Skip to main content
NCBI home page
Annals of Noninvasive Electrocardiology logoLink to Annals of Noninvasive Electrocardiology
. 2018 Oct 17;24(2):e12597. doi: 10.1111/anec.12597

Baseline fragmented QRS is associated with increased all‐cause mortality in heart failure with reduced ejection fraction: A systematic review and meta‐analysis

Chanavuth Kanitsoraphan 1, Pattara Rattanawong 2,3,✉, Poemlarp Mekraksakit 4, Pakawat Chongsathidkiet 5, Tanawan Riangwiwat 2, Napatt Kanjanahattakij 6, Wasawat Vutthikraivit 7, Saranapoom Klomjit 7, Subhanudh Thavaraputta 7
PMCID: PMC6931499  PMID: 30329201

Abstract

Background

Recent studies suggested that fragmented (fQRS) is associated with poor clinical outcomes in heart failure with reduced ejection fraction (HFrEF) patients. However, no systematic review or meta‐analysis has been done. We conducted a systematic review and meta‐analysis to assess the association between baseline fQRS and all‐cause mortality in HFrEF.

Methods

We comprehensively reviewed the databases of MEDLINE and EMBASE from inception to February 2018. Published studies of HFrEF that reported fQRS and outcome of all‐cause mortality and major arrhythmic event (sudden cardiac death, sudden cardiac arrest, ventricular fibrillation, or sustained ventricular tachycardia) were included. Data were integrated using the random‐effects, generic inverse‐variance method of DerSimonian and Laird.

Results

Ten studies from 2010 to 2017 were included. Baseline fQRS was associated with increased all‐cause mortality (risk ratio [RR] 1.63, 95% confidence interval [CI] 1.22–2.19, p < 0.0001, I 2 = 73%) as well as major arrhythmic events (RR = 1.74, 95% CI 1.09–2.80, I 2 = 89%). Baseline fQRS increased all‐cause mortality in both Asian and Caucasian cohorts (RR = 2.17 with 95% CI 1.33–3.55 and RR = 1.45 with 95% CI 1.05–1.99, respectively) as well as increased major arrhythmic events in Asian cohort (RR = 1.50, 95% CI 1.05–2.13). Baseline fQRS also increased all‐cause mortality in patients who had not received implantable cardioverter‐defibrillator, significantly more than in patients who had received implantable cardioverter‐defibrillator (RR = 2.46 with 95% CI 1.56–3.89 and 1.36 with 95% CI 1.08–1.71, respectively).

Conclusion

Baseline fQRS is associated with increased all‐cause mortality up to 1.63‐fold in HFrEF patients. Fragmented QRS could be a predictor of clinical outcome in patients with HFrEF.

Keywords: fragmented QRS, heart failure mortality

Abbreviations

CI

confidence interval

CRT

cardiac resynchronization therapy

DCM

dilated cardiomyopathy

ECG

electrocardiogram

EF

ejection fraction

fQRS

fragmented QRS

HF

heart failure

HFrEF

heart failure with reduced ejection fraction

ICM

ischemic cardiomyopathy

MAE

major arrhythmic events

RR

risk ratio

SCD

sudden cardiac death

VF

ventricular fibrillation

1. INTRODUCTION

Heart failure (HF) is defined as a clinical syndrome with impairment of blood ejection or ventricular filling (Yancy et al., 2013). HF is a common cardiac problem which causes significant morbidity and mortality globally. It is estimated that up to twenty‐three million people suffer from HF (McMurray, Petrie, Murdoch, & Davie, 1998), and approximately half of patients would die within 5 years of diagnosis (Levy et al., 2002; Roger et al., 2004). HF is mainly diagnosed on a clinical basis (Yancy et al., 2013). Its risk factors include, but is not limited to, coronary heart disease, cigarette smoking, hypertension, obesity, diabetes, and valvular heart disease (He et al., 2001). Heart failure with reduced ejection fraction (HFrEF), a subgroup of HF with documented ejection fraction (EF) less than forty percent, has been a focus of clinical trials. Most studied pharmacologic and device therapies specifically demonstrated benefits only in this group of HF patients (Yancy et al., 2013). Treatment of HFrEF includes lifestyle modification, management of underlying contributing factors, pharmacologic therapy, device therapy, cardiac rehabilitation, and preventive care. Several medications have been proven in their efficacy to reduce long‐term morbidity and mortality. Device therapy has been recommended in stage C HF patients with strict indications and the risk and benefit should be thoroughly considered. Since the prognosis for HF varies widely, identifying patients at risk of poor clinical outcomes would be crucial. Several risk stratification tools or prognostic models have been proposed (Alba et al., 2013). However, surface ECG parameters have not been widely developed in HF risk stratification, even though ECG is one of the most accessible tool and usually available in most healthcare settings.

