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European Journal of Heart Failure 2008 10(10):1007-1014; doi:10.1016/j.ejheart.2008.07.002
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© 2008 European Society of Cardiology

Non-sustained ventricular tachycardia as a predictor of sudden cardiac death in patients with left ventricular dysfunction: A meta-analysis

Marcos R. de Sousaa,b,*, Carlos A. Morilloc, Fábio T. Rabelob, Antônio M. Nogueira Filhod and Antonio L.P. Ribeiroa,b

a Post-Graduate Program in Internal Medicine, Universidade Federal de Minas Gerais Brazil
b Cardiology Service, Hospital das Clinicas, Universidade Federal de Minas Gerais Brazil
c Department of Medicine; Arrhythmia Service, Cardiology Division McMaster University — Population Health Research Institute Canada
d School of Medicine, Universidade Federal de Minas Gerais Brazil

* Corresponding author. Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Rua Aristides Duarte, 39/601 Bairro Prado, ZIP CODE 30.410-040, Belo Horizonte, Minas Gerais, Brazil. Tel.: +55 31 32753624; fax: +55 31 32879213. E-mail address: sousa.mr{at}uol.com.br (M.R. de Sousa).


   Abstract
Top
 Abstract
1. Introduction
 2. Methods
 3. Results
 4. Discussion
 References
 
Background: Identifying patients at risk of sudden cardiac death (SCD) remains a challenge.

Aim: To evaluate the performance of non-sustained ventricular tachycardia (NSVT) from 24 hour ambulatory electrocardiography as a predictor of SCD in patients with heart failure or non-ischaemic dilated cardiomyopathy with left ventricular systolic dysfunction (LVSD).

Methods and results: Study search and selection were performed by independent reviewers using a validated strategy. Eleven prognostic studies with > 100 patients with good quality data and multivariate analysis of predictors of SCD were included. Publication bias was evaluated by funnel plot with Kendall's tau b test. A summary ROC (sROC) curve was built to evaluate predictive performance of NSVT. There was threshold effect (Spearman's correlation between sensitivity and specificity=–0.818, p<0.01) which indicates that combining sensitivity and specificity was not appropriate. The area of 0.68±0.02 under the sROC curve indicates a statistically significant contribution of NSVT in the prediction of SCD. The true negative rate varied from 89 to 97%. Multivariate analysis and meta-regression suggested that the contribution of NSVT to risk stratification is independent of ejection fraction.

Conclusions: Absence of NSVT indicated a low probability of SCD in patients with LVSD. A risk score including NSVT should be evaluated in prospective studies.

Key Words: Death, sudden, cardiac • Ventricular dysfunction • Tachycardia, ventricular • Prognosis • Heart failure

Received May 12, 2008; Accepted July 1, 2008


   1. Introduction
Top
 Abstract
 1. Introduction
2. Methods
 3. Results
 4. Discussion
 References
 
Heart failure patients with reduced left ventricular ejection fraction (LVEF) have a high risk of sudden cardiac death (SCD). In the last decade, several seminal studies have demonstrated the benefit of implantable cardioverter defibrillators (ICDs), initially in patients with aborted SCD and ventricular tachycardia (VT), but more recently in those without a previous arrhythmic event. However, despite evidence of the significant benefit afforded by ICD primary prophylaxis, global adoption has been hampered by the associated cost [1-3]. The limited availability of resources has promoted the need for recognizing subjects who will derive the greatest benefit from receiving an ICD [4-6], and thus, identifying patients at risk of sudden cardiac death (SCD) remains a challenge.

Left ventricular ejection fraction (LVEF) has been used in most clinical trials as the main identification criterion of patients at high risk of arrhythmic events [7,8]. In the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT), the mean LVEF was 25±5%; and ICD insertion led to a 7%-absolute risk reduction in mortality [7]. However, almost 80% of the subjects assigned to an ICD did not receive any therapy from the device during a median follow-up period of 46 months [7]. In addition, there was a 14%-absolute increase in "linically significant" complication rate caused by ICD implantation [7]. Indeed, other risks may be related to the device implantation, the incidence of ICD infection in a recent population-based study was 8.9 (95% CI, 4.2-18.6) per 1000 device-years [9], almost 6% in six years, the mean device longevity. These findings further support the need of identifying an accurate and reproducible risk stratification strategy to select patients who will benefit the most from ICD primary prophylaxis [10].

