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Circulation. 1997;96:1557-1565

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(Circulation. 1997;96:1557-1565.)
© 1997 American Heart Association, Inc.

Articles

Beat-to-Beat QT Interval Variability

Novel Evidence for Repolarization Lability in Ischemic and Nonischemic Dilated Cardiomyopathy

Ronald D. Berger, MD, PhD; Edward K. Kasper, MD; Kenneth L. Baughman, MD; Eduardo Marban, MD, PhD; Hugh Calkins, MD; ; Gordon F. Tomaselli, MD

From Johns Hopkins School of Medicine, Baltimore, Md.

Correspondence to Ronald D. Berger, MD, PhD, Carnegie 592, Johns Hopkins Hospital, 600 N Wolfe St, Baltimore, MD 21287. E-mail ron{at}tachy.cdisc.jhu.edu


*    Abstract
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 *Abstract
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Background Dilated cardiomyopathy (DCM) is associated with a high incidence of malignant ventricular arrhythmias and sudden death. Abnormalities in repolarization of ventricular myocardium have been implicated in the development of these arrhythmias. Spatial heterogeneity in repolarization has been studied in DCM, but temporal fluctuations in repolarization in this setting have been largely ignored. We sought to test the hypothesis that beat-to-beat QT interval variability is increased in DCM patients compared with control subjects.

Methods and Results Eighty-three patients with ischemic and nonischemic DCM and 60 control subjects served as the study population. Beat-to-beat QT interval variability was measured by automated analysis on the basis of 256-second records of the surface ECG. A QT variability index (QTVI) was calculated for each subject as the logarithm of the ratio of normalized QT variance to heart rate variance. The coherence between heart rate and QT interval fluctuations was determined by spectral analysis. In patients, ejection fractions were assessed by echocardiography or ventriculography, and spatial QT dispersion was determined from the standard 12-lead ECG. DCM patients had greater QT variance than control subjects (60.4±63.1 versus 25.7±24.8 ms2, P<.0001) despite reduced heart rate variance (6.7±7.8 versus 10.5±10.4 bpm2, P=.01). The QTVI was higher in DCM patients than in control subjects, with a high degree of significance (-0.43±0.71 versus -1.29±0.51, P<10-12). QTVI did not correlate with ejection fraction or spatial QT dispersion but did depend on New York Heart Association functional class. QTVI did not differ between DCM patients with ischemic and those with nonischemic origin. Coherence between heart rate and QT interval fluctuations at physiological frequencies was lower in DCM patients compared with control subjects (0.28±0.14 versus 0.39±0.18, P<.0001).

Conclusions DCM is associated with beat-to-beat fluctuations in QT interval that are larger than normal and uncoupled from variations in heart rate. QT interval variability increases with worsening functional class but is independent of ejection fraction. These data indicate that DCM leads to temporal lability in ventricular repolarization.


Key Words: cardiomyopathy • electrocardiography • repolarization • intervals


*    Introduction
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Dilated cardiomyopathy (DCM) is a highly prevalent and lethal disease in the United States. When accompanied by congestive symptoms, DCM has been associated with annual mortality rates as high as 25% to 40%,1 2 regardless of origin. Roughly half of these deaths occur suddenly and unexpectedly.3 4 5 In patients with DCM who have not had a myocardial infarction, classic reentrant ventricular tachycardia (VT) is rare and not likely to be the dominant mechanism of sudden cardiac death (SCD). Even among patients with an ischemic cardiomyopathy and prior history of reentrant VT, malignant polymorphic ventricular arrhythmias frequently occur without antecedent monomorphic VT,6 suggesting again that SCD may be related to mechanisms other than excitable gap reentry.

