학술논문
Patient-Ventilator Asynchrony in Critical Care Settings: National Outcomes of Ventilator Waveform Analysis.
Document Type
Academic Journal
Author
Alqahtani JS; Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia; UCL Respiratory, University College London, London, UK. Electronic address: Alqahtani-Jaber@hotmail.com.; AlAhmari MD; Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia; Chief Executive Officer (CEO), Rural Healthcare Networks, Eastren Province Health Cluster, Saudi Arabia.; Alshamrani KH; Department of Respiratory Care, Prince Sultan Military Medical City, Riyadh, Saudi Arabia.; Alshehri AM; Department of Respiratory Care, Prince Sultan Military Medical City, Riyadh, Saudi Arabia.; Althumayri MA; Department of Respiratory Care, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.; Ghazwani AA; Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.; AlAmoudi AO; Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.; Alsomali A; Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.; Alenazi MH; Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.; AlZahrani YR; Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.; Alqahtani AS; Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.; AlRabeeah SM; Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia.; Arabi YM; College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Intensive Care Department, Ministry of the National Guard, Health Affairs, Riyadh, Saudi Arabia.
Source
Publisher: Mosby Country of Publication: United States NLM ID: 0330057 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1527-3288 (Electronic) Linking ISSN: 01479563 NLM ISO Abbreviation: Heart Lung Subsets: MEDLINE
Subject
Language
English
Abstract
Background: Patient-ventilator asynchrony (PVA) is a prevalent and often underrecognized problem in mechanically ventilated patients. Ventilator waveform analysis is a noninvasive and reliable means of detecting PVAs, but the use of this tool has not been broadly studied.
Methods: Our observational analysis leveraged a validated evaluation tool to assess the ability of critical care practitioners (CCPs) to detect different PVA types as presented in three videos. This tool consisted of three videos of common PVAs (i.e., double-triggering, auto-triggering, and ineffective triggering). Data were collected via an evaluation sheet distributed to 39 hospitals among the various CCPs, including respiratory therapists (RTs), nurses, and physicians.
Results: A total of 411 CCPs were assessed; of these, only 41 (10.2%) correctly identified the three PVA types, while 92 (22.4%) correctly detected two types and 174 (42.3%) correctly detected one; 25.3% did not recognize any PVA. There were statistically significant differences between trained and untrained CCPs in terms of recognition (three PVAs, p < 0.001; two PVAs, p = 0.001). The majority of CCPs who identified one or zero PVAs were untrained, and such differences among groups were statistically significant (one PVA, p = 0.001; zero PVAs, p = 0.004). Female gender and prior training on ventilator waveforms were found to increase the odds of identifying more than two PVAs correctly, with odds ratios (ORs) (95% confidence intervals [CIs]) of 1.93 (1.07-3.49) and 5.41 (3.26-8.98), respectively. Profession, experience, and hospital characteristics were not found to correlate with increased odds of detecting PVAs; this association generally held after applying a regression model on the RT profession, with the ORs (95% CIs) of prior training (2.89 [1.28-6.51]) and female gender (2.49 [1.15-5.39]) showing the increased odds of detecting two or more PVAs.
Conclusion: Common PVAs detection were found low in critical care settings, with about 25% of PVA going undetected by CCPs. Female gender and prior training on ventilator graphics were the only significant predictive factors among CCPs and RTs in correctly identifying PVAs. There is an urgent need to establish teaching and training programs, policies, and guidelines vis-à-vis the early detection and management of PVAs in mechanically ventilated patients, so as to improve their outcomes.
(Copyright © 2020 Elsevier Inc. All rights reserved.)
Methods: Our observational analysis leveraged a validated evaluation tool to assess the ability of critical care practitioners (CCPs) to detect different PVA types as presented in three videos. This tool consisted of three videos of common PVAs (i.e., double-triggering, auto-triggering, and ineffective triggering). Data were collected via an evaluation sheet distributed to 39 hospitals among the various CCPs, including respiratory therapists (RTs), nurses, and physicians.
Results: A total of 411 CCPs were assessed; of these, only 41 (10.2%) correctly identified the three PVA types, while 92 (22.4%) correctly detected two types and 174 (42.3%) correctly detected one; 25.3% did not recognize any PVA. There were statistically significant differences between trained and untrained CCPs in terms of recognition (three PVAs, p < 0.001; two PVAs, p = 0.001). The majority of CCPs who identified one or zero PVAs were untrained, and such differences among groups were statistically significant (one PVA, p = 0.001; zero PVAs, p = 0.004). Female gender and prior training on ventilator waveforms were found to increase the odds of identifying more than two PVAs correctly, with odds ratios (ORs) (95% confidence intervals [CIs]) of 1.93 (1.07-3.49) and 5.41 (3.26-8.98), respectively. Profession, experience, and hospital characteristics were not found to correlate with increased odds of detecting PVAs; this association generally held after applying a regression model on the RT profession, with the ORs (95% CIs) of prior training (2.89 [1.28-6.51]) and female gender (2.49 [1.15-5.39]) showing the increased odds of detecting two or more PVAs.
Conclusion: Common PVAs detection were found low in critical care settings, with about 25% of PVA going undetected by CCPs. Female gender and prior training on ventilator graphics were the only significant predictive factors among CCPs and RTs in correctly identifying PVAs. There is an urgent need to establish teaching and training programs, policies, and guidelines vis-à-vis the early detection and management of PVAs in mechanically ventilated patients, so as to improve their outcomes.
(Copyright © 2020 Elsevier Inc. All rights reserved.)