Several other markers have been proposed to identify high SCD risk individuals, namely, heart rate variability, baroreceptor reflex sensitivity, heart rate turbulence, and more recently, T-wave alternans [11-13]. The main limitations of these markers are their poor positive predictive value and limited generalizability. The role of NSVT as a SCD predictor in heart failure has been widely debated, but it remains controversial [10,14]. In order to evaluate the performance of NSVT on ambulatory electrocardiography monitoring (AECG) as a predictor of major arrhythmic events in reduced-LVEF patients, we performed a systematic review and meta-analysis of the current literature.


   2. Methods
Top
 Abstract
 1. Introduction
 2. Methods
3. Results
 4. Discussion
 References
 
2.1. Systematic review and study selection
PubMed archives from 1965 to July 31st 2007 were searched and the literature was selected and reviewed by two of the authors (M.R.S. and A.M.N.F.) independently. Prognostic studies evaluating NSVT as a risk factor of major arrhythmic events in heart failure patients were identified. Major arrhythmic events, defined as SCD or resuscitated ventricular fibrillation or sustained VT, are referred to as SCD. The following terms were searched: (((ventricular(Text Word)) AND ("arrhythmia" (MeSH Terms) OR arrhythmia (Text Word)) OR "ventricular tachycardia" (Text Word) OR "tachycardia, ventricular" (MeSH Terms) OR tachycardia, ventricular (Text Word)) AND (("ongestive heart failure" (Text Word) OR "heart failure, congestive" (MeSH Terms) OR heart failure, congestive (Text Word)) OR ("ventricular dysfunction" (MeSH Terms) OR ventricular dysfunction (Text Word)) OR ("dilated cardiomyopathy" (Text Word) OR "ardiomyopathy, dilated" (MeSH Terms) OR cardiomyopathy, dilated (Text Word))) AND (("sudden cardiac death" (Text Word) OR "death, sudden, cardiac" (MeSH Terms) OR death, sudden, cardiac (Text Word)) OR ("sudden death" (Text Word) OR "death, sudden" (MeSH Terms) OR death, sudden (Text Word)))) AND (incidence (MeSH) or mortality (MeSH) or follow-up studies (MeSH) or prognos* (Text Word) or predict* (Text Word) or course* (Text Word)) [15]. Eight hundred and twenty papers were retrieved with 5 additional related articles for each paper found, totaling 4920 papers. The related papers added 9 selected abstracts for full text reading. The reproducibility of this systematic strategy was confirmed. Additionally, "Biblioteca Virtual em Saúde (BVS)" (http://www.bireme.br/php/index.php) was searched using LILACS and SciELO databases. References of read papers were also used in the search. We excluded studies of patients with a recent myocardial infarction (Holter evaluation of NSVT during hospitalisation or soon after acute myocardial infarction) or previous sustained VT or SCD. Inclusion criteria were a) prognostic studies with at least 100 patients with either ischaemic or non-ischaemic, either symptomatic or asymptomatic LVSD [16], b) NSVT data of at least 24 hour AECG available at the beginning of follow-up, c) data availability on major arrhythmic event type, defined as either SCD, or ventricular fibrillation, or sustained VT, e) multivariate analysis of the NSVT and SCD association, f) data availability on SCD-predicting NSVT: true positive, false positive, false negative, true negative, g) either English or Portuguese or Spanish as publication languages.

Initially, two independent reviewers (M.R.S. and F.T.R) checked the title and abstract search result list to determine whether the articles contained relevant data. The selected articles were read in full to confirm eligibility and to extract data. Additional studies were located by manual searching the retrieved article references. In the case of disagreement on study inclusion or exclusion or data extraction between the 2 reviewers, differences were resolved by consensus with senior authors (A.L.P.R and C.A.M.). For each article, the numbers of true positive, true negative, false positive, and false negative results were either recorded or estimated from study data. If more than one publication presumably on the same patients by the same authors was found, only one study was used.

