A New Control System for Left Ventricular Assist Devices Based on Patient-specific Physiological Demand

A left ventricular assist device (LVAD) is a mechanical pump that helps patients with a heart failure (HF) condition. This pump works in parallel to the ailing heart and provides a continuous flow from the weak left ventricle to the ascending aorta. The current supplied to the pump motor controls the flow of blood. A new feedback control system is developed to automatically adjust the pump motor current to provide the blood flow required by the level of activity of the patient. The systemic vascular resistance (RS) is the only undeterministic variable parameter in a patient-specific model and also a key value that expresses the level of activity of the patient. The rest of the parameters are constants for a patient-specific model. To determine the level of activity of the patient, an inverse problem approach is followed. The output data (pump flow) are observed and using an optimized search technique, the best model to describe such output is selected. Furthermore, the estimated RS is used in another patient-specific cardiovascular model that assumes a healthy heart, to determine the blood flow demand. Once the physiological demand is established, the current supplied to the pump motor of the LVAD can be adjusted to achieve the desired blood flow through the cardiovascular system. This process can be performed automatically in a real-time basis using information that is readily available and thus rendering a high degree of applicability. Results from simulated data show that the feedback control system is fast and very stable

Synergistic Model of Cardiac Function with a Heart Assist Device

The breakdown of cardiac self-organization leads to heart diseases and failure, the number one cause of death worldwide. The left ventricular pressure–volume relation plays a key role in the diagnosis and treatment of heart diseases. Lumped-parameter models combined with pressure–volume loop analysis are very effective in simulating clinical scenarios with a view to treatment optimization and outcome prediction. Unfortunately, often invoked in this analysis is the traditional, time-varying elastance concept, in which the ratio of the ventricular pressure to its volume is prescribed by a periodic function of time, instead of being calculated consistently according to the change in feedback mechanisms (e.g., the lack or breakdown of self-organization) in heart diseases. Therefore, the application of the time-varying elastance for the analysis of left ventricular assist device (LVAD)–heart interactions has been questioned. We propose a paradigm shift from the time-varying elastance concept to a synergistic model of cardiac function by integrating the mechanical, electric, and chemical activity on microscale sarcomere and macroscale heart levels and investigating the effect of an axial rotary pump on a failing heart. We show that our synergistic model works better than the time-varying elastance model in reproducing LVAD–heart interactions with sufficient accuracy to describe the left ventricular pressure–volume relation

Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

Bio-sensing textile based patch with integrated optical detection system for sweat monitoring

Sensors, which can be integrated into clothing and used to measure biochemical changes in body fluids, such as sweat, constitute a major advancement in the area of wearable sensors. Initial applications for such technology exist in personal health and sports performance monitoring. However, sample collection is a complicated matter as analysis must be done in real-time in order to obtain a useful examination of its composition. This work outlines the development of a textile-based fluid handling platform which uses a passive pump to gather sweat and move it through a pre-defined channel for analysis. The system is tested both in vitro and in vivo. In addition, a pH sensor, which depends on the use of a pH sensitive dye and paired emitter-detector LEDs to measure colour changes, has been developed. In vitro and on-body trials have shown that the sensor has the potential to record real-time variations in sweat during exercise