Download Predictive Value of Beat-to-Beat QT Variability across Continuum of

Predictive Value of Beat-to-Beat QT Variability across the Continuum of Left
Ventricular Dysfunction: All-Cause Mortality, Cardiovascular Mortality, and Sudden
Cardiac Death
Tereshchenko: QT variability in LVEF > 35%
Larisa G. Tereschenko1, MD, PhD, Iwona Cygankiewicz2, MD, PhD, Scott McNitt3, MS,
Rafael Vazquez4, MD, PhD, Antoni Bayes-Genis5, MD, PhD, Lichy Han6, Sanjoli Sur6 , JeanPhilippe Couderc3, PhD, Ronald D. Berger1, MD, PhD, Antoni Bayes de Luna7, MD, PhD, and
Wojciech Zareba3, MD, PhD
from
1
The Division of Cardiology, Department of Medicine, Johns Hopkins University School of
Medicine, Baltimore, MD (L.G.T., R.D.B.); 2The Division of Electrocardiology, Medical
University of Lodz, Lodz, Poland (I.C.); 3The Division of Cardiology, University of Rochester
Medical Center, Rochester, NY (S.M., W.Z., J.C.), 4Cardiology Service, Valme University
Hospital, Seville, Spain (R.V.), 5Hospital Universitari Germans Trias i Pujol, Badalona, Spain
(A.B-G), 6Whiting School of Engineering, Johns Hopkins University, Baltimore, MD (L.H.,
S.S.), 7Institut Catala Ci`encies Cardiovasculars, Barcelona, Spain (B.d.L.).
Financial support & relationships with industry: none
Address for correspondence: Larisa G. Tereshchenko, MD, Carnegie 568, 600 N. Wolfe
St., Baltimore, MD 21287. E-mail: [email protected]. Phone: 410-502-2796; Fax: 410-6148039
Total words count: 4790
1
Abstract
Objectives. The goal of this study was to determine the predictive value of QT variability
in heart failure (HF) patients with left ventricular ejection fraction (LVEF) >35%.
Background. Increased beat-to-beat QT variability predicts mortality and appropriate ICD
therapies in HF patients with LVEF ≤ 35%.
Methods. Beat-to-beat QT variability index (QTVI), heart rate variance (LogHRV),
normalized QT variance (QTVN), and coherence between heart rate variability and QT
variability were measured at rest during sinus rhythm in 533 participants of the Muerte Subita en
Insuficiencia Cardiaca (MUSIC) HF study (mean age 63.1±11.7; males 70.6%; LVEF >35% in
254 [48%]) and in 181 healthy participants from the Intercity Digital Electrocardiogram Alliance
(IDEAL) database.
Results. During a median of 3.7 years of follow-up, 116 patients died, 49 from sudden
cardiac death (SCD). In multivariate Cox regression models, after adjustment for age, gender,
LVEF >35%, NYHA HF class, and history of myocardial infarction, the highest QTVI quartile
was associated with all-cause death [hazard ratio (HR) 2.08 (95% CI: 1.41-3.05), P<0.001] and
cardiovascular mortality [HR 2.36 (95% CI: 1.54-3.61), P<0.001]. Elevated QTVI separated
97.5% of healthy individuals from subjects at risk for cardiovascular [HR 1.96 (95% CI: 1.163.29), P=0.011] and all-cause death [HR 1.95 (95% CI: 1.23-3.09), P=0.005] in multivariate
analysis. No interaction between QTVI and LVEF was found. The lowest quartiles of LogHRV
and coherence were less powerful predictors of mortality than was QTVI.
Conclusions. Increased QTVI predicts all-cause and cardiovascular mortality in HF
across the continuum of left ventricular dysfunction. Abnormally amplified QTVI separates
97.5% of healthy individuals from HF patients at risk.
2
Key words: heart failure; left ventricular ejection fraction above 35%; QT variability;
sudden cardiac death; mortality.