There is growing evidence that myocardial repolarization is altered in the setting of heart failure.7 Cells isolated from failing animal and human hearts display significantly prolonged action potentials compared with those from normal hearts independent of the mechanism of the cardiomyopathy.8 9 10 In addition, several studies have demonstrated the presence of regional heterogeneity in repolarization in DCM patients on the basis of intracardiac monophasic action potential recordings11 and on measurements of the QT interval dispersion in the standard 12-lead surface ECG.12 13 14

We hypothesized that DCM also leads to abnormal beat-to-beat changes in ventricular repolarization. The action potential duration is normally closely coupled with the interbeat (or RR) interval through a variety of mechanisms. These mechanisms may be altered in the failing heart,7 leading to temporal as well as spatial variations in repolarization. Using a new robust algorithm to quantify beat-to-beat fluctuations in the QT interval, we compared QT interval variability in DCM patients with control subjects and analyzed the relations of QT variability with heart rate variability and with the spatial QT interval dispersion.


*    Methods
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 *Methods
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Patient Population and Data Acquisition
The study population included 83 patients with DCM referred to the Johns Hopkins Cardiomyopathy and Heart Transplant Service and 60 control subjects. The sex and age distributions of the group are presented in Table 1Down. In this study, DCM refers to left ventricular systolic dysfunction with ventricular dilation (end-diastolic diameter >=5.8 cm) of any origin. Twenty-nine patients had DCM of ischemic origin on the basis of either angiographic evidence of coronary artery disease sufficient to account for the ventricular dysfunction, or history of myocardial infarction. The remaining 54 patients had DCM of nonischemic causes. Patients were excluded if their ECG rhythm was other than normal sinus. The presence of atrial or ventricular ectopy was allowed unless such beats represented >5% of all beats over a 5-minute period. Patients taking class I or III antiarrhythmic agents were excluded. New York Heart Association functional class was assessed by the investigators, who were blinded to the results of subsequent ECG analysis. Left ventricular ejection fraction was assessed by either radionuclide or contrast ventriculography or by two-dimensional echocardiography with the use of Simpson's rule.15


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  		Table 1. Characteristics of the Study Population

The control group was drawn from healthy volunteers, subjects participating in the Baltimore Longitudinal Study on Aging,16 and patients seen in the Johns Hopkins Preventive Cardiology Clinic, all without a history or evidence of heart disease and with a normal 12-lead surface ECG. Demographics for this group are also presented in Table 1Up. There was a greater proportion of men in the DCM group than among control subjects, but age was not different between groups.

Two unfiltered ECG limb leads (leads I and II) were acquired simultaneously, digitized with 12-bit precision at 1000 samples/s, and stored on removable magnetic media for off-line analysis, with a multichannel data acquisition system (Biopac Systems, Inc) connected to a 486-based computer. Two consecutive 256-second epochs of ECG data were obtained in each subject while supine and awake. Within 2 weeks of these recordings, a standard 12-lead ECG was also obtained (Marquette Medical Systems).

QT Variability Algorithm
A novel algorithm for QT interval measurement was implemented on a workstation (Sun Microsystems) with a custom-designed graphical interface. The approach is to let the operator define a template QT interval by selecting the beginning of the QRS complex and the end of the T wave for one beat. The algorithm then finds the QT interval of all other beats by determining how much each beat must be stretched or compressed in time to best match the template. In this way, changes in QT interval are assessed using the entire T wave.

Specifically, the steps involved in analyzing a digitized ECG record involve (1) The operator selects which ECG lead to analyze (lead I or II) on the basis of signal quality. Baseline wander is removed by digital filtering, using a passband >0.3 Hz.

(2) The time of each R wave is identified with an automated peak detection algorithm. These time locations are denoted Ti.

(3) The operator selects the beginning and end of the QT interval for any one beat, denoted beat k. This template is denoted {phi}(n), where n is the sample number. Thus,

(1)
where x(n) is the ECG signal and n0 and n1 are the operator-defined beginning and end samples of the QT template, respectively. This template has n=n1-n0 points, and the template QT interval duration is N {Delta}t, where {Delta}t is the digitization interval (1 ms). For the purpose of matching all other beats to the template, the algorithm ignores the actual QRS complex and uses only the ST segment and T wave. Thus, only the region of the template from n=Tk+n{nabla} to n=n1 is subsequently used, where n{nabla} represents a preset blanking period of 50 ms.

