":"","lastName":"PHOEUN","suffix":""},{"id":587767248,"identity":"93589887-fd91-4510-860e-71ae1c57efcc","order_by":6,"name":"Moni Rath HENG","email":"","orcid":"","institution":"Preah Ang Duong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Moni","middleName":"Rath","lastName":"HENG","suffix":""},{"id":587767249,"identity":"da78f165-0b63-439c-9707-2ffcca4132de","order_by":7,"name":"SOVANNARA LENG","email":"","orcid":"","institution":"Preah Ang Duong Hospital","correspondingAuthor":false,"prefix":"","firstName":"SOVANNARA","middleName":"","lastName":"LENG","suffix":""}],"badges":[],"createdAt":"2026-01-31 13:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8750154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8750154/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12871-026-03846-4","type":"published","date":"2026-04-24T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102337871,"identity":"028bcc72-8c46-4ffd-9e00-5b07677af454","added_by":"auto","created_at":"2026-02-10 16:16:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109282,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve for Combined Predictive Models\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8750154/v1/ffb4555fe0ec995462b3ccda.jpg"},{"id":102337872,"identity":"daea0da9-72aa-4fd9-9871-4ce9a707402c","added_by":"auto","created_at":"2026-02-10 16:16:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20051,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve for Overall Predictive Models\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8750154/v1/4f3fb63b93367b3273041910.jpg"},{"id":102337873,"identity":"d3c5e74e-4688-4767-8130-add398c4f9d9","added_by":"auto","created_at":"2026-02-10 16:16:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80539,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Result section.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8750154/v1/48da9fe41ebbadea300294d2.jpg"},{"id":107927765,"identity":"efd167aa-3a52-4c9c-b0b0-80ab204ab4c8","added_by":"auto","created_at":"2026-04-27 16:04:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":492301,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8750154/v1/457574db-0d85-4abf-ab2f-640dcd863bde.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and bedside predictors of difficult direct laryngoscopy in a Cambodian tertiary center: a retrospective cohort study of 3,080 adult elective surgeries","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSecuring the airway is the most time-critical step in anesthetic management. Failure can precipitate hypoxemia, aspiration, brain injury, or death within minutes, making difficult direct laryngoscopy (DDL)\u0026mdash;defined here as Cormack\u0026ndash;Lehane grade III/IV or the need for \u0026ge;\u0026thinsp;3 laryngoscopic attempts\u0026mdash;a major patient-safety concern [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Unexpected difficulties during endotracheal intubation remain a primary concern in general anesthesia and can lead to catastrophic outcomes [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prevalence of DDL varies widely (1.5\u0026ndash;18%) across studies and settings [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For example, the DIFFICAIR trial reported a 1.86% prevalence of unanticipated difficult intubation, whereas studies from Ethiopia and India reported 12.2% and 2.6\u0026ndash;2.9%, respectively\u0026mdash;underscoring the influence of patient demographics and practice patterns [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Since no single predictor reliably identifies all DDL cases, combining anatomical, physiological, and demographic factors is essential to improve prediction accuracy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In some cohorts, use of the Laryngoscopic Exam Test (LET) yielded a 6.1% prevalence, further highlighting heterogeneity and the need for better tools [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePreoperative airway assessment allows tailored strategies and equipment selection. Common bedside tools include the Mallampati classification, thyromental distance (TMD), neck mobility, Upper Lip Bite Test (ULBT), and neck circumference (NC). However, their sensitivity is often limited; while Mallampati and ULBT can show high specificity (up to 92%), inadequate sensitivity contributes to missed cases [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Notably, one large study reported that 93% of difficult intubations were not predicted by routine preoperative assessment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Multivariate and ratio-based models\u0026mdash;such as the neck-circumference-to-thyromental-distance (NC/TMD) ratio\u0026mdash;have shown promise for improving prediction, including in obese and non-obese populations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCompared with Western literature, research on difficult airway prediction in Southeast Asia is limited. To date, no study has evaluated combined bedside predictors in a Cambodian cohort, supporting the need for context-specific risk assessment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study was conducted at Preah Ang Duong Hospital, a tertiary center with a high volume of otolaryngology (ENT) and maxillofacial procedures, settings inherently associated with increased airway difficulty due to anatomical distortion. Within the eligible study cohort (January\u0026ndash;June 2023), ENT (n\u0026thinsp;=\u0026thinsp;794; 25.78%) and maxillofacial (n\u0026thinsp;=\u0026thinsp;798; 25.90%) surgeries comprised over half (51.68%) of procedures. Surgeries with anatomical alteration\u0026mdash;such as large goiters (increasing neck circumference) and extensive facial trauma\u0026mdash;are particularly prone to difficult intubation; increased neck circumference correlates with difficult intubation, and LeFort II fractures alone account for 57% of difficult intubations in maxillofacial trauma populations [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eObjectives\u003c/h3\u003e\n\u003cp\u003eAccordingly, this retrospective cohort study aims to: (i) quantify the prevalence of DDL in adult elective surgical patients; (ii) identify independent bedside predictors\u0026mdash;including BMI, Mallampati class, TMD, ULBT, neck mobility, NC, and inter-incisor gap (IIG); and (iii) determine whether combining these predictors improves discrimination beyond individual tests, to refine preoperative risk stratification, inform institutional airway algorithms, and reduce perioperative complications in resource-constrained Southeast Asian settings.