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. 2011 Dec;4(6):858-66.
doi: 10.1161/CIRCEP.110.961763. Epub 2011 Aug 14.

Unstable QT interval dynamics precedes ventricular tachycardia onset in patients with acute myocardial infarction: a novel approach to detect instability in QT interval dynamics from clinical ECG

Xiaozhong Chen  1 , Yuxuan Hu, Barry J Fetics, Ronald D Berger, Natalia A Trayanova
Affiliations

Unstable QT interval dynamics precedes ventricular tachycardia onset in patients with acute myocardial infarction: a novel approach to detect instability in QT interval dynamics from clinical ECG

Xiaozhong Chen et al. Circ Arrhythm Electrophysiol. 2011 Dec.

Abstract

Background: Instability in ventricular repolarization in the presence of premature activations (PA) plays an important role in arrhythmogenesis. However, such instability cannot be detected clinically. This study developed a methodology for detecting QT interval (QTI) dynamics instability from the ECG and explored the contribution of PA and QTI instability to ventricular tachycardia (VT) onset.

Methods and results: To examine the contribution of PAs and QTI instability to VT onset, ECGs of 24 patients with acute myocardial infarction, 12 of whom had sustained VT (VT) and 12 nonsustained VT (NSVT), were used. From each patient ECG, 2 10-minute-long ECG recordings were extracted, 1 right before VT onset (onset epoch) and 1 at least 1 hour before it (control epoch). To ascertain how PA affects QTI dynamics stability, pseudo-ECGs were calculated from an MRI-based human ventricular model. Clinical and pseudo-ECGs were subdivided into 1-minute recordings (minECGs). QTI dynamics stability of each minECG was assessed with a novel approach. Frequency of PAs (f(PA)) and the number of minECGs with unstable QTI dynamics (N(us)) were determined for each patient. In the VT group, f(PA) and N(us) of the onset epoch were larger than in control. Positive regression relationships between f(PA) and N(us) were identified in both groups. The simulations showed that both f(PA) and the PA degree of prematurity contribute to QTI dynamics instability.

Conclusions: Increased PA frequency and QTI dynamics instability precede VT onset in patients with acute myocardial infarction, as determined by novel methodology for detecting instability in QTI dynamics from clinical ECGs.

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Figures

Figure 1
Figure 1
Clinical ECG annotation and intervals. See text for detail.
Figure 2
Figure 2
A. Epicardial (left) and transmural (right) views of the biophysically-detailed MRI-based human ventricular model (at left, ventricles are in pink and atria in brown). Atria were insulated from the ventricles during the simulation. ECG electrodes are E1 and E2; pacing electrode is E3. B. Action potentials of human endo-, M, and epicardial cells. C. RRI sequence with PAs used as a pacing train in the electrophysiological simulations (see text for detail). D. ECG annotation and QT interval in one beat from a pseudo-ECG.
Figure 2
Figure 2
A. Epicardial (left) and transmural (right) views of the biophysically-detailed MRI-based human ventricular model (at left, ventricles are in pink and atria in brown). Atria were insulated from the ventricles during the simulation. ECG electrodes are E1 and E2; pacing electrode is E3. B. Action potentials of human endo-, M, and epicardial cells. C. RRI sequence with PAs used as a pacing train in the electrophysiological simulations (see text for detail). D. ECG annotation and QT interval in one beat from a pseudo-ECG.
Figure 3
Figure 3
QTI dynamics predicted by the ARX model (QTI_p) compared with the QTI dynamics extracted from a minECG of the ONSET epoch from the VT group for A. Mmax=24 and B. M=3. C. Dependence of the mean square error in the prediction of QTI dynamics on the value of M (length of activation history).
Figure 3
Figure 3
QTI dynamics predicted by the ARX model (QTI_p) compared with the QTI dynamics extracted from a minECG of the ONSET epoch from the VT group for A. Mmax=24 and B. M=3. C. Dependence of the mean square error in the prediction of QTI dynamics on the value of M (length of activation history).
Figure 3
Figure 3
QTI dynamics predicted by the ARX model (QTI_p) compared with the QTI dynamics extracted from a minECG of the ONSET epoch from the VT group for A. Mmax=24 and B. M=3. C. Dependence of the mean square error in the prediction of QTI dynamics on the value of M (length of activation history).
Figure 4
Figure 4
A. Nus and B. its medians and interquartile ranges for the ONSET and CONTROL epochs of the VT group. The difference in Nus between the ONSET and CONTROL epochs is significant. C. fPA and D. its medians and interquartile ranges for the ONSET and CONTROL epochs of the VT group. The difference in fPA between the ONSET and CONTROL epochs is significant.
Figure 4
Figure 4
A. Nus and B. its medians and interquartile ranges for the ONSET and CONTROL epochs of the VT group. The difference in Nus between the ONSET and CONTROL epochs is significant. C. fPA and D. its medians and interquartile ranges for the ONSET and CONTROL epochs of the VT group. The difference in fPA between the ONSET and CONTROL epochs is significant.
Figure 5
Figure 5
Mean QTI instability index Pm as a function of time in the 10 min prior to onset of A. VT and B. NSVT.
Figure 5
Figure 5
Mean QTI instability index Pm as a function of time in the 10 min prior to onset of A. VT and B. NSVT.
Figure 6
Figure 6
The positive regression relationship between Nus and fPA of A. the ONSET epoch of the VT group, B. the CONTROL epoch of the VT group, C. the ONSET epoch of the NSVT group, and D. the CONTROL epoch of the NSVT group.
Figure 7
Figure 7
QTI restitution constructed from a minECG before VT onset.
Figure 8
Figure 8
The simulation results revealed the dependence of Pm on A. DOP and B. fPA.
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