Fragmented QRS (fQRS), which reflects myocardial scarring, has been found to be associated with poor prognostic outcome in several cardiac conditions, including coronary artery disease (Xu et al., 2015), non‐ischemic cardiomyopathy (Das et al., 2010), Brugada syndrome (Rattanawong et al., 2017), chronic total occlusion (Bonakdar et al., 2016), and hypertrophic cardiomyopathy (Rattanawong et al., 2018). Recent studies have demonstrated poor prognostic outcome in HFrEF patients whose baseline ECG was positive for fQRS. However, the results were inconclusive and no meta‐analysis has been performed. Hence, we conducted a systematic review of the literature and meta‐analysis to assess prognostic value of fQRS in HFrEF patients.

2. METHODS

2.1. Search strategy

We intended to identify all published reports relating the presence of fQRS and all‐cause mortality or arrhythmic events in patients with HFrEF. The published studies indexed in EMBASE and MEDLINE databases from inception to February 2018 were independently searched by two investigators (PC and PM), supplemented by manual searches through the references of the publications. We used a search strategy that included fragmented QRS and heart failure. Only English language publications involving human subjects were included.

2.2. Inclusion criteria

The eligibility criteria included the following:

  1. Cohort study (prospective or retrospective) or cross‐sectional study reporting incidence or prevalence of all‐cause mortality and major arrhythmic events (MAE), including ventricular fibrillation, sustained ventricular tachycardia, sudden cardiac arrest, or sudden cardiac death in heart failure with left ventricular dysfunction patients with and without baseline fQRS.

  2. Heart failure with left ventricular dysfunction patients including heart failure patients who have left ventricular EF ≤40% with or without implantable cardioverter‐defibrillator (ICD) and/or cardiac resynchronization therapy (CRT) for primary or secondary prevention, dilated cardiomyopathy (DCM), or ischemic cardiomyopathy (ICM) with follow‐up period ≥6 months.

  3. Relative risk, hazard ratio, odds ratio, incidence ratio, or standardized incidence ratio with 95% confidence intervals (CI) or sufficient raw data for the calculation were provided.

  4. Participants without baseline fQRS were used as controls.

The studies that include patients described fQRS on vectorcardiography, magnetocardiography, or signal‐averaged ECG were excluded.

Study eligibility was independently determined by two investigators (CK and PM) and differences were resolved by mutual consensus. Newcastle–Ottawa quality assessment scale was used to evaluate each study in three domains: recruitment and selection of the participants, similarity and comparability between the groups, and ascertainment of the outcome of interest among cohort studies (Table S1).

2.3. Data extraction

We used a standardized data collection form to obtain information from each study as followed: title of study, name of the first author, year of study, year of publication, country of origin, number of subjects enrolled, demographic data, and average follow‐up time.

To ensure accuracy of data, all investigators conducted a data extraction process independently. Should there was any data discrepancy, we referred to original articles.

2.4. Statistical analysis

We conducted a meta‐analysis using random‐effects model. We gathered the point estimates from each study by applying the generic inverse‐variance method of Der Simonian and Laird. I 2 statistic and Q statistic were used to calculate the heterogeneity of effect size. The I 2 statistic ranges from 0% to 100% (I 2 > 50% reflects substantial heterogeneity, I 2 from 25% to 50% indicates moderate heterogeneity, and I 2 < 25% means low heterogeneity). Funnel plot and Egger's regression test were used to assess publication bias. All data analysis processes were done by the Stata SE 14.1 software from Stata Corp LP.