\u003c/p\u003e "},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cp\u003e \u003cstrong\u003eStudy Design and Setting\u003c/strong\u003e \u003cp\u003eWe conducted a retrospective cohort study analyzing data from 1 January to 30 June 2023 at Preah Ang Duong Hospital, Phnom Penh, Cambodia. The hospital serves as a national referral center for otorhinolaryngology and maxillofacial surgery. The study protocol was approved by the institutional ethics committee, which waived the requirement for informed consent due to the retrospective analysis of de-identified routine clinical data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eParticipants\u003c/b\u003e: The cohort included consecutive adult patients (\u0026ge;\u0026thinsp;18 years) scheduled for elective surgery under general anesthesia requiring tracheal intubation via planned Macintosh direct laryngoscopy. \u003cb\u003eExclusion Criteria\u003c/b\u003e: Of 5,646 eligible patients scheduled for direct laryngoscopy, 2,342 were excluded due to ineligibility (pediatric, emergency, or obstetric cases). A further 224 patients (7.3%) were excluded due to incomplete documentation of key airway predictors. The final analysis included 3,080 adult elective patients.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData Collection and Predictor Definitions\u003c/strong\u003e \u003cp\u003ePreoperative airway assessments were extracted from standardized anesthesia records. We analyzed seven bedside predictors using a priori high-risk cutoffs tailored to the population where applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMallampati Class: High risk if Class III or IV (modified classification).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBody Mass Index (BMI): High risk if\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2; (WHO Asian population cutoff).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThyromental Distance (TMD): High risk if\u0026thinsp;\u0026le;\u0026thinsp;6.5 cm.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInter-Incisor Gap (IIG): High risk if\u0026thinsp;\u0026le;\u0026thinsp;3 cm.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNeck Mobility: High risk if extension is \u0026lt;\u0026thinsp;80\u0026deg;.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNeck Circumference (NC): High risk if\u0026thinsp;\u0026ge;\u0026thinsp;40 cm.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUpper Lip Bite Test (ULBT): High risk if Grade III (inability to bite the upper lip).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome Measures\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eOutcome Measures\u003c/div\u003e \u003cp\u003eThe primary outcome was Difficult Direct Laryngoscopy (DDL), defined as either:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA modified Cormack\u0026ndash;Lehane (C\u0026ndash;L) glottic view of Grade III or IV; OR\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe requirement for three or more (\u0026ge;) laryngoscopic attempts by the attending anesthesiologist.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using STATA (or SPSS V30) and Epi-Info. Continuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and categorical variables as frequencies/percentages.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eModel Development\u003c/b\u003e: Univariate associations were tested using Chi-square or Fisher\u0026rsquo;s exact tests. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were entered into a multivariable logistic regression model to identify independent predictors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eComposite Score Construction\u003c/b\u003e: The composite score (0\u0026ndash;3) was derived by assigning one point for each independent predictor: Mallampati class III/IV, BMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2;, and TMD\u0026thinsp;\u0026le;\u0026thinsp;6.5 cm. Receiver Operating Characteristic (ROC) analysis determined the optimal decision threshold. A score of \u0026ge;\u0026thinsp;1 was defined as 'High Risk,' prioritizing sensitivity to ensure difficult airways were not missed in this resource-limited context.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eModel Performance\u003c/b\u003e: Discrimination was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC). The performance of the composite score was compared against individual predictors. Calibration was evaluated using the Hosmer\u0026ndash;Lemeshow goodness-of-fit test. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Surgical Characteristics\u003c/h2\u003e \u003cp\u003eA total of 3,080 consecutive adult patients underwent elective surgery with planned direct laryngoscopy. The mean age was 53.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.7 years, with a balanced sex distribution (49.3% male). Using the Asian-specific BMI cutoff (\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2;), 25.3% (n\u0026thinsp;=\u0026thinsp;780) of the cohort was classified as obese. The case-mix was predominantly head-and-neck focused, with maxillofacial (25.9%) and ENT (25.8%) surgeries accounting for over half of the procedures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of Difficult Direct Laryngoscopy\u003c/h2\u003e \u003cp\u003eThe overall prevalence of DDL was 9.0% (n\u0026thinsp;=\u0026thinsp;278; 95% CI: 8.0\u0026ndash;10.0%). Analysis by surgical specialty revealed significant heterogeneity; DDL rates were highest in maxillofacial (13.5%) and ENT (11.3%) procedures, compared to gynecology (3.2%).