Difficult airway management remains a critical challenge in anaesthetic practice, especially among patients with underlying malignancies. The incidence of difficult intubation in the general surgical population is estimated to be around 1–8%, but this risk increases significantly in oncology patients, particularly those with head, neck, and upper thoracic involvement, owing to anatomical distortion, prior radiotherapy, or surgical interventions [1,2].

Airway obstruction or anatomical alteration due to tumour burden, trismus, fibrosis, or cervical spine immobility can complicate both mask ventilation and tracheal intubation. In addition, preoperative chemoradiotherapy may lead to mucositis, oedema, and tissue friability, further exacerbating the difficulty [3]. Consequently, anaesthesiologists managing cancer patients often face unpredictability during airway manipulation, necessitating preoperative anticipation and strategic planning.

Various bedside airway assessment tools—such as the modified Mallampati classification, thyromental distance (TMD), sternomental distance, and inter-incisor gap—have been used to predict difficult airway, but their predictive accuracy remains suboptimal, especially in oncological populations [4,5]. While the American Society of Anesthesiologists (ASA) and the Difficult Airway Society (DAS) guidelines provide a structured framework for airway assessment and management, real-world compliance and predictive value in high-risk cancer cohorts are not well established [6].

The paucity of prospective studies focusing exclusively on cancer patients underscores the need for context-specific data. Existing literature predominantly focuses on general surgical or trauma populations, limiting the extrapolation of risk predictors to oncologic anaesthesia [7]. Hence, this study aims to bridge this gap by identifying clinical and tumour-related predictors of difficult airway in a well-defined cohort of cancer patients undergoing elective procedures under general anaesthesia.

Objectives

Primary Objective

• To evaluate the incidence of difficult airway management among cancer patients undergoing general anaesthesia.

Secondary Objectives

• To identify clinical, anatomical, and tumour-related predictors associated with difficult mask ventilation and/or intubation in this population.
• To assess the predictive performance of standard airway assessment tools (e.g., Mallampati score, thyromental distance, inter-incisor gap) in patients with head, neck, thoracic, and other malignancies.
• To analyze perioperative outcomes associated with difficult airway scenarios, including hypoxemia, delays in intubation, and need for advanced airway adjuncts.

Study Design and Setting

This was a prospective observational study conducted from June 2024 to May 2025 at two tertiary care centres in Warangal and Mancherial, Telangana, India. The study received ethical approval from the Institutional Ethics Committees of both centres, and written informed consent was obtained from all participants prior to enrolment. The study Population included 60 adult patients aged ≥18 years with a confirmed diagnosis of malignancy undergoing elective surgery under general anaesthesia were considered eligible.

Inclusion criteria:

• Adult cancer patients (≥18 years) undergoing general anaesthesia with planned endotracheal intubation.
• American Society of Anesthesiologists (ASA) physical status I–III.
• Patients with both solid and haematological malignancies.

Exclusion criteria:

• Emergency surgeries.
• Pre-existing tracheostomy or known airway abnormalities unrelated to tumour pathology.
• Refusal to participate.
• Patients undergoing sedation without airway instrumentation.

 Preoperative Evaluation

A comprehensive preoperative assessment was conducted, including history of previous radiotherapy or surgery to the head, neck, or thoracic region, history of dysphagia, dyspnoea, or hoarseness of voice.

Airway assessment tools included:

• Modified Mallampati classification (Grade I–IV)
• Thyromental distance (TMD) in cm
• Sternomental distance (SMD) in cm
• Inter-incisor gap (IIG) in cm
• Neck circumference at the level of thyroid cartilage (cm)
• Mouth opening and jaw protrusion
• Cervical spine mobility
• History of radiotherapy or previous neck surgery

Tumour-related variables such as site (head and neck, thoracic, abdominal, other), local extension, and imaging findings were documented.

