Abnormal QT interval variability in patients with hypertrophic cardiomyopathy

Journal of Electrocardiology Vol. 37 No. 2 2004

Abnormal QT Interval Variability in Patients With Hypertrophic Cardiomyopathy Can Syncope Be Predicted?

Sergio Cuomo, MD, Fortunato Marciano, Marie L. Migaux, Filippo Finizio, MD, Elpidio Pezzella, MD, Maria A. Losi, MD, and Sandro Betocchi, MD, FACC

Abstract: We sought to determine QT variability pattern in patients with hypertrophic cardiomyopathy (HCM) and its relationship with the risk of syncope. QT interval variability was assessed from 24-hour Holter monitoring in 10 HCM patients with history of syncope, 10 HCM patients without history of syncope, and 10 healthy subjects. QT variability was higher in patients with HCM, in particular in those with history of syncope, than in healthy controls. Time domain QT variability did not vary between waking and sleeping hours in HCM patients, whereas it was significantly shorter while asleep in the control group. Increased QT SDANN identified HCM patients with history of syncope with an accuracy of 75%. Our data show that QT variability is abnormal in HCM patients and indirectly support the concept that arrhythmiarelated syncope in these patients may be, at least in part, related to an altered control of repolarization. Key words: QT interval variability, hypertrophic cardiomyopathy, syncope.

Hypertrophic cardiomyopathy (HCM) is the most common cause of sudden death in otherwise healthy young individuals (1). Sudden death is mainly caused by major ventricular arrhythmias (2). Because ventricular tachyarrhythmias can be effectively treated in HCM by implantable cardiac

defibrillators (2) the identification of an arrhythmic substrate and of patients at high risk of sudden death would be relevant for prognosis. Recent studies have drawn the attention of clinicians upon several risk factors for sudden death in HCM: massive hypertrophy, (3) family history of sudden death in at least two relatives, history of syncope, and exercise-induced hypotension (4) have been proved to carry an additive risk of sudden death in HCM. The identification of such risk, however, remains elusive in some patients. Abnormalities in QT duration and in spatial heterogeneity in repolarization of ventricular myocardium have been reported in a high percentage of HCM patients

From Division of Cardiology, Pellegrini’s Hospital, Naples, Italy; Institute of Cybernetics, CNR, Pozzuoli, Italy; Department of Cardiology and Cardiac Surgery, “Federico II” University School of Medicine, Naples, Italy. Reprint requests: Sergio Cuomo, MD, Piazzetta Mondragone 9, 80132 Napoli, Italia; e-mail: [email protected]. © 2004 Elsevier Inc. All rights reserved. 0022-0736/04/3702-0007$30.00/0 doi:10.1016/j.jelectrocard.2004.01.010

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114 Journal of Electrocardiology Vol. 37 No. 2 April 2004 (5–11) and have been associated with increased risk of ventricular arrhythmias and sudden death (12). Also temporal QT dispersion, expressed as increased QT variability, has been recently demonstrated to play a role in the mechanism of ventricular arrhythmias in a subset of patients with HCM caused by a particular ␤-myosin heavy-chain gene mutation which is associated to high risk of sudden death (13). However, only little information is available on the clinical significance of temporal fluctuations in ventricular repolarization in the setting of HCM. The purpose of the present study was 2-fold: 1) to compare the QT variability in patients with HCM and in healthy subjects and 2) to examine the association between QT variability and syncope in patients with HCM.

Materials and Methods Study Populations We reviewed the medical records of the 202 HCM patients from the HCM outpatient clinic of the Department of Clinical Medicine, Cardiovascular and Immunological Sciences, Federico II University of Naples, Italy. HCM was diagnosed on the basis of current diagnostic criteria including the evidence of left ventricular hypertrophy on two-dimensional echocardiograms in the absence of other cardiac or systemic diseases that cause left ventricular hypertrophy (14). From the above population, we selected all patients (n ⫽ 15) who have had a syncope during the follow-up. Of these 15 patients, 5 were excluded from the study because of poor technical quality of Holter recordings that could not be interpreted by the computer program, in consequence of absence or ambiguous termination of T waves. After exclusion of these patients, the remaining 10 HCM patients who had syncope during the follow-up constituted the group 1. Ten age (⫾ 3 years) and sex matched HCM patients without any history of syncope constituted the group 2. A group of 10 consecutive healthy subjects without structural heart disease served as control (group 3) to provide information on QT variability in normal persons with an age comparable to that of the HCM population. All study subjects were ambulatory and had a 24-hour Holter monitoring, showing sinus rhythm, normal QRS duration and clear end point of the T wave in at least 1 lead. None of the study patients received antiarrhythmic drugs at the time of the Holter recordings. All the study subjects were

