Chhina Anoop Kanwal, Jain Richa, Gautam Parshotam Lal, Garg Jony, Singh Nidhi, Grewal Anju
Department of Anaesthesiology, Dayanand Medical College and Hospital, Ludhiana, Punjab, India.
J Anaesthesiol Clin Pharmacol. 2018 Jan-Mar;34(1):62-67. doi: 10.4103/joacp.JOACP_230_16.
Various models were devised for prediction of difficult intubation but have low positive predictive value, sensitivity and specificity. We aimed to predict difficult intubation from various airway predictive indices, in isolation and combination, and to formulate a multivariate model that can aid in accurate prediction of difficult intubation.
A prospective double blinded study was conducted on 500 adult patients scheduled for elective surgery under general anaesthesia. Preoperatively, they were assessed for airway screening tests. After standardized induction of anaesthesia, laryngoscopic view was classified according to the Modified Cormack and Lehane (MCL) classification. Variables' association with intubation findings was evaluated using Chi-square statistic. Stepwise logistic regression identified the multivariate independent predictors of difficult intubation and combinations were made using forward selection process. 8 models were formulated and a receiver-operating characteristic (ROC) curve worked out for them. Sensitivity and specificity analysis validated the final model.
Age, sex, weight, BMI, snoring, obstructive sleep apnea (OSA), diabetes, hypertension, upper lip bite test (ULBT), Mallampati grade (MPS), thyromental distance (TMD), sternomental distance (SMD), neck movements (NM), neck circumference (NC) and inter-incisor gap (IIG) had significant correlation with difficult intubation. Based upon sensitivity and specificity analysis, model comprising of MPS, NM, NC and SMD was found to be most accurate. It had highest sensitivity 80%, specificity 87% and area under curve 0.90, thus validating the model.
Our study found that a combination of MPS, SMD, NM and NC permits reliable, accurate and quick preoperative prediction of difficult intubation.
已设计出多种模型用于预测困难插管，但这些模型的阳性预测值、敏感性和特异性较低。我们旨在单独及联合使用各种气道预测指标来预测困难插管，并构建一个有助于准确预测困难插管的多变量模型。
对500例计划在全身麻醉下接受择期手术的成年患者进行了一项前瞻性双盲研究。术前，对他们进行气道筛查测试。在标准化诱导麻醉后，根据改良的Cormack和Lehane（MCL）分类法对喉镜视野进行分类。使用卡方统计量评估变量与插管结果的相关性。逐步逻辑回归确定困难插管的多变量独立预测因素，并使用向前选择过程进行组合。制定了8个模型，并为它们绘制了受试者工作特征（ROC）曲线。敏感性和特异性分析验证了最终模型。
年龄、性别、体重、体重指数、打鼾、阻塞性睡眠呼吸暂停（OSA）、糖尿病、高血压、上唇咬合试验（ULBT）、Mallampati分级（MPS）、颏甲距离（TMD）、胸骨颏距离（SMD）、颈部活动度（NM）、颈围（NC）和门齿间距（IIG）与困难插管有显著相关性。基于敏感性和特异性分析，发现由MPS、NM、NC和SMD组成的模型最为准确。它具有最高的敏感性80%、特异性87%和曲线下面积0.90，从而验证了该模型。
我们的研究发现，MPS、SMD、NM和NC的组合可实现对困难插管的可靠、准确且快速的术前预测。