(4) For each beat, an error function {epsilon}i ({alpha}) is defined:

(2)
where {alpha} is the time-stretching factor. {epsilon}i ({alpha}) is thus the sum of squared differences between the template T wave and the stretched or compressed version of the T wave for beat i.

(5) A progressive search is performed to find the value of {alpha} that minimizes {epsilon}i({alpha}) for that beat. This best value of {alpha} is denoted i and is generally between 0.9 and 1.1. The search proceeds until the search step size for {alpha} is <0.0001.

(6) The QT interval for the ith beat is taken as

(3)
since, again, N {Delta}t is the duration of the template QT interval.

The algorithm thus finds the QT interval for each beat such that the T-wave shape best matches the template T wave under the time-stretch model. If the operator chooses the end point of the template T wave well before or well after the true end of the T-wave, all QT interval values computed will be biased proportionately low or high, respectively, but the beat-to-beat variability in computed QT intervals will be relatively unaffected. Furthermore, since much of the repolarization lability may reside in the latter portion of the T wave, or in the U wave if present, we include in the template all deflections that may relate to repolarization. U waves do not have a disproportionate effect on the QT interval determination because they are generally of low amplitude and therefore have less influence than the T wave on the sum of squared differences (in Equation 2Up).

The algorithm is demonstrated graphically in Fig 1Down. The top panel shows the operator-selected template QT interval with the region used to calculate the error function highlighted. A subsequent beat in the analysis is shown in the next panel, with several time-compressed versions of its T wave superimposed (dashed lines). The third panel shows the rather large area of difference between the template T wave and the uncompressed version of the index beat, while the bottom panel shows the close match between the template T wave and the optimally compressed version of this beat. The compression ratio, , which minimizes the difference between the template and the index beat in this case is 1.0984. The QT interval for this beat is thus taken as 1.0984 times the template QT interval.



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  		Figure 1. Beat-to-beat QT variability algorithm. Operator selects beginning and end points of template QT interval from one beat (top panel). For each of the other beats in the epoch, multiple time-compressed or time-stretched versions of the QT interval are generated (second panel) for comparison with the template QT interval. In this example, the uncompressed version of a new beat has a large area of difference between its T wave and that of the template (third panel), but the area of difference between the optimally compressed version of the new beat and the template is small (bottom panel). See text for details.

Time Series Analysis
An evenly sampled heart rate time series was constructed from the sequence of RR intervals as described previously,17 using a sampling frequency of 4 samples/s. The QT interval series was similarly resampled. Large abrupt deflections in the resampled instantaneous heart rate and QT interval series resulting from ectopic beats were eliminated with a linear spline approach.18 Linear trends found in the heart rate and QT interval series over each 256-second epoch were removed by subtraction of the best-fit line. The heart rate mean (HRm) and variance (HRv) and QT interval mean (QTm) and variance (QTv) were computed from the respective time series for each 256-second epoch. A normalized QT variability index (QTVI) was then derived for each epoch according to the equation:

(4)
The QTVI represents the log ratio between the QT interval and heart rate variabilities, each normalized by the squared mean of the respective time series. Results from the two 256-second epochs were averaged for each patient.

Power spectra of the heart rate and QT interval time series and the cross spectrum between the two processes were computed from 1024 point (256 seconds) segments with the Blackman-Tukey method.19 20 The coherence function {gamma}(f) was then computed according to the relation

(5)
where f is frequency, Pxx(f) is the heart rate spectrum, Pyy(f) is the QT interval spectrum, and Pxy(f) is the cross spectrum. The coherence provides a measure between zero and unity of the degree of linear interaction between heart rate and QT interval fluctuations as a function of the frequency of those fluctuations.21 A measure of mean coherence was obtained by averaging {gamma}(f) over the frequency band from 0 to 0.2 Hz.

