Display options
Cite
Patel B, Mumby S, Johnson N, et al. Decision support system to evaluate ventilation in the acute respiratory distress syndrome (DeVENT study)-trial protocol. Trials. 2022;23(1):47doi: 10.1186/s13063-021-05967-2.
Patel, B., Mumby, S., Johnson, N., Falaschetti, E., Hansen, J., Adcock, I., McAuley, D., Takata, M., Karbing, D. S., Jabaudon, M., Schellengowski, P., Rees, S. E. (2022). Decision support system to evaluate ventilation in the acute respiratory distress syndrome (DeVENT study)-trial protocol. Trials, 23(1), 47. https://doi.org/10.1186/s13063-021-05967-2
Patel, Brijesh, et al. "Decision support system to evaluate ventilation in the acute respiratory distress syndrome (DeVENT study)-trial protocol." Trials vol. 23,1 (2022): 47. doi: https://doi.org/10.1186/s13063-021-05967-2
Patel B, Mumby S, Johnson N, Falaschetti E, Hansen J, Adcock I, McAuley D, Takata M, Karbing DS, Jabaudon M, Schellengowski P, Rees SE. Decision support system to evaluate ventilation in the acute respiratory distress syndrome (DeVENT study)-trial protocol. Trials. 2022 Jan 17;23(1):47. doi: 10.1186/s13063-021-05967-2. PMID: 35039050.
Copy Download .nbib Format: NLM
Share it on

Trials. 2022 Jan 17;23(1):47. doi: 10.1186/s13063-021-05967-2.

Decision support system to evaluate ventilation in the acute respiratory distress syndrome (DeVENT study)-trial protocol.

Trials

Brijesh Patel, Sharon Mumby, Nicholas Johnson, Emanuela Falaschetti, Jorgen Hansen, Ian Adcock, Danny McAuley, Masao Takata, Dan S Karbing, Matthieu Jabaudon, Peter Schellengowski, Stephen E Rees,

Affiliations

  1. Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Imperial College, London, UK. [email protected].
  2. Airway Disease, National, Heart & Lung Institute, Imperial College, London, UK.
  3. Imperial Clinical Trials Unit, Stadium House, 68 Wood Lane, London, W12 7RH, UK.
  4. Mermaid Care A/S, Aalborg, Denmark.
  5. Wellcome-Wolfson Institute for Experimental Medicine, Queen's University, Belfast, UK.
  6. Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Imperial College, London, UK.
  7. Respiratory and Critical Care Group, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
  8. Department of Perioperative Medicine, University Hospital of Clermont-Ferrand, GReD, Université Clermont Auvergne, CNRS, INSERM, Clermont-Ferrand, France.
  9. Medical University of Vienna, Department of Medicine I, Waehringer Guertel 18-20, A-1090, Vienna, Austria.

PMID: 35039050 DOI: 10.1186/s13063-021-05967-2

Abstract

BACKGROUND: The acute respiratory distress syndrome (ARDS) occurs in response to a variety of insults, and mechanical ventilation is life-saving in this setting, but ventilator-induced lung injury can also contribute to the morbidity and mortality in the condition. The Beacon Caresystem is a model-based bedside decision support system using mathematical models tuned to the individual patient's physiology to advise on appropriate ventilator settings. Personalised approaches using individual patient description may be particularly advantageous in complex patients, including those who are difficult to mechanically ventilate and wean, in particular ARDS.

METHODS: We will conduct a multi-centre international randomised, controlled, allocation concealed, open, pragmatic clinical trial to compare mechanical ventilation in ARDS patients following application of the Beacon Caresystem to that of standard routine care to investigate whether use of the system results in a reduction in driving pressure across all severities and phases of ARDS.

