Dynamic features of cardiac vector as alternative markers of drug-induced spatial dispersion.

INTRODUCTION: The abnormal amplification of ventricular repolarization dispersion (VRD) has long been linked to proarrhythmia risk. Recently, the measure of VRD through electrocardiogram intervals has been strongly questioned. The search for an efficient and non-invasive surrogate marker of drug-induced dispersion effects constitute an urgent research challenge. METHODS: Herein, drug-induced ventricular dispersion is generated by d-Sotalol supply in an In-vitro rabbit heart model. A cilindrical chamber simulates the thorax and a multi-electrode net is used to obtain spatial electrocardiographic signals. Cardiac vector dynamics is captured by novel velocity cardiomarkers obtained by quaternion methods. Through statistical analysis and machine learning technics, we compute potential dispersion markers that could define proarrhythmic risk. RESULTS: The cardiomarkers with the greatest statistical significance, both obtained from the electrical cardiac vector, were: the QTω, which is the difference between first and last maxima of angular velocity and λ21vT, the roundness of linear velocity. When comparing with the performance of the current standards (89%), this pair was able to correctly separate 21 out of 22 experiments achieving a performance of 95%. Moreover, the QTω computes in a much more robust basis the QT interval, the current index for drug regulation. DISCUSSION: These velocity markers circumvent the problems of accuratelly finding the fiducial points such as the always tricky T-wave end. Given the high performance they achieved, it is provided a promising outcome for future applications to the detection of anomalous changes of heterogeneity that may be useful for the purposes of torsadogenic toxicity studies.

Biomarkers of pre-existing risk of Torsade de Pointes under Sotalol treatment.

INTRODUCTION: Antiarrhythmic drugs therapies are currently going through a turning point. The high risk that exists during the treatments has led to an ongoing search for new non-invasive toxicity risk biomarkers. METHODS: We propose the use of spatial biomarkers obtained through the quaternion algebra, evaluating the dynamics of the cardiac electrical vector in a non-invasive way in order to detect abnormal changes in ventricular heterogeneity. In groups of patients with and without history of Torsade de Pointes undergoing a Sotalol challenge, we compute the radius and the linear and angular velocities of QRS complex and T-wave loops. From these signals we extract significant features in order to compute a risk patient classifier. RESULTS: Using machine learning techniques and statistical analysis, the combinations of few indices reach a pair of sensitivity/specificity of 100%/100% when separating patients with arrhythmogenic substrate. Several biomarkers not only measure drug-induced changes significantly but also observe differences in at-risk patients outperforming current standards. DISCUSSION: Alternative biomarkers were able to describe pre-existing risk of patients. Given the high levels of significance and performance, these results could contribute to a better understanding of the torsadogenic substrate and to the safe development of drug therapies.

Knee stiffness and viscosity: New implementation and perspectives in prosthesis development.

The pendulum test is a method applied to measure passive resistance of the knee. A new and simple pendulum test with instrumentation based on infrared camera was used to evaluate knee stiffness and viscosity on a female human cadaver. The stiffness and viscosity were calculated based on the kinetic data. During the measurements, the periarticular and intraarticular soft tissue of the knee was gradually removed to determine the stiffness and viscosity as a function of the tissue removal rate. The measurements showed that the removal of tissue around the joint reduces the damping of leg oscillation, and therefore decreases the stiffness and viscosity. The contribution to knee joint damping was 10% for the skin, 20% for ligaments, and 40% for muscles and tendons. Tissue removal has a very large impact on the knee stiffness and viscosity.