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictors of Difficulty\u003c/h3\u003e\n\u003cp\u003eIn multivariable logistic regression, six factors emerged as independent predictors of DDL (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMallampati Class III/IV was the strongest predictor (Adjusted Odds Ratio [AOR] 4.14; 95% CI: 3.03\u0026ndash;5.64).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRestricted Neck Mobility (\u0026lt;\u0026thinsp;80\u0026deg; extension) doubled the odds of difficulty (AOR 2.18; 95% CI: 1.67\u0026ndash;2.82).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThyromental Distance (TMD)\u0026thinsp;\u0026le;\u0026thinsp;6.5 cm (AOR 1.95) and Obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2;; AOR 1.86) were also significant.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe Upper Lip Bite Test (ULBT) was not an independent predictor in this cohort (p\u0026thinsp;=\u0026thinsp;0.21).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003ePerformance of the Composite Score\u003c/h3\u003e\n\u003cp\u003eThe simplified composite score (summing Mallampati, BMI, and TMD; range 0\u0026ndash;3) demonstrated superior discrimination compared to any single predictor. The composite model achieved an AUC of 0.76 (95% CI: 0.73\u0026ndash;0.79). At the optimal cutoff, the model provided a sensitivity of 71%, a specificity of 87%, and an overall accuracy of 85%. This outperformed the Mallampati test alone (AUC 0.65) and BMI alone (AUC 0.70).\u003c/p\u003e \u003cp\u003eTable I: Bedside airway tests and association with DDL (univariate)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor (high-risk definition)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDDL prevalence (high-risk)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMallampati III/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.95\u0026ndash;5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeck mobility\u0026thinsp;\u0026lt;\u0026thinsp;80\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.66\u0026ndash;2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeck circumference\u0026thinsp;\u0026ge;\u0026thinsp;40 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.08\u0026ndash;3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInter-incisor gap\u0026thinsp;\u0026le;\u0026thinsp;3 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.01\u0026ndash;3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyromental distance\u0026thinsp;\u0026le;\u0026thinsp;6.5 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47\u0026ndash;2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUpper Lip Bite Test (Grade III) was \u003cb\u003enot significant\u003c/b\u003e (P\u0026thinsp;=\u0026thinsp;0.204).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStudy flowchart detailing patient selection and the composition of \u0026lsquo;study\u0026rsquo; and \u0026lsquo;direct laryngoscopy\u0026rsquo; groups. Reasons for exclusion of various patient attendances are provided\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable analysis\u003c/h2\u003e \u003cp\u003eAll seven predictors (including ULBT) entered a logistic model. Model fit was acceptable (Hosmer\u0026ndash;Lemeshow p\u0026thinsp;=\u0026thinsp;0.08972). Discrimination was AUC 0.78 (fair) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMulticollinearity was low (all VIF\u0026thinsp;\u0026lt;\u0026thinsp;2).\u003c/p\u003e \u003cp\u003e5.4.1 Independent predictors\u003c/p\u003e \u003cp\u003eSix variables independently predicted DDL:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMallampati III/IV: AOR 4.139 (95% CI 3.032\u0026ndash;5.644), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNeck mobility\u0026thinsp;\u0026lt;\u0026thinsp;80\u0026deg;: AOR 2.181 (95% CI 1.671\u0026ndash;2.822), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThyromental distance\u0026thinsp;\u0026le;\u0026thinsp;6.5 cm: AOR 1.954 (95% CI 1.468\u0026ndash;2.566), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2;: AOR 1.86 (95% CI 1.23\u0026ndash;2.81), p\u0026thinsp;=\u0026thinsp;0.003\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNeck circumference\u0026thinsp;\u0026ge;\u0026thinsp;40 cm: AOR 1.391 (95% CI 1.074\u0026ndash;1.802), p\u0026thinsp;=\u0026thinsp;0.012\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInter-incisor gap\u0026thinsp;\u0026le;\u0026thinsp;3 cm: AOR 1.375 (95% CI 1.038\u0026ndash;1.784), p\u0026thinsp;=\u0026thinsp;0.022\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eULBT (Grade III) remained non-significant (AOR 1.162, p\u0026thinsp;=\u0026thinsp;0.211).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCombined predictors\u003c/h2\u003e \u003cp\u003eWe observed that combining multiple predictors enhanced predictive accuracy compared to using single tests. As demonstrated in Table II and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the simple composite model combining Mallampati\u0026thinsp;+\u0026thinsp;BMI\u0026thinsp;+\u0026thinsp;TMD yielded an AUC of 0.76. This was superior to the discrimination offered by Mallampati alone (AUC 0.65), BMI alone (AUC 0.70), or TMD alone (AUC 0.57). This combined model achieved an overall accuracy of 85%, with a sensitivity of 71% and a specificity of 87%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable II:. Performance of Individual and Combined Models for Predicting DDL.