QT Dispersion
Spatial variation in repolarization was assessed in each patient by measurement of the QT dispersion ({Delta}QT) from the 12-lead ECG.12 22 {Delta}QT was taken as the difference between maximum and minimum QT interval measured manually among the 12 standard surface ECG leads. No correction was applied for heart rate or the number of usable leads, although the {Delta}QT measurement obtained was excluded from further analysis if fewer than 6 leads had discernible T waves.

Statistical Analysis
All data are expressed as mean±SD. An unpaired t test was used to compare variables between groups. ANOVA was used to test for group effect when patients were subgrouped by NYHA class. Post hoc subgroup comparisons were made with unpaired t tests with Bonferroni correction. Correlation between continuous variables was tested by linear regression. Statistical significance for all tests was accepted at the P<.05 level.


*    Results
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 *Results
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Temporal Variability
Heart rate and QT variability during a 256-second epoch in a normal subject are shown in Fig 2aDown. The heart rate exhibits substantial beat-to-beat variability, which is mirrored in the instantaneous QT interval; that is, when heart rate rises QT falls and vice versa. Heart rate and QT interval series from a DCM patient are shown in Fig 2bDown. In this case, not surprisingly, heart rate variability is small. However, in sharp contrast to the findings in the normal subject, in the DCM patient the QT interval varies widely and erratically, without any discernible relation with instantaneous heart rate.



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  		Figure 2. Single epoch of heart rate and QT interval samples in a control subject (a) and in a dilated cardiomyopathy (DCM) patient (b). In the control subject, heart rate variability is large and QT variability is small, whereas in the DCM patient, heart rate varies little and QT interval fluctuates widely and unpredictably.

Summary data on heart rate and QT interval means and variances, comparing DCM patients with control subjects, are shown in Fig 3Down and tabulated in Table 2Down.HRm was significantly higher among DCM patients than control subjects (P<.0001). QTm was slightly greater in control subjects than in DCM patients (P=.04). However, QTm for each epoch was dependent on the operator's choice for T-wave terminus, which was somewhat subjective as described above. QTv was higher in DCM patients compared with control subjects (P<.0001). This excess QT interval variability appeared despite lower HRv in DCM patients than in control subjects (P=.01).



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  		Figure 3. Group differences in mean heart rate (a), mean QT interval (b), heart rate variance (HRV) (c), and QT interval variance (QTV) (d). In these and subsequent box plots, central line represents distribution median; box spans from 25 to 75 percentile points; and error bars extend from 10 to 90 percentile points. DCM indicates dilated cardiomyopathy.


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  		Table 2. Study Variables in Control Subjects and DCM Patients

The QTVI was greater in DCM patients than in control subjects, with a high degree of significance (P<10-12), as shown in Fig 4Down. Thus, normalizing the absolute QT interval variability by the degree of heart rate variability that was present accentuated the excess repolarization lability found in DCM patients compared with control subjects. There were no differences in HRm, HRv, QTm, QTv, or QTVI between men and women in the entire study population, control subjects, or DCM patients. Group differences in these parameters between control subjects and DCM patients, therefore, cannot be explained on the basis of sex distribution differences.



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  		Figure 4. QT variability index (QTVI) distributions in control subjects and dilated cardiomyopathy (DCM) patients.

In an effort to stratify DCM patients according to severity of disease, we examined the relation between QTVI and ejection fraction among DCM patients (Fig 5Down) but found no correlation (P=.4). Patients were then subgrouped according to NYHA functional class (Fig 6Down). There was a significant group effect (P<.01) with this stratification. Post hoc analysis demonstrated that the QTVI in class I patients was not different from that in control subjects. The QTVIs for patients in classes II, III, and IV were significantly different from control levels and increased monotonically but were not significantly different from each other. Thus most of the overlap in QTVI between DCM patients and control subjects is attributable to those patients in NYHA class I (without functional impairment).



