›› 2020, Vol. 40 ›› Issue (3): 358-.

• Original article (Clinical research) •

### Study on risk factors analysis and prediction model of difficult airway

NI Hong-wei, HE Guang-bao, GAO Hong-mei, ZHU Yi-jun, SHI Dong-ping, HANG Yan-nan

1. 1. Department of Anesthesiology, Jiading District Central Hospital, Shanghai 201800, China; 2. Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
• Online:2020-03-28 Published:2020-04-09
• Supported by:
Jiading District Science and Technology Commission Health System Research Project (JDKW-2016-W15); Teacher Talent Project of Hospital Affiliated Shanghai University of Medicine & Health Sciences.

Abstract: Objective &middot; To explore the risk factors of difficult airway (DA) and establish its prediction model. Methods &middot; May to Oct. 2018, 211 patients were selected for elective surgery under general anesthesia in Jiading District Central Hospital, and their basic data such as age, sex, height, weight, body mass index (BMI) were collected. Conventional airway assessment indicators were evaluated, including the modified Mallampati test (MMT), cervical mobility, inter-incisor distance and thyromental distance. Ultrasound was utilized to measure the distance between the skin and thyroid cartilage (DST), the distance between the thyroid cartilage and epiglottis (DTE) and the distance between the skin and epiglottis (DSE) in the parasagittal plane. The first laryngoscope was used to observe the laryngeal state of the patients, and Cormack-Lehane (CL) grade was performed. Logistic regression model was used to analyze the influencing factors that might caDA, establish the best model to predict DA, and carry out risk assessment and judgment on the indexes and their coefficients in the model. Results &middot; Forty-four patients were classified as CL grade Ⅲ or Ⅵ. Logistic regression analysis showed that the best model for predicting DA was determinedsex, BMI, DSE and MMT. The sensitivity and specificity of the diagnostic value of the optimal model were 90.9% and 90.4%, and the area under the receiver operator characteristic curve was 0.934. Conclusion &middot; The prediction model determinedfour risk factors of sex, BMI, DSE and MMT can evaluate DA more comprehensively and effectively.