Abbreviations
HF=Heart failure
SCD=sudden cardiac death
ICD=implantable cardioverter-defibrillator
NYHA=New York Heart Association
ECG=electrocardiogram
LVEF=left ventricular ejection fraction
MI=myocardial infarction
HR=hazard ratio
MUSIC= Muerte Subita en Insuficiencia Cardiaca=Sudden Death in Heart Failure study
IDEAL= Intercity Digital Electrocardiogram Alliance database
THEW = the Telemetric and Holter ECG Warehouse
ISAR-Risk= Improved Stratification of Autonomic Regulation for risk prediction in postinfarction patients with preserved left ventricular function study
3
An aging population with a high prevalence of obesity, diabetes, and hypertension, along
with advancements in the treatment of acute cardiovascular diseases, has resulted in an increased
incidence and prevalence of heart failure (HF) over the past decades (1, 2). Studies estimate that
about 2-3% of the population suffer from HF (3). Despite advances in therapy and management,
HF carries substantial morbidity and mortality, as well as high rates of hospitalizations and
hospital readmissions, which together represent a large burden to the health-care system (3, 4).
Though mortality rates in HF with moderate and severe LV systolic dysfunction are
slightly higher (5) than those in mild HF with relatively preserved LVEF (6-8), the absolute
number of deaths attributable to mild HF with LVEF >35% is large and continues to grow (9).
While a certain amount of success has been achieved in the management of HF patients with
LVEF ≤35%, resulting in improved survival over time, there are no effective therapies confirmed
to improve the natural history of HF patients with LVEF >35% (7, 10). Implantable
cardioverter-defibrillators (ICDs) could potentially be game-changers. However, strategies for
effective risk stratification in the population of patients with HF and LVEF >35% have not been
developed (11).
It was previously shown that increased beat-to-beat QT variability (12) predicts
ventricular tachyarrhythmias, SCD, and cardiovascular and all-cause mortality in moderate and
severe systolic heart failure (13-15). Still, data on the predictive value of QT variability in
patients with LVEF >35% are limited. The threshold of abnormal QT variability is usually set up
at its highest quartile in a studied population. However, it is unknown whether QT variability
could separate 97.5% of healthy individuals from subjects at risk for cardiovascular and all-cause
death.
Methods
4
We performed ad-hoc analysis using the prospectively collected data of 2 studies. The
multicenter prospective observational cohort study Muerte Subita en Insuficiencia Cardiaca
(MUSIC; Sudden Death in Heart Failure) was designed to assess risk predictors of cardiac
mortality and SCD in ambulatory patients with mild to moderate HF (16). To determine the
threshold of abnormal QT variability that would allow us to separate 97.5% of healthy
individuals from those at risk, we analyzed healthy subjects from the Intercity Digital
Electrocardiogram Alliance (IDEAL) database (17). The studies’ protocols were approved by
institutional investigational committees, and all participants gave written informed consent
before entering the study.
MUSIC study population
Design of this prospective multicenter observational cohort study has been previously
described (16, 18, 19). Adult stable ambulatory symptomatic NYHA class II-III HF patients with
either depressed or preserved LVEF, either ischemic or non-ischemic cardiomyopathy, were
enrolled. High resolution (1000 Hz) orthogonal ECG recordings were performed using ELA
Spiderview recorders (ELA Medical, Sorin Group, Paris, France) during 10 minutes at rest at the
time of enrollment. Patients were followed every 6 months in outpatient HF clinics. All death
cases were adjudicated by the MUSIC study core endpoints adjudication committee as
previously described (16). Total mortality, cardiovascular mortality, and SCD served as endpoints in this study.
IDEAL healthy subjects database
The Intercity Digital Electrocardiogram Alliance (IDEAL) database study (17) was
conducted by the University of Rochester (Rochester, NY, USA) and was provided for this
5
analysis by the Telemetric and Holter ECG Warehouse (THEW). Healthy individuals were
eligible for enrollment if they had no history of cardiovascular disorders, no history of blood
pressure above 150/90 mm Hg, no history of any chronic illness, did not take any medications,
and fulfilled objective criteria of cardiovascular health: normal physical examination; no
pregnancy; normal 12-lead ECG (specifically, without signs of ventricular hypertrophy, inverted
T-waves, and conduction abnormalities). In the presence of borderline ECG changes, normal
echocardiogram and normal ECG exercise testing were required for inclusion. High resolution
(1000 Hz) orthogonal ECG recordings (SpaceLab-Burdick, Inc., Deerfield, WI) were performed
at rest during 10 minutes.
