Exploring clinicians’ beliefs and practices regarding Non-Invasive Ventilation devices: An international survey study

Kevin Benavente, Eric W. Robbins, Bradley Fujiuchi, Kamran Manzoor, Ehab G. Daoud

Cite

Benavente K, Robbins ER, Fujiuchi B, Manzoor K, Daoud EG. Exploring clinicians’ beliefs and practices regarding Non-Invasive Ventilation devices: An international survey study. J Mech Vent 2023; 4(2):84-91.

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1745 Downloads

Abstract

Introduction

Non-invasive ventilation (NIV) has a significant role in supporting patients with respiratory failure with the goal of avoiding mechanical ventilation. Traditionally, NIV has been applied using dedicated NIV-specific devices but over the last decade, newer generation critical care ventilators have updated their capabilities to include NIV options with improved synchrony and leak compensation. No recent trials have compared the efficacy of new generation critical care ventilators to NIV ventilators. The purpose of this study was to evaluate clinicians attitudes and perceptions toward the use of NIV between the dedicated NIV and critical care ventilators.

Methods

An online survey of clinicians with seven questions regarding their thoughts and experience in using NIV in acute care settings was posted online and promoted through emails and social media. The survey was anonymous and an exemption of consent was obtained from the Institutional Review Board. Analysis of variants (ANOVA) was done for the total responses in each question, followed by multivariate analysis of variants (MANOVA) for responses per occupation.

Results

514 responses from 54 countries were recorded. 151 from North America, 109 from South America, 125 from Europe, 97 from Asia, 21 from Africa, and 11 from Australia. 218 responders were physicians, 218 were respiratory therapists, 28 were nurses, and 50 were reported as other professionals (engineers, biomedical technicians). 346 (67.3%) reported using both types of ventilators for NIV, 91 (17.7%) use only NIV -specific devices, and 77 (15%) only use critical care ventilators (P 0.097), responses per occupation (P < 0.001). 290 (56.4%) have automatic synchronization software on either of their ventilators, 113 (22%) do not, while 111 (21.6%) are unsure if they do (P 0.22), with significant variation by occupation (P 0.008). Regarding synchrony, 233 (45.3%) said NIV ventilators are better, and 165 (32.1%) said critical care ventilators are better, while 116 (22.5%) said both are similar (P 0.59) with significant variation by occupation (P 0.04). Regarding leak compensation, 241 (46.9%) said NIV ventilators are better, and 146 (284%) said critical care ventilators are better, while 127 (24.7%) said both are similar (P 0.6) without significant variation by occupation (P 0.07). Regarding the general opinion of superiority, 273 (53.1%) said NIV ventilators are better, 131 (25.5%) said critical care ventilators are better, and 110 (21.4%) said both are similar (P 0.42) without significant variation by occupation (P 0.098).

Conclusion

Despite the lack of evidence, there is wide variability in opinion with no clear consensus regarding the clinicians’ attitude towards which ventilators are superior to use during NIV, especially according to surveyed occupation.