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMallampati\u0026thinsp;+\u0026thinsp;BMI\u0026thinsp;+\u0026thinsp;TMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMallampati alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMD alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOverview of key findings\u003c/h2\u003e \u003cp\u003eWe examined 3,080 adult elective cases requiring planned direct laryngoscopy and quantified both the prevalence of difficult direct laryngoscopy (DDL) and its independent bedside predictors in a high-volume Cambodian tertiary center. We also evaluated a simple composite model that improves preoperative risk stratification in this setting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of DDL\u003c/h2\u003e \u003cp\u003eDDL occurred in \u003cb\u003e9.03%\u003c/b\u003e (278/3,080; 95% CI, 8.01\u0026ndash;10.04%). This lies within the global range (1.5\u0026ndash;18%) yet exceeds rates in several Western series (e.g., DIFFICAIR\u0026thinsp;~\u0026thinsp;1.87%) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The difference reflects case-mix: \u003cb\u003e51.68%\u003c/b\u003e of cases were ENT (25.78%) or maxillofacial (25.90%), specialties with frequent anatomic distortion and intrinsically higher airway risk. Comparisons with Germany (4.9%) and Thailand (3.2%) support this interpretation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], while higher rates in Ethiopia (12.2%) illustrate the impact of setting and resources [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A meta-analysis reported a global prevalence of 5.51% [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIndependent predictors of DDL\u003c/h2\u003e \u003cp\u003eSix bedside factors were independently associated with DDL:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMallampati III\u0026ndash;IV: AOR 4.139 (95% CI 3.032\u0026ndash;5.644; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNeck mobility\u0026thinsp;\u0026lt;\u0026thinsp;80\u0026deg;: AOR 2.181 (95% CI 1.671\u0026ndash;2.822; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThyromental distance\u0026thinsp;\u0026le;\u0026thinsp;6.5 cm: AOR 1.954 (95% CI 1.468\u0026ndash;2.566; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2; (Asian cutoff): AOR 1.86 (95% CI 1.23\u0026ndash;2.81; p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNeck circumference\u0026thinsp;\u0026ge;\u0026thinsp;40 cm: AOR 1.391 (95% CI 1.074\u0026ndash;1.802; p\u0026thinsp;=\u0026thinsp;0.012).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInter-incisor gap\u0026thinsp;\u0026le;\u0026thinsp;3 cm: AOR 1.375 (95% CI 1.038\u0026ndash;1.784; p\u0026thinsp;=\u0026thinsp;0.022).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe \u003cb\u003eupper lip bite test\u003c/b\u003e was not significant (p\u0026thinsp;=\u0026thinsp;0.211).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePredictive challenges and model performance\u003c/h2\u003e \u003cp\u003eSingle tools show strong associations yet modest sensitivity. Large-scale work reported that up to \u003cb\u003e93%\u003c/b\u003e of difficult intubations were missed when relying on single predictors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Internal validation supported a multidimensional model (AUC \u003cb\u003e0.78\u003c/b\u003e; Hosmer\u0026ndash;Lemeshow p\u0026thinsp;=\u0026thinsp;0.09).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCombined predictors improve accuracy\u003c/h2\u003e \u003cp\u003eComposite assessment outperformed single tests. A simple index combining Mallampati, BMI, and TMD yielded AUC 0.76, sensitivity 71%, specificity 87%, and overall accuracy 85%, consistent with the existing literature emphasizing combined approaches such as SARI/LEMON and the DIFFICAIR program [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eComparison with existing literature\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003eDemographics\u003c/h2\u003e \u003cp\u003eOur cohort (mean age\u0026thinsp;~\u0026thinsp;53 years; near-equal sex distribution; mean BMI 25.3 kg/m\u0026sup2; with 25.3% obese by Asian cut-offs) broadly aligns with regional reports, though the female proportion is higher than in some series. Neither age nor sex was an independent predictor in our final model.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSurgical specialties\u003c/h2\u003e \u003cp\u003eThe high proportions of ENT and maxillofacial surgery distinguish this population and elevate expected DDL risk. The observed specialty-specific rates align with reports linking thyroid enlargement, tracheal deviation, and facial fractures to difficult laryngoscopy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDDL prevalence across settings\u003c/h2\u003e \u003cp\u003eThe \u003cb\u003e9.03%\u003c/b\u003e prevalence exceeds many high-resource datasets (e.g., Denmark\u0026thinsp;~\u0026thinsp;1.87%; Germany\u0026thinsp;~\u0026thinsp;4.9%) but is similar to several low- and middle-income settings (e.g., Ethiopia\u0026thinsp;~\u0026thinsp;12.2%) and higher than Thailand (~\u0026thinsp;3.2%). Differences likely reflect case-mix and context, reinforcing the need for region-specific tools. ([\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations merit consideration. First, the retrospective design depended on routine documentation, inviting missing data and measurement variability (e.g., Mallampati, TMD, neck circumference). Second, this is a single-center study with a head-and-neck\u0026ndash;heavy case mix, limiting generalizability. Third, residual confounding may persist despite adjustment (e.g., comorbidities, operator experience). These constraints support future prospective, multicenter validation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePredictive factors\u003c/h2\u003e \u003cp\u003eFindings corroborate with prior evidence:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMallampati III\u0026ndash;IV remained the strongest predictor (AOR 4.139); specificity is high, sensitivity