Estimation of pulmonary diffusion resistance and shunt in an oxygen status model

Andreassen, Steen; Egeberg, J.; Schröter, M. P.; Andersen, Per Thorgaard

doi:10.1016/0169-2607(96)01765-8

Cited by 14 publications

(17 citation statements)

References 9 publications

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“…The first model developed by Andreassen et (1996), estimates pulmonary shunt and oxygen diffusion resistance by measuring variations in the fraction of inspired oxygen (F i O 2 ) and arterial oxygen saturation (S a O 2 ). The diffusion model takes inputs of cardiac output, F i O 2 and other ventilation data.…”

Section: Model Based Methodsmentioning

confidence: 99%

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Prediction of cardiac output changes with response to PEEP on patients under mechanical ventilation

Sundaresan¹,

Chase²,

Hann³

2010

UKACC International Conference on CONTROL 2010

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The application of positive end expiratory pressure (PEEP) in mechanically ventilated (MV) patients with acute respiratory distress syndrome (ARDS) decreases cardiac output (CO). Accurate measurement of CO is invasive and is not ideal for all MV critically ill patients. However, the link between the PEEP used in MV, and CO provides an opportunity to assess CO via MV therapy and other existing measurements, creating a non-invasive CO measure. This paper examines combining models of diffusion resistance with lung mechanics, to help predict CO changes due to PEEP. The CO estimator uses an initial measurement of pulmonary shunt, and estimations of shunt changes due to PEEP to predict CO at different levels of PEEP. Inputs to the cardiac model are the PV loops from the ventilator, as well as the oxygen saturation values using known respiratory inspired oxygen content. The output is estimates of pulmonary shunt and CO changes due to changes in applied PEEP. Data from two published studies are used to assess and initially validate this model.The model shows the effect on oxygenation due to decreased CO and decreased shunt, resulting from increased PEEP. It concludes that there is a trade off on oxygenation parameters. More clinically importantly, the model also looks at how the rate of CO drop with increased PEEP may also be used as a method to determine optimal PEEP, and optimise MV therapy with respect to the gas exchange achieved, while also accounting for its impact on cardiovascular system management.

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Section: Model Based Methodsmentioning

confidence: 99%

“…• In Section 2 it should be Andreassen et al (1996). It appears the at the incorrect style file has been used.…”

Section: Reviewer Comments and Responsementioning

confidence: 98%

Prediction of cardiac output changes with response to PEEP on patients under mechanical ventilation

Sundaresan¹,

Chase²,

Hann³

2010

UKACC International Conference on CONTROL 2010

View full text Add to dashboard Cite

show abstract

“…The first model, developed by Andreassen et al [14] looks at estimations of pulmonary shunt and oxygen diffusion resistance by measuring variations in the fraction of inspired oxygen (FIO 2 ) and arterial oxygen saturation (S a O 2 ). The diffusion model takes inputs of CO, F I O 2 and other ventilation data, to estimate pulmonary shunt and diffusion resistance as outputs.…”

Section: Model Based Methodsmentioning

confidence: 99%

“…The model developed by Andreassen et al [14] uses a compartmental oxygen status model (OSM) as shown in Figure 1.…”

Section: Model Based Methodsmentioning

confidence: 99%

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Cardiac output estimation using pulmonary mechanics in mechanically ventilated patients

Sundaresan

Chase

Hann

2010

BioMedical Engineering OnLine

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The application of positive end expiratory pressure (PEEP) in mechanically ventilated (MV) patients with acute respiratory distress syndrome (ARDS) decreases cardiac output (CO). Accurate measurement of CO is highly invasive and is not ideal for all MV critically ill patients. However, the link between the PEEP used in MV, and CO provides an opportunity to assess CO via MV therapy and other existing measurements, creating a CO measure without further invasiveness.This paper examines combining models of diffusion resistance and lung mechanics, to help predict CO changes due to PEEP. The CO estimator uses an initial measurement of pulmonary shunt, and estimations of shunt changes due to PEEP to predict CO at different levels of PEEP. Inputs to the cardiac model are the PV loops from the ventilator, as well as the oxygen saturation values using known respiratory inspired oxygen content. The outputs are estimates of pulmonary shunt and CO changes due to changes in applied PEEP. Data from two published studies are used to assess and initially validate this model.The model shows the effect on oxygenation due to decreased CO and decreased shunt, resulting from increased PEEP. It concludes that there is a trade off on oxygenation parameters. More clinically importantly, the model also examines how the rate of CO drop with increased PEEP can be used as a method to determine optimal PEEP, which may be used to optimise MV therapy with respect to the gas exchange achieved, as well as accounting for the impact on the cardiovascular system and its management.

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The Pulse Oxygen Saturation: Inspired Oxygen Pressure (SpO2:P1O2) Diagram: Application in the Ambulatory Assessment of Pulmonary Vascular Disease

Skjodt

Ritz

Vethanayagam³

2008

Integration in Respiratory Control

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Estimation of pulmonary diffusion resistance and shunt in an oxygen status model

Cited by 14 publications

References 9 publications

Prediction of cardiac output changes with response to PEEP on patients under mechanical ventilation

Prediction of cardiac output changes with response to PEEP on patients under mechanical ventilation

Cardiac output estimation using pulmonary mechanics in mechanically ventilated patients

The Pulse Oxygen Saturation: Inspired Oxygen Pressure (SpO2:P1O2) Diagram: Application in the Ambulatory Assessment of Pulmonary Vascular Disease

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