A virtual patient model for mechanical ventilation

In-silico; Intensive care; Mechanical ventilation; PEEP; Prediction; Virtual patient; Biological organs; Forecasting; Patient treatment; Ventilation; Virtual patients; Physiological models

Abstract :

[en] Background and Objectives: Mechanical ventilation (MV) is a primary therapy for patients with acute respiratory failure. However, poorly selected ventilator settings can cause further lung damage due to heterogeneity of healthy and damaged alveoli. Varying positive-end-expiratory-pressure (PEEP) to a point of minimum elastance is a lung protective ventilator strategy. However, even low levels of PEEP can lead to ventilator induced lung injury for individuals with highly inflamed pulmonary tissue. Hence, models that could accurately predict peak inspiratory pressures after changes to PEEP could improve clinician confidence in attempting potentially beneficial treatment strategies. Methods: This study develops and validates a physiologically relevant respiratory model that captures elastance and resistance via basis functions within a well-validated single compartment lung model. The model can be personalised using information available at a low PEEP to predict lung mechanics at a higher PEEP. Proof of concept validation is undertaken with data from four patients and eight recruitment manoeuvre arms. Results: Results show low error when predicting upwards over the clinically relevant pressure range, with the model able to predict peak inspiratory pressure with less than 10% error over 90% of the range of PEEP changes up to 12 cmH2O. Conclusions: The results provide an in-silico model-based means of predicting clinically relevant responses to changes in MV therapy, which is the foundation of a first virtual patient for MV. © 2018 Elsevier B.V.

Disciplines :

Anesthesia & intensive care

Author, co-author :

Morton, S. E.; Department of Mechanical Engineering, University of Canterbury, New Zealand

Dickson, J.; Department of Mechanical Engineering, University of Canterbury, New Zealand

Chase, J. G.; Department of Mechanical Engineering, University of Canterbury, New Zealand

Docherty, P.; Department of Mechanical Engineering, University of Canterbury, New Zealand

Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles

Howe, S. L.; Department of Mechanical Engineering, University of Canterbury, New Zealand

Shaw, G. M.; Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand

Tawhai, M.; Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand

Language :

English

Title :

A virtual patient model for mechanical ventilation

Publication date :

2018

Journal title :

Computer Methods and Programs in Biomedicine

ISSN :

0169-2607

eISSN :

1872-7565

Publisher :

Elsevier Ireland Ltd

Volume :

165

Pages :

77-87

Peer reviewed :

Peer Reviewed verified by ORBi

Name of the research project :

TEC fund MedTech CoRE (Centre of Research Expertise); NZ National Science Challenge 7, Science for Technology and Innovation; eTIME 318943; EU FP7 International Research StaffEx- change Scheme (IRSES) grant [#PIRSES-GA-2012-318943]

Funders :

TEC - Tertiary Education Commission [NZ]
UE - Union Européenne [BE]

Available on ORBi :

since 24 May 2019

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