limited, favoring composite use[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTMD\u0026thinsp;\u0026le;\u0026thinsp;6.5 cm indicates restricted mandibular space and poorer glottic view [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2; and NC\u0026thinsp;\u0026ge;\u0026thinsp;40 cm capture soft-tissue load and landmark obscuration[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLimited neck mobility remains critical to align axes for direct laryngoscopy[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe superiority of the Mallampati\u0026thinsp;+\u0026thinsp;BMI\u0026thinsp;+\u0026thinsp;TMD index (AUC 0.76) mirrors multifactor approaches (e.g., SARI, LEMON) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis retrospective cohort from Preah Ang Duong Hospital quantified the burden of difficult direct laryngoscopy (DDL) and identified independent predictors in adult elective surgical patients. DDL occurred in 9.03% (95% CI 8.01\u0026ndash;10.04), driven by a head-and-neck\u0026ndash;heavy case-mix; maxillofacial (13.53%) and ENT (11.34%) procedures had the highest rates. Six bedside factors were independently associated with DDL: Mallampati III\u0026ndash;IV (AOR 4.139, 95% CI 3.032\u0026ndash;5.644), limited neck mobility\u0026thinsp;\u0026lt;\u0026thinsp;80\u0026deg; (AOR 2.181, 95% CI 1.671\u0026ndash;2.822), TMD\u0026thinsp;\u0026le;\u0026thinsp;6.5 cm (AOR 1.954, 95% CI 1.468\u0026ndash;2.566), BMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2; (AOR 1.86, 95% CI 1.23\u0026ndash;2.81), NC\u0026thinsp;\u0026ge;\u0026thinsp;40 cm (AOR 1.391, 95% CI 1.074\u0026ndash;1.802), and IIG\u0026thinsp;\u0026le;\u0026thinsp;3 cm (AOR 1.375, 95% CI 1.038\u0026ndash;1.784). The ULBT was not significant (p\u0026thinsp;=\u0026thinsp;0.211). A simple composite (Mallampati\u0026thinsp;+\u0026thinsp;BMI\u0026thinsp;+\u0026thinsp;TMD) showed superior discrimination (AUC 0.76, 71% sensitivity, 87% specificity, 85% accuracy). Age and sex were not independent predictors. These data support routine, multifactor screening to anticipate DDL and guide preparation in resource-constrained Southeast-Asian settings.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eRecommendation\u003c/h2\u003e \u003cp\u003eWe recommend embedding the composite screening test into routine pre-operative checklists and documentation to proactively flag high-risk patients. For those identified as high risk, conduct a focused pre-induction team briefing, prepare advanced airway devices (e.g., video laryngoscope and flexible fiberoptic scope), and ensure experienced personnel are present at induction. Optimize equipment and processes by standardizing difficult-airway carts, maintaining rapid access to video laryngoscopes and emergency cricothyrotomy sets, and performing regular readiness checks. Target high-risk subgroups\u0026mdash;particularly maxillofacial and ENT cases\u0026mdash;by considering adjunctive evaluation when anatomical distortion is suspected and allocating senior airway support. Strengthen training through regular simulation-based education emphasizing multifactor assessment, device proficiency, escalation algorithms, and crisis resource management. Finally, pursue prospective, multi-center validation of the Mallampati\u0026thinsp;+\u0026thinsp;BMI\u0026thinsp;+\u0026thinsp;TMD model and explore objective adjuncts such as ultrasound-based airway evaluation to further refine prediction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study was reviewed and approved by the National Ethics Committee for Health Research (NECHR), Cambodia \u003cem\u003e(Reference Number: 478)\u003c/em\u003e and the Preah Ang Duong Hospital Research Ethics Committee \u003cem\u003e(Approval Date: 11 December 2024\u003c/em\u003e). The requirement for informed consent was waived by these committees due to the retrospective design of the study and the use of de-identified data. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable: the manuscript contains no individual person\u0026rsquo;s data in any form (including images).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**PN** : conceptualization, methodology, formal analysis, investigation, data curation, writing\u0026mdash;original draft; **BL** : investigation, data curation, validation, writing\u0026mdash;review \u0026amp;amp; editing; **SV** : investigation, data curation, validation, writing\u0026mdash;review \u0026amp;amp; editing; **SS** : investigation, data curation, validation, writing\u0026mdash;review \u0026amp;amp; editing; **CM** : writing\u0026mdash;review \u0026amp;amp; editing; **PS** : writing\u0026mdash;review \u0026amp;amp; editing; **HMR** : writing\u0026mdash;review \u0026amp;amp; editing; **LS** : writing\u0026mdash;review \u0026amp;amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the anesthesia and surgical teams at Preah Ang Duong Hospital for their support and collaboration throughout data collection and manuscript preparation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e De-identified dataset, analysis code, and the data dictionary are available from the corresponding author on reasonable request, in accordance with institutional policy and ethics approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\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.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCommittee on Standards and Practice Parameters, Apfelbaum JL, Connis RT, Nickinovich DG, American Society of Anesthesiologists Task Force on Preanesthesia Evaluation, Pasternak LR, et al. Practice advisory for preanesthesia evaluation: an updated report by the American Society of Anesthesiologists Task Force on Preanesthesia Evaluation. Anesthesiology. 