3. RESULTS

3.1. Description of included studies

Our search strategy generated ninety potentially relevant articles (fifty‐four from EMBASE and thirty‐eight from MEDLINE). Sixteen duplicated articles and two case reports were excluded, and 74 remaining articles underwent title and abstract review. Forty‐nine studies were excluded by title and abstract review. Twenty‐five remaining articles underwent full‐length article review. Finally, seven retrospective cohorts, two prospective cohorts, and one cross‐sectional study were included in the meta‐analysis. Studies’ characteristics are described in Table 1. In one study (Pei et al., 2012), we used the information from the subgroup analysis, since the characteristics of the subgroup fit with our criteria, while the characteristics of the main cohort did not.

Table 1.

The clinical characteristics and summary of included studiesw

First author Country of origin Year Study type Participant description Exclusion criteria Total population Male (%) Mean age (years) fQRS definition fQRS (n) Mean duration of follow‐up (months) Outcome definition Conclusion by authors
Das et al. USA 2010 Retrospective cohort CAD and DCM receiving an ICD for primary or secondary prevention. LVEF ≤40% Paced rhythm 361 91 63.3 ± 11.4 RSR' patterns (QRS duration ≤120 ms), additional R', notching of the R or S wave, or more than two R' in two contiguous leads 84 16.6 ± 10.2 Arrhythmic events, all‐cause mortality fQRS predicts arrhythmic event in patients with ICM and NICM who received ICD for primary or secondary prevention of SCD, but does not predict mortality.
Cheema et al. USA 2010 Prospective cohort LVEF ≤35% of both ischemic and nonischemic etiologies. N/A 842 77.8 66 ± 12 RSR' patterns (QRS duration ≤120 ms), additional R', notching of the R or S wave, or more than two R' in two contiguous leads 274 40 ± 17 All‐cause mortality, cause‐specific mortality, appropriate shock in patients with ICD fQRS was not associated with a higher risk for either all‐cause or arrhythmic mortality.
Sha et al. China 2011 Retrospective cohort IDCM with LVEF ≤40% HF due to CAD, secondary cardiomyopathy, valvular or congenital heart disease, or patients with orthotopic cardiac transplantation during follow‐up. Paced rhythm at baseline. 128 68 53.6 ± 13.9 RSR' patterns (QRS duration ≤120 ms), additional R', notching of the R or S wave, or more than two R' in two contiguous leads 51 14 ± 5 All‐cause mortality, ventricular arrhythmia, including appropriate ICD therapy for ventricular arrhythmia fQRS predicts combined end point of all‐cause mortality and ventricular tachyarrhythmias in IDCM patients with left ventricular dysfunction
Forleo et al. Italy 2011 Retrospective cohort IDCM or NIDCM, chronic stable HF with first ICD implantation. LVEF ≤35% ICD for secondary prevention 394 84.8 66.4 ± 11.0 More than two peaks or notches present in the R or the S wave in two contiguous leads 103 26.3 ± 17.5 All‐cause mortality, any appropriate ICD‐delivered therapy fQRS is not associated with an increased risk of subsequent appropriate ICD therapy or all‐cause death.
Pei et al. China 2012 Prospective cohort CHF patients with DCM and ICM. (*Subgroup with LVEF ≤30% was selected for analysis) Malignant tumors, severe liver and kidney dysfunctions, or other uncontrollable systemic diseases, pregnancy 1570 (N/A for analyzed subgroup) 78.0 (N/A for analyzed subgroup) N/A for analyzed subgroup RSR' patterns (QRS duration ≤120 ms), additional R', notching of the R or S wave, or more than two R' in two contiguous leads N/A for analyzed subgroup 36 All‐cause mortality, sudden cardiac death, including 3 appropriate ICD discharge, nonsudden cardiac death Presence of J wave or fQRS in the inferior leads predicts SCD in DCM and ICM patients. fQRS might be an independent predictor for SCD in patients with CHF
Brenyo et al. USA 2012 Retrospective cohort History of prior MI 1 month or more. LVEF ≤30% N/A 1,040 97 N/A RSR' patterns (QRS duration ≤120 ms), additional R', notching of the R or S wave, or more than two R' in two contiguous leads 339 20 SCD, combined appropriate ICD shock or SCD, all‐cause mortality fQRS predicts SCD or ICD shock for ventricular arrhythmia in patients with ischemic cardiovascular disease and depressed LV function. Inferior fQRS is strongly predictive of all‐cause mortality and SCD in LBBB patients.
Ozcan et al. Turkey 2013 Retrospective cohort Hospitalized for decompensated HF due to ischemic or nonischemic DCM Bundle branch block, WPW syndrome, Brugada syndrome, or QRSd>120 ms, suspected subclinical myocardial involvement or presence of significant valvular heart disease or permanent pacemaker 227 68.7 64.26 RSR' patterns (QRS duration ≤120 ms), additional R', notching of the R or S wave, or more than two R' in two contiguous leads 114 44.76 ± 16.92 Cardiovascular mortality, sudden cardiac death, rehospitalization for HF Narrow fQRS predicts cardiovascular mortality in patients hospitalized for decompensated HF of both ischemic and nonischemic causes.
Ozcan et al. Turkey 2014 Cross‐sectional study LV systolic failure in whom ICD had been implanted for primary prophylaxis. ICD for secondary prevention 215 72.5 58.2 ± 11.6 RSR' patterns (QRS duration ≤120 ms), additional R', notching of the R or S wave, or more than two R' in two contiguous leads 123 23.5 ± 12.1 All‐cause mortality, appropriate ICD shock, combined endpoints of appropriate ICD shock and all‐cause mortality fQRS predicts arrhythmic events and appropriate ICD shocks, but not all‐cause mortality in patients with systolic heart failure undergoing ICD implantation for primary prophylaxis
Vanderberk et al. Belgium 2017 Retrospective cohort Indicated for an ICD in primary prevention of SCD in ICM and non‐ICM N/A 407 84.3 60.6 ± 11.9

