Analysis of different model-based approaches for estimating dFRC for real-time application

Drunen, Erwin J. van; Chase, J. Geoffrey; Chiew, Yeong Shiong; Desaive, Thomas

doi:10.1186/1475-925x-12-9

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…Prediction across PEEP levels requires calculation of the change in V frc or the volume recruited by a PEEP step change relative to the current PEEP. 30,56 It is assumed that the change in V frc is positive or zero when PEEP is increased, and negative or zero when PEEP is decreased.…”

Section: Model Identification Identification and Fittingmentioning

confidence: 99%

“…In particular, RMs can be highly effective, resulting in additional end-expiratory lung volume that contributes to gas exchange function, or recruitment of dynamic functional residual capacity (V frc ) at a given PEEP due to an increase in open alveoli. 18,56,59 However, both healthy and injured alveoli are subjected to the same pressures and volumes. Hence, RMs also subject the patient to risk due to the higher pressures or volumes induced.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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 cmH 2 O, 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.

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Section: Model Identification Identification and Fittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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%

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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“…In this model, P aw is the airway pressure, t is time, V is the air volume, andV is the air flow. P 0 is an offset pressure, usually positive end-expiratory pressure (PEEP) if there is no auto-PEEP [25]. E is respiratory system elastance and R is respiratory system resistance.…”

Section: Linear Single Compartment Modelmentioning

confidence: 99%

Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort

Redmond

Chiew

Major

et al. 2019

Computer Methods and Programs in Biomedicine

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Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.

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Analysis of different model-based approaches for estimating dFRC for real-time application

Cited by 14 publications

References 23 publications

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

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

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

Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort

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