Ramirez Ivan I, Arellano Daniel H, Adasme Rodrigo S, Landeros Jose M, Salinas Francisco A, Vargas Alvaro G, Vasquez Francisco J, Lobos Ignacio A, Oyarzun Magdalena L, Restrepo Ruben D
Division of Critical Care Medicine, Hospital Clinico Universidad de Chile, Santiago, Chile.
Division of Critical Care Medicine, Hospital Clinico Universidad Catolica, Santiago, Chile and Epidemiology Master Degree, Faculty of Medicine, Universidad de Los Andes.
Respir Care. 2017 Feb;62(2):144-149. doi: 10.4187/respcare.04750. Epub 2016 Oct 25.
Waveform analysis by visual inspection can be a reliable, noninvasive, and useful tool for detecting patient-ventilator asynchrony. However, it is a skill that requires a properly trained professional.
This observational study was conducted in 17 urban ICUs. Health-care professionals (HCPs) working in these ICUs were asked to recognize different types of asynchrony shown in 3 evaluation videos. The health-care professionals were categorized according to years of experience, prior training in mechanical ventilation, profession, and number of asynchronies identified correctly.
A total of 366 HCPs were evaluated. Statistically significant differences were found when HCPs with and without prior training in mechanical ventilation (trained vs non-trained HCPs) were compared according to the number of asynchronies detected correctly (of the HCPs who identified 3 asynchronies, 63 [81%] trained vs 15 [19%] non-trained, P < .001; 2 asynchronies, 72 [65%] trained vs 39 [35%] non-trained, P = .034; 1 asynchrony, 55 [47%] trained vs 61 [53%] non-trained, P = .02; 0 asynchronies, 17 [28%] trained vs 44 [72%] non-trained, P < .001). HCPs who had prior training in mechanical ventilation also increased, nearly 4-fold, their odds of identifying ≥2 asynchronies correctly (odds ratio 3.67, 95% CI 1.93-6.96, P < .001). However, neither years of experience nor profession were associated with the ability of HCPs to identify asynchrony.
HCPs who have specific training in mechanical ventilation increase their ability to identify asynchrony using waveform analysis. Neither experience nor profession proved to be a relevant factor to identify asynchrony correctly using waveform analysis.
通过目视检查进行波形分析可能是检测患者 - 呼吸机不同步的一种可靠、无创且有用的工具。然而，这是一项需要经过适当培训的专业人员才能掌握的技能。
这项观察性研究在17个城市重症监护病房进行。要求在这些重症监护病房工作的医护人员识别3个评估视频中展示的不同类型的不同步。医护人员根据工作年限、机械通气方面的既往培训、职业以及正确识别的不同步数量进行分类。
共评估了366名医护人员。根据正确检测到的不同步数量比较有和没有机械通气既往培训的医护人员（受过培训与未受过培训的医护人员）时，发现了统计学上的显著差异（在识别出3个不同步的医护人员中，受过培训的有63名[81%]，未受过培训的有15名[19%]，P <.001；2个不同步，受过培训的有72名[65%]，未受过培训的有39名[35%]，P =.034；1个不同步，受过培训的有55名[47%]，未受过培训的有61名[53%]，P =.02；0个不同步，受过培训的有17名[28%]，未受过培训的有44名[72%]，P <.001）。有机械通气既往培训的医护人员正确识别≥2个不同步的几率也增加了近4倍（优势比3.67，95%可信区间1.93 - 6.96，P <.001）。然而，工作年限和职业均与医护人员识别不同步的能力无关。
接受过机械通气特定培训的医护人员使用波形分析识别不同步的能力有所提高。经验和职业都不是使用波形分析正确识别不同步的相关因素。