(1) QRS duration ≤120 ms: RSR′ pattern, ≥1 R prime or notching of R or S wave

(2) QRS duration >120 ms: RSR’ patterns with N2 R waves (R′) or N2 notches in the R wave, or N2 notches in the downstroke or upstroke of the S wave

 190 50 ± 40 All‐cause mortality, first appropriate ICD shock Inferior fQRS was a predictor of early arrhythmia. Anterior fQRS was related to mortality
Igarashi et al. Japan 2017 Retrospective cohort HF patients with NICM who received CRT N/A 137 67.2 63.8 ± 15.0

(1) QRS duration ≤120 ms: RSR' patterns, additional R', notching of the R or S wave, or more than two R' in two contiguous leads

(2) QRS duration >120 ms: QRS, >2 notches in the R, or S waves in two contiguous leads

 67 18 Composite of SCD or Vas including appropriate ICD or CRTD therapies fQRS‐post is independently associated with SCD or ventricular arrhythmic events.
Open in a new tab

3.2. Quality assessment of included studies

We evaluated all included studies by using the Newcastle–Ottawa scale (zero‐to‐nine). The higher scores reflect higher study quality. Three domains were considered; selection, comparability, and ascertainment of outcomes. Included studies’ score ranged from 7 to 8, indicating their high quality. Intrastudy risks of bias, including population characteristics, outcome definitions and assessment, follow‐up duration, loss during follow‐up, and identified limitations, were individually assessed. No intrastudy risk of bias was identified (Table S1).

3.3. Meta‐analysis results

Ten studies were included into the meta‐analysis. Nine of 10 studies showed increased risk of all‐cause mortality in patient with baseline fQRS. However, only one study (Cheema et al., 2010) showed non‐significant negative association. Four cohorts from three studies showed significantly increased risk of all‐cause mortality (Ozcan et al., 2013; Pei et al., 2012; Vandenberk et al., 2017). According to the overall analysis, baseline fQRS was significantly associated with the primary outcome of all‐cause mortality (risk ratio [RR] = 1.63, 95% CI 1.22–2.19). Baseline fQRS was also significantly associated with secondary outcome of MAE (RR = 1.74, 95% CI 1.09–2.80). The statistical heterogeneity was substantial (I 2 = 73%) for primary outcome and high (I 2 = 89%) for secondary outcome. No publication bias was detected by Egger test and funnel plot. To validate the result, we conducted a sensitivity analysis by omitting one study at a time. The results were not significantly different from the main results.

