Cycle-averaged dynamics of a periodically driven, closed-loop circulation model

A Novel Mean-Value Model of the Cardiovascular System Including a Left Ventricular Assist Device

Time-varying elastance models (TVEMs) are often used for simulation studies of the cardiovascular system with a left ventricular assist device (LVAD). Because these models are computationally expensive, they cannot be used for long-term simulation studies. In addition, their equilibria are periodic solutions, which prevent the extraction of a linear time-invariant model that could be used e.g. for the design of a physiological controller. In the current paper, we present a new type of model to overcome these problems: the mean-value model (MVM). The MVM captures the behavior of the cardiovascular system by representative mean values that do not change within the cardiac cycle. For this purpose, each time-varying element is manually converted to its mean-value counterpart. We compare the derived MVM to a similar TVEM in two simulation experiments. In both cases, the MVM is able to fully capture the inter-cycle dynamics of the TVEM. We hope that the new MVM will become a useful tool for researchers working on physiological control algorithms. This paper provides a plant model that enables for the first time the use of tools from classical control theory in the field of physiological LVAD control.

Model-based data integration in clinical environments

As a result of improved hospital information-technology infrastructure and declining costs of storage media, vast amounts of physiological waveform and trend data can now be continuously collected and archived from bedside monitors in operating rooms, intensive care units, or even regular hospital rooms. The real-time or off-line processing of such volumes of high-resolution data, in attempts to turn raw data into clinically actionable information, poses significant challenges. However, it also presents researchers - and eventually clinicians - with unprecedented opportunities to move beyond the traditional individual-channel analysis of waveform data, and towards an integrative patient-monitoring framework, with likely improvements in patient care and safety. We outline some of the challenges and opportunities, and propose strategies for model-based integration of physiological data to improve patient monitoring.

A cycle-averaged model of hypoplastic left heart syndrome (HLHS)

This paper is concerned with computational modeling of a severe congenital defect called Hypoplastic left heart syndrome (HLHS) that is the most common cardiac malformation with the highest likelihood of deaths in newborns. A lumped parameter model of the HLHS circulation has been developed to study the hemodynamic variables in the various sections of the cardio-pulmonary circulation system. We applied a short-term, cycle-averaging operation to the differential equations of the HLHS model to obtain the cycle-averaged model. Study has been carried out to analyze the variation of blood flow rate in different parts due to parameter changes. Results show that the developed model, could bring a good insight into understanding of the HLHS disease.

A simple cycle-averaged model for cardiovascular dynamics

Lumped-parameter time-varying electrical circuit analogs for cardiovascular systems are frequently used in computational models for simulating and analyzing hemodynamics. These pulsatile models provide details of the beat-by-beat or intracycle dynamics. In other settings, however, such as when tracking a hospital patient's hemodynamic state over time, it is more useful to track trends in the intercycle dynamics. In this paper, we apply a cycle-averaging method to a simple pulsatile cardiovascular model to derive a cycle-averaged model for cardiovascular dynamics. The resulting cycle-averaged model captures the intercycle dynamics with relatively small approximation errors for a large range of perturbations in parameters such as systemic vascular resistance.