2012;116(3):522\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN\u0026oslash;rskov AK, Rosenstock CV, Wetterslev J, Astrup G, Afshari A, Lundstr\u0026oslash;m 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(3):272\u0026ndash;81. Epub 2014 Dec 16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaw JA, Broemling N, Cooper RM, Drolet P, Duggan LV, Griesdale DE, et al. The difficult airway with recommendations for management-part 2-the anticipated difficult airway. Can J Anaesth. 2013;60(11):1119\u0026ndash;38. Epub 2013 Oct 17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN\u0026oslash;rskov AK, Rosenstock CV, Wetterslev J, Lundstr\u0026oslash;m LH. Incidence of unanticipated difficult airway using an objective airway score versus a standard clinical airway assessment: the DIFFICAIR trial - trial protocol for a cluster randomized clinical trial. Trials. 2013;14:347.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlemayehu T, Sitot M, Zemedkun A, Tesfaye S, Angasa D, Abebe F. Assessment of predictors for difficult intubation and laryngoscopy in adult elective surgical patients at Tikur Anbessa Specialized Hospital, Ethiopia: A cross-sectional study. Ann Med Surg (Lond). 2022;77:103682.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarkhede HH, Patel RD, Narkhede HR. A prospective observational study of predictors of difficult intubation in Indian patients. J Anaesthesiol Clin Pharmacol 2019 Jan-Mar;35(1):119\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhlaghi M, Abedinzadeh M, Ahmadi A, Heidari Z. Predicting Difficult Laryngoscopy and Intubation With Laryngoscopic Exam Test: A New Method. Acta Med Iran. 2017;55(7):453\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePradeep S, Bhar Kundu S, Nivetha C. Evaluation of neck-circumference- thyromental-distance ratio as a predictor of difficult intubation: A prospective, observational study. Indian J Anaesth. 2023;67(5):445\u0026ndash;51. Epub 2023 May 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Radaideh K, Dheeb E, Shbool H, Garaibeh S, Bataineh A, Khraise W, et al. Evaluation of different airway tests to determine difficult intubation in apparently normal adult patients: undergoing surgical procedures. Patient Saf Surg. 2020;14(1):43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiga T, Wajima Z, Inoue T, Sakamoto A. Predicting difficult intubation in apparently normal patients: a meta-analysis of bedside screening test performance. Anesthesiology. 2005;103(2):429\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed AM, Zaky MN, El-Mekawy NM, Ollaek MA, Sami WM, Mohamed DM. Evaluation of thyromental height test in prediction of difficult airway in obese surgical patients: An observational study. Indian J Anaesth. 2021;65(12):880\u0026ndash;5. Epub 2021 Dec 22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOria MS, Halimi SA, Negin F, Asady A. Predisposing Factors of Difficult Tracheal Intubation Among Adult Patients in Aliabad Teaching Hospital in Kabul, Afghanistan - A Prospective Observational Study. Int J Gen Med. 2022;15:1161\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan YZ, Tian Y, Xu M, Ni C, Li M, Wang J, et al. Neck circumference to inter-incisor gap ratio: a new predictor of difficult laryngoscopy in cervical spondylosis patients. BMC Anesthesiol. 2017;17(1):55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhiwal AK, Sharma C, Tripathi A, BK A, Choudhary V, Gupta S. Evaluation of thyromental height test as a single anatomical measure for prediction of difficult laryngoscopy: a prospective observational study. Ain-Shams J Anesthesiology. 2023;15(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarković D, Šurbatović M, Milisavljević D, Marjanović V, Stošić B, Stanković M. Prediction of a Difficult Airway Using the ARNE Score and Flexible Laryngoscopy in Patients with Laryngeal Pathology. Med (Kaunas). 2024;60(4):619.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlusomi BB, Aliyu SZ, Babajide AM, Sulaiman AO, Adegboyega OS, Gbenga HO, et al. Goitre-Related Factors for Predicting Difficult Intubation in Patients Scheduled for Thyroidectomy in a Resource-Challenged Health Institution in North Central Nigeria. Ethiop J Health Sci. 2018;28(2):169\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Trivedi A, Alvarez Z, Bhattacharyya R, Sartorato F, Gargano F, et al. Predicting Difficult Airway Intubation Based on Maxillofacial Trauma: A Retrospective Study. Cureus. 2022;14(5):e24844.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuzuner-Oncul AM, Kucukyavuz Z. Prevalence and prediction of difficult intubation in maxillofacial surgery patients. J Oral Maxillofac Surg. 2008;66(8):1652\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaithani U, Gupta G, Keerti, Gupta M, Meena K, Sharma CP, et al. Predicting difficult intubation in surgical patients scheduled for general anaesthesia: a prospective study of 435 patients. J Evol Med Dent Sci. 2013;2(14):2270\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeinrich S, Birkholz T, Irouschek A, Ackermann A, Schmidt J. Incidences and predictors of difficult laryngoscopy in adult patients undergoing general anesthesia: a single-center analysis of 102,305 cases. J Anesth. 2013;27(6):815\u0026ndash;21. Epub 2013 Jun 9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Cassai A, Boscolo A, Rose K, Carron M, Navalesi P. Predictive parameters of difficult intubation in thyroid surgery: a meta-analysis. Minerva Anestesiol. 2020;86(3):317\u0026ndash;26. Epub 2020 Jan 8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIttichaikulthol W, Chanpradub S, Amnoundetchakorn S, Arayajarernwong N, Wongkum W. Modified Mallampati test and thyromental distance as a predictor of difficult laryngoscopy in Thai patients. J Med Assoc Thai. 2010;93(1):84\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelvi O, Kahraman T, Senturk O, Tulgar S, Serifsoy E, Ozer Z. Evaluation of the reliability of preoperative descriptive airway assessment tests in prediction of the Cormack-Lehane score: A prospective randomized clinical study. J Clin Anesth. 