• Siyi Yan, Mengzhuo Guo, Zhifeng Gao, Haotian Wu, Xian Liu, Guoping Yin, Jingying Ye, Xiaofei Zhang, Zhuozhao Zheng, and Huan Zhang.
• Department of Anesthesiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
• Minerva Anestesiol. 2023 Jun 1; 89 (6): 498509498-509.

BackgroundObstructive sleep apnea-hypopnea syndrome (OSAHS) has been linked to increased risk of perioperative morbidity and mortality because of difficult intubation (DI). However, there is a lack of clinically validated tools to identify OSAHS patients who are likely to have an increased the risk of DI.MethodsFor model development, a prospective cohort study included patients with OSAHS who underwent elective surgery between September 2018 to December 2020. The outcome was DI and classified according to the Cormack-Lehane grading. Conventional airway assessment tests, skeletal features, and the severity of OSAHS were recorded, and LASSO regression was used. Validation was performed on an external sample of patients from the same hospital between January 2021 and December 2021.ResultsThe development (prevalence of DI: 44%) and validation cohorts (prevalence of DI: 32%) included 247 and 82 patients, respectively. Based on the result of LASSO, age and four skeletal features (thyromental height, maximum mandibular protrusion, mandibulohyoid distance, and neck hypokinesis grade) were included in the final model. Discrimination and calibration of the model were satisfactory with high AUC (0.97), sensitivity (88.5%), specificity (94.6%), accuracy (92.7%), PPV (88.5%) and NPV (94.6%) from external validation.ConclusionsOur study developed and externally validated a DI prediction model using skeletal features in OSAHS patients. The final model had an NPV of nearly 95%, suggesting that a simple nomogram including only five predictors was quite helpful for ruling out the presence of difficult intubation in OSAHS patients who underwent elective surgery.

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