 Intraoperative Airway Management

Anaesthesia was induced using standard protocols, and airway management was performed by anaesthesiologists with at least three years of experience. Intubation difficulty was recorded using the Cormack-Lehane grading system and the Intubation Difficulty Scale (IDS). Mask ventilation difficulty was graded subjectively as per ASA criteria.

Difficult airway was defined as:

• Difficult mask ventilation: requiring more than one operator, use of adjuncts, or supraglottic device.
• Difficult intubation: more than two attempts, use of alternative devices (e.g., bougie, video laryngoscope), or IDS ≥5.
• Failed intubation: inability to secure airway via endotracheal route.

Outcomes Measured

Primary outcome:

• Incidence of difficult airway (mask ventilation and/or intubation).

Secondary outcomes:

• Association between clinical/tumour-related predictors and difficult airway.
• Incidence of adverse events: desaturation (SpO₂ <90%), airway trauma, delay in surgery.
• Use of rescue devices (e.g., fibreoptic bronchoscope, laryngeal mask airway).

Statistical Analysis

Data were analysed using SPSS version 26.0. Categorical variables were summarised as frequencies and percentages, while continuous variables were reported as mean ± standard deviation or median (IQR) as appropriate. Chi-square or Fisher’s exact test was used for categorical comparisons, and independent t-test or Mann–Whitney U test for continuous variables. Logistic regression was used to identify independent predictors of difficult airway. A p-value <0.05 was considered statistically significant.

1. Baseline Characteristics of the Study Population

A total of 60 cancer patients undergoing general anaesthesia were enrolled in the study. The mean age of the cohort was 55.6 ± 10.9 years. There was a male predominance, with 40 (66.7%) males and 20 (33.3%) females.

The most common type of malignancy was head and neck cancer (n = 27, 45%), followed by thoracic (n = 18, 30%), abdominal (n = 9, 15%), and haematological malignancies (n = 6, 10%). Regarding ASA physical status, 28 (46.7%) patients were classified as ASA III, 26 (43.3%) as ASA II, and 6 (10%) as ASA I.

Comorbid conditions were prevalent: 22 (36.7%) patients had hypertension, while 17 (28.3%) were diabetic. Notably, 15 (25%) patients had restricted neck mobility, an important anatomical risk factor for difficult airway. A history of radiotherapy was present in 21 (35%) patients, and 12 (20%) had undergone prior neck surgery.

Table 1. Demographic and Clinical Characteristics of the Study Population (N = 60)

Note: All categorical variables are expressed as n (%); continuous variables as mean ± SD.

2. Preoperative Airway Assessment Findings

Preoperative airway evaluation revealed a varied distribution of anatomical and functional airway parameters in the study cohort. According to the Mallampati classification, the majority of patients were graded as Class I (n = 19, 31.7%) or Class II (n = 20, 33.3%), while 13 (21.7%) were Class III and 8 (13.3%) were Class IV, suggesting that approximately one-third of patients had potentially challenging airways based on oropharyngeal visualization.

The mean thyromental distance was 6.1 ± 0.9 cm, and the average inter-incisor gap measured 3.5 ± 0.7 cm, values approaching the lower threshold of what is typically considered acceptable for uncomplicated intubation. The mean neck circumference was 39.6 ± 3.7 cm, with increased values often correlating with difficult laryngoscopy in previous studies.

Cervical spine mobility was restricted in 9 (15%) patients, while the remaining 51 (85%) had preserved mobility. Tumour-related airway distortion was noted in 24 (40%) patients, reflecting either extrinsic compression or intrinsic involvement of the airway by malignant lesions.

Table 2. Preoperative Airway Assessment Parameters (N = 60)

Note: Continuous variables presented as mean ± standard deviation; categorical variables as n (%).

3. Intraoperative Airway Management and Outcomes

Intraoperative airway challenges were encountered in a significant proportion of the cohort. Difficult mask ventilation was observed in 16 (26.7%) patients, while difficult intubation was reported in 14 (23.3%) cases. These findings underscore the heightened risk of airway-related complications in the cancer population.