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  		Figure 5. QT variability index (QTVI) vs ejection fraction in dilated cardiomyopathy patients show no correlation between these two variables.



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  		Figure 6. QT variability index (QTVI) distributions in control subjects and dilated cardiomyopathy patients as a function of NYHA functional class.

The effect of disease origin on the QTVI is shown in Fig 7Down. In this graph, patients with nonischemic DCM are compared against patients with ischemic cardiomyopathy. Nonischemic DCM patients were younger than those with ischemic DCM (49.2±14.0 versus 64.8±9.4 years, P<.0001), and nonischemics had lower NYHA class (2.3±0.8 versus 2.9±0.9, P<.01) despite somewhat lower ejection fraction (21.8±9.0% versus 26.6±10.7%, P=.03) than their ischemic counterparts. However, no difference was observed in the QTVI between the two subgroups (ischemic, -0.44±0.74 versus nonischemic, -0.41±0.67; P=.9).



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  		Figure 7. QT variability index (QTVI) distributions in dilated cardiomyopathy patients as a function of disease origin.

Coherence
The heart rate and QT interval series obtained in a control subject (shown in Fig 2aUp) were subjected to Fourier analysis to yield the power spectra shown in Fig 8aDown. The heart rate spectrum exhibits two peaks, one centered at 0.02 Hz and the other at the respiratory frequency of 0.1 Hz, with significant power extending to 0.2 Hz. The QT interval spectrum is similar to the heart rate spectrum. This suggests that variability in the two time series has a similar distribution of power density in the frequency domain. This is not unexpected, since fluctuations in the two waveforms observed in the time domain mirror each other closely. The coherence spectrum is also displayed in Fig 8aDown. Over most of the frequency band from 0 to 0.2 Hz, the coherence approaches unity, indicating strong linear coupling between heart rate and QT interval fluctuations. Beyond 0.2 Hz, there is little power in either heart rate or QT interval variability, so estimates of coherence are not meaningful in this region. The mean coherence in the band from 0 to 0.2 Hz in this example is 0.83.



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  		Figure 8. Power spectra of heart rate and QT interval fluctuations and coherence spectrum in a control subject (a) and a dilated cardiomyopathy patient (b).

Power and coherence spectra for the heart rate and QT interval series obtained in a DCM patient (Fig 2bUp) are shown in Fig 8bUp. In this case, virtually all the heart rate variability is confined to a single peak below 0.1 Hz. QT interval variability, on the other hand, is distributed over a broad frequency band from 0 to roughly 0.3 Hz. The coherence is low at almost all frequencies, particularly in the band in which the coherence estimates are most reliable, from 0 to 0.2 Hz. The mean coherence in this band for this patient is 0.17. Note that the coherence remains low even at frequencies at which there is substantial power density in both heart rate and QT interval variability, for example, 0.05 Hz. This shows that fluctuations in QT interval occur without any linear relation to those in heart rate.

Group distributions for mean coherence are shown in Fig 9Down and summarized in Table 2Up. While there was considerable overlap in this parameter between DCM patients and control subjects, it was lower in DCM patients, with a high degree of significance (P<.0001). Thus, a coupling between heart rate and QT interval variations exists in control subjects and is reduced in patients with DCM.



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  		Figure 9. Mean coherence distributions in control subjects and dilated cardiomyopathy (DCM) patients.

QT Dispersion
The QT dispersion, {Delta}QT, was determined from the 12-lead surface ECG in each patient, as described above. {Delta}QT was not significantly different between DCM patients and control subjects (Table 2Up). No significant correlation was found between QTVI and {Delta}QT (P=.4).


*    Discussion
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 *Discussion
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Effects of DCM on Heart Rate and QT Interval Variability
We found that patients with DCM had a higher mean heart rate and lower heart rate variability compared with control subjects. These data are consistent with the findings of prior studies18 23 24 and probably reflect withdrawal of parasympathetic tone accompanying heart failure. DCM patients in our study also had a slightly shorter mean QT interval than control subjects, although this difference barely met statistical significance and may have been secondary to the increased heart rate among these patients.