2017;36:21\u0026ndash;6. Epub 2016 Oct 31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlock PA Jr, Benumof JL. Definition and incidence of the difficult airway. Benumof's Airway Management. Paris: Elsevier; 2007. pp. 215\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKheterpal S, Martin L, Shanks AM, Tremper KK. Prediction and outcomes of impossible mask ventilation: a review of 50,000 anesthetics. Anesthesiology. 2009;110(4):891\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Jin Y, Zheng Y, Chen H, Feng J, Sun J. Evaluation of preoperative difficult airway prediction methods for adult patients without obvious airway abnormalities: a systematic review and meta-analysis. BMC Anesthesiol. 2024;24(1):242.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKharrat I, Achour I, Trabelsi JJ, Trigui M, Thabet W, Mnejja M, et al. Prediction of difficulty in direct laryngoscopy. Sci Rep. 2022;12(1):10722.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoth D, Pace NL, Lee A, Hovhannisyan K, Warenits AM, Arrich J, et al. Bedside tests for predicting difficult airways: an abridged Cochrane diagnostic test accuracy systematic review. Anaesthesia. 2019;74(7):915\u0026ndash;28. Epub 2019 Mar 6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKheterpal S, Healy D, Aziz MF, Shanks AM, Freundlich RE, Linton F, et al. Incidence, predictors, and outcome of difficult mask ventilation combined with difficult laryngoscopy: a report from the multicenter perioperative outcomes group. Anesthesiology. 2013;119(6):1360\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuvin P, Lavaut E, Dupont H, Lefevre P, Demetriou M, Dumoulin JL, et al. Difficult tracheal intubation is more common in obese than in lean patients. Anesth Analg. 2003;97(2):595\u0026ndash;600.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang LY, Zhang KD, Zhang ZH, Zhang DX, Wang HL, Qi F. Evaluation of the reliability of the upper lip bite test and the modified mallampati test in predicting difficult intubation under direct laryngoscopy in apparently normal patients: a prospective observational clinical study. BMC Anesthesiol. 2022;22(1):314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaghaei M, Safavi MR. Prediction of prolonged laryngoscopy. Anaesthesia. 2001;56(12):1198\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKheterpal S, Healy D, Aziz MF, Shanks AM, Freundlich RE, Linton F, et al. Incidence, predictors, and outcome of difficult mask ventilation combined with difficult laryngoscopy: a report from the multicenter perioperative outcomes group. Anesthesiology. 2013;119(6):1360\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah JP, Patel SG, Singh B, Wong RJ, editors. Jatin Shah's Head and Neck Surgery and Oncology. Philadelphia: Elsevier; 2020. pp. 365\u0026ndash;439.\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.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHung OR, Murphy MF, editors. Hung's Difficult and Failed Airway Management. 3rd ed. New York: McGraw-Hill Education; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmerman B, Chason H, Schick A, Asselin N, Lindquist D, Musica N. Assessment of the Thyromental Height Test as an Effective Airway Evaluation Tool. Ann Emerg Med. 2021;77(3):305\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShehabi Y, Gatt S, Buckman T, Isert P. Effect of adrenaline, fentanyl and warming of injectate on shivering following extradural analgesia in labour. Anaesth Intensive Care. 1990;18(1):31\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrebaugh SL. Definition, incidence, and predictors of the difficult airway. Atlas of airway management: techniques and tools. Philadelphia: Lippincott Williams \u0026amp; Wilkins; 2007. p. 45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan ZH, Mohammadi M, Rasouli MR, Farrokhnia F, Khan RH. The Diagnostic Value of the Upper Lip Bite Test Combined with Sternomental Distance, Thyromental Distance, and Interincisor Distance for Prediction of Easy Laryngoscopy and Intubation: A Prospective Study. Anesth Analgesia. 2009;109(3):822\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdapted from Zimmerman B, Chason H, Schick A, Asselin N, Lindquist D, Musica N. Title of the article. Ann Emerg Med. 2021;77(3):305\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDom\u0026iacute;nguez-P\u0026eacute;rez M, Gonz\u0026aacute;lez-Dzib RDS. Correlation between Predictive Index of Difficult Intubation and Cormack. Rev Med Inst Mex Seguro Soc. 2023;61(1):15\u0026ndash;20. Spanish.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen-Noun L, Laor A. Relationship of neck circumference to cardiovascular risk factors. Obes Res. 2003;11(2):226\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson ME, Spiegelhalter D, Robertson JA, Lesser P. Predicting difficult intubation. Br J Anaesth. 1988;61(2):211\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi WX, Wang DD, Li X, Li WX, Han Y, Cai YR. Risk factors for difficult mask ventilation and difficult intubation among patients undergoing pharyngeal and laryngeal surgery. Heliyon. 2023;9(3):e14408.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdnet F, Borron SW, Racine SX, Clemessy JL, Fournier JL, Plaisance P, et al. The intubation difficulty scale (IDS): proposal and evaluation of a new score characterizing the complexity of endotracheal intubation. Anesthesiology. 1997;87(6):1290\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReed MJ, Dunn MJG, McKeown DW. Can an airway assessment score predict difficulty at intubation in the emergency department? Emerg Med J. 2005;22(2):99\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaha AR. Difficult airway and intubation in thyroid surgery. Ann Otol Rhinol Laryngol. 