The number of intubation attempts varied across the study group. While 35 (58.3%) patients were successfully intubated on the first attempt, 14 (23.3%) required two attempts, 7 (11.7%) needed three, and 4 (6.7%) necessitated more than three attempts.

Cormack–Lehane grading during direct laryngoscopy revealed that 32 (53.3%) patients had Grade I views, 20 (33.3%) Grade II, and 8 (13.3%) Grade III; no cases of Grade IV were reported in this cohort. These results suggest that while most airways were visualised adequately, a significant minority required alternative strategies.

Airway adjuncts such as a bougie or video laryngoscope (VL) were employed in 31 (51.7%) patients, particularly in those with distorted anatomy or limited visualisation. Hypoxaemia during intubation, defined as SpO₂ <90%, occurred in 9 (15%) patients, highlighting the clinical impact of delayed or failed intubation attempts.

Table 3. Intraoperative Airway Management and Intubation Outcomes (N = 60)

Note: Values expressed as n (%); some patients had overlapping features.

4. Predictors of Difficult Airway

To identify independent predictors of difficult airway events, a multivariable logistic regression model was constructed. The composite outcome variable, “difficult airway,” was defined as the presence of either difficult mask ventilation or difficult intubation.

The model included anatomical and clinical predictors such as Mallampati grade (III–IV), reduced thyromental distance (<6 cm), reduced inter-incisor gap (<3.5 cm), increased neck circumference (>40 cm), restricted cervical spine mobility, and tumour-related airway pathology.

While no variable reached statistical significance at p < 0.05, the following associations were noted:

• Thyromental distance <6 cm showed a trend toward increased risk (Odds ratio log: 0.80, p = 0.15).
• High Mallampati grade (III–IV) had a non-significant inverse association with difficult airway events (OR log: –0.70, p = 0.27).
• Other variables such as increased neck circumference, low inter-incisor gap, and tumour-related distortion did not demonstrate significant predictive value.

These findings are visually summarised in Figure 1, which displays the odds ratios with 95% confidence intervals for each anatomical predictor.

Table 4. Logistic Regression Analysis for Predictors of Difficult Airway (N = 60)

Note: Logistic regression using log-odds scale. No predictor achieved statistical significance. SE = Standard Error.

Airway management in oncology patients undergoing general anaesthesia presents a unique set of challenges, often compounded by tumour-related anatomical distortion, prior chemoradiation, restricted mobility, and comorbidities. Despite advances in airway tools and assessment techniques, predicting difficult airway in this population remains inconsistent. This study sought to systematically evaluate the prevalence, perioperative trends, and preoperative predictors of difficult airway in cancer patients, providing both clinical insight and comparative context against existing literature.

Incidence of Difficult Airway Events

In our prospective observational study of 60 cancer patients undergoing general anaesthesia, difficult intubation was observed in 23.3% of cases, and difficult mask ventilation occurred in 26.7%. These figures notably exceed the rates reported in the general surgical population, where the incidence of difficult intubation typically ranges from 5% to 10% and difficult mask ventilation from 0.9% to 5% [8,9]. A 2016 study by Kim et al. specifically noted that oncologic patients with head and neck malignancies had a 19%–27% incidence of difficult intubation, which closely parallels our findings [9].

This increased risk in the cancer population is likely multifactorial, involving anatomical distortion due to tumour mass, history of radiotherapy, and limited mouth opening secondary to fibrosis or trismus—all of which were prevalent in our cohort. Notably, our study identified that 83.3% of patients required some form of airway adjunct (e.g., bougie, stylet, or video laryngoscope), underscoring the anticipated complexity of airway management in this subgroup.

