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Войти в исследования Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons · 2025
Computed Tomography-Based Predictors of Difficult Intubation in Oral and Maxillofacial Surgery Patients.
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Wang Jiayi, Zhao Pengcheng, Zhou Ren, Yan Jia, Jiang Hong
Аннотация
BACKGROUND: The incidence of difficult airway is higher in oral and maxillofacial surgery (OMS) operations than those in general surgery. Current predictors, including clinical tools like the LEMON (Look Externally, Evaluate Mallampati Classification, Obstruction, Neck Mobility, Motion of Jaw) score, are often inaccurate, leading to underdiagnosis and increased risks to patient safety.
PURPOSE: This study aimed to identify computed tomography (CT) findings associated with difficult intubation in patients receiving OMS operations.
STUDY DESIGN, SETTING, SAMPLE: This prospective cohort study was conducted at the Shanghai Ninth People's Hospital from October 2018 to October 2021 and included patients undergoing OMS operations under general anesthesia. Exclusion criteria included individuals who received regional anesthesia, had contraindications to mask ventilation, lacked preoperative CT imaging, or did not provide informed consent.
PREDICTOR VARIABLES: The predictor variables were 21 midsagittal plane CT parameters and five 3-dimensional airway reconstruction parameters.
OUTCOME VARIABLE: The outcome variable was ease of intubation, categorized as difficult intubation (grade III or IV laryngeal view under direct laryngoscopy) and easy intubation (grade I or II laryngeal view under direct laryngoscopy).
COVARIATES: Covariates included demographic variables (age, weight, body mass index, and sex) and anatomical variables (modified Mallampati classification, hyoid thyroid cartilage distance, hyoid mental distance, thyromental distance, atlanto-occipital joint extension, and interincisor distance).
ANALYSES: Least absolute shrinkage and selection operator was used to select significant variables. Logistic regression was then used to construct the predictive model. The model's performance was evaluated using receiver operating characteristic curve.
RESULTS: The study included 630 patients, with 35.2% experiencing difficult intubation. The model was composed of 10 midsagittal plane and two 3-dimensional airway reconstruction parameters from CT images, with area under the curve values of 0.78 in the training cohort (n = 336) and 0.76 in the validation cohort (n = 72). In the validation cohort, the model achieved a sensitivity of 0.74 and a specificity of 0.75. In contrast, the area under the curve value of LEMON score was 0.62.
CONCLUSIONS AND RELEVANCE: This study developed a CT-based model that identifies patients at risk of difficult intubation in OMS, outperforming the LEMON score and underscoring the relevance of CT parameters, particularly those involving the tongue, uvula, and hyoid bone, in guiding clinical decision-making.
PURPOSE: This study aimed to identify computed tomography (CT) findings associated with difficult intubation in patients receiving OMS operations.
STUDY DESIGN, SETTING, SAMPLE: This prospective cohort study was conducted at the Shanghai Ninth People's Hospital from October 2018 to October 2021 and included patients undergoing OMS operations under general anesthesia. Exclusion criteria included individuals who received regional anesthesia, had contraindications to mask ventilation, lacked preoperative CT imaging, or did not provide informed consent.
PREDICTOR VARIABLES: The predictor variables were 21 midsagittal plane CT parameters and five 3-dimensional airway reconstruction parameters.
OUTCOME VARIABLE: The outcome variable was ease of intubation, categorized as difficult intubation (grade III or IV laryngeal view under direct laryngoscopy) and easy intubation (grade I or II laryngeal view under direct laryngoscopy).
COVARIATES: Covariates included demographic variables (age, weight, body mass index, and sex) and anatomical variables (modified Mallampati classification, hyoid thyroid cartilage distance, hyoid mental distance, thyromental distance, atlanto-occipital joint extension, and interincisor distance).
ANALYSES: Least absolute shrinkage and selection operator was used to select significant variables. Logistic regression was then used to construct the predictive model. The model's performance was evaluated using receiver operating characteristic curve.
RESULTS: The study included 630 patients, with 35.2% experiencing difficult intubation. The model was composed of 10 midsagittal plane and two 3-dimensional airway reconstruction parameters from CT images, with area under the curve values of 0.78 in the training cohort (n = 336) and 0.76 in the validation cohort (n = 72). In the validation cohort, the model achieved a sensitivity of 0.74 and a specificity of 0.75. In contrast, the area under the curve value of LEMON score was 0.62.
CONCLUSIONS AND RELEVANCE: This study developed a CT-based model that identifies patients at risk of difficult intubation in OMS, outperforming the LEMON score and underscoring the relevance of CT parameters, particularly those involving the tongue, uvula, and hyoid bone, in guiding clinical decision-making.