Aortic valve resistance in aortic stenosis: Doppler echocardiographic study and surgical correlation

Stimulus-effect relations for left ventricular growth obtained with a simple multi-scale model: the influence of hemodynamic feedback

Cardiac growth is an important mechanism for the human body to respond to changes in blood flow demand. Being able to predict the development of chronic growth is clinically relevant, but so far models to predict growth have not reached consensus on the stimulus–effect relation. In a previously published study, we modeled cardiac and hemodynamic function through a lumped parameter approach. We evaluated cardiac growth in response to valve disease using various stimulus–effect relations and observed an unphysiological decline pump function. Here we extend that model with a model of hemodynamic feedback that maintains mean arterial pressure and cardiac output through adaptation of peripheral resistance and circulatory unstressed volume. With the combined model, we obtain stable growth and restoration of pump function for most growth laws. We conclude that a mixed combination of stress and strain stimuli to drive cardiac growth is most promising since it (1) reproduces clinical observations on cardiac growth well, (2) requires only a small, clinically realistic adaptation of the properties of the circulatory system and (3) is robust in the sense that results were fairly insensitive to the exact choice of the chosen mechanics loading measure. This finding may be used to guide the choice of growth laws in more complex finite element models of cardiac growth, suitable for predicting the response to spatially varying changes in tissue load. Eventually, the current model may form a basis for a tool to predict patient-specific growth in response to spatially homogeneous changes in tissue load, since it is computationally inexpensive.

Evaluation of stimulus-effect relations in left ventricular growth using a simple multiscale model

Cardiac growth is the natural capability of the heart to change size in response to changes in blood flow demand of the growing body. Cardiac diseases can trigger the same process leading to an abnormal type of growth. Prediction of cardiac growth would be clinically valuable, but so far published models on cardiac growth differ with respect to the stimulus-effect relation and constraints used for maximum growth. In this study, we use a zero-dimensional, multiscale model of the left ventricle to evaluate cardiac growth in response to three valve diseases, aortic and mitral regurgitation along with aortic stenosis. We investigate how different combinations of stress- and strain-based stimuli affect growth in terms of cavity volume and wall volume and hemodynamic performance. All of our simulations are able to reach a converged state without any growth constraint, with the most promising results obtained while considering at least one stress-based stimulus. With this study, we demonstrate how a simple model of left ventricular mechanics can be used to have a first evaluation on a designed growth law.

Stress Echocardiography in Aortic Stenosis: Insights into Valve Mechanics and Hemodynamics

Stress interventions have been classically combined with cardiac catheterization recordings to understand the hemodynamic principles of valvular stenosis. Indices of aortic stenosis such as pressure gradient and valve area were based on simple hydraulic principles and have proved to be clinically useful for patient management during a number of decades. With the advent of Doppler echocardiography, these hemodynamic indices can be readily obtained noninvasively. Abundant evidence obtained using exercise and pharmacological stress echocardiography has demonstrated that the assumptions of classic hemodynamic models of aortic stenosis were wrong. Consequently, it is recognized that conventional indices may be misleading indicators of aortic stenosis significance in particular clinical situations. To improve diagnostic accuracy, several alternative hemodynamic models have been developed in the past few years, including valve resistance and left ventricular stroke work loss, among others. Nevertheless, these more-accurate indices should be obtainable noninvasively and need to demonstrate greater diagnostic and prognostic power than conventional indices; preliminary data suggest such superiority. Stress echocardiography is well established as the tool of choice for testing hypothesis and physical models of cardiac valve function. Although the final role of alternative indices is not yet well established, the new insights into valvular hemodynamics provided by this technique may change the clinical assessment of aortic stenosis.

Neural network pattern recognition analysis of graft flow characteristics improves intra-operative anastomotic error detection in minimally invasive CABG

OBJECTIVE

The intra-operative assessment of the quality of anastomosis in minimally invasive coronary artery bypass surgery (CABG) is critical. Recent investigations demonstrated that flow probes used intra-operatively to assess anastomotic errors may give the surgeon a false sense of confidence as only severely stenotic anastomoses (>90%) could be reliably detected. We developed a neural network system using graft flow data and assessed its potential to improve anastomotic error detection.

METHODS

Mammary to LAD grafts (n = 46) were constructed in mongrel dogs off-pump. Continuous beat-to-beat graft flow was recorded using transit-time flow probes. Various degrees of anastomotic stenoses (0-100%) were created by an additional suture. The degree of anastomotic stenosis was confirmed by postoperative angiography. A learning vector quantization neural network was created using heart rate, mean aortic pressure, mean systolic, maximum systolic, minimum systolic, mean diastolic, maximum diastolic, minimum diastolic, and mean graft flows. In addition, a spectral analysis of the flow waveforms was performed and the magnitude and phase of the first five harmonics were used to further develop the neural network.

RESULTS

The neural network pattern recognition system was 94% accurate in detecting any stenosis >50%. To validate the model, a testing set was used with 20% of the data values, and the accuracy remained at 100% above chance alone.

CONCLUSION

Pattern recognition of transit-time flow probe tracings using neural network systems can detect anastomotic errors significantly better than the surgeon's visual assessment, thereby improving the clinical outcome of minimally invasive CABG.