Beat-to-beat QT variability analysis
QT variability analysis was performed at the Johns Hopkins Hospital by investigators
(L.G.T., L.H., S.S) fully blinded to the study population’s clinical characteristics and outcomes.
Software for this analysis was developed in Tereshchenko’s laboratory. Fiducial points
[beginning of Q or R wave (20) and end of T wave (21)] were detected automatically by a
customized software application written in Matlab (MathWorks, Inc, Natick, MA) on each beat
during 3-minute ECG epochs. Premature atrial and ventricular beats with one subsequent sinus
beat were excluded from analysis. The quality of the automated analysis was reviewed with the
aid of a graphical display. Mean heart rate (HRm), heart rate variance (HRv), mean duration of
QT interval (QTm), and QT variance (QTv) were measured on each of the XYZ leads and
averaged. Normalized by the mean QT interval, QT variance (QTVN) was calculated according
to equation: QTVN = QTv/QTm2. The heart rate variance was log-transformed (LogHRV) to
normalize distribution. The QT variability index (QTVI) was calculated as described by Berger
(12) according to equation: QTVI=log10 [(QTv/QTm2 )/(HRv/HRm2 )]. To assess coherence
6
between the heart rate variability and QT variability, spectral analysis of the RR’ and QT
intervals was performed, and a cross spectrum was generated using the Blackman-Tukey
method (22). Coherence was calculated as previously reported (12) according to the equation:
γ(f) = |Pxy(f)|2/[Pxx(f)
Pyy(f)].
Statistical analysis
Statistical analysis for this study was performed in the Statistical Core Laboratory of the
Heart Research Follow-up Program at the University of Rochester. Results are presented as
mean±standard deviation (SD) after confirmation of normal distribution. Continuous variables
were compared using the independent samples t test. The Pearson chi-square test was used to
compare categorical variables. To dissect which component of the QTVI formula contributes the
most to the predictive power of QTVI, we pre-specified high-risk subgroups by identifying
individuals in the highest quartile (≥75th percentile) of QTVI and of QTVN and in the lowest
quartile (<25th percentile) of mean coherence and of LogHRV. To determine whether abnormal
QTVI could robustly separate healthy individuals from HF patients at risk of death, an additional
threshold of abnormal QTVI was determined at the 97.5th percentile of IDEAL healthy
individuals’ QTVI values. Kaplan-Meier survival analysis was used to test predictive value of
risk markers for all-cause, cardiovascular mortality, and SCD. The multivariate Cox regression
analyses were performed to adjust for clinical and demographic variables associated with endpoints in the MUSIC study as shown previously (age >65 years, gender, LVEF ≤35%, NYHA
HF class, history of MI) (16, 18, 23). Interaction between LVEF and QTVI was tested in the Cox
regression models for all-cause mortality, cardiovascular death, and SCD. A P-value of <0.05
was considered significant. Data were analyzed using SAS 9.2 (SAS Institute Inc, Cary, NC).
Results
7
MUSIC study population
Clinical characteristics of MUSIC study participants have been previously described(16).
We analyzed high resolution ECGs of 924 MUSIC study participants. Patients with non-eligible
for QT variability analysis ECGs (due to atrial fibrillation, frequent premature beats, or noise;
n=391) were excluded from this study, and data of remaining 533 patients were further analyzed.
Mean age was 62.8±12.0 years. The majority of patients were males (N=377; 70.7%), in NYHA
HF class II (N=428; 80.3%), with LVEF > 35% in about half (N=254; 47.7%), and history of
myocardial infarction (MI) in 238 (44.7%) patients. There were 116 deaths overall during
median 44-months follow-up (6.7% per person-year of follow-up), including 93 cardiac deaths
(5.4% per person-year of follow-up), and 49 cases of SCD (2.9 % per person-year of follow-up).