2.2. Statistical methods
Publication bias was evaluated by funnel plot using Comprehensive Meta Analysis Software® Version 2.2.046 (June 24, 2007). The funnel plot is a plot of a measure of study size (usually standard error or precision) on the vertical axis as a function of log diagnostic odds ratio on the horizontal axis [17,18]. In the absence of publication bias we would expect the studies to be distributed symmetrically about the combined effect size. Rank correlation and regression procedures can test for the presence of bias. Kendall's tau b test was used.

The true positive, false positive, false negative, true negative numbers of each study were entered in the Meta-Disc 1.4 analysis software [19]. The threshold effect refers to variations in sensitivity and specificity caused by different cut-off points to define NSVT (some authors used more than 100 beats/min, other authors used 120 and 150 beats/min). To explore the threshold effect, we calculated Spearman's correlation coefficient between sensitivity and specificity. If the threshold effect exists, an inverse correlation appears [20]. The threshold effect may be caused by explicit differences in either NSVT cut-off definitions or implicit differences between studies. This effect was also evaluated by ROC plane plot. Combining study results for threshold effect involves fitting a ROC curve rather than pooling sensitivities and specificities. We chose the Moses model to build a sROC curve. The confidence interval of the symmetrical sROC curve is calculated by introducing the 95% upper and lower limits of confidence interval of overall diagnostic odds ratio in the curve equation [19]. The area under the sROC curve (AUC) was computed by numeric integration of the curve equation by the trapezoidal method. The Q index, which also summarizes the diagnostic performance, was calculated [21]. Due to differences in NSVT cut-off definitions between studies, the pooled sensitivity and specificity was not calculated [22]. Positive and negative likelihood ratios (LR) and the DOR were pooled by the DerSimonian Laird method with the random effects model to incorporate variations between studies [23]. The homogeneity was tested using Inconsistency (I2) test based on Cochran's Q test with inverse variance weights, which also has a chi-squared distribution with k–1 degrees of freedom [24].

A meta-regression using Comprehensive Meta Analysis Software® was done do evaluate the relationship between mean ejection fraction in each study and the log odds ratio of NSVT as a predictor of sudden death. We also did subgroup analysis of the patients with exclusively non-ischaemic dilated cardiomyopathy compared to patients with LVSD of both ischaemic and non-ischaemic aetiologies using summary diagnostic odds ratio for sudden death. A secondary analysis was done for the nine studies that evaluated transplant-free survival, by building a sROC curve. This is a secondary end-point because our inclusion criteria were studies evaluating sudden death.


   3. Results
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
4. Discussion
 References
 
The study selection process is summarized in Fig. 1. Eleven studies of different patient populations that met the inclusion criteria were considered in the meta-analysis. There was a symmetrical distribution of studies evaluated by funnel plot (Kendall's tau b was 0.29, p=0.21). The summary diagnostic odds ratio (DOR) was 3.031 (95% CI 2.441-3.763) homogeneously (heterogeneity chi-squared=10.76 (df=10) p=0.377 Inconsistency (I2)=7.1%).


Figure 01
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  		Fig. 1 Study selection process.

 
Study features and clinical characteristics are summarized in Table 1. The mean patient age ranged from 41 to 66 years, with a large majority of males, all of them with enlarged ventricles or LVSD. Heart failure and LVSD aetiology included both ischaemic and non-ischaemic cardiomyopathy in six studies and exclusively non-ischaemic aetiology in five studies. The definition of NSVT varied widely across studies, and included NSVT with frequencies from 70 bpm [25] to over 150 bpm [26]; however, in most studies, it ranged from 100 to 120 bpm. Spearman's correlation between sensitivity and specificity was –0.818 (p=0.002), suggesting the existence of a threshold effect. In spite of the threshold effect, the rather consistent and homogeneous rate of true negative values, ranging from 89 to 97%, allowed the calculation of pooled summary negative LR: 0.617 (95% CI, 0.550-0.693) (Fig. 2) without statistical heterogeneity (chi-squared=9.26, df=10, p=0.507, Inconsistency (I2)=0.0%). Summary positive LR was 1.858 (95% CI 1.560-2.213) with significant heterogeneity (chi-squared=40.54, df=10, p<0.001, Inconsistency (I2I2)=75.3%).


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  		Table 1 Features and clinical population characteristics for the 11 selected studies

 


Figure 02
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  		Fig. 2 Negative Likelihood Ratio plots. In spite of the clinical heterogeneity and the occurrence of threshold effect, the negative likelihood ratio was homogeneous across studies and statistically significant.