Keywords

NIV, synchrony, leak compensation

References

1. Girault C, Briel A, Hellot MF, et al. Noninvasive mechanical ventilation in clinical practice: a 2-year experience in a medical intensive care unit. Crit Care Med 2003; 31(2):552-559.
https://doi.org/10.1097/01.CCM.0000050288.49328.F0
PMid:12576965
2. Rose L, Gerdtz MF. Review of non-invasive ventilation in the emergency department: clinical considerations and management priorities. J Clin Nurs 2009; 18(23):3216-3224.
https://doi.org/10.1111/j.1365-2702.2008.02766.x
PMid:19538560
3. Chawla R, Dixit SB, Zirpe KG, et al. ISCCM Guidelines for the use of non-invasive ventilation in acute respiratory failure in adult ICUs. Indian J Crit Care Me 2020; 24(Suppl 1):S61-S81.
https://doi.org/10.5005/jp-journals-10071-G23186
PMid:32205957 PMCid:PMC7085817
4. Scott JB. Ventilators for noninvasive ventilation in adult acute care. Respir Care 2019; 64(6):712-722.
https://doi.org/10.4187/respcare.06652
PMid:31110039
5. Olivieri C, Costa R, Conti G, et al. Bench studies evaluating devices for non-invasive ventilation: critical analysis and future perspectives. Intensive Care Med 2012; 38(1):160-167.
https://doi.org/10.1007/s00134-011-2416-9
PMid:22124770
6. Scala R, Naldi M. Ventilators for noninvasive ventilation to treat acute respiratory failure. Respir Care 2008; 53(8):1054-1080.
7. Vignaux L, Tassaux D, Jolliet P. Performance of noninvasive ventilation modes on ICU ventilators during pressure support: a bench model study. Intensive Care Med 2007; 33(8):1444-1451.
https://doi.org/10.1007/s00134-007-0713-0
PMid:17563875
8. Chatburn RL. Which ventilators and modes can be used to deliver noninvasive ventilation? Respir Care 2009; 54(1):85-101.
9. Owens RL, Wilson KC, Gurubhagavatula I, et al. Philips Respironics recall of positive airway pressure and noninvasive ventilation devices: a brief statement to inform response efforts and identify key steps forward. Am J Respir Crit Care Med 2021; 204(8):887-890.
https://doi.org/10.1164/rccm.202107-1666ED
PMid:34461022 PMCid:PMC8534631
10. Chen CW, Lin WC, Hsu CH, et al. Detecting ineffective triggering in the expiratory phase in mechanically ventilated patients based on airway flow and pressure deflection: feasibility of using a computer algorithm. Crit Care Med 2008; 36(2):455-461.
https://doi.org/10.1097/01.CCM.0000299734.34469.D9
PMid:18091543
11. Vignaux L, Vargas F, Roeseler J, et al. Patient-ventilator asynchrony during non-invasive ventilation for acute respiratory failure: a multicenter study. Intensive Care Med 2009; 35:840-846.
https://doi.org/10.1007/s00134-009-1416-5
PMid:19183949
12. Cuvelier A, Achour L, Rabarimanantsoa H, et al. A noninvasive method to identify ineffective triggering in patients with noninvasive pressure support ventilation. Respiration 2010; 80(3):198-206.
https://doi.org/10.1159/000264606
PMid:19955701
13. Vignaux L, Tassaux D, Carteaux G. et al. Performance of noninvasive ventilation algorithms on ICU ventilators during pressure support: a clinical study. Intensive Care Med 2010; 36:2053-2059.
https://doi.org/10.1007/s00134-010-1994-2
PMid:20689921
14. Carteaux G, Lyazidi A, Cordoba-Izquierdo A, et al. Patient-ventilator asynchrony during noninvasive ventilation: a bench and clinical study. Chest 2012; 142(2):367-376.
https://doi.org/10.1378/chest.11-2279
PMid:22406958
15. Doorduin J, Sinderby CA, Beck J. et al. Automated patient-ventilator interaction analysis during neurally adjusted non-invasive ventilation and pressure support ventilation in chronic obstructive pulmonary disease. Crit Care 2014; 18(5):550.
https://doi.org/10.1186/s13054-014-0550-9
PMid:25307894 PMCid:PMC4207887
16. Longhini F, Colombo D, Pisani L, et al. Efficacy of ventilator waveform observation for detection of patient-ventilator asynchrony during NIV: a multicentre study. ERJ Open Res 2017; 3(4):00075-2017.
