A simplified model for mitral valve dynamics.

Located between the left atrium and the left ventricle, the mitral valve controls flow between these two cardiac chambers. Mitral valve dysfunction is a major cause of cardiac dysfunction and its dynamics are little known. A simple non-linear rotational spring model is developed and implemented to capture the dynamics of the mitral valve. A measured pressure difference curve was used as the input into the model, which represents an applied torque to the anatomical valve chords. A range of mechanical model hysteresis states were investigated to find a model that best matches reported animal data of chord movement during a heartbeat. The study is limited by the use of one dataset found in the literature due to the highly invasive nature of getting this data. However, results clearly highlight fundamental physiological issues, such as the damping and chord stiffness changing within one cardiac cycle, that would be directly represented in any mitral valve model and affect behaviour in dysfunction. Very good correlation was achieved between modeled and experimental valve angle with 1-10% absolute error in the best case, indicating good promise for future simulation of cardiac valvular dysfunction, such as mitral regurgitation or stenosis. In particular, the model provides a pathway to capturing these dysfunctions in terms of modeled stiffness or elastance that can be directly related to anatomical, structural defects and dysfunction.

Breath ammonia and trimethylamine allow real-time monitoring of haemodialysis efficacy.

Non-invasive monitoring of breath ammonia and trimethylamine using Selected-ion-flow-tube mass spectroscopy (SIFT-MS) could provide a real-time alternative to current invasive techniques. Breath ammonia and trimethylamine were monitored by SIFT-MS before, during and after haemodialysis in 20 patients. In 15 patients (41 sessions), breath was collected hourly into Tedlar bags and analysed immediately (group A). During multiple dialyses over 8 days, five patients breathed directly into the SIFT-MS analyser every 30 min (group B). Pre- and post-dialysis direct breath concentrations were compared with urea reduction, Kt/V and creatinine concentrations. Dialysis decreased breath ammonia, but a transient increase occurred mid treatment in some patients. Trimethylamine decreased more rapidly than reported previously. Pre-dialysis breath ammonia correlated with pre-dialysis urea in group B (r(2) = 0.71) and with change in urea (group A, r(2) = 0.24; group B, r(2) = 0.74). In group B, ammonia correlated with change in creatinine (r(2) = 0.35), weight (r(2) = 0.52) and Kt/V (r(2) = 0.30). The ammonia reduction ratio correlated with the urea reduction ratio (URR) (r(2) = 0.42) and Kt/V (r(2) = 0.38). Pre-dialysis trimethylamine correlated with Kt/V (r(2) = 0.21), and the trimethylamine reduction ratio with URR (r(2) = 0.49) and Kt/V (r(2) = 0.36). Real-time breath analysis revealed previously unmeasurable differences in clearance kinetics of ammonia and trimethylamine. Breath ammonia is potentially useful in assessment of dialysis efficacy.

Classifying algorithms for SIFT-MS technology and medical diagnosis.

Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) is an analytical technique for real-time quantification of trace gases in air or breath samples. SIFT-MS system thus offers unique potential for early, rapid detection of disease states. Identification of volatile organic compound (VOC) masses that contribute strongly towards a successful classification clearly highlights potential new biomarkers. A method utilising kernel density estimates is thus presented for classifying unknown samples. It is validated in a simple known case and a clinical setting before-after dialysis. The simple case with nitrogen in Tedlar bags returned a 100% success rate, as expected. The clinical proof-of-concept with seven tests on one patient had an ROC curve area of 0.89. These results validate the method presented and illustrate the emerging clinical potential of this technology.

Metabolic model of autoregulation in the Circle of Willis.