3.4. Subgroups analyses

3.4.1. Ejection fraction

Subgroup analysis based on EF was performed to compare between HFrEF patient with EF < 35% and nonspecific EF (EF ≤ 40%). Baseline fQRS significantly increased all‐cause mortality and MAE (RR = 1.65 and 1.41, 95% CI 1.20–2.26 and 1.02–1.95, respectively) among patients with EF < 35%. Among HFrEF patients with nonspecific EF, baseline fQRS did not significantly increase all‐cause mortality (RR 1.37, 95% CI 0.58–3.22), but significantly increased MAE (RR 7.63, 95% CI 5.15–11.29).

3.4.2. ICD status

In subgroup of patients who had not received ICD placement, baseline fQRS significantly increased all‐cause mortality more than in patients who had received ICD placement (RR = 2.46 and 1.36, 95% CI 1.56–3.89 and 1.08–1.71, respectively). Baseline fQRS increased MAE significantly in subgroup of patients who had not received ICD, but insignificantly in subgroup of patients who had received ICD (RR = 1.60 and 1.57, 95% CI 1.14–2.25 and 0.90–2.74, respectively).

3.4.3. Ethnicities

Our analysis also indicated that baseline fQRS increased all‐cause mortality in both Asian and Caucasian ethnicities (RR = 2.17 and 1.45, 95% CI 1.33–3.55 and 1.05–1.99, respectively). Baseline fQRS also increased MAE in Asian cohort (RR = 1.50, 95% CI 1.05–2.13). Nonetheless, in Caucasian cohort, baseline fQRS did not significantly increase MAE (RR = 1.85, 95% CI 0.97–3.52).

4. DISCUSSION

This is the first meta‐analysis to investigate the association between fQRS and prognostic outcomes in HFrEF patients. From included studies, most studies showed that fQRS was associated with increased all‐cause mortality and increased MAE (Ahn, Kim, Joung, Lee, & Kim, 2013; Brenyo et al., 2012; Das et al., 2010; Forleo et al., 2011; Igarashi et al., 2017; Ozcan et al., 2013, 2014 ; Pei et al., 2012; Sha et al., 2011; Vandenberk et al., 2017), whereas one study revealed conflicting results (Cheema et al., 2010). The meta‐analysis demonstrated that baseline fQRS was associated with increased all‐cause mortality in HFrEF patients up to 1.63‐fold. We also found that fQRS increased MAE up to 1.74‐fold. The result suggested that fQRS could potentially be integrated as a risk stratification tool in such patients to aid clinical decision.

In this meta‐analysis, we analyze fQRS regardless of its location. Pei et al. (2012), Vandenberk et al. (2017), and Brenyo et al. (2012), however, suggested particularly strong associations between fQRS in inferior lead and arrhythmic events or SCD. Pei et al. suggested that fQRS in inferior leads could be an independent predictor of SCD in CHF patients with ICM or DCM (Pei et al., 2012). MADIT II study, which included patients with history of myocardial infarction within 1 month whose EF ≤ 30%, suggested a particularly strong association between inferior fQRS and SCD or appropriate ICD therapy (Brenyo et al., 2012). Vandenberg et al. also reported a strong association between inferior fQRS and appropriate ICD therapy after implantation in patients receiving ICD for primary prevention of SCD (Vandenberk et al., 2017). Nonetheless, there were not enough data to conclude related clinical outcomes regarding specific location of fQRS. Thus, large‐scale study is needed to warrant impact of fQRS in specific territory.

It is apparent that the association of fQRS and all‐cause mortality was more pronounced in patients without ICD (2.46‐fold when compared to 1.36‐fold in patients with ICD). This could be accountable from beneficial effect of ICD to the patients who are susceptible to MAE resulting in dramatically decreased mortality.

The explanation of the pathophysiology of fQRS associated with major arrhythmic events and all‐cause mortality is still unclear. Recently, fQRS has been reported more in its prognostic effects of poor clinical outcomes in several cardiac conditions (Bonakdar et al., 2016; Das et al., 2010; Rattanawong et al., 2017; Xu et al., 2015). Suggested mechanism of fQRS is explained by electrical dyssynchrony of interventricular conduction (Sinha et al., 2016), which could result from a myocardial scar. Baseline fQRS was reported as a sensitive and specific tool to detect myocardial scars with sensitivity and specificity up to 85.6% and 89%, respectively (Das, Khan, Jacob, Kumar, & Mahenthiran, 2006; Sadeghi, Dabbagh, Tayyebi, Zakavi, & Ayati, 2016).