DISCUSSION: Despite 20 years of clinical trial data showing significant improvements in ARDS mortality through mitigation of ventilator-induced lung injury, there remains a gap in its personalised application at the bedside. Importantly, the protective effects of higher positive end-expiratory pressure (PEEP) were noted only when there were associated decreases in driving pressure. Hence, the pressures set on the ventilator should be determined by the diseased lungs' pressure-volume relationship which is often unknown or difficult to determine. Knowledge of extent of recruitable lung could improve the ventilator driving pressure. Hence, personalised management demands the application of mechanical ventilation according to the physiological state of the diseased lung at that time. Hence, there is significant rationale for the development of point-of-care clinical decision support systems which help personalise ventilatory strategy according to the current physiology. Furthermore, the potential for the application of the Beacon Caresystem to facilitate local and remote management of large numbers of ventilated patients (as seen during this COVID-19 pandemic) could change the outcome of mechanically ventilated patients during the course of this and future pandemics.

TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT04115709. Registered on 4 October 2019, version 4.0.

© 2022. The Author(s).

Keywords: COVID-19; Critical care; Decision support; Mechanical ventilation; Acute respiratory distress syndrome; Pandemic

References

  1. Network ARDS. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. The Acute Respiratory Distress Syndrome Network. N Engl J Med. 2000;342(18):1301–8. - PubMed
  2. Amato MBP, Meade MO, Slutsky AS, Brochard L, Costa ELV, Schoenfeld DA, et al. Driving pressure and survival in the acute respiratory distress syndrome. N Engl J Med. 2015;372(8):747–55. https://doi.org/10.1056/NEJMsa1410639 . - PubMed
  3. Lung NH, Blood Institute Acute Respiratory Distress Syndrome ARDS Clinical Trials Network, Wiedemann HP, Wheeler AP, Bernard GR, Thompson BT, Hayden D, et al. Comparison of two fluid-management strategies in acute lung injury. N Engl J Med. 2006;354(24):2564–75. https://doi.org/10.1056/NEJMoa062200 . - PubMed
  4. Briel M, Meade M, Mercat A, Brower RG, Talmor D, Walter SD, et al. Higher vs lower positive end-expiratory pressure in patients with acute lung injury and acute respiratory distress syndrome: systematic review and meta-analysis. JAMA. 2010;303(9):865–73. https://doi.org/10.1001/jama.2010.218 . - PubMed
  5. Guerin C, Reignier J, Richard J-C, Beuret P, Gacouin A, Boulain T, et al. Prone positioning in severe acute respiratory distress syndrome. N Engl J Med. 2013;368(23):2159–68. https://doi.org/10.1056/NEJMoa1214103 . - PubMed
  6. Needham DM, Colantuoni E, Mendez-Tellez PA, Dinglas VD, Sevransky JE, Dennison Himmelfarb CR, et al. Lung protective mechanical ventilation and two year survival in patients with acute lung injury: prospective cohort study. BMJ. 2012;344(apr05 2):e2124. - PubMed
  7. Cavalcanti AB, Suzumura ÉA, Laranjeira LN, Paisani D, de M, Damiani LP, et al. Effect of lung recruitment and titrated positive end-expiratory pressure (PEEP) vs low PEEP on mortality in patients with acute respiratory distress syndrome: a randomized clinical trial. JAMA. 2017;318(14):1335–45. https://doi.org/10.1001/jama.2017.14171 . - PubMed
  8. Zampieri FG, Costa EL, Iwashyna TJ, Carvalho CRR, Damiani LP, Taniguchi LU, et al. Heterogeneous effects of alveolar recruitment in acute respiratory distress syndrome: a machine learning reanalysis of the Alveolar Recruitment for Acute Respiratory Distress Syndrome Trial. Br J Anaesth. 2019;5(1):88–95. https://doi.org/10.1016/j.bja.2019.02.026 . - PubMed
  9. Rees SE, Allerød C, Murley D, Zhao Y, Smith BW, Kjaergaard S, et al. Using physiological models and decision theory for selecting appropriate ventilator settings. J Clin Monit Comput. 2006;20(6):421–9. https://doi.org/10.1007/s10877-006-9049-5 . - PubMed