The major finding of this study is that patients with DCM demonstrated an increase in QT interval variability. In control subjects, QT interval fluctuations mirrored those in heart rate and became greater in magnitude when heart rate fluctuations grew larger. The absolute QT interval variance (QTv) was thus sometimes large enough in normal subjects to be comparable to that observed in DCM patients. However, when the QT interval variance was normalized by the heart rate variance (HRv), as was done in the QTVI calculation (see Equation 4Up), a consistently low value was found among control subjects. In contrast, in DCM patients the combination of a large absolute QT variance and a generally small heart rate variance led to a high (less negative) QTVI value. The QTVI is a logarithmic index, so an increase of one unit corresponds to a 10-fold increase in the quotient that forms the argument of the logarithm.

When DCM patients were stratified by NYHA functional class, class II patients (mild impairment) had significantly greater QTVI than control subjects, and little further increase in QTVI was observed among class III and class IV patients. This suggests that on average, most of the repolarization lability develops as DCM patients become mildly symptomatic, with relatively less change as patients further decompensate. To the extent that altered control of repolarization may underlie the development of malignant ventricular arrhythmias, these data are consistent with the findings of Gradman et al25 that sudden death is just as prevalent among minimally symptomatic patients with congestive heart failure as it is among those with greater impairment. The fact that QTVI correlated with functional class but not with ejection fraction suggests that the neurohumoral response to ventricular dysfunction may be more important than the degree of dysfunction in the development of labile repolarization.

Our data also show that DCM leads to repolarization lability regardless of whether ischemic or nonischemic mechanisms underlie the cardiomyopathy. Prolongation of the action potential and abnormalities in calcium and potassium currents have been demonstrated in cells isolated from failing human hearts of ischemic and nonischemic origin.9 10 26 27 In particular, Beuckelmann et al26 have shown that the inward rectifier (IK1) and transient outward (Ito) potassium currents, both important in the repolarization process, are significantly reduced in both ischemic and nonischemic myopathic heart cells. It is unknown, however, whether there are abnormal beat-to-beat fluctuations in these or other ionic currents in such cells.

Heart Rate–QT Interval Coherence
In control subjects, heart rate and QT interval fluctuations had similar frequency content and moderately high coherence. This suggests either that the mechanisms driving heart rate changes also modulate QT interval or that the heart rate fluctuations themselves secondarily drive QT interval oscillations. The latter mechanism relates to the well-known phenomenon of electrical restitution, that is, the dependence of action potential duration on the diastolic interval of the preceding beat,28 29 30 31 which has been shown to govern QT interval changes in the normal human ventricle.32 33 On the other hand, mechanisms that drive both heart rate and QT interval fluctuations primarily relate to the autonomic nervous system. The effects of sympathetic and vagal modulation on normal heart rate variability have been well characterized,20 34 35 as has the marked reduction in autonomically mediated heart rate variability associated with congestive heart failure.18 Autonomic influences on ventricular repolarization have also been studied and recently reviewed.36 Given the multiple mechanisms modulating repolarization that generally occur in association with heart rate changes, it is not surprising that heart rate and QT interval fluctuations are coherent among normal individuals, in whom these mechanisms are intact.

Compared with the control subjects, DCM patients had significantly lower coherence between heart rate and QT interval. Examination of individual instantaneous heart rate and QT interval tracings and corresponding power spectra consistently demonstrated the presence of broad-band QT interval fluctuations in the face of nearly constant or slowly changing heart rates in these patients. These data suggest there is a loss in the normal coupling between heart rate and QT interval in the setting of DCM. If QT interval fluctuations reflect beat-to-beat changes in the cellular action potential duration of at least some of the ventricular myocardium, then these cells apparently exhibit fluctuating action potential durations despite little if any change in RR interval. Electrical restitution must therefore be altered in these cells since there is no longer a unique action potential duration for a given diastolic interval.