Several prognostic models or scoring systems have been introduced to predict the outcomes from clinical data in HF patients. Nonetheless, they faced several challenges, especially their applicability and generalizability to HF patients in real clinical settings (Alba et al., 2013). The Seattle Heart Failure Model (SHFM), which was proposed in 2006, was developed based on a database of 1,125 patients in the PRAISE clinical trial (Levy et al., 2006). The recruited patients in the trial were limited to patients in the United States and Canada with ejection <30% with the New York Heart Association classification functional class IIIB to IV heart failure thus external validity of the study was relatively limited. Published in 2012, the Meta‐analysis Global Group in Chronic Heart Failure (MAGGIC) from 39,372 patients in 30 studies proposed the score based on 13 independent predictors of mortality in HF patients (Pocock et al., 2013). These predictors, by the order of predictive strength, included age, lower EF, the New York Heart Association classification, serum creatinine, diabetic status, not‐prescribed beta‐blocker, lower systolic BP, lower body mass, time since diagnosis, current smoker, chronic obstructive pulmonary disease, male gender, and having not being prescribed with ACE‐inhibitors or angiotensin‐receptor blockers (Pocock et al., 2013). Several other models were developed to predict outcomes in patients with acute decompensated heart failure (Passantino, Monitillo, Iacoviello, & Scrutinio, 2015) or chronic heart failure—yet, none have become standard in clinical practice. This stressed the complexity in predicting outcome and the necessity to develop practical and widely applicable tools, and surface ECG clues could potentially be integrated into those prognostic predictors.

5. LIMITATIONS

There are a few limitations in our study. First, as an observational nature of cohort studies, we did not demonstrate causal relationship between fQRS and poor prognostic outcome. Second, there is slight heterogeneity from variation among subjects included in each study and differences in definitions of MAE or cardiac events in each study. Third, we analyzed fQRS regardless of its location, since there was not enough information to identify specific location. Fourth, the indications of ICD implantation have often been revised in the past decades, hence, may affect the outcomes among recruited studies that included implanted ICD patients.

6. CONCLUSION

Our study suggests that fQRS is significantly associated with increased all‐cause mortality and MAE in HFrEF patients. Hence, fQRS could be considered as a potential element in risk stratification of HF patients. Since HFrEF patients are relatively heterogeneous, further studies would be needed to validate its role in risk stratifying HFrEF patients in specific subsets of HFrEF for the best implementation in clinical settings.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest.

AUTHOR CONTRIBUTIONS

Chanavuth Kanitsoraphan involved in conception design, data acquisition, and data interpretation, and drafted the manuscript. Pattara Rattanawong involved in conception design, data interpretation, statistical analysis, and corresponding. Poemlarp Mekraksakit involved in data acquisition and data interpretation, and drafted the manuscript. Pakawat Chongsathidkiet and Tanawan Riangwiwat acquired the data. Napatt Kanjanahattakij, Wasawat Vutthikraivit, Saranapoom Klomjit, and Subhanudh Thavaraputta interpreted the data.

7.

Figure 1.

Figure 1

Open in a new tab

Search methodology and selection process

Figure 2.

Figure 2

Open in a new tab

All‐cause mortality (main result)

Figure 3.

Figure 3

Open in a new tab

Major arrhythmic events (main result)

Figure 4.

Figure 4

Open in a new tab

All‐cause mortality and major arrhythmic event (subgroup analysis by EF)

Figure 5.

Figure 5

Open in a new tab

All‐cause mortality and major arrhythmic event (subgroup analysis by ethnicities)

Figure 6.

Figure 6

Open in a new tab

All‐cause mortality and major arrhythmic event (subgroup analysis by ICD status)

Supporting information

 

Click here for additional data file. (18.8KB, docx)

ACKNOWLEDGMENT

None.