asked to maintain their normal activities and their normal asleep-awake rhythm during the recording. Holter Recording and QT Measurements Three-channel 24-hour Holter monitoring was performed in all subjects with a Del Mar Avionics (Irvine, CA) model 459 recorder. Holter recordings were analyzed on a homemade analyzer built around a Motorola 68030-50 Mhz microprocessor (Motorola Inc, Libertyville, IL). Operating characteristics of this analyzer have been previously reported (15). Briefly, 3 recorded electrocardiographic (ECG) analog channels were read via a modified Teac-Tascam 234 Syncaset tape deck (Teac Co, Tokyo, Japan) and digitised at 200 samples/s. Besides evaluation of the usual ECG parameters, including identification of QRS widths and shapes and of RR interval abnormalities, QT intervals at each beat were evaluated as follows. The interval between the peak of the R wave (Ra) and the end of the T wave (Te)was found in a 2 steps process. First, a table relating the values of the intervals between Raand the peak of the T wave Tato RR intervals was built into the program. The (Ta) point was then searched within a moving window centered around the value given by the table. Then the interval Ta Tewas measured. The maximum slope value was found computing slopes after Taas moving averages of the slopes measured at 4 consecutive sample points, reducing in so doing the influence of artifacts and noise. It was, then possible to derive a threshold as a fraction of this maximum value. Several consecutive slopes after the maximum were then compared with this threshold. The first point where the slope was smaller then the threshold was taken as Te. Finally Q Ravalue is added to Ra Teto form the QT interval. The sequences of all RR and QT intervals were stored with a code number identifying its normality or their class of abnormality. Premature ventricular complexes and their adjacent beats, used only for time keeping purposes, were rejected just as noise and other aberrant ECG signals. Data losses did not exceed 15% of the total recordings. The sequences of all normal RR (NN) and their corresponding QT intervals were analyzed to compute time and frequency domain measures of QT variability for the entire 24 h recordings as well as for awake and sleep periods. The 24 h power spectrum estimates were computed by means of the fast Fourier transforms and by averaging a sufficient number of spectra so as to reduce the estimation error. Time functions samples

QT Variability in Hypertrophic Cardiomyopathy • Table 1. Frequency Domain Measures Variable

Area Under the Power Spectrum in the Range

Total power (ms2) Ultra low frequency power (ms2) Very low frequency power (ms2) Low frequency power (ms2) High frequency power (ms2)

0.00066 to 0.34 Hz 0.00066 to ⬍0.0033 Hz 0.0033 to ⬍0.04 Hz 0.04 to ⬍0.15 Hz 0.15 to 0.34 Hz

were obtained from the QT sequences by linear interpolation with time steps of 100 ms; this is a low-pass filtering operation that attenuates any variability above the chosen value of the sampling frequency. The frequency bands explored are reported in Table 1. The following time domain parameters of QT variability were calculated: SDNN, SDNN index, SDANN, rMSSD, pNN50. The definition and statistical procedures of these parameters are listed in Table 2.

Statistical Analysis Chi-square test was used for comparison of proportions. Normal gaussian distribution of the data was evaluated by Kolmogoroff-Smirnoff goodnessof-fit test. Whenever the data were not normally distributed (z value ⬎1.2), a logarithmic transformation was performed before the statistical analyses. The differences between the 3 groups were assessed by ANOVA followed by Bonferroni’s post hoc multiple-range tests. Student’s paired t tests were used to assess differences in clinical variables between HCM patients with and without syncope. Student’s paired t tests were also used to identify differences between nighttime and daytime values of QT variability parameters. A measure of the linear association between syncope and clinical and QT variability parameters was evaluated using the Pearson correlation coefficient. The normal range for different QT variability measures was determined by calculation of the 95% tolerance limits of the mean 24-hour values of the healthy subjects. These values were used to determine sensitivity, specificity, predictive values, and accuracy of QT variability parameters for syncope in HCM patients. Receiver operator characteristic curve was used to assess the usefulness of 24-hour QT-SDANN. The area under the curve, which indicates the predictive accuracy, is reported. All analyses were done using SPSS software, version 9.0 (SPSS Inc, Chicago, IL).

