ALBERT

Description: Sudden cardiac death (SCD) is a challenging health problem in the western world. Analysis of cardiac repolarization from the electrocardiogram (ECG) provides valuable information for stratifying patients according to their risk of suffering from arrhythmic events that could end in SCD, as well as for assessing efficacy of antiarrhythmic therapies. In this paper, we start by exploring the cellular basis of ECG repolarization waveforms under both normal and pathological conditions. We then describe basic preprocessing steps that need to be accomplished on the ECG signal before extracting repolarization indices. A comprehensive review of techniques aimed to characterize spatial or temporal repolarization dispersion is provided, together with a summary of their usefulness for clinical risk stratification. Techniques that describe spatial dispersion of repolarization are based on either differences in repolarization duration or T-wave loop morphology. Techniques that evaluate temporal dispersion of repolarization include the analysis of QT interval adaptation to heart rate changes, QT interval and T-wave variability, and T-wave alternans.

Description: In this study, several modifications were introduced to a recently proposed human ventricular action potential (AP) model so as to render it suitable for the study of ventricular arrhythmias. These modifications were driven by new sets of experimental data available from the literature and the analysis of several well-established cellular arrhythmic risk biomarkers, namely AP duration at 90 per cent repolarization (APD 90 ), AP triangulation, calcium dynamics, restitution properties, APD 90 adaptation to abrupt heart rate changes, and rate dependence of intracellular sodium and calcium concentrations. The proposed methodology represents a novel framework for the development of cardiac cell models. Five stimulation protocols were applied to the original model and the ventricular AP model developed here to compute the described arrhythmic risk biomarkers. In addition, those models were tested in a one-dimensional fibre in which hyperkalaemia was simulated by increasing the extracellular potassium concentration, [K + ] o . The effective refractory period (ERP), conduction velocity (CV) and the occurrence of APD alternans were investigated. Results show that modifications improved model behaviour as verified by: (i) AP triangulation well within experimental limits (the difference between APD at 50 and 90 per cent repolarization being 78.1 ms); (ii) APD 90 rate adaptation dynamics characterized by fast and slow time constants within physiological ranges (10.1 and 105.9 s); and (iii) maximum S1S2 restitution slope in accordance with experimental data ( S S1S2 =1.0). In simulated tissues under hyperkalaemic conditions, APD 90 progressively shortened with the degree of hyperkalaemia, whereas ERP increased once a threshold in [K + ] o was reached ([K + ] o ≈6 mM). CV decreased with [K + ] o , and conduction was blocked for [K + ] o 〉10.4 mM. APD 90 alternans were observed for [K + ] o 〉9.8 mM. Those results adequately reproduce experimental observations. This study demonstrated the value of basing the development of AP models on the computation of arrhythmic risk biomarkers, as opposed to joining together independently derived ion channel descriptions to produce a whole-cell AP model, with the new framework providing a better picture of the model performance under a variety of stimulation conditions. On top of replicating experimental data at single-cell level, the model developed here was able to predict the occurrence of APD 90 alternans and areas of conduction block associated with high [K + ] o in tissue, which is of relevance for the investigation of the arrhythmogenic substrate in ischaemic hearts.

Description: Stress test electrocardiogram (ECG) analysis is widely used for coronary artery disease (CAD) diagnosis despite its limited accuracy. Alterations in autonomic modulation of cardiac electrical activity have been reported in CAD patients during acute ischemia. We hypothesized that those alterations could be reflected in changes in ventricular repolarization dynamics during stress testing that could be measured through QT interval variability (QTV). However, QTV is largely dependent on RR interval variability (RRV), which might hinder intrinsic ventricular repolarization dynamics. In this study, we investigated whether different markers accounting for low-frequency (LF) oscillations of QTV unrelated to RRV during stress testing could be used to separate patients with and without CAD. Power spectral density of QTV unrelated to RRV was obtained based on time-frequency coherence estimation. Instantaneous LF power of QTV and QTV unrelated to RRV were obtained. LF power of QTV unrelated to RRV normalized by LF power of QTV was also studied. Stress test ECG of 100 patients were analysed. Patients referred to coronary angiography were classified into non-CAD or CAD group. LF oscillations in QTV did not show significant differences between CAD and non-CAD groups. However, LF oscillations in QTV unrelated to RRV were significantly higher in the CAD group as compared with the non-CAD group when measured during the first phases of exercise and last phases of recovery. ROC analysis of these indices revealed area under the curve values ranging from 61 to 73%. Binomial logistic regression analysis revealed LF power of QTV unrelated to RRV, both during the first phase of exercise and last phase of recovery, as independent predictors of CAD. In conclusion, this study highlights the importance of removing the influence of RRV when measuring QTV during stress testing for CAD identification and supports the added value of LF oscillations of QTV unrelated to RRV to diagnose CAD from the first minutes of exercise. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.