Visualisation of Time-Variant Respiratory System Elastance in ARDS Models

Drunen, Erwin J. van; Chiew, Yeong Shiong; Zhao, Zhe; Lambermont, Bernard; Janssen, Nathalie; Pretty, Christopher G.; Desaive, Thomas; Möeller, Knut; Chase, J. Geoffrey

doi:10.1515/bmt-2013-4328

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…In metabolic systems, insulin sensitivity trajectories have captured the impact of other drug therapies [ 132 , 259 ], provided insight into the evolution of patient condition over time and thus provided hints how to target better treatment [ 131 , 133 , 139 ], and diagnosed the absence of sepsis [ 88 ]. In pulmonary mechanics model-based elastance has been investigated to reveal the impact and effect of recruitment manoeuvres and how they decline over time [ 152 , 260 , 261 ]. The impact of different breathing modes and recruitment manoeuvres [ 80 , 142 , 147 , 261 – 263 ], where clinically assessed, simpler elastance metrics have been insufficient [ 264 ].…”

Section: Virtual Patients Virtual Cohorts and Their Validationmentioning

confidence: 99%

“…In pulmonary mechanics model-based elastance has been investigated to reveal the impact and effect of recruitment manoeuvres and how they decline over time [ 152 , 260 , 261 ]. The impact of different breathing modes and recruitment manoeuvres [ 80 , 142 , 147 , 261 – 263 ], where clinically assessed, simpler elastance metrics have been insufficient [ 264 ]. In the cardiovascular area, model-based methods of determining beat to beat stroke volume or stressed blood volume [ 71 , 161 ], and other more detailed lumped parameter physiological models used for real-time diagnosis (e.g.…”

Section: Virtual Patients Virtual Cohorts and Their Validationmentioning

confidence: 99%

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Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

et al. 2018

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Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current “one size fits all” protocolised care to adaptive, model-based “one method fits all” personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

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Section: Virtual Patients Virtual Cohorts and Their Validationmentioning

confidence: 99%

Section: Virtual Patients Virtual Cohorts and Their Validationmentioning

confidence: 99%

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

et al. 2018

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“…Patient-specific respiratory elastance is calculated from the single compartment lung model using only the inspiratory portion of breathing (Chiew et al, 2011, van Drunen et al, 2013. Therefore, to enable the automated calculation of realtime elastance, the breathing data must be split into inspiration sections.…”

Section: Data Processingmentioning

confidence: 99%

“…CURE Soft uses a time-varying elastance model (van Drunen et al, 2013, Chiew et al, 2011. The inspiratory sections of pressure and flow are used to calculate elastance for each breath.…”

Section: Time-varying Elastance Modelmentioning

confidence: 99%

Managing Patient-Specific Mechanical Ventilation: Clinical Utilisation of Respiratory Elastance (CURE) – Model and Software Development

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et al. 2014

IFAC Proceedings Volumes

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Continuous Stroke Volume Estimation from Aortic Pressure Using Zero Dimensional Cardiovascular Model: Proof of Concept Study from Porcine Experiments

et al. 2014

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IntroductionAccurate, continuous, left ventricular stroke volume (SV) measurements can convey large amounts of information about patient hemodynamic status and response to therapy. However, direct measurements are highly invasive in clinical practice, and current procedures for estimating SV require specialized devices and significant approximation.MethodThis study investigates the accuracy of a three element Windkessel model combined with an aortic pressure waveform to estimate SV. Aortic pressure is separated into two components capturing; 1) resistance and compliance, 2) characteristic impedance. This separation provides model-element relationships enabling SV to be estimated while requiring only one of the three element values to be known or estimated. Beat-to-beat SV estimation was performed using population-representative optimal values for each model element. This method was validated using measured SV data from porcine experiments (N = 3 female Pietrain pigs, 29–37 kg) in which both ventricular volume and aortic pressure waveforms were measured simultaneously.ResultsThe median difference between measured SV from left ventricle (LV) output and estimated SV was 0.6 ml with a 90% range (5th–95th percentile) −12.4 ml–14.3 ml. During periods when changes in SV were induced, cross correlations in between estimated and measured SV were above R = 0.65 for all cases.ConclusionThe method presented demonstrates that the magnitude and trends of SV can be accurately estimated from pressure waveforms alone, without the need for identification of complex physiological metrics where strength of correlations may vary significantly from patient to patient.

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Visualisation of Time-Variant Respiratory System Elastance in ARDS Models

Cited by 6 publications

References 9 publications

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Managing Patient-Specific Mechanical Ventilation: Clinical Utilisation of Respiratory Elastance (CURE) – Model and Software Development

Continuous Stroke Volume Estimation from Aortic Pressure Using Zero Dimensional Cardiovascular Model: Proof of Concept Study from Porcine Experiments

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