A Study of the Dynamics of Cardiac Ischemia using Experimental and Modeling Approaches

Shome, Shibaji; Stinstra, J.G.; Hopenfeld, Bruce; Punske, Bonnie B.; MacLeod, Rob S.

doi:10.1109/iembs.2004.1404008

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…The mean effect is defined to be the conditional expectation of that output, conditional on the input variable x w , after averaging over the remaining variables; that is, mean effect = E{f (x)|x w }. For a particular emulator, this is calculated for each input, by allowing x w to vary over [0,1], while the other inputs are taken to be independently normally distributed with a mean of 0.5 and a variance of 0.04 [18].…”

Section: Gaussian Process Emulatormentioning

confidence: 99%

“…Myocardial ischaemia results from a critical reduction of blood supply to the heart and/or an increase in the metabolic demand, which cannot be met by the heart [1]. When the ischaemia is transmural, clinicians are able to determine the location of the ischaemia using the elevation of the ST segment in superficial electrocardiogram (ECG) leads [2].…”

Section: Introductionmentioning

confidence: 99%

“…equating the normal and transverse conductivities) instead of the full set of six. Another suggestion that the conductivities in the ischaemic tissue may be sufficiently different from those in healthy tissue as to have an effect on the EPDs has also been investigated [1,3].…”

Section: Introductionmentioning

confidence: 99%

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Determining the most significant input parameters in models of subendocardial ischaemia and their effect on ST segment epicardial potential distributions

Johnston

2018

Computers in Biology and Medicine

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There is considerable interest in simulating ischaemia in the ventricle and its effect on the electrocardiogram, because a better understanding of the connection between the two may lead to improvements in diagnosis of myocardial ischaemia. In this work we studied subendocardial ischaemia, in a simplified half-ellipsoidal bidomain model of a ventricle, and its effect on ST segment epicardial potential distributions (EPDs). We found that the EPD changed as the ischaemic depth increased, from a single minimum (min1) over the ischaemic region to a maximum (max) there, with min1 over the border of the region. Lastly, a second minimum (min2) developed on the opposite side of the ischaemic region, in addition to min1 and max. We replicated these results in a realistic ventricular model and showed that the min1 only case could be found for ischaemic depths of up to around 35% of the ventricular wall. In addition, we systematically examined the sensitivity of EPD parameters, such as the potentials and positions of min1, max and min2, to various inputs to the half-ellipsoidal model, such as fibre rotation angle, ischaemic depth and conductivities. We found that the EPD parameters were not sensitive to the blood or transverse bidomain conductivities and were most sensitive to either ischaemic depth and/or fibre rotation angle. This allowed us to conclude that the asynchronous development of the two minima might provide a way of distinguishing between low and high thickness subendocardial ischaemia, and that this method may well be valid despite variability in the population.

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Section: Gaussian Process Emulatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determining the most significant input parameters in models of subendocardial ischaemia and their effect on ST segment epicardial potential distributions

Johnston

2018

Computers in Biology and Medicine

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“…Preliminary results from our group using intramyocardial recordings do not support the assertion that nontransmural ischemia arises only in the subendocardium [11] and have motivated a comprehensive evaluation of the spatial distribution of acute ischemia based on high-resolution measurements under a range of conditions. Our goal in this study was to evaluate the conventional mechanisms for nontransmural ischemia using intramural electrodes to measure three-dimensional potential distributions in the ventricles of animals exposed to acute ischemia.…”

Section: Introductionmentioning

confidence: 99%

Spatial organization of acute myocardial ischemia

Aras

Burton

Swenson

et al. 2016

Journal of Electrocardiology

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Introduction Myocardial ischemia is a pathological condition initiated by supply and demand imbalance of the blood to the heart. Previous studies suggest that ischemia originates in the subendocardium, i.e., that nontransmural ischemia is limited to the subendocardium. By contrast, we hypothesized that acute myocardial ischemia is not limited to the subendocardium and sought to document its spatial distribution in an animal preparation. The goal of these experiments was to investigate the spatial organization of ischemia and its relationship to the resulting shifts in ST segment potentials during short episodes of acute ischemia. Methods We conducted acute ischemia studies in open-chest canines (N=19) and swines (N=10), which entailed creating carefully controlled ischemia using demand, supply or complete occlusion ischemia protocols and recording intramyocardial and epicardial potentials. Elevation of the potentials at 40% of the ST segment between the J-point and the peak of the T-wave (ST40%) provided the metric for local ischemia. The threshold for ischemic ST segment elevations was defined as two standard deviations away from the baseline values. Results The relative frequency of occurrence of acute ischemia was higher in the subendocardium (78% for canines and 94% for swines) and the mid-wall (87% for canines and 97% for swines) in comparison with the subepicardium (30% for canines and 22% for swines). In addition, acute ischemia was seen arising throughout the myocardium (distributed pattern) in 87% of the canine and 94% of the swine episodes. Alternately, acute ischemia was seen originating only in the subendocardium (subendocardial pattern) in 13% of the canine episodes and 6% of the swine episodes (p < 0.05). Conclusions Our findings suggest that the spatial distribution of acute ischemia is a complex phenomenon arising throughout the myocardial wall and is not limited to the subendocardium.

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“…A study by Shome et al [10] comparing the results of the simulated epicardial potentials with the epicardial potentials measured in isolated canine hearts showed similar dynamic changes during acute ischemia lasting several minutes.…”

Section: Whole Heart Simulationsmentioning

confidence: 76%

Using models of the passive cardiac conductivity and full heart anisotropic bidomain to study the epicardial potentials in ischemia

Stinstra¹,

Hopenfeld

MacLeod

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In this paper we present a multi-scale approach for cardiac modeling. Based on the histology of cardiac tissue we created a geometrical model at a cellular scale to compute the effective conductivity of a piece of cardiac tissue. In turn, the conductivity values obtained from this cellular scale model were used in a whole heart model in which we simulated regional, subendocardial ischemia. Histological changes at a cellular level led to changes in the effective conductivity tensor of the tissue, which in turn resulted in changes in the epicardial potential patterns during the ST-interval. Two effects were studied using this multi-scale approach: (1) the influence of a dynamically growing ischemic region on the epicardial potentials, and (2) the influence of a dynamically changing conductivity in the ischemic zone due to changes in the underlying pathology. One specific finding was the presence of epicardial patterns consisting of a central elevation and two opposite depressions at the edges of the ischemic zone which rotated as the ischemia became more transmural. In addition, the epicardial potentials decreased in magnitude with the duration of the ischemia due to changes in the effective conductivity of the ischemic tissue predicted by the cellular level model.

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A Study of the Dynamics of Cardiac Ischemia using Experimental and Modeling Approaches

Cited by 6 publications

References 8 publications

Determining the most significant input parameters in models of subendocardial ischaemia and their effect on ST segment epicardial potential distributions

Determining the most significant input parameters in models of subendocardial ischaemia and their effect on ST segment epicardial potential distributions

Spatial organization of acute myocardial ischemia

Using models of the passive cardiac conductivity and full heart anisotropic bidomain to study the epicardial potentials in ischemia

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