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A novel adaptive control system for noisy pressure-controlled ventilation: a numerical simulation and bench test study

Alessandro Beda| Peter M. Spieth| Thomas Handzsuj| Paolo Pelosi| Nadja C. Carvalho| Edmund Koch| Thea Koch| Marcelo Gama de Abreu
Physiological and Technical Notes
Volume 36, Issue 1 / January , 2010

Pages 164 - 168

Abstract

Purpose

There is growing interest in the use of both variable and pressure-controlled ventilation (PCV). The combination of these approaches as “noisy PCV” requires adaptation of the mechanical ventilator to the respiratory system mechanics. Thus, we developed and evaluated a new control system based on the least-mean-squares adaptive approach, which automatically and continuously adjusts the driving pressure during PCV to achieve the desired variability pattern of tidal volume (VT).

Methods

The controller was tested during numerical simulations and with a physical model reproducing the mechanical properties of the respiratory system. We applied step changes in respiratory system mechanics and mechanical ventilation settings. The time needed to converge to the desired VT variability pattern after each change (tc) and the difference in minute ventilation between the measured and target pattern of VT (ΔMV) were determined.

Results

During numerical simulations, the control system for noisy PCV achieved the desired variable VT pattern in less than 30 respiratory cycles, with limited influence of the dynamic elastance (E*) on tc, except when E* was underestimated by >25%. We also found that, during tests in the physical model, the control system converged in <60 respiratory cycles and was not influenced by airways resistance. In all measurements, the absolute value of ΔMV was <25%.

Conclusion

The new control system for noisy PCV can prove useful for controlled mechanical ventilation in the intensive care unit.

Keywords

References

  1. Gama de Abreu M, Spieth PM, Pelosi P, Carvalho AR, Walter C, Schreiber-Ferstl A, Aikele P, Neykova B, Hubler M, Koch T (2008) Noisy pressure support ventilation: a pilot study on a new assisted ventilation mode in experimental lung injury. Crit Care Med 36:818–827
    • View reference on publisher's website
    • View reference on PubMed
  2. Spieth P, Carvalho AR, Güldner A, Pelosi P, Kirichuck O, Koch T, Gama de Abreu M (2009) Effects of different levels of pressure support variability in experimental lung injury. Anesthesiology 110:342–350
    • View reference on PubMed
  3. Spieth PM, Carvalho AR, Pelosi P, Hoehn C, Meissner C, Kasper M, Hubler M, von Neindorff M, Dassow C, Barrenschee M, Uhlig S, Koch T, Gama de Abreu M (2009) Variable tidal volumes improve lung protective ventilation strategies in experimental lung injury. Am J Respir Crit Care Med 179:684–693
    • View reference on publisher's website
    • View reference on PubMed
  4. Lefevre GR, Kowalski SE, Girling LG, Thiessen DB, Mutch WA (1996) Improved arterial oxygenation after oleic acid lung injury in the pig using a computer-controlled mechanical ventilator. Am J Respir Crit Care Med 154:1567–1572
    • View reference on PubMed
  5. Tugrul M, Camci E, Karadeniz H, Senturk M, Pembeci K, Akpir K (1997) Comparison of volume controlled with pressure controlled ventilation during one-lung anaesthesia. Br J Anaesth 79:306–310
    • View reference on PubMed
  6. Prella M, Feihl F, Domenighetti G (2002) Effects of short-term pressure-controlled ventilation on gas exchange, airway pressures, and gas distribution in patients with acute lung injury/ARDS. Chest 122:1382–1388
    • View reference on publisher's website
    • View reference on PubMed
  7. Putensen C, Zech S, Wrigge H, Zinserling J, Stuber F, von Spiegel T, Mutz N (2001) Long-term effects of spontaneous breathing during ventilatory support in patients with acute lung injury. Am J Respir Crit Care Med 164:43–49
    • View reference on PubMed
  8. Widrow B, Stearns SD (1985) Adaptive signal processing. Prentice-Hall, Englewood Cliffs
  9. Khoo MC (1999) Physiological control systems: analysis simulation and estimation. Wiley-IEEE Press, New York
  10. Tobin MJ, Mador MJ, Guenther SM, Lodato RF, Sackner MA (1988) Variability of resting respiratory drive and timing in healthy subjects. J Appl Physiol 65:309–317
    • View reference on PubMed
  11. Boker A, Graham MR, Walley KR, McManus BM, Girling LG, Walker E, Lefevre GR, Mutch WA (2002) Improved arterial oxygenation with biologically variable or fractal ventilation using low tidal volumes in a porcine model of acute respiratory distress syndrome. Am J Respir Crit Care Med 165:456–462
    • View reference on PubMed
  12. Dojat M, Brochard L, Lemaire F, Harf A (1992) A knowledge-based system for assisted ventilation of patients in intensive care units. Int J Clin Monit Comput 9:239–250
    • View reference on publisher's website
    • View reference on PubMed
  13. Jandre FC, Pino AV, Lacorte I, Neves JH, Giannella-Neto A (2004) A closed-loop mechanical ventilation controller with explicit objective functions. IEEE Trans Biomed Eng 51:823–831
    • View reference on publisher's website
    • View reference on PubMed
  14. Laubscher TP, Heinrichs W, Weiler N, Hartmann G, Brunner JX (1994) An adaptive lung ventilation controller. IEEE Trans Biomed Eng 41:51–59
    • View reference on publisher's website
    • View reference on PubMed
  15. Mersmann S, Dojat M (2004) SmartCare––automated clinical guidelines in critical care. In: Proceedings of 16th Eur Conf on artificial intelligence, pp 745–749
  16. Romero PV, Rodriguez B, Lopez-Aguilar J, Manresa F (1998) Parallel airways inhomogeneity and lung tissue mechanics in transition to constricted state in rabbits. J Appl Physiol 84:1040–1047
    • View reference on PubMed
  17. Feuer A, Weinstein E (1985) Convergence analysis of LMS filters with uncorrelated Gaussian data. IEEE Trans Acoust Speech 33:222–230
    • View reference on publisher's website

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