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See detailProcessing aortic and pulmonary artery waveforms to derive the ventricle time-varying elastance
Stevenson, D. J.; Hann, C. E.; Chase, G. J. et al

in IFAC Proceedings Volumes (IFAC-PapersOnline) (2011), 18(PART 1), 587-592

Time-varying elastance of the ventricles is an important metric both clinically and as an input for a previously developed cardiovascular model. However, currently time-varying elastance is not normally ... [more ▼]

Time-varying elastance of the ventricles is an important metric both clinically and as an input for a previously developed cardiovascular model. However, currently time-varying elastance is not normally available in an Intensive Care Unit (ICU) setting, as it is an invasive and ethically challenging metric to measure. A previous paper developed a method to map less invasive metrics to the driver function, enabling an estimate to be achieved without invasive measurements. This method requires reliable and accurate processing of the aortic and pulmonary artery pressure waveforms to locate the specific points that are required to estimate the driver function. This paper details the method by which these waveforms are processed, using a data set of five pigs induced with pulmonary embolism, and five pigs induced with septic shock (with haemofiltration), adding up to 88 waveforms (for each of aortic and pulmonary artery pressure), and 616 points in total to locate. 98.2% of all points were located to within 1% of their true value, 0.81% were between 1% and 5%, 0.65% were between 5% and 10%, the remaining 0.32% were below 20%.© 2011 IFAC. [less ▲]

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See detailUnique parameter identification for model-based cardiac diagnosis in critical care
Hann, C. E.; Chase, J. G.; Desaive, Thomas ULg et al

in IFAC Proceedings Volumes (IFAC-PapersOnline) (2009), 7(PART 1), 169-174

Lumped parameter approaches for modeling the cardiovascular system typically have many parameters of which many are not identifiable. The conventional approach is to only identify a small subset of ... [more ▼]

Lumped parameter approaches for modeling the cardiovascular system typically have many parameters of which many are not identifiable. The conventional approach is to only identify a small subset of parameters to match measured data, and to set the remaining parameters at population values. These values are often based on animal data or the "average human" response. The problem, is that setting many parameters at nominal fixed values, may introduce dynamics that are not present in a specific patient. As parameter numbers and model complexity increase, more clinical data is required for validation and the model limitations are harder to quantify. This paper considers the modeling and the parameter identification simultaneously, and creates models that are one to one with the measurements. That is, every input parameter into the model is uniquely optimized to capture the clinical data and no parameters are set at population values. The result is a geometrical characterization of a previously developed six chamber heart model, and a completely patient specific approach to cardiac diagnosis in critical care. In addition, simplified sub-structures of the six chamber model are created to provide very fast and accurate parameter identification from arbitrary starting points and with no prior knowledge on the parameters. Furthermore, by utilizing continuous information from the arterial/pulmonary pressure waveforms and the end-diastolic time, it is shown that only the stroke volumes of the ventricles are required for adequate cardiac diagnosis. This reduced data set is more practical for an intensive care unit as the maximum and minimum volumes are no longer needed, which was a requirement in prior work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing a generating set of waveforms that the complex models can match to. Most importantly, this approach does not have any predefined assumptions on patient dynamics other than the basic model structure, and is thus suitable for improving cardiovascular management in critical care by optimizing therapy for individual patients. © 2009 IFAC. [less ▲]

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