  10. Rees SE, Karbing DS. Determining the appropriate model complexity for patient-specific advice on mechanical ventilation. Biomed Tech. 2017;62(2):183–98. https://doi.org/10.1515/bmt-2016-0061 . - PubMed
  11. Spadaro S, Karbing DS, Mauri T, Marangoni E, Mojoli F, Valpiani G, et al. Effect of positive end-expiratory pressure on pulmonary shunt and dynamic compliance during abdominal surgery. Br J Anaesth. 2016;116(6):855–61. https://doi.org/10.1093/bja/aew123 . - PubMed
  12. Spadaro S, Grasso S, Karbing DS, Fogagnolo A, Contoli M, Bollini G, et al. Physiologic evaluation of ventilation perfusion mismatch and respiratory mechanics at different positive end-expiratory pressure in patients undergoing protective one-lung ventilation. Anesthesiology. 2018;128(3):531–8. https://doi.org/10.1097/ALN.0000000000002011 . - PubMed
  13. Kjaergaard S, Rees SE, Nielsen JA, Freundlich M, Thorgaard P, Andreassen S. Modelling of hypoxaemia after gynaecological laparotomy. Acta Anaesthesiol Scand. 2001;45(3):349–56. https://doi.org/10.1034/j.1399-6576.2001.045003349.x . - PubMed
  14. Karbing DS, Kjaergaard S, Andreassen S, Espersen K, Rees SE. Minimal model quantification of pulmonary gas exchange in intensive care patients. Med Eng Phys. 2011;33(2):240–8. https://doi.org/10.1016/j.medengphy.2010.10.007 . - PubMed
  15. Karbing DS, Kjaergaard S, Smith BW, Espersen K, Allerød C, Andreassen S, et al. Variation in the PaO2/FiO2 ratio with FiO2: mathematical and experimental description, and clinical relevance. Crit Care. 2007;11(6):R118. https://doi.org/10.1186/cc6174 . - PubMed
  16. Kjaergaard S, Rees S, Malczynski J, Nielsen JA, Thorgaard P, Toft E, et al. Non-invasive estimation of shunt and ventilation-perfusion mismatch. Intensive Care Med. 2003;29(5):727–34. https://doi.org/10.1007/s00134-003-1708-0 . - PubMed
  17. Rees SE, Kjaergaard S, Andreassen S, Hedenstierna G. Reproduction of inert gas and oxygenation data: a comparison of the MIGET and a simple model of pulmonary gas exchange. Intensive Care Med. 2010;36(12):2117–24. https://doi.org/10.1007/s00134-010-1981-7 . - PubMed
  18. Rees SE, Kjaergaard S, Andreassen S, Hedenstierna G. Reproduction of MIGET retention and excretion data using a simple mathematical model of gas exchange in lung damage caused by oleic acid infusion. J Appl Physiol. 2006;101(3):826–32. https://doi.org/10.1152/japplphysiol.01481.2005 . - PubMed
  19. Rees SE, Andreassen S. Mathematical models of oxygen and carbon dioxide storage and transport: the acid-base chemistry of blood. Crit Rev Biomed Eng. 2005;33(3):209–64. https://doi.org/10.1615/CritRevBiomedEng.v33.i3.10 . - PubMed
  20. Rees SE, Klaestrup E, Handy J, Andreassen S, Kristensen SR. Mathematical modelling of the acid-base chemistry and oxygenation of blood: a mass balance, mass action approach including plasma and red blood cells. Eur J Appl Physiol. 2010;108(3):483–94. https://doi.org/10.1007/s00421-009-1244-x . - PubMed
  21. Larraza S, Dey N, Karbing DS, Jensen JB, Nygaard M, Winding R, et al. A mathematical model approach quantifying patients' response to changes in mechanical ventilation: evaluation in volume support. Med Eng Phys. 2015;37(4):341–9. https://doi.org/10.1016/j.medengphy.2014.12.006 . - PubMed
  22. Karbing DS, Spadaro S, Dey N, Ragazzi R, Marangoni E, Dalla Corte F, et al. An open-loop, physiologic model-based decision support system can provide appropriate ventilator settings. Crit Care Med. 2018;46(7):e642–8. https://doi.org/10.1097/CCM.0000000000003133 . - PubMed
  23. Spadaro S, Karbing DS, Dalla Corte F, Mauri T, Moro F, Gioia A, et al. An open-loop, physiological model based decision support system can reduce pressure support while acting to preserve respiratory muscle function. J Crit Care. 2018;48:407–13. https://doi.org/10.1016/j.jcrc.2018.10.003 . - PubMed

Publication Types

Grant support