摘要/Abstract
摘要： 目的·探索困难气道（difficult airway，DA）的风险因素并建立预测模型。方法·选择2018年5月—10月在上海市嘉定区中心医院拟全身麻醉下行择期手术的患者211例，收集其年龄、性别、身高、体质量和体质量指数（body mass index，BMI）等基本资料。采集患者的常规气道评估指标，包括改良的Mallampati分级（modified Mallampati test，MMT）、头颈活动度、张口度和甲颏距离。采用超声技术在旁矢状位测量皮肤到甲状软骨的距离（the distance between the skin and thyroid cartilage，DST）、皮肤到会厌的距离（the distance between the skin and epiglottis，DSE）以及甲状软骨到会厌的距离（the distance between the thyroid cartilage and epiglottis，DTE）。采用第一眼喉镜对患者喉部状态进行观察，并行Cormack-Lehane（CL）分级判定。采用Logistic 回归模型对可能引起DA的影响因素进行分析，建立预测DA的最佳模型，对模型中的指标及其系数进行风险评估及判定。结果·44例患者的CL分级为Ⅲ级或Ⅳ级。Logistic回归分析显示，预测DA的最佳模型由性别、BMI、DSE和MMT共4个风险因素确定，该最佳模型的诊断价值为灵敏度90.9%、特异度90.4%，受试者工作特征曲线下面积达到0.934。结论·由性别、BMI、DSE和MMT共4个风险因素确定的预测模型能够更为全面、有效地评估DA。
关键词: 困难气道, 全身麻醉, 超声
Abstract:
Objective · To explore the risk factors of difficult airway (DA) and establish its prediction model. Methods · 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 · 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 · The prediction model determinedfour risk factors of sex, BMI, DSE and MMT can evaluate DA more comprehensively and effectively.
Key words: difficult airway (DA), general anesthesia, ultrasound
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