https://doi.org/10.1183/23120541.00075-2017
PMid:29204431 PMCid:PMC5703352
17. Mulqueeny Q, Ceriana P, Carlucci A. et al. Automatic detection of ineffective triggering and double triggering during mechanical ventilation. Intensive Care Med 2007; 33(11):2014-2018.
https://doi.org/10.1007/s00134-007-0767-z
PMid:17611736
18. Letellier C, Lujan M, Arnal JM, et al. Patient-ventilator synchronization during non-invasive ventilation: A pilot study of an automated analysis system. Front Med Technol 2021; 3:690442.
https://doi.org/10.3389/fmedt.2021.690442
PMid:35047935 PMCid:PMC8757845
19. Chouvarda IG, Babalis D, Papaioannou V et al. Multiparametric modeling of the ineffective efforts in assisted ventilation within an ICU. Med Biol Eng Comput 2016; 54(2-3):441-451.
https://doi.org/10.1007/s11517-015-1328-1
PMid:26081905
20. Gholami B, Phan TS, Haddad WM, et al. Replicating human expertise of mechanical ventilation waveform analysis in detecting patient-ventilator cycling asynchrony using machine learning. Comput Biol Med 2018; 97:137-144.
https://doi.org/10.1016/j.compbiomed.2018.04.016
PMid:29729488
21. Bakkes THGF, Montree RJH, Mischi M, et al. A machine learning method for automatic detection and classification of patient-ventilator asynchrony. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:150-153.
https://doi.org/10.1109/EMBC44109.2020.9175796
PMid:33017952
22. Zhang L, Mao K, Duan K, et al. Detection of patient-ventilator asynchrony from mechanical ventilation waveforms using a two-layer long short-term memory neural network. Comput Biol Med 2020; 120:103721.
https://doi.org/10.1016/j.compbiomed.2020.103721
PMid:32250853
23. Pan Q, Zhang L, Jia M, et al. An interpretable 1D convolutional neural network for detecting patient-ventilator asynchrony in mechanical ventilation. Comput Methods Programs Biomed 2021; 204:106057.
https://doi.org/10.1016/j.cmpb.2021.106057
PMid:33836375
24. Bakkes T, van Diepen A, De Bie A, et al. Automated detection and classification of patient-ventilator asynchrony by means of machine learning and simulated data. Comput Methods Programs Biomed 2023; 230:107333.
https://doi.org/10.1016/j.cmpb.2022.107333
PMid:36640603
25. De Luca A, Sall FS, Khoury A. Leak Compensation Algorithms: The Key Remedy to Noninvasive Ventilation Failure? Respir Care 2017; 62(1):135-136.
https://doi.org/10.4187/respcare.05289
PMid:28003559
26. Oto J, Chenelle CT, Marchese AD, et al. A comparison of leak compensation in acute care ventilators during noninvasive and invasive ventilation: a lung model study. Respir Care 2013; 58(12):2027-2037.
https://doi.org/10.4187/respcare.02466
PMid:23696688
27. Zhu K, Rabec C, Gonzalez-Bermejo J, et al. Combined effects of leaks, respiratory system properties and upper airway patency on the performance of home ventilators: a bench study. BMC Pulm Med 2017; 17(1):145.
https://doi.org/10.1186/s12890-017-0487-2
PMid:29157220 PMCid:PMC5697337
28. Miyoshi E, Fujino Y, Uchiyama A, et al. Effects of gas leak on triggering function, humidification, and inspiratory oxygen fraction during noninvasive positive airway pressure ventilation. Chest 2005: 128:3691-3698
https://doi.org/10.1378/chest.128.5.3691
PMid:16304335
29. Ferreira JC, Chipman DW, Hill NS, et al. Bilevel vs ICU ventilators providing noninvasive ventilation: effect of system leaks: a COPD lung model comparison. Chest 2009;136(2):448-456.
https://doi.org/10.1378/chest.08-3018
PMid:19429723
30. Crimi C, Noto A, Princi P, et al. A European survey of noninvasive ventilation practices. Eur Respir J 2010; 36(2):362-369.
https://doi.org/10.1183/09031936.00123509
PMid:20075052
31. Lofaso F, Brochard L, Touchard D, et al. Evaluation of carbon dioxide rebreathing during pressure support ventilation with airway management system (BiPAP) devices. Chest 1995; 108(3):772-778.
https://doi.org/10.1378/chest.108.3.772
PMid:7656632