Ivan I Ramirez1, Daniel H Arellano2, Rodrigo S Adasme3, Jose M Landeros4, Francisco A Salinas5, Alvaro G Vargas6, Francisco J Vasquez7, Ignacio A Lobos8, Magdalena L Oyarzun9, Ruben D Restrepo10. 1. Division of Critical Care Medicine, Hospital Clinico Universidad de Chile, Santiago, Chile. 2. Division of Critical Care Medicine, Hospital Clinico Universidad de Chile, Santiago, Chile. darellano@vtr.net. 3. Division of Critical Care Medicine, Hospital Clinico Universidad Catolica, Santiago, Chile and Epidemiology Master Degree, Faculty of Medicine, Universidad de Los Andes. 4. Division of Critical Care Medicine Hospital Roberto del Rio, Santiago, Chile. 5. Division of Critical Care Medicine, Instituto Nacional del Torax, Santiago, Chile. 6. Division of Critical Care Medicine, Hospital Higueras de Talcahuano, Chile. 7. Division of Critical Care Medicine, Hospital de Talca, Talca, Chile. 8. Division of Critical Care Medicine, Hospital Clinico de la Florida, Santiago, Chile. 9. Division of Critical Care Medicine, Clinica Bicentenario, Santiago Chile. 10. Department of Respiratory Care, University of Texas Health Sciences Center at San Antonio, San Antonio, Texas.
Abstract
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.
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.
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