 
In the presence of a threshold effect, the best approach was to build a summary ROC curve, as shown in Fig. 3. The area under the curve (standard error=0.015) indicates that the presence of NSVT had a statistically significant diagnostic contribution to SCD risk stratification in patients with LVSD. The diagnostic odds ratio for patients with non-ischaemic dilated cardiomyopathy and for patients with both ischaemic and non-ischaemic aetiologies is shown in Fig. 4.


Figure 03
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  		Fig. 3 Summary ROC curve of non-sustained ventricular tachycardia as a predictor of sudden death and transplant-free survival in patients with left ventricular dysfunction. The primary end-point result was the area under the curve for sudden cardiac death (left). The outer lines represent the 95% confidence interval. The AUC was 0.641 for the nine studies that evaluated also transplant-free survival (right).

 


Figure 04
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  		Fig. 4 Results of summary diagnostic odds ratio in the subgroup of studies with non-ischaemic dilated cardiomyopathy and the subgroup with both ischaemic and non-ischaemic aetiologies. The results are similar, although there is more heterogeneity in the results for the non-ischaemic subgroup (Q=6.6; df=4; I2=39%) than in the subgroup with both aetiologies (Q=3.95; df=5; I2=0%).

 
In Fig. 3, we also evaluated transplant-free survival for the nine studies that presented data for this end-point. The results of the sROC curve were essentially similar (AUC 0.6414±0.02), although with more heterogeneity than for the sudden death end-point. Another interesting analysis is the subgroup of patients with non-ischaemic dilated cardiomyopathy (Fig. 4). Again, we did separate analysis of this subgroup and the results were essentially similar (AUC 0.71±0.0313), and the positive LR were stronger (2.29, 95% CI, 1.61-3.24), without heterogeneity (I2=0.5%). The meta-regression shows that there was no statistically significant influence of LVEF in the diagnostic odds ratio of NSVT as a predictor of sudden death (Fig. 5).


Figure 05
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  		Fig. 5 Meta-regression between mean ejection fraction in each study and log diagnostic odds ratio of NSVT as predictor of sudden death. There was no statistically significant influence of mean left ventricular ejection fraction on the predictive capacity of NSVT. p=18.

 

   4. Discussion
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
References
 
The main finding of this study is that NSVT is a statistically significant predictor for arrhythmic events in patients with LVSD, regardless of aetiology and LVEF. Out of the 11 studies that evaluated the predictive capacity of NSVT using a multivariable analysis model including LVEF, 8 were positive [25-32], 2 were negative [33,34], and one revealed a positive trend [35].

4.1. Threshold effect and pooled summary negative LR
It is important to note that the included studies used different thresholds to define the presence of NSVT. The NSVT definition varied across studies (Table 1), including two studies that used the definition of three consecutive beats at any frequency. These two studies may have included some patients with accelerated idioventricular rhythm, the prognostic value of which may differ from that of NSVT [25,33]. Furthermore, differences in the populations studied may have accounted for the threshold effect. The rate of true negative (not having SCD and absence of NSVT) varied from 89 to 97% and was homogeneous across studies. Because of a high true negative rate even in the presence of threshold effect, we decided to pool the negative LR. Usually, a sensitive test is a better predictor when the result is negative. Furthermore, the advantage of the LR is in estimating the post-test probability [post-test odds=pre-test oddsxLR, odds=probability: (1 — probability)] [36].

4.2. NSVT and risk stratification of SCD
The sROC curve [21] shows a moderate, but statistically significant, capacity for discrimination of NSVT. This fact should be counter pointed with the capacity for discrimination of LVEF, which is also fair. Indeed, LVEF is considered a relatively insensitive and unspecific SCD risk factor [5,37]. In a large SCD registry, 50% of SCDs occurred in patients with LVEF>30% [38,39], and 20% occurred in patients with LVEF>50% [38]. In contrast, only 21% of the patients (LVEF- and functional class-based selection) treated with an ICD received appropriate shocks in the SCD-HeFT study, giving a 79% false positive rate [7]. This rate may be underestimated based on the fact that appropriate shocks are not a good surrogate for SCD and may overestimate the true benefit of ICDs [40]. Additionally, a recent post-hoc analysis of an important study that evaluated risk stratification after myocardial infarction for ICD insertion, suggested that LVEF alone is insufficient to predict the risk of SCD [37]. We decided to analyse the end-point of sudden death because we wanted to know if NSVT could be used to help in selecting patients for defibrillator implantation. The results found for the end-point of transplant-free survival is similar, although this result is a secondary post-hoc analysis. NSVT has been suggested to be more prevalent in non-ischaemic dilated cardiomyopathy perhaps making it less potent as a predictor of SCD. But our results show the same or even better predictive performance of NSVT in non-ischaemic dilated cardiomyopathy.

