Using physiological models and decision theory for selecting appropriate ventilator settings

Rees, Stephen Edward; Allerød, Charlotte; Murley, David; Zhao, Yichun; Smith, Bram Wallace; Kjærgaard, Søren; Thorgaard, Per; Andreassen, Steen

doi:10.1007/s10877-006-9049-5

Cited by 89 publications

(54 citation statements)

References 25 publications

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“…This rulebased system created fuzzy sets based on patient's measured heart rate, tidal volume, respiratory rate, and arterial oxygen saturation to determine the pressure support level provided by the ventilator during weaning. Another IDSS that was based on a physiological model of the patient was presented several years later (Rees et al, 2006). A simplified model of respiratory mechanics and mathematical models of oxygen and carbon dioxide transport were used in this system to simulate the www.intechopen.com effects of changing some of the ventilatory parameters such as F IO2 on the patient's blood gases.…”

Section: An Overview Of Various Idsss For Mechanical Ventilationmentioning

confidence: 99%

Computerized Decision Support Systems for Mechanical Ventilation

Tehrani¹

2011

Efficient Decision Support Systems - Practice and Challenges in Biomedical Related Domain

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Section: An Overview Of Various Idsss For Mechanical Ventilationmentioning

confidence: 99%

Computerized Decision Support Systems for Mechanical Ventilation

Tehrani¹

2011

Efficient Decision Support Systems - Practice and Challenges in Biomedical Related Domain

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“…Physiological modeling of the respiratory system is one way to counteract that problem and to find out the best possible settings for mechanical ventilation [16] [17] [18]. In physiological modeling the simplest model to describe the behavior of the respiratory system (lung and airways) is a first order model (FOM) [19], where the airway passage is symbolized by a single (constant) resistance term and the tissue behavior regarding the resistance to expansion is described by a constant elastance.…”

Section: Introductionmentioning

confidence: 99%

What Does a First Order Model Tell Us about PEEP Wave Maneuvers?

Laufer¹,

Kretschmer²,

Docherty³

et al. 2017

JBiSE

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Patients with acute respiratory distress syndrome (ARDS) are currently treated with a lung protective ventilation strategy and the application of positive end-expiratory pressure (PEEP), sometimes in combination with recruitment maneuvers. In this study, the respiratory system elastance and airway resistance of each breath before, during and after a specific recruitment maneuver (PEEP wave maneuver) were analyzed in two patient groups, ARDS and control group. A reduction of elastance after the maneuver was observed in ARDS patients. In addition, only healthy lungs exhibited a reduction of the elastance during the course of the maneuver, while the lungs of ARDS patients didn't show that reduction of elastance. The capability of PEEP wave maneuvers to improve lung ventilation was shown and the dynamic behavior of the elastance after the maneuver was illustrated. Healthy lungs adapt faster to changes in mechanical ventilation than the lungs of ARDS patients.

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“…When sub-optimal ventilator settings cause injury to the lungs, this is known as ventilator induced lung injury (VILI) (Slutsky and Ranieri, 2013). A lung model that captures patient-specific behaviour could enable individualised mechanical ventilation, reduce the incidence of VILI, and help reduce patient morbidity and mortality (Fenstermacher and Hong, 2004) (Rees et al, 2006). Spontaneously breathing (SB) patients apply their own inspiratory efforts on top of a ventilator supported breathing cycle.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Non-Linear Autoregressive Model with Modified Gauss-Newton Parameter Identification to Determine Pulmonary Mechanics of Respiratory Patients that are Intermittently Resisting Ventilator Flow Patterns

et al. 2015

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Modelling the respiratory system of intensive care patients can enable individualized mechanical ventilation therapy and reduce ventilator induced lung injuries. However, spontaneous breathing (SB) efforts result in asynchronous pressure waveforms that mask underlying respiratory mechanics. In this study, a nonlinear auto-regressive (NARX) model was identified using a modified Gauss-Newton (GN) approach, and demonstrated on data from one SB patient. The NARX model uses three pressure dependent basis functions to capture respiratory system elastance, and contains a single resistance coefficient and positive end expiratory pressure (PEEP) coefficient. The modified GN method exponentially reduces the contribution of large residuals on the step in the coefficients at each GN iteration. This approach allows the model to effectively ignore the anomaly in the pressure waveform due to SB efforts, while successfully describing the shape of normal breathing cycles. This method has the potential to be used in the ICU to more robustly capture patient-specific behaviour, and thus enable clinicians to select optimal ventilator settings and improve patient care.

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Using physiological models and decision theory for selecting appropriate ventilator settings

Abstract: The system illustrates how mathematical models combined with decision theory can aid in the difficult compromises necessary when deciding on ventilator settings.

Cited by 89 publications

References 25 publications

Computerized Decision Support Systems for Mechanical Ventilation

Computerized Decision Support Systems for Mechanical Ventilation

What Does a First Order Model Tell Us about PEEP Wave Maneuvers?

Implementation of a Non-Linear Autoregressive Model with Modified Gauss-Newton Parameter Identification to Determine Pulmonary Mechanics of Respiratory Patients that are Intermittently Resisting Ventilator Flow Patterns

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