Advertisement

A Modular Patient Simulator for Evaluation of Decision Support Algorithms in Mechanically Ventilated Patients

  • Jörn KretschmerEmail author
  • Thomas Lehmann
  • Daniel Redmond
  • Patrick Stehle
  • Knut Möller
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 57)

Abstract

Mechanical ventilation is a life-saving intervention, which, despite being routinely used in ICUs, poses the risk of causing further damage to the lung tissue if the ventilator is set inappropriately. Medical decision support systems may help in optimizing ventilator settings according to therapy goals given by the clinician. Before using the decision support algorithms in commercially available systems, extensive tests are necessary to ensure patient safety and correct decision making. Model-based patient simulators can assist in evaluating such decision support systems by creating different clinical scenarios. We propose a new Java based patient simulator that implements various models of respiratory mechanics, gas exchange and cardiovascular dynamics to form a complex patient model. The implemented models interact with one another to allow simulation of the ventilators influence on various physiological processes. Model simulations are running in real-time and simulation results can be extracted via multiple interfaces. Each of the implemented models has been validated to exhibit physiologically correct behavior. Results of the combined model system also showed to be physiologically plausible.

Keywords

Patient simulator Physiological modeling Model-based decision support Mechanical ventilation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    D. Dreyfuss and G. Saumon (1998) From ventilator-induced lung injury to multiple organ dysfunction. Intensive Care Med 24:102-4Google Scholar
  2. 2.
    A. S. Slutsky and L. Tremblay (1998) Multiple System Organ Failure – is mechanical ventilation a contributing factor. Am J Respir Crit Care Med 157:1721-5Google Scholar
  3. 3.
    G. Nash, J. B. Blennerhassett, and H. Pontoppidan (1967) Pulmonary lesions associated with oxygen therapy and artificial ventilation. N Engl J Med 276:368-74Google Scholar
  4. 4.
    R. S. Campbell, R. D. Branson, and J. A. Johannigman (2001) Adaptive support ventilation. Respir Care Clin N Am 7:425-40, ixGoogle Scholar
  5. 5.
    S. Lozano, K. Möller, A. Brendle et al. (2008) AUTOPILOT-BT: a system for knowledge and model based mechanical ventilation. Technol Health Care 16:1-11Google Scholar
  6. 6.
    S. E. Rees, C. Allerød, D. Murley et al. (2006) Using physiological models and decision theory for selecting appropriate ventilator settings. J Clin Monit Comput 20:421-9Google Scholar
  7. 7.
    G. W. Rutledge, G. E. Thomsen, B. R. Farr et al. (1993) The design and implementation of a ventilator-management advisor. Artif Intell Med 5:67-82Google Scholar
  8. 8.
    F. T. Tehrani and S. Abbasi (2012) A model-based decision support system for critiquing mechanical ventilation treatments. J Clin Monit Comput 26:207-15Google Scholar
  9. 9.
    F. T. Tehrani and J. H. Roum (2008) Flex: a new computerized system for mechanical ventilation. J Clin Monit Comput 22:121-30Google Scholar
  10. 10.
    C. Schranz, T. Becher, D. Schadler et al. (2014) Model-based setting of inspiratory pressure and respiratory rate in pressure-controlled ventilation. Physiol Meas 35:383-97Google Scholar
  11. 11.
    F. Galia (2010) Supervision automatique de la ventilation artificielle en soins intensifs: investigation d’un système existant et propositions d’extensions., PhD Thesis, Engineering Sciences, Université Paris-Est, Paris, 2010Google Scholar
  12. 12.
    T. L. Davis (1985) Teaching Physiology Through Interactive Simulation of Hemodynamics, Master Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Boston, 1985Google Scholar
  13. 13.
    T. Heldt, R. Mukkamala, G. B. Moody et al. (2010) CVSim: An Open-Source Cardiovascular Simulator for Teaching and Research. Open Pacing Electrophysiol Ther J 3:45-54Google Scholar
  14. 14.
    H. F. Kowk, M. Mahfouf, K. M. Goode et al. (2003) The use of a patient simulator for knowledge acquisition from the clinicians. Int J Simulation 4:50-61Google Scholar
  15. 15.
    M. C. K. Khoo (1999) Physiological control systems: Analysis, simulation, and estimation. John Wiley & Sons, Hoboken, New JerseyGoogle Scholar
  16. 16.
    C. Schranz, P. D. Docherty, Y. S. Chiew et al. (2012) Iterative integral parameter identification of a respiratory mechanics model. Biomed Eng Online 11:38Google Scholar
  17. 17.