Amongst 254 HF patients with LVEF>35%, 36 died during follow-up, cardiovascular cause of
death was established in 28 patients, and SCD- in 15 patients.
IDEAL study population
We analyzed data of 181 healthy subjects in the IDEAL database (mean age 38.7±15.7;
males 51%; 93.8% whites), predominantly non-smokers (71.4%) with a mean body mass index
of 24.1±4.5.
Beat-to-beat QT variability
QTVI was significantly higher in MUSIC HF patients than in healthy IDEAL participants
(0.827±0.521 vs. -1.541±0.689; P<0.00001) (Figure 1). There were no differences in the clinical
characteristics of HF patients with QTVI in the highest quartile (Table 1). As expected, the
highest QTVI quartile was characterized by significantly increased QTVN, faster heart rate,
decreased QTm, diminished LogHRV, and reduced coherence (Table 2). Even though the heart
rate at rest was slower in patients with LVEF >35%, there were no significant differences in QT
8
variability parameters between HF patients with LVEF ≤35% and those with LVEF >35% (Table
2).
Predictive value of QT variability
Overall mortality was almost twice as high in patients with QTVI in the highest quartile
[42 deaths (31.6%, or 8.5% per person-year of follow-up) vs. 74 deaths (18.5%, or 5.0% per
person-year of follow-up); P=0.002]. In the Kaplan-Meier survival analysis in patients with
LVEF ≤35% the highest QTVI quartile was associated with significantly higher all-cause and
cardiovascular mortality (Figure 2A,B). Kaplan-Meier curves suggested a dynamic pattern in
QTVI prediction of SCD in patients with LVEF ≤35% (Figure 2C): separation of survival curves
was noticed at 1 – 3 years of follow-up, but disappeared beyond 3 years. However, no significant
interaction of the highest QTVI quartile with the follow-up time was found (P=0.182). In HF
patients with LVEF >35%, the highest quartile of QTVI predicted all-case and cardiovascular
death, but not SCD (Figure 3). There was no significant interaction found between the highest
QTVI quartile and LVEF above or below 35% (P=0.515 for all-cause mortality, and P=0.428 for
cardiovascular mortality), which confirmed that QTVI predicts mortality across the continuum of
left ventricular dysfunction equally well.
In order to separately examine the predictive value of the numerator and denominator in
the QTVI formula, one-by-one we evaluated the predictive values of QTVI, QTVN, LogHRV,
and coherence in univariate and multivariate Cox regression analyses. Surprisingly, even in
univariate analysis the highest quartile of QTVN did not predict any end-point (Tables 3-5),
whereas the lowest quartile of LogHRV was significantly associated with all-cause and
cardiovascular mortality. The lowest quartile of coherence was a significant predictor of allcause and cardiovascular mortality as well. Importantly, adjusted risk of all-cause and
9
cardiovascular death was twice as high in patients with the highest QTVI quartile. The predictive
power of QTVI was stronger than the predictive power of LogHRV and coherence (Tables 3-4).
Importantly, we have found a similar risk of all-cause and cardiovascular death for HF patients
with the highest QTVI quartile and for patients with QTVI above the 97.5th percentile of healthy
individuals’ values (Tables 3-5).
Discussion
Our study showed that increased QTVI predicts all-cause and cardiovascular mortality across a
continuum of LV dysfunction in HF patients with LVEF either below or above 35%. Abnormally
amplified QTVI separates 97.5% of healthy individuals from HF patients at risk, and therefore
QTVI could be recommended for screening of cardiovascular death risk in the general
population.
Increased QT variability index: absolute increase in repolarization lability or
predominantly decreased heart rate variability?
