An Extension to the First Order Model of Pulmonary Mechanics to Capture a Pressure dependent Elastance in the Human Lung

Knörzer, Andreas; Docherty, Paul; Chiew, Yeong Shiong; Chase, J. Geoffrey; Möller, Knut

doi:10.3182/20140824-6-za-1003.01834

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…The pressure-variant dynamic elastance model in combination with the α-method (3) rests upon a constant airway resistance [16]. Due to the lack of the dynamic elastance model to get a continuous prediction curve of E(P) (Fig.…”

Section: 'α-Method'mentioning

confidence: 99%

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Lung mechanics - airway resistance in the dynamic elastance model

et al. 2016

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The selection of optimal positive end-expiratory pressure (PEEP) levels during mechanical ventilation therapy of patients with acute respiratory distress syndrome (ARDS) remains a problem for clinicians. A particular mooted strategy states that minimizing the energy transferred to the lung during mechanical ventilation could potentially be used to determine the optimal, patient-specific PEEP levels. Furthermore, the dynamic elastance model of pulmonary mechanics could possibly be applied to minimize the energy by localization of the patients' minimum dynamic elastance range. The sensitivity of the dynamic elastance model to variance in the airway resistance was analyzed. Additionally, the airway resistance was determined by using three other established identification methods and was compared to the constant resistance obtained by the dynamic elastance model. For increasing PEEP, the alternative identification methods showed similar decreasing trends of the resistance during inspiration. This declining trend is apparently an exponential decrease. Results showed that the constant airway resistance, presumed by the dynamic elastance model, has to be rechecked and investigated.

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Section: 'α-Method'mentioning

confidence: 99%

“…Subsequent studies mitigated this problem by introducing various correction terms [16][17][18]. For example, Knörzer et al [16] introduced the α-method (3) to improve the dynamic elastance model in order to obtain the desired continuous prediction curve of E(P).…”

Section: Introductionmentioning

confidence: 99%

Lung mechanics - airway resistance in the dynamic elastance model

et al. 2016

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“…Therefore, a well-supported assumption was made that the optimal PEEP level can be set in the region of the tidal pressure where the minimum of the E(P) curve appears. Subsequent studies modelled the E(P) across different PEEP-levels to obtain a continuous prediction curve (Knörzer 2014;Laufer 2015) where their overall goal was to find the minimal E(P) using different extensions to the FOM. This analysis further investigates a volume correction method (V-method) that determines E(P) in concert with R across a number of breaths and PEEP levels and was initially hypothesised by Laufer (2015).…”

Section: Introductionmentioning

confidence: 99%

Identifying pressure dependent elastance in lung mechanics with reduced influence of unmodelled effects

et al. 2015

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The selection of optimal positive end expiratory pressure (PEEP) levels during ventilation therapy of patients with ARDS (acute respiratory distress syndrome) remains a problem for clinicians. One particular mooted strategy states that minimizing the energy transferred to the lung by mechanical ventilation could potentially be used to determine the optimal PEEP level. This minimization could potentially be undertaken by finding the minimum range of dynamic elastance.In this study, we compare an adapted Gauss-Newton method with the typical gauss newton method in terms of the level of agreement obtained in elastance-pressure curves across different PEEP levels in 10 patients. The Gauss-Newton adaptation effectively ignored characteristics in the data that are un-modelled. The adapted method successfully determined regions of the data that were un-modelled, as expected. In ignoring this un-modelled behavior, the adapted method captured the desired elastance-pressure curves with more consistency than the typical least-squares Gauss Newton method.

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Modelling and prediction of physiological behaviour in critically ill patients

Langdon¹

2017

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An Extension to the First Order Model of Pulmonary Mechanics to Capture a Pressure dependent Elastance in the Human Lung

Cited by 4 publications

References 15 publications

Lung mechanics - airway resistance in the dynamic elastance model

Lung mechanics - airway resistance in the dynamic elastance model

Identifying pressure dependent elastance in lung mechanics with reduced influence of unmodelled effects

Modelling and prediction of physiological behaviour in critically ill patients

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