    C. Schranz, P. D. Docherty, Y. S. Chiew et al. (2012) Structural Identifiability and Practical Applicability of an Alveolar Recruitment Model for ARDS Patients. IEEE Trans Biomed Eng. 59:3396-404Google Scholar
  18. 18.
    D. Redmond, J. Kretschmer, Y. S. Chiew et al. (2015) Modelling expiration using viscoelastic pressure dependant recruitment models - is it the same as inspiration, 25th Congress of the International Society of Biomechanics, Glasgow, 2015Google Scholar
  19. 19.
    C. Schranz, J. Kretschmer, and K. Moller (2013) Hierarchical individualization of a recruitment model with a viscoelastic component for ARDS patients. Conf Proc IEEE Eng Med Biol Soc 2013:5220-3Google Scholar
  20. 20.
    L. Chiari, G. Avanzolini, and M. Ursino (1997) A comprehensive simulator of the human respiratory system: validation with experimental and simulated data. Ann Biomed Eng 25:985-99Google Scholar
  21. 21.
    H. Benallal and T. Busso (2000) Analysis of end-tidal and arterial PCO2 gradients using a breathing model. Eur J Appl Physiol 83:402-8Google Scholar
  22. 22.
    H. Benallal, C. Denis, F. Prieur et al. (2002) Modeling of end-tidal and arterial PCO2 gradient: comparison with experimental data. Med Sci Sports Exerc 34:622-9Google Scholar
  23. 23.
    M. F. V. Melo, J. A. Loeppky, A. Caprihan et al. (1993) Alveolar ventilation to perfusion heterogeneity and diffusion impairment in a mathematical model of gas exchange. Comput Biomed Res 26:103-20Google Scholar
  24. 24.
    J. Kretschmer (2013) Komplexe Modellsysteme zur Automatisierung mechanischer Beatmung, PhD Thesis, Klinik für Anästhesiologie und Intensivpflege, Dresden University of Technology, Dresden, 2013Google Scholar
  25. 25.
    J. Kretschmer, A. Riedlinger, T. Becher et al. (2013) A Family of Physiological Models to Simulate Human Gas Exchange. Biomed Tech 58 (Suppl. 1)Google Scholar
  26. 26.
    R. W. deBoer, J. M. Karemaker, and J. Strackee (1987) Hemodynamic fluctuations and baroreflex sensitivity in humans: a beat-to-beat model. Am J Physiol 253:H680-9Google Scholar
  27. 27.
    T. Parlikar and G. Verghese (2005) A simple cycle-averaged model for cardiovascular dynamics. Conf Proc IEEE Eng Med Biol Soc 5:5490-4Google Scholar
  28. 28.
    M. Danielsen and J. T. Ottesen (2004), “A cardiovascular model” in Applied mathematical models in human physiology. Society for Industrial and Applied Mathematics, Philadelphia, 113-26Google Scholar
  29. 29.
    M. S. Leaning, H. E. Pullen, E. R. Carson et al. (1983) Modelling a complex biological system: the human cardiovascular system — 1. Methodology and model description. T I Meas Control 5:71-86Google Scholar
  30. 30.
    J. Kretschmer, T. Haunsberger, E. Drost et al. (2014) Simulating physiological interactions in a hybrid system of mathematical models. J Clin Monit Comput 28:513-23Google Scholar
  31. 31.
    C. Schranz, J. Guttmann, and K. Möller (2010) An Approach towards Parameter Identification in Hierarchical Models of Respiratory Mechanics. Biomed Tech. 55 (Suppl. 1)Google Scholar
  32. 32.
    D. Laude, M. Goldman, P. Escourrou et al. (1993) Effect of breathing pattern on blood pressure and heart rate oscillations in humans. Clin Exp Pharmacol Physiol 20:619-26Google Scholar
  33. 33.
    M. J. Purves (1966) Fluctuations of arterial oxygen tension which have the same period as respiration. Respir Physiol 1:281-96Google Scholar
  34. 34.
    E. M. Williams, J. P. Viale, R. M. Hamilton et al. (2000) Within-breath arterial PO2 oscillations in an experimental model of acute respiratory distress syndrome. Br J Anaesth 85:456-9Google Scholar
  35. 35.
    N. Gut, J. Kretschmer, G. Vanyi et al. (2012) Design of a mechanical lung simulator. A concept study, 6th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), Shanghai, China, 2012, 767-70Google Scholar
  36. 36.
    C. Knöbel and K. Möller (2013) Control of an Active Lung Simulator using a Real Time Controller, 4th Dutch Conference on Bio-Medical Engineering, Egmond aan Zee, The Netherlands, 2013, 128Google Scholar
  37. 37.
    J. Kretschmer, A. Riedlinger, and K. Möller (2015) Evaluation of an algorithm to choose between competing models of respiratory mechanics. Current Directions in Biomedical Engineering 1:428Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jörn Kretschmer
    • 1
    Email author
  • Thomas Lehmann
    • 2
  • Daniel Redmond
    • 3
  • Patrick Stehle
    • 1
  • Knut Möller
    • 1
  1. 1.Institute of Technical MedicineFurtwangen UniversityVillingen-SchwenningenGermany
  2. 2.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.Department of Mechanical EngineeringUniversity of CanterburyChristchurchNew Zealand

Personalised recommendations