Regional lung opening and closing pressures in patients with acute lung injury

Pulletz, Sven; Adler, Andy; Kott, Matthias; Elke, Gunnar; Gawelczyk, Barbara; Schädler, Dirk; Zick, Günther; Weiler, Norbert; Frerichs, Inéz

doi:10.1016/j.jcrc.2011.09.002

Cited by 54 publications

(51 citation statements)

References 34 publications

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“…However, no such simpler model has been proposed at this stage. Likewise Electro-Impedance Tomography (EIT) would also be able to provide estimates on TOP directly at the bedside [4].…”

Section: Discussionmentioning

confidence: 99%

“…However, the physiological interpretations of the model parameters are only valid if the model assumptions are correct. Although, the true recruitment mechanisms are still unknown, several clinical studies [4,[25][26] support the recruitment principle according to Hickling's definition.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, optimal ventilator settings for individual ARDS patients are an unresolved issue. Furthermore, any settings should be re-evaluated regularly to account for changes in patient condition and physiology [3][4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Structural Identifiability and Practical Applicability of an Alveolar Recruitment Model for ARDS Patients

Schranz

Docherty

Chiew

et al. 2012

IEEE Trans. Biomed. Eng.

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Mathematical models of respiratory mechanics can offer substantial insight into patient state and pulmonary dynamics that are not directly measurable. Thus, they offer significant potential to evaluate and guide patient-specific lung protective ventilator strategies for Acute Respiratory Distress Syndrome (ARDS) patients. To assure bedside-applicability, the model has to be computationally efficient and identifiable from the available data, while also capturing dominant dynamics observed in ARDS patients.In this work, a recruitment model is enhanced by considering alveolar distension and implemented in a time-continuous respiratory mechanics model. A hierarchical gradient decent approach is used to fit the model to low-flow test responses of 12 ARDS patients.The reported parameter values were physiologically plausible and capable of reproducing the measured pressure responses with high accuracy. Structural identifiability of the model is proven, but a practical identifiability analysis of the results shows a lack of convexity on the error-surface. Covariance analyses reveal limited influence of particular model parameters during parameter identification indicating that successful parameter identification is currently not assured in all test sets.Overall, the presented model is physiologically and clinically relevant, captures ARDS dynamics, and uses clinically descriptive parameters. The patient-specific models show its ability to capture pulmonary dynamics directly relevant to patient condition and clinical guidance. These characteristics can currently not be directly measured or established without such a validated model.

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“…However, no such simpler model has been proposed at this stage. Likewise Electro-Impedance Tomography (EIT) would also be able to provide estimates on TOP directly at the bedside [4].…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Structural Identifiability and Practical Applicability of an Alveolar Recruitment Model for ARDS Patients

Schranz

Docherty

Chiew

et al. 2012

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

“…Although, the true recruitment mechanisms are still unknown, several clinical studies (Crotti et al, 2001, Schiller et al, 2003, Pulletz et al, 2011 support the recruitment principle according to Hickling's definition. Thus, the clinical relevance of the PRM has yet to be validated.…”

Section: Discussionmentioning

confidence: 99%

Identifiability Analysis of a Pressure-Depending Alveolar Recruitment Model

Schranz

Docherty

Chiew

et al. 2012

IFAC Proceedings Volumes

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Patient-specific physiological models of respiratory mechanics can offer insight into patient state and pulmonary dynamics that are not directly measurable. Thus, significant potential exists to evaluate and guide patient-specific lung protective ventilator strategies for Acute Respiratory Distress Syndrome (ARDS) patients. To assure bedside-applicability, the physiological model must be computationally efficient and identifiable from the limited available data, while also capturing dominant dynamics and trends observed in ARDS patients. In this work, an existing static recruitment model is enhanced by considering alveolar distension and implemented in a novel time-continuous dynamic respiratory mechanics model. A hierarchical gradient descent approach is used to fit the model to lowflow test responses of 12 ARDS patients. Identified parameter values were physiologically plausible and capable of reproducing the measured pressure responses with very high accuracy (Overall median percentage fitting error: MPE = 1.84% [IQR: 1.77% to 2.18%]). Structural identifiability of the model is proven, but a practical identifiability analysis of the results shows a lack of convexity on the error-surface for some patients due to reduced information content within the measured data set. Overall, the model presented is physiologically and clinically relevant, captures ARDS dynamics, and uses clinically descriptive parameters. The patient-specific models show their ability to capture pulmonary dynamics directly relevant to patient condition and clinical guidance. These characteristics cannot be directly measured without such a validated model.

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“…It is a heterogeneous condition that varies between patients. Hence, determination of optimal ventilator settings must be patientspecific (Brower et al, 2003, Pulletz et al, 2012.…”

Section: Introductionmentioning

confidence: 99%

Interpolation within a Recruitment Manoeuvre using a Non-Linear Autoregressive Model of Pulmonary Mechanics

et al. 2015

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Mathematical models that capture patient-specific information can enable personalised mechanical ventilation, improving care for patients in the intensive care unit (ICU). A nonlinear autoregressive (NARX) model that uses pressure dependent elastance, and multiple time dependent resistance coefficients has been fit to 25 patient data sets containing increasing PEEP steps. This model was more successful than a well-accepted first order model (FOM) at describing the shape of the airway pressure curve. In this study, the NARX model and FOM were identified on the first 20% and last 20% of the available data (IDD40). The parameterized model was then interpolated over the evaluation data (EVD) that consisted of the middle 60% of the data. The model-data residuals were compared to the result of identification using 100% of the data (IDD100). There were significant differences between the average root mean square (RMS) residuals for most IDD100, IDD40, and EVD combinations (p < 0.05). However, the magnitude of the differences was small in a clinical sense. Importantly, the NARX model was able to provide consistent results across all 25 patients. In contrast the FOM interpolation results were worse when the patient suffered over-distension in the high PEEP IDD40 data. The results suggest the NARX model could be suitable for use in the ICU, to estimate behaviour when data is not available, allowing clinicians to make informed decisions regarding ventilator PEEP settings.

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Regional lung opening and closing pressures in patients with acute lung injury

Cited by 54 publications

References 34 publications

Structural Identifiability and Practical Applicability of an Alveolar Recruitment Model for ARDS Patients

Structural Identifiability and Practical Applicability of an Alveolar Recruitment Model for ARDS Patients

Identifiability Analysis of a Pressure-Depending Alveolar Recruitment Model

Interpolation within a Recruitment Manoeuvre using a Non-Linear Autoregressive Model of Pulmonary Mechanics

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