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Annals of Biomedical Engineering

, Volume 47, Issue 7, pp 1626–1641 | Cite as

Predictive Virtual Patient Modelling of Mechanical Ventilation: Impact of Recruitment Function

  • Sophie E. MortonEmail author
  • Jennifer L. Knopp
  • J. Geoffrey Chase
  • Knut Möller
  • Paul Docherty
  • Geoffrey M. Shaw
  • Merryn Tawhai
Article

Abstract

Mechanical ventilation is a life-support therapy for intensive care patients suffering from respiratory failure. To reduce the current rate of ventilator-induced lung injury requires ventilator settings that are patient-, time-, and disease-specific. A common lung protective strategy is to optimise the level of positive end-expiratory pressure (PEEP) through a recruitment manoeuvre to prevent alveolar collapse at the end of expiration and to improve gas exchange through recruitment of additional alveoli. However, this process can subject parts of the lung to excessively high pressures or volumes. This research significantly extends and more robustly validates a previously developed pulmonary mechanics model to predict lung mechanics throughout recruitment manoeuvres. In particular, the process of recruitment is more thoroughly investigated and the impact of the inclusion of expiratory data when estimating peak inspiratory pressure is assessed. Data from the McREM trial and CURE pilot trial were used to test model predictive capability and assumptions. For PEEP changes of up to and including 14 cmH2O, the parabolic model was shown to improve peak inspiratory pressure prediction resulting in less than 10% absolute error in the CURE cohort and 16% in the McREM cohort. The parabolic model also better captured expiratory mechanics than the exponential model for both cohorts.

Keywords

In-silico Intensive care Mechanical ventilation PEEP Prediction Virtual patient 

Notes

Acknowledgments

This work was supported by the NZ Tertiary Education Commission (TEC) fund MedTech CoRE (E6391) (Centre of Research Expertise) and the NZ National Science Challenge 7, Science for Technology and Innovation (E6525). The authors also acknowledge support from the Engineering Technology-based Innovation in Medicine (eTIME) consortium grant [eTIME 318943]; the EU FP7 International Research Staff Exchange Scheme (IRSES) Grant [#PIRSES-GA-2012-318943].

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Copyright information

© Biomedical Engineering Society 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of CanterburyChristchurchNew Zealand
  2. 2.Institute of Technical MedicineFurtwangen UniversityVillingen-SchwenningenGermany
  3. 3.Department of Intensive CareChristchurch HospitalChristchurchNew Zealand
  4. 4.Auckland Bioengineering InstituteThe University of AucklandAucklandNew Zealand

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