%0 Journal Article %A Ivan I Ramirez %A Daniel H Arellano %A Rodrigo S Adasme %A Jose M Landeros %A Francisco A Salinas %A Alvaro G Vargas %A Francisco J Vasquez %A Ignacio A Lobos %A Magdalena L Oyarzun %A Ruben D Restrepo %T Ability of ICU Health-Care Professionals to Identify Patient-Ventilator Asynchrony Using Waveform Analysis %D 2017 %R 10.4187/respcare.04750 %J Respiratory Care %P 144-149 %V 62 %N 2 %X BACKGROUND: 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.METHODS: 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.RESULTS: 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.CONCLUSIONS: 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. %U https://rc.rcjournal.com/content/respcare/62/2/144.full.pdf