Berger et al (12) in 1997 proposed QTVI as a measure of repolarization lability. During
next 15 years increased QTVI was shown to be strong predictor of increased risk of
cardiovascular death, SCD, and ventricular arrhythmia in patients with ischemic and nonischemic cardiomyopathy (13-15). The mechanisms and arrhythmogenic potential of elevated
beat-to-beat QT variability have been recently reviewed (24). At the same time, increased QTVI
was observed in a wide variety of conditions associated with autonomic imbalance.
Unfortunately, not every study reported data of QTVI formula numerator and denominator. Two
scenarios could result in increased QTVI: (1) true dramatic increase in absolute beat-to-beat QT
variability [e.g., as reported in MADIT II (13) and in a study of ischemic cardiomyopathy
10
patients with LVEF between 35% and 40% (14)] and (2) predominantly decreased heart rate
variability and out-of-proportion unchanged or mildly increased QT variance [e.g., as reported in
MADIT II females (25)]. The second scenario was observed in MUSIC HF patients in this study.
Importantly, in this study we demonstrated an incremental predictive value of QTVI as compared
to heart rate variability (LogHRV) for cardiovascular and all-cause mortality in patients with
LVEF above and below 35%. Indeed, not simply flat heart rate, but in particular reduced heart
rate variance, associated with out-of-proportion unchanged or mildly augmented QT variance,
carries risk of death. In agreement with our previous study (25), low coherence between QT
variability and heart rate variability showed predictive value in multivariate Cox analyses as
well, revealing pathologic uncoupling of RR’ and QT intervals’ behavior in subjects at risk.
QTVI separates 97.5% of healthy individuals from HF patients at risk
Our study showed that QTVI separates 97.5% of healthy individuals from HF patients at
risk of cardiovascular and all-cause death. The predictive value of QTVI is independent of
LVEF. Therefore, QTVI could be a valuable marker for screening for the risk of cardiovascular
death in the general population. On the other hand, QTVI could be easily monitored and
therefore could help guide management of patients.
Prediction of sudden cardiac death in patients with LVEF >35%
Very few studies have explored the predictive value of ECG-parameters in HF patients
with LVEF >35%. In a GISSI-HF study QTVI predicted cardiovascular and all-cause death (26).
The prognostic significance of clinical and ECG parameters for SCD was previously investigated
in the MUSIC study (16, 19). A decreased heart rate turbulence slope was shown to be associated
with an increased risk of SCD in multivariate Cox regression analysis, after adjustment for
gender, LVEF, NYHA class, and history of MI (18). A combination of ≥2 abnormal markers
11
(turbulence slope, QT/RR slope, or SDNN) was associated with increased SCD risk (19). In the
ISAR-Risk study(27) severe autonomic failure predicted SCD in patients with LVEF above and
below 30%. In contrast with successful prediction of SCD by markers of autonomic failure,
markers of repolarization heterogeneity were not helpful for prediction of SCD in HF with LVEF
>35%. Contradictory results of T-wave alternans predictive power in HF patients with LVEF
>35% were previously reported (28, 29). In our study increased QTVI did not predict SCD in HF
patients with LVEF >35% (Figure 3C). At the same time, MUSIC study patients with a history
of a prior acute vascular event (which included acute MI or stroke) had a twice-higher risk of
SCD after adjustment for other significant SCD predictors (16). This finding supports the notion
about the complex etiology of sudden death in patients with LVEF >35%, which might include
not-arrhythmic sudden death. As the ICD is an effective preventive strategy for sudden death due
to ventricular arrhythmia, but does not help to prevent not-arrhythmic sudden death, future
studies of HF patients with LVEF >35% should further adjudicate SCD as arrhythmic SCD vs.
not-arrhythmic sudden death.
Limitations
This was an ad-hoc analysis of a prospectively conducted cohort study. The statistical power of
survival analysis with the SCD end-point was limited and required prediction with hazard ratio
above 2.5 in order to demonstrate statistical significance. Therefore, survival analysis of SCD
was inconclusive in this study. Dynamic pattern in QTVI prediction of SCD and interaction of
the QTVI with the time and LVEF should be further explored in a larger prospective study.