These findings support the hypothesis that it would be desirable to build a risk score based on more than one non-invasive risk stratifier [26] that would include LVEF and NSVT and possibly other risk markers [41]. Several studies have demonstrated the feasibility of this proposal. In 680 patients with dilated cardiomyopathy, Watanabe et al. showed that combining predictors provides a high predictive capacity and that an isolated predictor such as LVEF is insufficient [26]. In the Marburg Cardiomyopathy Study (MACAS) [35] in patients with non-ischaemic left ventricular dysfunction, combining LVEF with NSVT provided a higher predictive rate than either of these variables alone. Considering the landmark studies on the benefit of ICD in preventing SCD, those that used combined predictors [42,43] showed a significantly higher incidence of SCD (about 30%) than those in which LVEF was the main inclusion criterion (about 20%) [7,8]. Thus, there is robust evidence suggesting that combining predictors [26,35,42-44] in scores would probably allow a better selection of patients at risk of SCD and, consequently, a more rational use of ICD therapy [45].

Considering the need for a more accurate stratification strategy to help in identifying heart failure and LVSD patients at high risk of SCD, AECG-documented NSVT has two potential advantages over other risk markers. Firstly, it has resisted the challenge of time. We selected studies published between 1989 and 2007, and the absence of NSVT was significantly and independently related to the absence of SCD. This observation was independent of the radical changes in heart failure and LVSD treatment which occurred during this period. Secondly, AECG is a non-invasive and inexpensive method, which is widely available around the world and is frequently performed in patients with LVSD, in whom NSVT is usually easily recognized. This is a significant advantage over other more complex and not readily available techniques such as electrophysiological testing, baroreceptor reflex sensitivity, and T-wave alternans, which require invasive procedures and/or specific devices not available in many medical facilities.

This study has some limitations. AECG recording duration and the number of tests done may influence detection of NSVT. Another limitation in almost all of the studies is the lack of data on the prognostic significance of the length of NSVT runs as well as the number of NSVT episodes.

The limitations of this study are common to prognostic test meta-analyses. Evaluating the quality of prognostic study papers is still a challenge [46,47]. The funnel plot and Kendall's tau b test suggest absence of publication bias. We selected good quality studies that included multivariable analysis with LVEF as a covariate. Studies with less than 100 patients were excluded, since publication bias affects small studies more frequently [18].

The selected studies showed a large DOR range, from 1.66 to 7.71, which probably reflects their heterogeneity. It would be desirable to perform subgroup meta-analysis including variables such as NSVT frequency [25,33], predominance of patients in NYHA class IV [28,34], and beta-blocker use; however, the number of studies in each of these sub-analyses is too small to do this kind of analysis. Nonetheless, this heterogeneity seems to be more of an advantage than a limitation, according to Irwig et al. who state: "the more variation there is in study populations, the greater the potential to know how the test will perform in various settings" [48].

Our results do not apply to patients with a recent myocardial infarction. Patients early after infarction (within 6 weeks) do not benefit from defibrillator implantation [49]. In DINAMIT, patients showed a slight improvement in left ventricular function, from 0.28 at baseline to 0.30 by 6-8 weeks following their acute infarction, showing that LV dysfunction may be potentially reversible in some patients, in the early period following infarction [50]. The ocurrence of NSVT could also change in this period, so we excluded these studies.

In conclusion, this meta-analysis shows that NSVT plays a consistent role in the risk stratification of SCD in LVSD patients, suggesting that it may be possible to build a robust risk score including both LVEF and NSVT [37]. Prospective studies are needed to confirm the efficacy of this strategy.


   References
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 References
 

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