Kanitsoraphan C, Rattanawong P, Mekraksakit P, et al. Baseline fragmented QRS is associated with increased all‐cause mortality in heart failure with reduced ejection fraction: A systematic review and meta‐analysis. Ann Noninvasive Electrocardiol. 2019;24:e12597 10.1111/anec.12597

REFERENCES

  1. Ahn, M. S. , Kim, J. B. , Joung, B. , Lee, M. H. , & Kim, S. S. (2013). Prognostic implications of fragmented QRS and its relationship with delayed contrast‐enhanced cardiovascular magnetic resonance imaging in patients with non‐ischemic dilated cardiomyopathy. International Journal of Cardiology, 167(4), 1417–1422. 10.1016/j.ijcard.2012.04.064 10.1016/j.ijcard.2012.04.064 [DOI] [PubMed] [Google Scholar]
  2. Alba, A. C. , Agoritsas, T. , Jankowski, M. , Courvoisier, D. , Walter, S. D. , Guyatt, G. H. , & H. J. (2013). Risk prediction models for mortality in ambulatory patients with heart failure: A systematic review. Circulation: Heart Failure, 6(5), 881–889. 10.1161/CIRCHEARTFAILURE.112.000043 10.1161/CIRCHEARTFAILURE.112.000043 [DOI] [PubMed] [Google Scholar]
  3. Bonakdar, H. , Moladoust, H. , Kheirkhah, J. , Abbaspour, E. , Assadian Rad, M. , Salari, A. , … Shad, B. (2016). Significance of a fragmented QRS complex in patients with chronic total occlusion of coronary artery without prior myocardial infarction. Anatolian Journal of Cardiology, 16(2), 106–112. 10.5152/akd.2015.5887 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Brenyo, A. , Pietrasik, G. , Barsheshet, A. , Huang, D. T. , Polonsky, B. , McNitt, S. , & Zareba, W. (2012). QRS fragmentation and the risk of sudden cardiac death in MADIT II. Journal of Cardiovascular Electrophysiology, 23(12), 1343–1348. 10.1111/j.1540-8167.2012.02390.x 10.1111/j.1540-8167.2012.02390.x [DOI] [PubMed] [Google Scholar]
  5. Cheema, A. , Khalid, A. , Wimmer, A. , Bartone, C. , Chow, T. , Spertus, J. A. , & Chan, P. S. (2010). Fragmented QRS and mortality risk in patients with left ventricular dysfunction. Circulation: Arrhythmia and Electrophysiology, 3(4), 339–344. 10.1161/CIRCEP.110.940478 10.1161/CIRCEP.110.940478 [DOI] [PubMed] [Google Scholar]
  6. Das, M. K. , Khan, B. , Jacob, S. , Kumar, A. , & Mahenthiran, J. (2006). Significance of a fragmented QRS complex versus a Q wave in patients with coronary artery disease. Circulation, 113(21), 2495–2501. 10.1161/CIRCULATIONAHA.105.595892 [DOI] [PubMed] [Google Scholar]
  7. Das, M. K. , Maskoun, W. , Shen, C. , Michael, M. A. , Suradi, H. , Desai, M. , … Bhakta, D. (2010). Fragmented QRS on twelve‐lead electrocardiogram predicts arrhythmic events in patients with ischemic and nonischemic cardiomyopathy. Heart Rhythm: The Official Journal of the Heart Rhythm Society, 7(1), 74–80. 10.1016/j.hrthm.2009.09.065 10.1016/j.hrthm.2009.09.065 [DOI] [PubMed] [Google Scholar]
  8. Forleo, G. B. , Della Rocca, D. G. , Papavasileiou, L. P. , Panattoni, G. , Sergi, D. , Duro, L. , … Romeo, F. (2011). Predictive value of fragmented QRS in primary prevention implantable cardioverter defibrillator recipients with left ventricular dysfunction. Journla of Cardiovascular Medicine, 12(11), 779–784. 10.2459/JCM.0b013e32834ae458 10.2459/JCM.0b013e32834ae458 [DOI] [PubMed] [Google Scholar]
  9. He, J. , Ogden, L. G. , Bazzano, L. A. , Vupputuri, S. , Loria, C. , & Whelton, P. K. (2001). Risk factors for congestive heart failure in US men and women: NHANES I epidemiologic follow‐up study. Archives of Internal Medicine, 161(7), 996–1002. 10.1001/archinte.161.7.996 10.1001/archinte.161.7.996 [DOI] [PubMed] [Google Scholar]
