A mathematical model approach quantifying patients' response to changes in mechanical ventilation: Evaluation in pressure support

Larraza, Sebastian; Dey, Nilanjan; Karbing, Dan Stieper; Jensen, Jakob B.; Nygaard, Morten; Winding, Robert; Rees, Stephen Edward

doi:10.1016/j.jcrc.2015.05.010

Cited by 7 publications

(11 citation statements)

References 40 publications

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“…When the values of SIDcsf and Tc were estimated from measurements of ventilation and a single ABG at a baseline ventilator setting, the model was shown to accurately simulate values of fR, arterial pH, and end-tidal CO 2 at five different levels of ventilator support. This was shown in 12 patients ventilated using volume support ventilation [28] and a further 12 patients in PS ventilation [27]. The range of values of parameters seen in these patients was for SIDcsf from 26.1 mmol/l to 43.9 mmol/l, and for Tc from 34.9 nmol/l to 53.8 nmol/l.…”

Section: Respiratory Drivementioning

confidence: 81%

“…For patients in supported modes of mechanical ventilation, values of Cdyn can be much higher than those reflecting only the mechanical properties of the respiratory system. These values no longer reflect respiratory system mechanics alone but also the driving pressure (∆P) of the respiratory muscles (Pmus) [27]. Larraza et al [27] reported calculated values of Cdyn as high as 200 ml/ cm H 2 O on reducing pressure support (PS) to 3-4 cm H 2 O.…”

Section: Mathematical Models Of Respiratory System Mechanicsmentioning

confidence: 99%

“…These values no longer reflect respiratory system mechanics alone but also the driving pressure (∆P) of the respiratory muscles (Pmus) [27]. Larraza et al [27] reported calculated values of Cdyn as high as 200 ml/ cm H 2 O on reducing pressure support (PS) to 3-4 cm H 2 O. Values of Cdyn in excess of 70 ml/cm H 2 O are unlikely to be due to the mechanical properties of the respiratory system.…”

Section: Mathematical Models Of Respiratory System Mechanicsmentioning

confidence: 99%

“…In this case, pH CSF may respond well to changes in arterial PCO 2 , but the brain signal response to respiratory muscles can be reduced and, as such, A may not be sufficient so as to normalize either pH CSF , or pHa [44]. Figure 3 illustrates the mathematical models of CSF (3A) and respiratory drive (3B) included in the Beacon system, as described previously [26][27][28]. The CSF model is a modified version of that of Duffin [12].…”

Section: Respiratory Drivementioning

confidence: 99%

“…This model has been validated in two clinical studies [27,28]. When the values of SIDcsf and Tc were estimated from measurements of ventilation and a single ABG at a baseline ventilator setting, the model was shown to accurately simulate values of fR, arterial pH, and end-tidal CO 2 at five different levels of ventilator support.…”

Section: Respiratory Drivementioning

confidence: 99%

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Determining the appropriate model complexity for patient-specific advice on mechanical ventilation

Rees

Karbing

2017

Biomedical Engineering / Biomedizinische Technik

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Mathematical physiological models can be applied in medical decision support systems. To do so requires consideration of the necessary model complexity. Models that simulate changes in the individual patient are required, meaning that models should have a complexity where parameters can be uniquely identified at the bedside from clinical data and where the models adequately represent the individual patient's (patho)physiology. This paper describes the models included in a system for providing decision support for mechanical ventilation. Models of pulmonary gas exchange, respiratory mechanics, acid-base, and respiratory control are described. The parameters of these models are presented along with the necessary clinical data required for their estimation and the parameter estimation process. In doing so, the paper highlights the need for simple, minimal models for application at the bedside, directed toward well-defined clinical problems.

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Section: Respiratory Drivementioning

confidence: 81%

Section: Mathematical Models Of Respiratory System Mechanicsmentioning

confidence: 99%

Section: Mathematical Models Of Respiratory System Mechanicsmentioning

confidence: 99%

Section: Respiratory Drivementioning

confidence: 99%

Section: Respiratory Drivementioning

confidence: 99%

See 3 more Smart Citations

Determining the appropriate model complexity for patient-specific advice on mechanical ventilation

Rees

Karbing

2017

Biomedical Engineering / Biomedizinische Technik

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Digital Twins and Automation of Care in the Intensive Care Unit

Chase¹,

Zhou²,

Knopp³

et al. 2023

Cyber–Physical–Human Systems

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Typical patterns of expiratory flow and carbon dioxide in mechanically ventilated patients with spontaneous breathing

Rees

Larraza

Dey

et al. 2016

J Clin Monit Comput

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Incomplete expiration of tidal volume can lead to dynamic hyperinflation and auto-PEEP. Methods are available for assessing these, but are not appropriate for patients with respiratory muscle activity, as occurs in pressure support. Information may exist in expiratory flow and carbon dioxide measurements, which, when taken together, may help characterize dynamic hyperinflation. This paper postulates such patterns and investigates whether these can be seen systematically in data. Two variables are proposed summarizing the number of incomplete expirations quantified as a lack of return to zero flow in expiration (IncExp), and the end tidal CO variability (varETCO), over 20 breaths. Using these variables, three patterns of ventilation are postulated: (a) few incomplete expirations (IncExp < 2) and small varETCO; (b) a variable number of incomplete expirations (2 ≤ IncExp ≤ 18) and large varETCO; and (c) a large number of incomplete expirations (IncExp > 18) and small varETCO. IncExp and varETCO were calculated from data describing respiratory flow and CO signals in 11 patients mechanically ventilated at 5 levels of pressure support. Data analysis showed that the three patterns presented systematically in the data, with periods of IncExp < 2 or IncExp > 18 having significantly lower variability in end-tidal CO than periods with 2 ≤ IncExp ≤ 18 (p < 0.05). It was also shown that sudden change in IncExp from either IncExp < 2 or IncExp > 18 to 2 ≤ IncExp ≤ 18 results in significant, rapid, change in the variability of end-tidal CO p < 0.05. This study illustrates that systematic patterns of expiratory flow and end-tidal CO are present in patients in supported mechanical ventilation, and that changes between these patterns can be identified. Further studies are required to see if these patterns characterize dynamic hyperinflation. If so, then their combination may provide a useful addition to understanding the patient at the bedside.

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A mathematical model approach quantifying patients' response to changes in mechanical ventilation: Evaluation in pressure support

Cited by 7 publications

References 40 publications

Determining the appropriate model complexity for patient-specific advice on mechanical ventilation

Determining the appropriate model complexity for patient-specific advice on mechanical ventilation

Digital Twins and Automation of Care in the Intensive Care Unit

Typical patterns of expiratory flow and carbon dioxide in mechanically ventilated patients with spontaneous breathing

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