2015;124(4):334\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmathieu R, Combes X, Abdi W, Housseini LE, Rezzoug A, Dinca A, et al. An algorithm for difficult airway management, modified for modern optical devices (Airtraq laryngoscope; LMA CTrach\u0026trade;): a 2-year prospective validation in patients for elective abdominal, gynecologic, and thyroid surgery. Anesthesiology. 2011;114(1):25\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu DX, Ye Y, Zhu YH, Li J, He HY, Dong L, et al. Intubation of non-difficult airways using video laryngoscope versus direct laryngoscope: a randomized, parallel-group study. BMC Anesthesiol. 2019;19(1):75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTasche KK, Dorneden AM, Swift WM, Boyd NH, Shonka DC, Pagedar NA. Airway Management in Substernal Goiter Surgery. Ann Otol Rhinol Laryngol. 2022;131(2):225\u0026ndash;8. Epub 2021 May 25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayaraj AK, Siddiqui N, Abdelghany SMO, Balki M. Management of difficult and failed intubation in the general surgical population: a historical cohort study in a tertiary care centre. Can J Anaesth. 2022;69(4):427\u0026ndash;37. English. Epub 2021 Dec 14.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Airway management, Intubation, Intratracheal, Laryngoscopy, Mallampati test, Asian Continental Ancestry Group, Cambodia","lastPublishedDoi":"10.21203/rs.3.rs-8750154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8750154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFailure to anticipate difficult direct laryngoscopy (DDL) leads to catastrophic airway events. While Western algorithms exist, evidence from Southeast Asia\u0026mdash;particularly in settings with high volumes of head-and-neck pathology\u0026mdash;is limited. We investigated DDL prevalence and validated a simplified cumulative risk score in a Cambodian tertiary center.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study of 3,080 adults undergoing elective surgery with planned Macintosh laryngoscopy (January\u0026ndash;June 2023) at Preah Ang Duong Hospital, Phnom Penh. DDL was defined as Cormack\u0026ndash;Lehane grade III/IV or \u0026ge;\u0026thinsp;3 attempts. Seven bedside predictors were analyzed using multivariable logistic regression. A composite risk score (range 0\u0026ndash;3) was derived from the strongest independent predictors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDDL prevalence was 9.0% (278/3,080), rising to 13.5% in maxillofacial and 11.3% in ENT procedures\u003csup\u003e1\u003c/sup\u003e. Independent predictors included Mallampati class III\u0026ndash;IV (Adjusted Odds Ratio [AOR] 4.14), limited neck mobility (AOR 2.18), Thyromental Distance (TMD)\u0026thinsp;\u0026le;\u0026thinsp;6.5 cm (AOR 1.95), and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2;; AOR 1.86)\u003csup\u003e2\u003c/sup\u003e. The Upper Lip Bite Test was not predictive (p\u0026thinsp;=\u0026thinsp;0.21)\u003csup\u003e3\u003c/sup\u003e. A simplified composite score (Mallampati\u0026thinsp;+\u0026thinsp;BMI\u0026thinsp;+\u0026thinsp;TMD) demonstrated superior discrimination (AUC 0.76) compared to single predictors. At a cutoff of \u0026ge;\u0026thinsp;1, the score yielded a sensitivity of 71% and specificity of 87%\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDDL affects nearly 1 in 11 elective surgical patients in this cohort, driven by a complex case-mix. A simple, three-point composite score offers a zero-cost tool to enhance preoperative risk stratification in resource-limited settings where advanced airway equipment may be scarce.\u003c/p\u003e","manuscriptTitle":"Prevalence and bedside predictors of difficult direct laryngoscopy in a Cambodian tertiary center: a retrospective cohort study of 3,080 adult elective surgeries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 16:16:09","doi":"10.21203/rs.3.rs-8750154/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-25T06:21:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T03:17:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310271931543182531581475815658304129999","date":"2026-03-11T13:53:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T22:55:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T11:42:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245330803820028124200851922468377993630","date":"2026-03-06T13:15:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220717783201828005936254015144614156390","date":"2026-03-06T11:31:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238733380499280174178493252617606878026","date":"2026-03-06T10:50:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191183479862656847671380364776974460455","date":"2026-02-08T16:40:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71161765789832639044336302532409703274","date":"2026-02-08T05:13:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272062987289343944291190043376417305133","date":"2026-02-06T15:26:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T14:00:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-04T06:39:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-04T06:32:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T04:36:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2026-02-04T04:29:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c907cac-88e5-4082-a2e0-c31090c28d7e","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:02:00+00:00","versionOfRecord":{"articleIdentity":"rs-8750154","link":"https://doi.org/10.1186/s12871-026-03846-4","journal":{"identity":"bmc-anesthesiology","isVorOnly":false,"title":"BMC Anesthesiology"},"publishedOn":"2026-04-24 15:57:27","publishedOnDateReadable":"April 24th, 2026"},"versionCreatedAt":"2026-02-10 16:16:09","video":"","vorDoi":"10.1186/s12871-026-03846-4","vorDoiUrl":"https://doi.org/10.1186/s12871-026-03846-4","workflowStages":[]},"version":"v1","identity":"rs-8750154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8750154","identity":"rs-8750154","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}