Cattano Davide, Killoran Peter V, Cai Chunyan, Katsiampoura Anastasia D, Corso Ruggero M, Hagberg Carin A
Department of Anesthesiology, University of Texas Medical School at Houston, Houston, 77030, USA.
Division of Clinical and Translational Sciences, , Department of Internal Medicine, University of Texas Medical School at Houston, Houston, 77030, USA.
F1000Res. 2014 Aug 27;3:204. doi: 10.12688/f1000research.5131.1. eCollection 2014.
There are few predictors of difficult mask ventilation and a simple, objective, predictive system to identify patients at risk of difficult mask ventilation does not currently exist. We present a retrospective - subgroup analysis aimed at identifying predictive factors for difficult mask ventilation (DMV) in patients undergoing pre-operative airway assessment before elective surgery at a major teaching hospital.
Data for this retrospective analysis were derived from a database of airway assessments, management plans, and outcomes that were collected prospectively from August 2008 to May 2010 at a Level 1 academic trauma center. Patients were stratified into two groups based on the difficulty of mask ventilation and the cohorts were analyzed using univariate analysis and stepwise selection method.
A total of 1399 pre-operative assessments were completed with documentation stating that mask ventilation was attempted. Of those 1399, 124 (8.9%) patients were found to be difficult to mask ventilate. A comparison of patients with and without difficult mask ventilation identified seven risk factors for DMV: age, body mass index (BMI), neck circumference, history of difficult intubation, presence of facial hair, perceived short neck and obstructive sleep apnea. Although seven risk factors were identified, no individual subject had more than four risk factors.
The results of this study confirm that in a real world clinical setting, the incidence of DMV is not negligible and suggest the use of a simple bedside predictive score to improve the accuracy of DMV prediction, thereby improving patient safety. Further prospective studies to validate this score would be useful.
预测面罩通气困难的指标较少,目前尚不存在一种简单、客观的预测系统来识别有面罩通气困难风险的患者。我们进行了一项回顾性亚组分析,旨在确定一家大型教学医院中接受择期手术术前气道评估的患者面罩通气困难(DMV)的预测因素。
这项回顾性分析的数据来自于2008年8月至2010年5月在一级学术创伤中心前瞻性收集的气道评估、管理计划和结果数据库。根据面罩通气的难度将患者分为两组,并使用单因素分析和逐步选择法对队列进行分析。
共完成了1399例术前评估,其中有记录表明尝试了面罩通气。在这1399例中,发现124例(8.9%)患者面罩通气困难。对有和没有面罩通气困难的患者进行比较,确定了DMV的七个风险因素:年龄、体重指数(BMI)、颈围、困难插管史、面部毛发、感觉颈部短和阻塞性睡眠呼吸暂停。虽然确定了七个风险因素,但没有个体有超过四个风险因素。
本研究结果证实,在现实临床环境中,DMV的发生率不可忽视,并建议使用简单的床边预测评分来提高DMV预测的准确性,从而提高患者安全性。进一步进行前瞻性研究以验证该评分将是有用的。