The Circle of Willis (CoW) is a ringlike structure of blood vessels found at the base of the brain. Its main function is to distribute oxygen-rich arterial blood to the cerebral mass. In a previous study, a one-dimensional (1D) model of the CoW was created to simulate a series of possible clinical scenarios such as occlusions in afferent arteries, absent or stringlike circulus vessels, or arterial infarctions (Moorhead et al., 2004, Comput. Methods Biomech. Biomed. Eng., 7(3), pp. 121-130). The model captured cerebral haemodynamic autoregulation by using a proportional-integral-derivative (PID) controller to modify efferent artery resistances. Although some good results and correlations were achieved, the model was too simple to capture all the transient dynamics of autoregulation. Hence a more physiologically accurate model has been created that additionally includes the oxygen dynamics that drive the autoregulatory response. Results very closely match accepted physiological response and limited clinical data. In addition, a set of boundary conditions and geometry is presented for which the autoregulated system cannot provide sufficient perfusion, representing a condition with increased risk of stroke and highlighting the importance of modeling the haemodynamics of the CoW. The system model created is computationally simple so it can be used to identify at-risk cerebral arterial geometries and conditions prior to surgery or other clinical procedures.

One-dimensional and three-dimensional models of cerebrovascular flow.

The Circle of Willis is a ring-like structure of blood vessels found beneath the hypothalamus at the base of the brain. Its main function is to distribute oxygen-rich arterial blood to the cerebral mass. One-dimensional (1D) and three-dimensional (3D) computational fluid dynamics (CFD) models of the Circle of Willis have been created to provide a simulation tool which can potentially be used to identify at-risk cerebral arterial geometries and conditions and replicate clinical scenarios, such as occlusions in afferent arteries and absent circulus vessels. Both models capture cerebral haemodynamic autoregulation using a proportional-integral (PI) controller to modify efferent artery resistances to maintain optimal efferent flow rates for a given circle geometry and afferent blood pressure. The models can be used to identify at-risk cerebral arterial geometries and conditions prior to surgery or other clinical procedures. The 1D model is particularly relevant in this instance, with its fast solution time suitable for real-time clinical decisions. Results show the excellent correlation between models for the transient efferent flux profile. The assumption of strictly Poiseuille flow in the 1D model allows more flow through the geometrically extreme communicating arteries than the 3D model. This discrepancy was overcome by increasing the resistance to flow in the anterior communicating artery in the 1D model to better match the resistance seen in the 3D results.

Lumped parameter and feedback control models of the auto-regulatory response in the Circle of Willis.

The Circle of Willis (CoW) is a ring-like structure of blood vessels found beneath the hypothalamus at the base of the brain. Its main function is to distribute oxygen-rich arterial blood to the cerebral mass. A 1-dimensional model of the CoW has been created to simulate a series of possible clinical scenarios such as occlusions in afferent arteries, absent or string-like circulus vessels, or arterial infarctions. The model captures cerebral haemodynamic auto-regulation by using a proportional-integral-derivative (PID) controller to modify efferent resistances and maintain optimal efferent flowrates for a given circle geometry and afferent blood pressure. Results match limited clinical data and results obtained in prior studies to within 6%. In addition, a set of boundary conditions and geometry is presented for which the auto-regulated system cannot provide the necessary efferent flowrates and perfusion, representing a condition with increased risk of stroke and highlighting the importance of modelling the haemodynamics of the CoW. The system model created is computationally simple so it can be used to identify at-risk cerebral arterial geometries and conditions prior to surgery or other clinical procedures.

1D and 3D models of auto-regulated cerebrovascular flow.

The Circle of Willis (CoW) is a ring-like structure blood vessels at the base of the brain that distributes arterial blood to the cerebral mass. 1D and 3D CFD models of the Circle of Willis have been created to simulate clinical scenarios such as inclusions in afferent arteries and absent circulus vessels. Both models capture cerebral haemodynamic auto-regulation using proportional-integral controller to modify efferent artery distances to maintain optimal efferent flowrates for a given cycle geometry and afferent blood pressure. The models can be used to identify at-risk cerebral arterial geometries and conditions prior to surgery or other clinical procedures. The model is particularly relevant in this instance, with its fast execution time suitable for real-time clinical decisions and senario testing, as long as it captures the necessary details as a model would. Results show excellent correlation between models for the transient efferent flux profile with differences more than 5%. The assumption of strictly Poiseulile flow in the model allows more flow through the geometrically extreme communicating arteries than the 3D model. This discrepancy is overcome by increasing the resistance to flow in the ACoA the 1D model to better match the resistance seen in the 3D model, significantly improving correlation of the results.