Cuomo et al. 115

Results The clinical characteristics of the study patients are summarized in Table 3. Age was not significantly different between the 3 study groups. There were no significant statistical differences in the clinical and echocardiographic characteristics between HCM patients with and without history of syncope. Summary data on RR and QT intervals means and time domain QT variability, comparing HCM patients with control subjects, are tabulated in Table 4. The 24-hour mean values of RR intervals were similar in all 3 groups. The 24-hour mean QT interval was significantly longer in HCM patients with syncope that in normal subjects. Time domain measures of QT variability were significantly higher in HCM patients with syncope than in healthy controls. A slight, although significant, increase of time domain measures of QT variability was also observed in patients without syncope as compared with the control group, except SDANN and rMSSD. As shown in Figure 1, time domain measures of QT variability were significantly shorter while asleep than while awake in the control group. In contrast, QT variability did not significantly vary between waking and sleeping hours in HCM patients, except QT-SDNN that resulted significantly shorter while asleep than while awake in HCM patients without syncope. When a frequency domain analysis was used, an even more significant increase in QT variability was observed in HCM patients with syncope in compar-

Table 2. Definition of Time Domain Variables of QT Interval Variability Variable SDNN (ms) SDANN (ms) SDNN index (ms)

rMSSD (ms)

pNN50(%)

Definition Standard deviation from the mean of all NN QT intervals of sinus beats over 24 hours Standard deviation of the averaged NN QT intervals of sinus beats for all 5-min segments Mean of the standard deviations of all NN QT intervals of sinus beats for all 5-minute segments of a 24-hour ECG recording The square root of the mean of the squared differences between adjacent NN QT intervals of sinus beats over the entire 24hour ECG recording Proportion of adjacent QT intervals ⬎ 50 ms different, computed over the entire 24-hour ECG recording

ECG, electrocardiographic.

116 Journal of Electrocardiology Vol. 37 No. 2 April 2004 Table 3. Clinical Characteristics of 30 Study Patients

Number Age (years) Gender (men/women) Months of follow-up Family history of sudden death Beta-blocker therapy Abnormal BP during exercise Nonsustained VT on Holter monitoring Maximal LV wall thickness (mm) Maximal wall thickness ⬎30 mm Peak LV outflow tract gradient (mm Hg) Peak LV outflow tract gradient ⬎30 mm Hg

Group 1

Group 2

Group 3

10 39.4 ⫾ 12 7/3 27 ⫾ 14 5 (50%) 2 (20%) 2 (20%) 3 (20%) 26 ⫾ 8 2 (20%) 10 ⫾ 10 2 (20%)

10 39.8 ⫾ 13 7/3 20 ⫾ 14 4 (40%) 5 (50%) 5 (50%) 2 (20%) 21 ⫾ 5 1 (10%) 25 ⫾ 35 1 (10%)

10 34.6 ⫾ 13 5/5 -

No significant difference was observed between the groups. Group 1 ⫽ patients with hypertrophic cardiomyopathy and history of syncope; Group 2 ⫽ patients with hypertrophic cardiomyopathy without history of syncope; Group 3 ⫽ control subjects; BP ⫽ blood pressure; LV ⫽ left ventricular; VT ⫽ ventricular tachycardia.

ison to control subjects (Table 5). Frequency domain measures of QT variability were also slightly greater in patients without syncope than in control subjects. HCM patients with syncope showed a trend toward higher QT variability, both in time and frequency domain, in comparison to HCM patients without syncope. However, these differences were not statistically significant, except 24-hour SDANN (Tables 4 and 5). Table 6 provides the normal ranges for different QT variability measures, determined by calculating the 95% confidence interval for the mean 24-hour values in 10 healthy subjects. The predictive values of time- and frequencydomain measures of QT variability for identifying HCM patients with history of syncope are shown in Table 7. Twenty-four-hour QT-SDANN provided the best accuracy (75 %) for identifying HCM patients with history of syncope. Twenty-four-hour QT-SDANN was accompanied by an area under the receiver operator characteristic curve of 78% (95% confidence interval ⫽ 56% to 99%; P ⫽ .034) for the prediction of the occurrence of syncope.

