Alteration of Right Ventricular-Pulmonary Vascular Coupling in a Porcine Model of Progressive Pressure Overloading

Ghuysen, Alexandre; Lambermont, Bernard; Kolh, Philippe; Tchana‐Sato, Vincent; Magis, David; Gérard, Paul; Mommens, Véronique; Janssen, Nathalie; Desaive, Thomas; D’Orio, Vincent

doi:10.1097/shk.0b013e318070c790

Cited by 49 publications

(49 citation statements)

References 36 publications

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“…1. The model and methods of the left ventricle-systemic system are then tested on clinical data from a pulmonary embolism animal model experiment [39].…”

Section: Resultsmentioning

confidence: 99%

“…4 with noise levels defined in Eq. (39). To optimize accuracy, a cubic smoothing spline is performed on the noisy aortic pressure waveform using standard in-built functions in Matlab.…”

Section: Healthy and Diseased Human With Noise-no Volume Measurementmentioning

confidence: 99%

“…The data is obtained from the Hemodynamics Research Laboratory, University of Liege, Belgium. In the experiments, a pig is injected with autologous blood clots every 2 h to simulate pulmonary embolism [38].…”

Section: Further Validation On An Animal Model and Clinical Implementmentioning

confidence: 99%

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Unique parameter identification for cardiac diagnosis in critical care using minimal data sets

Hann

Chase

Desaive

et al. 2010

Computer Methods and Programs in Biomedicine

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Lumped parameter approaches for modelling the cardiovascular system typically have many parameters of which a significant percentage are often not identifiable from limited data sets. Hence, significant parts of the model are required to be simulated with little overall effect on the accuracy of data fitting, as well as dramatically increasing the complexity of parameter identification. This separates sub-structures of more complex cardiovascular system models to create uniquely identifiable simplified models that are one to one with the measurements. In addition, a new concept of parameter identification is presented where the changes in the parameters are treated as an actuation force into a feed back control system, and the reference output is taken to be steady state values of measured volume and pressure. The major advantage of the method is that when it converges, it must be at the global minimum so that the solution that best fits the data is always found.By utilizing continuous information from the arterial/pulmonary pressure waveforms and the end-diastolic time, it is shown that potentially, the ventricle volume is not required in the data set, which was a requirement in earlier published work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing an initial set of patient specific parameters that can reveal trends and interactions in the data over time. The goal is to apply the simplified models to retrospective data on groups of patients to help characterize population trends or un-modelled dynamics within known bounds. These trends can assist in improved prediction of patient responses to cardiac disturbance and therapy intervention with potentially smaller and less invasive data sets. In this way a more complex model that takes into account individual patient variation can be developed, and applied to the improvement of cardiovascular management in critical care. Keywords :Model-based cardiac diagnosis ; Cardiovascular system ; Integral-based parameter identification ; Pressure waveform ; ECG ; Intensive care unit IntroductionIn critical care, cardiovascular dysfunction can be easily misdiagnosed due to incomplete information and the complexities involved, leading to premature discharge or non-optimal treatment [1][2][3]. It is also a major cause of increased length of stay and death [4,5]. Demand for critical care is growing dramatically severely affecting healthcare delivery [6][7][8]. The overall goal of this research is to use computational cardiac models to better aggregate available clinical data in an intensive care unit (ICU) into a more readily understood physiological context for clinicians. The computational models can be used to reveal non-linear dynamics and interactions that are not readily apparent in the data.A major difficulty faced with cardiovascular modelling in general, is the level of detail these models typically include. For example multi-scale modelling approaches utilizing finite elements have successfully expl...

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“…1. The model and methods of the left ventricle-systemic system are then tested on clinical data from a pulmonary embolism animal model experiment [39].…”

Section: Resultsmentioning

confidence: 99%

“…4 with noise levels defined in Eq. (39). To optimize accuracy, a cubic smoothing spline is performed on the noisy aortic pressure waveform using standard in-built functions in Matlab.…”

Section: Healthy and Diseased Human With Noise-no Volume Measurementmentioning

confidence: 99%

See 1 more Smart Citation

Unique parameter identification for cardiac diagnosis in critical care using minimal data sets

Hann

Chase

Desaive

et al. 2010

Computer Methods and Programs in Biomedicine

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show abstract

“…Thus, it provides a good set of data to watch to progression of the disease state. For details see (Desaive et al (2005); Ghuysen et al (2008);Lambermont et al (2003)). …”

Section: Methodsmentioning

confidence: 99%

“…The study was conducted on data taken from a cohort of 9 porcine subjects. These 9 subjects were divided into two groups with 5 pigs induced with pulmonary embolism (Desaive et al (2005); Ghuysen et al (2008)). The remaining 4 are induced with septic shock, and treated with haemofiltration starting at two hours (Lambermont et al (2003)).…”

Section: Methodsmentioning

confidence: 99%

Estimating afterload, systemic vascular resistance and pulmonary vascular resistance in an intensive care setting

Stevenson

Revie

Chase

et al. 2012

IFAC Proceedings Volumes

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Poor management and delayed diagnosis for both pulmonary embolism and septic shock are common and lead to increased cost, length of stay and mortality. Despite a wealth of information coming from commonly placed catheters much of this information remains unknown to an intensive care clinician. Data was gather from two porcine trials, 5 subjects induced with pulmonary embolism, and 5 with septic shock (treated with haemofiltration). Methods for real-time estimating afterload, systemic vascular resistance and pulmonary vascular resistance are presented. Knowledge of these parameters would greatly increase management of patients with pulmonary embolism and septic shock, and help the accuracy and speed of diagnosis. All estimations tracked trends very well. The estimating for afterload has a percentage error of 21.6% in pulmonary embolism and 11.8% in septic shock, systemic vascular resistance has a percentage error of 12.51% and 13.5% for pulmonary embolism and septic shock respectively while pulmonary vascular resistance showed percentage errors of 12.2% and 44.5%. From these estimations, the drop in systemic vascular resistance and afterload can be clearly identified in the septic shock cohort, as well as the recovery after haemofiltration was started, while the pulmonary embolism cohort showed the expected increase in pulmonary vascular resistance.

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Intensive care management of right ventricular failure and pulmonary hypertension crises

Coleman

Chartan

Mourani

2021

Pediatric Pulmonology

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Pulmonary hypertension (PH), an often unrelenting disease that carries with it significant morbidity and mortality, affects not only the pulmonary vasculature but, in turn, the right ventricle as well. The survival of patients with PH is closely related to the right ventricular function. Therefore, having an understanding of how to manage right ventricular failure (RVF) and acute pulmonary hypertensive crises is imperative for clinicians who encounter these patients. This review addresses the management of these patients in detail, addressing: (a) the pathophysiology of RVF, (b) intensive care monitoring of these patients in the intensive care unit, (c) imaging of the right ventricle, (d) intubation and mechanical ventilation, (e) inotrope and vasopressor selection, (f) pulmonary vasodilator use, (g) interventional and surgical procedures for the acutely failing right ventricle, and (h) mechanical support for RVF.

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Alteration of Right Ventricular-Pulmonary Vascular Coupling in a Porcine Model of Progressive Pressure Overloading

Cited by 49 publications

References 36 publications

Unique parameter identification for cardiac diagnosis in critical care using minimal data sets

Unique parameter identification for cardiac diagnosis in critical care using minimal data sets

Estimating afterload, systemic vascular resistance and pulmonary vascular resistance in an intensive care setting

Intensive care management of right ventricular failure and pulmonary hypertension crises

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