Relation Between QT Variability and QT Dispersion
Recently, spatial QT dispersion has been tested as a predictor of arrhythmic risk in patients with heart failure.12 13 14 37 38 However, methodology to quantify QT dispersion has not been standardized, and differences in technique can lead to disparate results.39 40 Some studies concluded that increased QT dispersion correlated with increased risk of arrhythmias12 13 14 while others did not.37 38 Furthermore, QT dispersion was increased in DCM patients compared with control subjects in one report14 but did not correlate with the presence or severity of disease in others.37 38

In our hands, QT dispersion was not different between DCM patients and control subjects. We also found no correlation between QTVI and QT dispersion. Thus, the mechanisms responsible for increases in spatial QT dispersion and temporal QT variability are not the same. These two measurements may provide independent pieces of information regarding ventricular electrical stability.

Relation to Prior Work on Repolarization Variability
While numerous studies have examined the physiological and prognostic value of heart rate variability, there is a paucity of literature regarding beat-to-beat QT interval variability. Presumably, this is due in part to the technical difficulties in developing a robust automated scheme for QT interval determination. In assessing QT interval variability, determination of absolute QT duration is relatively unimportant, but the method must be sensitive to subtle changes in QT interval from one beat to the next and relatively insensitive to signal noise.

Previously described techniques for automated beat-to-beat QT interval assessment are largely based on criteria to detect T-wave terminus. These methods search for a fall in either the ECG waveform itself41 42 43 or the differentiated ECG signal.43 44 Under these strategies, the QT measurement is highly dependent on waveform morphology near the end of the T wave, when the signal is low in amplitude and slope. Erroneous measurements are likely in the setting of signal artifacts or noise, even if the noise is low in amplitude.

Several recent studies have examined beat-to-beat fluctuations in ventricular repolarization by automated analysis of the interval from peak of the R wave to peak of the T wave (RT interval).45 46 47 Identifying the T-wave peak can be as problematic as finding the T-wave terminus. Since the T wave is a low-frequency event, locating the time of its peak is easily corrupted by artifacts that often cannot be distinguished from the true waveform. While this may have a relatively small effect on absolute RT interval determination, it may largely obscure subtle beat-to-beat changes in this measurement. Furthermore, this approach ignores fluctuations in repolarization that principally influence the latter part of the T wave or that give rise to U waves. These features may be of lesser importance in studying repolarization variability in normal subjects but are likely to be of increasing significance when considering individuals with structural heart disease or metabolic abnormalities in whom afterdepolarizations are common.7 48

The template-matching strategy presented in this report takes the entire T wave into consideration and thus tracks beat-to-beat timing shifts in T-wave peak and terminus without requiring any one point of the T wave to serve as a fiducial marker. In addition, timing fluctuations in U waves, if present, can be assessed by inclusion of the U wave in the template.

Diurnal variation in QT interval has been observed for some time.49 50 However, beat-to-beat fluctuations in repolarization duration have gone largely unnoticed. Merri et al46 studied RT variability and the coherence between heart rate and RT interval in 10 young, healthy subjects. The amplitude of heart rate and RT interval fluctuations they found were similar to those in heart rate and QT interval that we observed in our control population. In addition, power spectra that they obtained of the heart rate and RT interval traces showed similar frequency components, and there was generally high coherence between these waveforms. Sarma et al47 studied RT variability in 12 patients with stable angina. They specifically excluded patients with heart failure or conduction defects. Nonetheless, in this patient group, RT interval fluctuations were present over a broader frequency band than were heart rate fluctuations, and the coherence was poor except at very low frequencies (near 0.05 Hz). These studies reconcile with ours and suggest that heart disease of diverse origins, perhaps even if mild, leads to a loss of the normally high coherence between heart rate and repolarization duration.