Discussion The main finding of this study is that in patients with HCM QT interval variability, as assessed by time domain and spectral analysis, is increased as compared to normal subjects. Furthermore, to our knowledge, this is the first study to show that repolarization abnormalities could be associated in HCM with a predisposition to syncope. It should be emphasized that pathogenesis of syncopal attacks is complex and dependent on several factors, not exclusively on ventricular arrhythmias. For example, syncope in HCM may relate to an inadequate ability to increase cardiac output during exertion. However, we did not classified the mechanisms of syncope in our patients, because the optimal way of clarifying the mechanism of syncope remains sometimes elusive and consequently the workup for syncope may result negative in some patients. Indeed, it is unlikely that one single test will be able to detect different hemodynamic, electrophysiologic, anatomic, and functional substrates that may be prosyncopal. This may account for the relatively low accuracy of most

Table 4. Twenty-Four-Hour Average RR and QT Intervals and Time Domain QT Variability in HCM Patients With (Group 1) and Without (Group 2) Syncope and in Control Subjects (Group 3)

Mean 24-hour RR interval (ms) Mean 24-hour QT interval (ms) 24-hour QT-SDNN (ms) 24-hour QT-SDANN (ms) 24-hour QT-SDNN index (ms) 24-hour QT-rMSSD (ms) 24-hour QT-pNN50(%) * P ⬍ .01 vs. Group 3;

†

P ⬍ .05 vs. Group 3;

‡

Group 1

Group 2

Group 3

767 ⫾ 106 428 ⫾ 67* 55 ⫾ 26† 39 ⫾ 11‡§ 29 ⫾ 18* 38 ⫾ 24† 11 ⫾ 8§

802 ⫾ 99 394 ⫾ 34 48 ⫾ 24† 28 ⫾ 6 20 ⫾ 6† 28 ⫾ 9 5 ⫾ 5†

782 ⫾ 75 361 ⫾ 19 28 ⫾ 7 24 ⫾ 7 12 ⫾ 2 18 ⫾ 4 1⫾1

P ⬍ .05 vs. Group 2;

§

P ⬍ .005 vs. Group 3

QT Variability in Hypertrophic Cardiomyopathy •

Cuomo et al. 117

Table 6. Normal Ranges for Different 24-hour QT Variability Measures, Determined by Calculation of the 95% Confidence Interval for Mean Values in 10 Healthy Subjects Lower Upper Mean Bound Bound 24-hour 24-hour 24-hour 24-hour 24-hour 24-hour 24-hour 24-hour 24-hour 24-hour

Fig. 1. Diurnal variations of time-domain QT interval variability. QT variability is significantly shorter while asleep than while awake in the control group, whereas it does not change in patients with hypertrophic cardiomyopathy (HCM), except SDNN in HCM patients without syncope.

of time- and frequency-domain measures of QT variability for prediction of syncope. However, unlike the other QT variability measures, QT-SDANN showed a sufficiently high accuracy for identifying HCM patients prone to syncope from any cause. We

QT-SDNN QT-SDANN QT-SDNN index QT-rMSSD QT-pNN50(%) QT total power ln QT ultra low frequency power QT very low frequency power QT low frequency power ln QT high frequency power ln

28 24 12 18 1 6.9 6.4 5.1 4.7 4.6

23 19 11 15 0.4 6.8 6.3 5.0 4.6 4.4

33 29 14 21 1.6 7.0 6.6 5.2 4.8 4.7

believe that this finding has potentially important management implications. There is growing evidence that myocardial repolarization is altered in the setting of HCM (5–13). However, while much has been written regarding the association between prolongation or dispersion in ventricular repolarization and incidence of malignant ventricular arrhythmias, little is known to what extent beat-to-beat QT interval variability may represent a marker to identify HCM patients at high risk for arrhythmic events. Momiyama et al. (11) have found in a series of 14 HCM patients that exercise-induced T-wave alternans is frequently present in high risk patients. This observation provides evidence that unstable repolarization may be a useful marker for ventricular arrhythmic risk in these patients. More recently, Atiga et al. (13)showed that HCM patients with a known ␤-MHC mutation associated with a particularly high risk for sudden death exhibit an abnormally high QT interval variability, confirming that temporal ventricular repolarization abnormalities may be useful as a tool for risk stratification. Presently, however, increased QT interval variability has not yet established as an independent marker of an increased risk of cardiac and especially arrhythmic death in HCM patients. In addition, it is

Table 5. Frequency Domain QT Variability in HCM Patients With (Group 1) and Without (Group 2) Syncope and in Control Subjects (Group 3)

24-hour 24-hour 24-hour 24-hour 24-hour

QT QT QT QT QT

total power ultra low frequency power very low frequency power low frequency power high frequency power

* P ⬍ .0001 vs. Group 3;

†

P ⬍ .05 vs. Group 3;