Difficulties in adjudication of SCD are well recognized. It was previously shown that in HF
patients with relatively preserved LVEF, cases of not-arrhythmic death are frequent (8). The
racial and ethnic composition of the MUSIC study may affect extrapolation of study findings to
12
the entire US population. Specific characteristics of SCD in a Mediterranean Spanish population
were recently described (30).
Conclusion
Increased QTVI predicts all-cause and cardiovascular mortality in HF across a continuum of left
ventricular dysfunction. Abnormally elevated QTVI separates 97.5% of healthy individuals from
subjects at risk for cardiovascular and total mortality. Further investigation of QTVI as a
potential screening tool in the general population is warranted.
Acknowledgments
We thank all investigators of the MUSIC study.
13
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16
Figure legends
Figure 1. Histograms showing QTVI distribution. Histograms showing the distribution
of the QTVI in healthy IDEAL subjects (empty bars) and heart failure participants of MUSIC
study patients (full bars).
Figure 2. Survival in heart failure with LVEF ≤35%. Kaplan‐Meier curves for the
probabilities of all‐cause death (A), cardiovascular death (B), and sudden cardiac death (C) in
patients with the highest QTVI quartile and those with the lower 3 QTVI quartiles as measured
in heart failure patients with LVEF ≤35%.
Figure 3. Survival in heart failure with LVEF >35%. Kaplan‐Meier curves for the
probabilities of all‐cause death (A), cardiovascular death (B), and sudden cardiac death (C) in
patients with the highest QTVI quartile and those with the lower 3 QTVI quartiles as measured
in heart failure patients with LVEF >35%.
17
Table 1. Clinical characteristics of patients with QTVI, dichotomized at the 75th percentile
Characteristic
QTVI <75th percentile
QTVI ≥75th
(n=400)
percentile
P
(n=133)
Mean age ±SD, y
63.4±11.3
62.3±12.7
0.434
Female, n(%)
110(27.5)
46(34.6)
0.120
Ischemic cardiomyopathy, n(%)
196(49.0)
70(52.6)
0.468
History of MI, n(%)
176(44.0)
61(45.9)
0.708
LVEF ≤35%, n(%)
213(53.3)
66(49.6)
0.468
NYHA class II, n(%)
327(81.8)
102(76.7)
0.202
NYHA class III, n (%)
73(18.3)
31(23.3)
0.202
Beta-blockers use, n(%)
283(70.8)
84(63.2)
0.101
Heart rate±SD, bpm
65.5±10.9
78.5±13.5
<0.001
MI = myocardial infarction. LVEF = left ventricular ejection fraction. NYHA class = New York
Heart Association heart failure class.
18
Table 2. ECG parameters of MUSIC heart failure patients with QTVI dichotomized at the 75th percentile, and LVEF,
dichotomized at 35%
QTVI <75th
QTVI ≥75th
LVEF ≤35%
LVEF >35%
percentile
percentile
(n=279)
(n=254)
(n=400)
(n=133)
HRm±SD, bpm
65.5±10.9
78.5±13.5
<0.00001
70.34±12.68
66.91±12.96
0.001
LogHRV±SD
6.6±0.95
5.0±0.79
<0.00001
6.21±1.14
6.22±1.16
0.932
QTm±SD,ms
439.7±47.8
413.4±46.7
<0.00001
432.28±48.42
434.09±49.40
0.528
QTVN±SD
0.230±0.024
0.245±0.027
<0.00001
0.234±0.025
0.233±0.027
0.528
Coherence±SD
0.297±0.111
0.252±0.090
<0.00001
0.283±0.109
0.288±0.107
0.414
-1.83±0.50
-0.66±0.37
<0.00001
-1.564±0.712
-1.517±0.663
0.328
QTVI±SD
P
P
HRm = mean heart rate. LogHRV = log-transformed heart rate variance. QTm = mean QT interval duration. QTVN = QT variance,
normalized by mean QT interval. QTVI = QT variability index.