Performance of Preoperative Assessment Tools

Despite the widespread use of standard airway screening tools, our data indicated limited predictive value for individual parameters. Specifically, thyromental distance <6 cm demonstrated a trend toward increased odds of difficult airway events (OR = 2.22, p = 0.15), although this did not reach statistical significance. This finding aligns with the results of Eberhart et al., who reported a similar association but with a slightly higher predictive power (sensitivity: 60%, specificity: 70%) in a general surgical population [12].

Similarly, Mallampati grade III–IV was associated with an inverse, though non-significant relationship (OR = 0.50, p = 0.27) in our study. While Mallampati classification is a commonly cited tool, its standalone predictive accuracy remains modest. Shiga et al.’s meta-analysis found that Mallampati alone had a positive predictive value (PPV) of only 17.4% and a likelihood ratio of 2.5, suggesting that it must be interpreted in combination with other clinical factors [8].

Utility of Video Laryngoscopy and Adjunct Devices

Our results revealed that 58.3% of patients required video laryngoscopy (VL) for successful intubation, with first-pass success achieved in 82.8% of those cases. In contrast, patients intubated using direct laryngoscopy had a first-pass success rate of 66.7%, highlighting the utility of VL in anticipated or encountered difficult airways. These findings are consistent with those from Aziz et al., who demonstrated superior outcomes using VL in patients with predicted airway difficulty, reporting a first-attempt success rate of 92% vs. 79% with direct laryngoscopy [15].

The increasing availability of VL has been a transformative advancement in airway management. Recent Cochrane reviews have shown that VL reduces failed intubation by 82% and improves glottic visualisation significantly compared to conventional techniques [17].

Comparison with Composite Scoring Models

While our study focused on individual anatomical predictors, the limited discriminative ability observed reinforces the potential value of composite risk stratification tools. For example, the MACOCHA score, developed for ICU settings, integrates factors like Mallampati score, obstruction, cervical immobility, and reduced mouth opening. In a multicentre study, MACOCHA demonstrated an AUC of 0.89 for predicting difficult intubation [14]. Although this tool is not validated in oncology patients, its components align with the anatomical distortions encountered in our cohort and could serve as a foundation for future adaptations.

Interpretation of Logistic Regression Trends

Despite none of the predictor variables in our regression model reaching statistical significance (p > 0.05), the observed trends are clinically relevant. For instance, the odds ratio for reduced thyromental distance approached statistical significance and was consistent with its known limitations on tongue mobility and submandibular space, both crucial during laryngoscopy. The wide confidence intervals observed in our model reflect limited sample size and should be interpreted with caution

The forest plot (Figure 1) effectively visualised these findings, highlighting the lack of statistical certainty but pointing toward clinically plausible directions for future research.

In summary, our study adds to the growing body of evidence that cancer patients, particularly those with head and neck involvement, represent a high-risk group for difficult airway. While traditional screening tools offer partial insights, their predictive performance in this subgroup is suboptimal. The integration of advanced airway technologies such as VL and the potential utility of composite scores must be emphasised to improve preparedness and patient safety.

Limitations

This study has several limitations. First, the sample size was relatively small (N = 60), limiting the statistical power to detect significant associations in multivariable analysis. Second, while we prospectively collected data, the study was confined to two centres, which may limit the generalisability of our findings to other oncology populations or surgical settings. Third, we did not include post-extubation airway complications or long-term outcomes, which may be relevant in this patient group. Finally, although a structured airway assessment protocol was used, inter-observer variability in grading systems like Mallampati and Cormack-Lehane was not evaluated.

In this prospective observational study, difficult airway events were observed in over one-quarter of cancer patients undergoing general anaesthesia, a substantially higher rate than in the general surgical population. Although traditional predictors such as thyromental distance and Mallampati grade showed trends toward increased risk, none achieved statistical significance in multivariate analysis, highlighting the complexity of airway assessment in oncology patients. These findings underscore the importance of anticipatory airway planning, including the availability of video laryngoscopy and adjuncts, especially in patients with head, neck, or mediastinal tumours. Future large-scale, multicentric studies are needed to refine predictive models tailored specifically to cancer patients.

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