‡

Group 1

Group 2

Group 3

7.5 ⫾ 4* 7 ⫾ .4* 5.8 ⫾ .5‡ 5.4 ⫾ .5‡ 5.3 ⫾ .5*

7.3 ⫾ .2 6.8 ⫾ .2† 5.5 ⫾ .3† 5.2 ⫾ .3† 5.1 ⫾ .3†

6.9 ⫾ .2 6.4 ⫾ .2 5.1 ⫾ .1 4.7 ⫾ .2 4.6 ⫾ .2

P ⬍ .005 vs. Group 3

†

118 Journal of Electrocardiology Vol. 37 No. 2 April 2004 Table 7. Sensitivity, Specificity and Accuracy of Time- and Frequency-Domain Measures of QT Variability to Identify HCM Patients With History of Syncope

24-hour 24-hour 24-hour 24-hour 24-hour 24-hour 24-hour 24-hour 24-hour 24-hour

QT-SDNN QT-SDANN QT-SDNN index QT-rMSSD QT-pNN50(%) QT total power QT ultra low frequency power QT very low frequency power QT low frequency power QT high frequency power

Sensitivity %

Specificity %

Positive predictive value %

Negative predictive value %

Accuracy %

80 80 90 90 90 100 80 90 90 90

40 70 10 20 30 10 20 20 20 10

57 73 50 53 56 53 50 53 53 50

67 78 50 67 75 100 50 67 67 50

60 75 50 55 60 55 50 55 55 50

not known the direct role of QT variability in the genesis of the arrhythmic events, neither if the measures of QT variability are stable and reproducible, or may be influenced by therapy. Thus, future research will be needed to determine how best QT variability should be used in risk stratification and how to optimally reduce risk among HCM patients who have this finding. Apart from HCM, an increase in QT interval variability has also been shown in patients with left ventricular hypertrophy due to causes different from HCM, such as arterial hypertension (16) and dilated cardiomyopathy (17). Indeed, as Berger at al. note (17), the potential importance of temporal repolarization lability in the genesis of arrhythmic events remains to be defined. It is important to note that Berger et al. (17). Atiga et al. (13) and Piccirillo et al. (16) measured QT variability from 1 or 2 consecutive 256-second epochs of ECG data. As opposed to these previous studies, in the current investigation automated measurement of beat-to-beat QT interval variability was performed over the entire duration of 24-hour Holter recordings. Because of the probable dependence of the QT variability measures on the length of the recording period, failure to analyze results in a recording duration-specific manner may yield misleading conclusions. Moreover, the results of our long-term analysis cannot be used as reference values for short-term analysis. Of note is the high negative predictive value of QT total power. Potentially, this variable would be useful to identify HCM patients with a low likelihood of arrhythmogenic causes of syncope. Another interesting finding of the present study is the different circadian profile of time domain measures of QT variability among controls and HCM patients. During sleep, controls showed a significant decrease of QT variability. In contrast, absence of day-night differences in time domain QT variability was found

among patients with HCM. At present, the reasons of this finding remain unexplained and deserve further investigations. However, this finding closely resembles the absence of circadian change in heart rate variability in patients with increased risk of death after myocardial infarction (18,19). Absence of circadian variations of time domain indices of QT variability, as well as increased QT variability, suggest a central autonomic regulatory impairment in HCM and may have prognostic implications.

Potential Study Limitations Several limitations should be considered in assessing this report. First, this study is limited by the small number of patients included. Thus, our results should be interpreted with caution. As the number of patients in each group was small, differences in QT variability among HCM patients with and without history of syncope might fail to achieve statistical significance. Second, 7 of 20 HCM patients have taken beta-blockers compared to none of the healthy controls. Also, more HCM patients without syncope received betablockers than patients with syncope. Although the effect of beta-blockers on QT variability is not proven, the inclusion of patients treated with betablocker in the analysis would have biased the results. Third, while our findings show that QT variability analysis can provide valuable information about the risk of syncope in HCM patients, we have not proven that there is a causeand-effect relationship. Fourth, no prospective data are available to indicate the predictive power of QT variability for arrhythmias and syncope in patients with HCM. Patients enrolled in the present study are being followed longitudinally to determine the clinical usefulness of QT variability in identifying HCM patients prone to syncope from ventricular

QT Variability in Hypertrophic Cardiomyopathy •

arrhythmias. Fifth, a main methodologic limitation in the analysis of QT dynamics lies in its dependency on the quality of data of QT interval series: there are still technical difficulties in the evaluation of QT interval duration from long term ambulatory recordings when T-wave morphology is poor.

Conclusions Because of the small number of patients, the present study should be considered as hypothesis generating rather than conclusive. Despite this, our data show that QT variability is abnormal in HCM patients and indirectly support the concept that altered control of repolarization may underlie arrhythmia-related syncope in these patients. If measurement of QT variability could provide valuable information about the risk of syncope in HCM, patients should be tested in a larger-scale prospective study.

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