Performance of variations of the dynamic elastance model in lung mechanics

Laufer, Bernhard; Docherty, Paul; Knörzer, Andreas; Chiew, Yeong Shiong; Langdon, Ruby; Möller, Knut; Chase, J. Geoffrey

doi:10.1016/j.conengprac.2016.03.004

Cited by 12 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Excessive shear stress can be mitigated using lung protective strategies (M. B. P. Brower et al, 2000;Donahoe, 2011;Laufer et al, 2017;M. O. Meade, Cook, Griffith, et al, 2008;Petrucci & De Feo, 2013;Pintado et al, 2013;Sundaresan & Chase, 2012), such as the use of lower tidal volumes, personalised PEEP, and moderating driving pressure (M. B. P. Amato et al, 2015;Brower et al, 2000Brower et al, , 2004Futier et al, 2013;Girard & Bernard, 2007;Iglesias et al, 2008;Jobe, 2009; M. O.…”

Section: Ventilator-induced Lung Injurymentioning

confidence: 99%

“…In addition, closed alveoli can be subjected to very high levels of shear stress when exposed to the same pressures as adjacent open healthy alveoli ( Mols et al., 2006 ). Excessive shear stress can be mitigated using lung protective strategies ( Amato et al., 1998a , Brower et al., 2000 , Donahoe, 2011 , Laufer et al., 2017 , Meade et al., 2008b , Petrucci and De Feo, 2013 , Pintado et al., 2013 , Sundaresan and Chase, 2012 ), such as the use of lower tidal volumes, personalised PEEP, and moderating driving pressure ( Amato et al., 2015 , Brower et al., 2000 , Brower et al., 2004 , Futier et al., 2013 , Girard and Bernard, 2007 , Iglesias et al., 2008 , Jobe, 2009 , Meade et al., 2008a , Mercat et al., 2008 , Nieman et al., 2018 , Petrucci and De Feo, 2013 , Zick et al., 2013 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimising mechanical ventilation through model-based methods and automation

Morton

Knopp

Chase

et al. 2019

Annual Reviews in Control

Self Cite

View full text Add to dashboard Cite

Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload.Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

show abstract

Section: Ventilator-induced Lung Injurymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimising mechanical ventilation through model-based methods and automation

Morton

Knopp

Chase

et al. 2019

Annual Reviews in Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…In contrast, the model is reaching its limits at higher PEEP values, which might induce other strong biophysical events [54] altering the cardiopulmonary interactions in such a way the model defined is no longer relevant. Such high PEEP levels (25 cmH 2 O) are typically not used in critically ill patients [55].…”

Section: Parameter Estimation Processmentioning

confidence: 99%

Parameter estimation in a minimal model of cardio-pulmonary interactions

Bournonville

Pironet

Pretty

et al. 2019

Mathematical Biosciences

Self Cite

View full text Add to dashboard Cite

Mechanical ventilation is a widely used breathing support for patients in intensive care. Its effects on the respiratory and cardiovascular systems are complex and difficult to predict. This work first presents a minimal mathematical model representing the mechanics of both systems and their interaction, in terms of flows, pressures and volumes. The aim of this model is to get insight on the two systems status when mechanical ventilation settings, such as positive end-expiratory pressure, are changing. The parameters of the model represent cardiac elastances and vessel compliances and resistances. As a second step, these parameters are estimated from 16 experimental datasets. The data come from three pig experiments reproducing intensive care conditions, where a large range of positive end-expiratory pressures was imposed by the mechanical ventilator. The data used for parameter estimation is limited to information available in the intensive care unit, such as stroke volume, central venous pressure and systemic arterial pressure. The model is able to satisfactorily reproduce this experimental data, with mean relative errors ranging from 1 to 26 %. The model also reproduces the dynamics of the cardio-vascular and respiratory systems, and their interaction. By looking at the estimated parameter values, one can quantitatively track how the two coupled systems mechanically react to changes in external conditions imposed by the ventilator. This work thus allows real-time, model-based management of ventilator settings.

show abstract

“…Finally, some models of all types capture lung mechanics well with good personalization of parameters, but are poor in predicting the response to changes in care [63][64][65], lacking the means to offer guidance to clinicians, and suggesting the identified parameters may not be correct. However, accurate prediction is a major need in guiding and improving the safety and efficacy of clinical MV treatment [66][67][68]. Thus, there is a need for simpler, identifiable, physiologically relevant models which can offer accurate prediction to changes in care by capturing the evolution of lung mechanics as MV parameters change [34,52].…”

Section: Introductionmentioning

confidence: 99%