19
Table 3. Univariate and multivariate hazard ratios of the QT variability and heart rate
variability parameters for all-cause mortality in MUSIC heart failure patients
Unadjusted hazard ratio (95% CI),
Adjusted* hazard ratio (95% CI),
and P value
and P value
Q4 QTVI
1.96(1.34-2.86), P=0.001
2.08(1.41-3.05), P<0.001
97.5‰ QTVI
2.16(1.38-3.38), P=0.001
1.95(1.23-3.09), P=0.005
Q4 QTVN
1.12(0.74-1.69), P=0.598
1.01(0.67-1.54), P=0.949
Q1 LogHRV
1.70(1.16-2.50), P=0.007
1.76(1.19-2.59), P=0.005
Q1 Coherence
1.59(1.08-2.34), P=0.019
1.50(1.01-2.22), P=0.043
Predictor
*- Adjusted for age >65 y, sex, left ventricular ejection fraction below 35%, New York Heart
Association heart failure class, and history of myocardial infarction.
Q4 QTVI = the highest quartile of QT variability index. 97.5‰ QTVI = QT variability index
above the 97.5th percentile of healthy individuals’ values. Q4 QTVN = the highest quartile of
normalized QT variance. Q1 LogHRV = the lowest quartile of log-transformed heart rate
variance. Q1 Coherence = the lowest quartile of coherence between heart rate variability and QT
variability.
20
Table 4. Univariate and multivariate hazard ratios of the QT variability and heart rate
variability parameters for cardiovascular mortality in MUSIC heart failure patients
Unadjusted hazard ratio (95% CI),
Adjusted* hazard ratio (95% CI),
and P value
and P value
Q4 QTVI
2.16(1.43-3.29), P<0.001
2.36(1.54-3.61), P<0.001
97.5‰ QTVI
2.10(1.27-3.48), P=0.004
1.96(1.16-3.29), P=0.011
Q4 QTVN
1.19(0.75-1.87), P=0.463
1.07(0.68-1.69), P=0.776
Q1 LogHRV
1.85(1.21-2.83), P=0.004
1.88(1.22-2.89), P=0.004
Q1 Coherence
1.63(1.06-2.51), P=0.027
1.55(1.01-2.40), P=0.048
Predictor
*- Adjusted for age >65 y, sex, left ventricular ejection fraction below 35%, New York Heart
Association heart failure class, and history of myocardial infarction.
Q4 QTVI = the highest quartile of QT variability index. 97.5‰ QTVI = QT variability index
above the 97.5th percentile of healthy individuals’ values. Q4 QTVN = the highest quartile of
normalized QT variance. Q1 LogHRV = the lowest quartile of log-transformed heart rate
variance. Q1 Coherence = the lowest quartile of coherence between heart rate variability and QT
variability.
21
Table 5. Univariate and multivariate hazard ratios of the QT variability and heart rate
variability parameters for sudden cardiac death in MUSIC heart failure patients
Unadjusted hazard ratio (95% CI),
Adjusted* hazard ratio (95% CI),
and P value
and P value
Q4 QTVI
1.21(0.64-2.29), P=0.554
1.28(0.67-2.42), P=0.459
97.5‰ QTVI
1.11(0.47-2.61), P=0.811
1.03(0.43-2.45), P=0.952
Q4 QTVN
1.22(0.65-2.26), P=0.535
1.10(0.59-2.05), P=0.771
Q1 LogHRV
1.54(0.84-2.79), P=0.160
1.52(0.83-2.78), P=0.179
Q1 Coherence
1.05(0.55-2.01), P=0.886
1.05(0.55-2.03), P=0.881
Predictor
*- Adjusted for age >65 y, sex, left ventricular ejection fraction below 35%, New York Heart
Association heart failure class, and history of myocardial infarction.
Q4 QTVI = the highest quartile of QT variability index. 97.5‰ QTVI = QT variability index
above the 97.5th percentile of healthy individuals’ values. Q4 QTVN = the highest quartile of
normalized QT variance. Q1 LogHRV = the lowest quartile of log-transformed heart rate
variance. Q1 Coherence = the lowest quartile of coherence between heart rate variability and QT
variability.
22
Figure 1.
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Figure 2.
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Figure 3.
25