Cy Li

Source of Electrocardiographic ST Changes in Subendocardial Ischemia

To clarify the source of electrocardiographic ST depression associated with ischemia, a sheep model of subendocardial ischemia was developed in which simultaneous epicardial and endocardial ST potentials were mapped, and a computer model using the bidomain technique was developed to explain the results. To produce ischemia in different territories of the myocardium in the same animal, the left anterior descending coronary artery and left circumflex coronary artery were partially constricted in sequence. Results from 36 sheep and the computer simulation are reported. The distributions of epicardial potentials from either ischemic source were very similar (r=0.77+/-0.14, P<0.0001), with both showing ST depression on the free wall of the left ventricle and no association between the ST depression and the ischemic region. However, endocardial potentials showed that ST elevation was directly associated with the region of reduced blood flow. Insulating the heart from the surrounding tissue with plastic increased the magnitude of epicardial ST potentials, which was consistent with an intramyocardial source. Increasing the percent stenosis of a coronary artery increased epicardial ST depression at the lateral boundary and resulted in ST elevation starting from the ischemic center as ischemia became transmural. Computer simulation using the bidomain model reproduced the epicardial ST patterns and suggested that the ST depression was generated at the lateral boundary between ischemic and normal territories. ST depression on the epicardium reflected the position of this lateral boundary. The boundaries of ischemic territories are shared, and only those appearing on the free wall contribute to external ST potential fields. These effects explain why body surface ST depression does not localize cardiac ischemia in humans.

The importance of anisotropy in modeling ST segment shift in subendocardial ischaemia

In this paper, a simple mathematical model of a slab of cardiac tissue is presented in an attempt to better understand the relationship between subendocardial ischaemia and the resulting epicardial potential distributions. The cardiac tissue is represented by the bidomain model where tissue anisotropy and fiber rotation have been incorporated with a view to predicting the epicardial surface potential distribution. The source of electric potential in this steady-state problem is the difference between plateau potentials in normal and ischaemic tissue, where it is assumed that ischaemic tissue has a lower plateau potential. Simulations with tissue anisotropy and no fiber rotation are also considered. Simulations are performed for various thicknesses of the transition region between normal and ischaemic tissue and for various sizes of the ischaemic region. The simulated epicardial potential distributions, based on an anisotropic model of the cardiac tissue, show that there are large, potential gradients above the border of the ischaemic region and that there are dips in the potential distribution above the region of ischaemia. It could be concluded from the simulations that it would be possible to predict the region of subendocardial ischaemia from the epicardial potential distribution, a conclusion contrary to observed experimental data. Possible reasons for this discrepancy are discussed. In the interests of mathematical simplicity, isotropic models of the cardiac tissue are also considered, but results from these simulations predict epicardial potential distributions vastly different from experimental observations. A major conclusion from this work is that tissue anisotropy and fiber rotation must be included to obtain meaningful and realistic epicardial potential distributions.

Epicardial ST Depression in Acute Myocardial Infarction

The presence of electrocardiographic ST depression in acute infarction remains controversial and poorly explained. A combined animal and modeling study was performed to evaluate the source of ST changes in acute infarction. In anaesthetized sheep, small infarcts showed uniform ST elevation over the infarction whereas larger infarcts showed marked ST depression over the normal myocardium in addition to the ST elevation. These findings were replicated by bidomain models of the heart. A hollow sphere was used to model a gradually increasing infarct, and this showed that there was a decrease in the ratio of ST elevation to ST depression as the infarct was increased. The current flowing out of the heart must be identical to the current flowing back into the heart. This means that any infarction will produce ST depression as well as ST elevation, the ratio between the two being related to the size of the infarction. Small infarction is associated with a small region of ST elevation and minor ST depression of the remaining myocardium, and as the infarct region increases, the amplitude of the epicardial ST elevation falls and the amplitude of the ST depression increases. Infarction size is proportional to both the height of the ST depression on the epicardium and the strength of the epicardial ST segment dipole.

Fast Fourier Transform Analysis of Ventricular Fibrillation Intervals to Predict Ventricular Refractoriness and Its Spatial Dispersion

A technique of fast Fourier transform analysis has been used to derive mean ventricular fibrillation (VF) intervals, and to confirm that these VF intervals predict ventricular refractory periods. Twenty episodes of VF were induced by a rapid ventricular pacing in 12 sheep. VF activations in a 10‐second period were simultaneously acquired from 64 epicardial sites with an electrode sock. The VF electrograms were analyzed by a fast Fourier transform analysis. The dominant peak frequency of the VF spectrum in each epicardial site was converted into milliseconds and served as a mean VF interval. The dominant peak frequency of VF electrograms ranged from 8.1 to 11.5 Hz, and the corresponding mean VF intervals were 87 to 124 ms. In five sheep, the mean VF intervals and the effective refractory periods were determined by the extrastimulus technique obtained from 29 epicardial sites. There was a very good correlation between the two parameters when the effective refractory periods were determined at a basic cycle length of 300 ms (r = 0.89, P < 0.001) and 400 ms (r = 0.87, P < 0.001), respectively. VF was induced twice in eight sheep. The maximum difference in the mean VF intervals between the two VF episodes in the same sheep was 3 ms (P > 0.05). In conclusion, mean VF intervals determined by the fast Fourier transform analysis have a good reproducibility and a good correlation with ventricular refractory periods measured by the classic extrastimulus technique. The mean VF intervals could serve as an index of ventricular refractoriness.