Repolarization variability has also been examined in terms of T-wave alternans, particularly as a marker of ischemia-induced susceptibility to arrhythmias.51 52 53 54 Work in this area has concentrated on beat-to-beat changes in T-wave energy or morphology, not duration, and only at the alternans frequency (half the heart rate). Nonetheless, it is conceivable that the same destabilizing mechanisms that lead to T-wave alternans may be responsible for the increased QT variability that we observed in DCM patients. We did not attempt to measure subtle T-wave alternans in these patients, and none had visually obvious alternans. However, T-wave alternans may not become manifest without atrial pacing or some other chronotropic challenge.54

Clinical Implications
Patients with DCM may present with various ventricular tachyarrhythmias, including monomorphic VT, polymorphic VT, and ventricular fibrillation. While monomorphic and polymorphic VT often occur in the same patient, the mechanisms responsible for these arrhythmias probably differ. Monomorphic VT is predominantly due to reentry occurring in the presence of a zone of sufficiently slowed conduction to enable an excitable gap to develop.55 A reentrant substrate can be demonstrated by induction of monomorphic VT with programmed electrical stimulation.56

The mechanistic causes of polymorphic VT and ventricular fibrillation are less clear. Abnormalities in cellular repolarization, including triggered activity and spatial dispersion of refractoriness, have been implicated in these arrhythmias.7 48 57 The potential importance of temporal repolarization lability, such as we found among DCM patients, in the genesis of these arrhythmias remains unexplored. Conceivably, spatial and temporal heterogeneity in repolarization may each contribute to render the substrate arrhythmogenic. Patients included in the present study are being followed longitudinally to assess the clinical value of QT dispersion and QT variability measurements in predicting arrhythmia-free survival.

Study Limitations
Several important factors must be considered in interpreting our results. First, many of the DCM patients were taking a variety of medications for their heart failure. While patients taking class I or class III antiarrhythmic agents were excluded from the study, some of the medications taken by the patients could have influenced ventricular repolarization. Even if the pharmacological effect on mean QT interval was subtle, the influence of these drugs on QT interval variability is unknown. Many of the patients with an ischemic cardiomyopathy were taking ß-adrenergic–blocking agents, which could have influenced the QTVI measurement through their effect on mean heart rate and on heart rate variability.

The second potentially confounding factor is the presence of bundle branch blocks and intraventricular conduction defects in a sizable proportion of the DCM patients. Altered depolarization is often associated with altered repolarization, and we made no effort to exclude patients with conduction defects from the study. However, at this point we see no reason to expect a conduction defect to either increase or decrease the degree of QT interval variability that would otherwise be present. This represents an important issue to be addressed in future research.

A third issue that remains to be resolved is the reproducibility of the QT variance and QTVI measurements. Data presented in this study are based on two 256-second epochs obtained in a single encounter with each patient. It is possible that repolarization lability itself varies over the course of the day or from one day to another. While these effects may render QT variability measurements in an individual less reliable, the finding that DCM patients as a group have a higher QTVI than control subjects cannot be explained on this basis.

Finally, we wish to point out that it is premature to consider measurements of QT interval variability, or the QTVI in particular, as a stratifier of clinical arrhythmia risk. At the present time, however, the presence of enhanced QT variability in the setting of DCM represents a previously unrecognized phenomenon. An understanding of its origin may lead to greater insights into the mechanisms that underlie malignant ventricular arrhythmias in this patient population.


*    Acknowledgments
 
Dr Berger is the recipient of a FIRST Award (R29-HL-54584) from the National Heart, Lung, and Blood Institute and is a Solo Cup Clinician Scientist. This work was also supported by grant P50-HL-52307 of the National Heart, Lung, and Blood Institute. The authors wish to thank Renee Ferguson for her expert technical assistance in the data collection and analysis.

Received January 9, 1997; revision received March 6, 1997; accepted March 30, 1997.


*    References
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