Naguib Mohamed, Scamman Franklin L, O'Sullivan Cormac, Aker John, Ross Alan F, Kosmach Steven, Ensor Joe E
Department of Anesthesiology and Pain Medicine, Unit 409, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA.
Anesth Analg. 2006 Mar;102(3):818-24. doi: 10.1213/01.ane.0000196507.19771.b2.
We performed a case-controlled, double-blind study to examine the performance of three multivariate clinical models (Wilson, Arné, and Naguib models) in the prediction of unanticipated difficult intubation. The study group consisted of 97 patients in whom an unanticipated difficult intubation had occurred. For each difficult intubation patient, a matched control patient was selected in whom tracheal intubation had been easily accomplished. Postoperatively, a blinded investigator evaluated both patients. The clinical assessment included the patient's weight, height, age, Mallampati score, interincisor gap, thyromental distance, thyrosternal distance, neck circumference, Wilson risk sum score, history of previous difficult intubation, and diseases associated with difficult laryngoscopy or intubation. The Naguib model was significantly more sensitive (81.4%; P < 0.0001) than the Arné (54.6%) or Wilson (40.2%) models. Both the Naguib (76.8%) and Arné (74.7%) model classified more intubations correctly (P = 0.01) than the Wilson model (66.5%). The specificity of Arné, Wilson, and Naguib model was 94.9%, 92.8%, and 72.2%, respectively (P < 0.0001). The corresponding area under the receiver operating characteristic curve was 0.87, 0.79, and 0.82, respectively. Our new model for prediction of difficult intubation was developed using logistic regression and includes thyromental distance, Mallampati score, interincisor gap, and height. This model is 82.5% sensitive and 85.6% specific with an area under the receiver operating characteristic curve of 0.90.
我们进行了一项病例对照双盲研究，以检验三种多变量临床模型（威尔逊模型、阿尔内模型和纳吉布模型）预测意外困难插管的性能。研究组由97例发生意外困难插管的患者组成。对于每例困难插管患者，选择一名气管插管顺利完成的匹配对照患者。术后，一名盲法研究者对两名患者进行评估。临床评估包括患者的体重、身高、年龄、马兰帕蒂评分、门齿间距、颏甲距离、胸骨上切迹距离、颈围、威尔逊风险总分、既往困难插管史以及与困难喉镜检查或插管相关的疾病。纳吉布模型的敏感性（81.4%；P<0.0001）显著高于阿尔内模型（54.6%）或威尔逊模型（40.2%）。纳吉布模型（76.8%）和阿尔内模型（74.7%）正确分类的插管病例数均多于威尔逊模型（66.5%）（P = 0.01）。阿尔内模型、威尔逊模型和纳吉布模型的特异性分别为94.9%、92.8%和72.2%（P<0.0001）。相应的受试者操作特征曲线下面积分别为0.87、0.79和0.82。我们使用逻辑回归开发了预测困难插管的新模型，该模型包括颏甲距离、马兰帕蒂评分、门齿间距和身高。该模型的敏感性为82.5%，特异性为85.6%，受试者操作特征曲线下面积为0.90。