  10. Igarashi, M. , Tada, H. , Yamasaki, H. , Kuroki, K. , Ishizu, T. , Seo, Y. , … Aonuma, K. (2017). Fragmented QRS Is a novel risk factor for ventricular arrhythmic events after receiving cardiac resynchronization therapy in nonischemic cardiomyopathy. Journal of Cardiovascular Electrophysiology, 28(3), 327–335. 10.1111/jce.13139 10.1111/jce.13139 [DOI] [PubMed] [Google Scholar]
  11. Levy, D. , Kenchaiah, S. , Larson, M. G. , Benjamin, E. J. , Kupka, M. J. , Ho, K. K. , … Vasan, R. S. (2002). Long‐term trends in the incidence of and survival with heart failure. New England Journal of Medicine, 347(18), 1397–1402. 10.1056/NEJMoa020265 10.1056/NEJMoa020265 [DOI] [PubMed] [Google Scholar]
  12. Levy, W. C. , Mozaffarian, D. , Linker, D. T. , Sutradhar, S. C. , Anker, S. D. , Cropp, A. B. , … Packer, M. (2006). The Seattle Heart Failure Model: Prediction of survival in heart failure. Circulation, 113(11), 1424–1433. 10.1161/CIRCULATIONAHA.105.584102 [DOI] [PubMed] [Google Scholar]
  13. McMurray, J. J. , Petrie, M. C. , Murdoch, D. R. , & Davie, A. P. (1998). Clinical epidemiology of heart failure: Public and private health burden. European Heart Journal, 19(Suppl. P), P9–P16. [PubMed] [Google Scholar]
  14. Ozcan, S. , Cakmak, H. A. , Ikitimur, B. , Yurtseven, E. , Stavileci, B. , Tufekcioglu, E. Y. , & Enar, R. (2013). The prognostic significance of narrow fragmented QRS on admission electrocardiogram in patients hospitalized for decompensated systolic heart failure. Clinical Cardiology, 36(9), 560–564. 10.1002/clc.22158 10.1002/clc.22158 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Ozcan, F. , Turak, O. , Canpolat, U. , Avci, S. , Tok, D. , Isleyen, A. , … Aydogdu, S. (2014). Fragmented QRS predicts the arrhythmic events in patients with heart failure undergoing ICD implantation for primary prophylaxis: More fragments more appropriate ICD shocks. Annals of Noninvasive Electrocardiology, 19(4), 351–357. 10.1111/anec.12141 10.1111/anec.12141 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Passantino, A. , Monitillo, F. , Iacoviello, M. , & Scrutinio, D. (2015). Predicting mortality in patients with acute heart failure: Role of risk scores. World Journal of Cardiology, 7(12), 902–911. 10.4330/wjc.v7.i12.902 10.4330/wjc.v7.i12.902 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Pei, J. , Li, N. , Gao, Y. , Wang, Z. , Li, X. , Zhang, Y. , … Pu, J. (2012). The J wave and fragmented QRS complexes in inferior leads associated with sudden cardiac death in patients with chronic heart failure. Europace, 14(8), 1180–1187. 10.1093/europace/eur437 10.1093/europace/eur437 [DOI] [PubMed] [Google Scholar]
  18. Pocock, S. J. , Ariti, C. A. , McMurray, J. J. , Maggioni, A. , Kober, L. , Squire, I. B. , … Doughty, R. N. (2013). Predicting survival in heart failure: A risk score based on 39 372 patients from 30 studies. European Heart Journal, 34(19), 1404–1413. 10.1093/eurheartj/ehs337 10.1093/eurheartj/ehs337 [DOI] [PubMed] [Google Scholar]
  19. Rattanawong, P. , Riangwiwat, T. , Kanitsoraphan, C. , Chongsathidkiet, P. , Kanjanahattakij, N. , Vutthikraivit, W. , … Chung, E. H. (2018). Baseline fragmented QRS increases the risk of major arrhythmic events in hypertrophic cardiomyopathy: Systematic review and meta‐analysis. Annals of Noninvasive Electrocardiology, 23(4), e12533 10.1111/anec.12533 10.1111/anec.12533 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Rattanawong, P. , Riangwiwat, T. , Prasitlumkum, N. , Limpruttidham, N. , Kanjanahattakij, N. , Chongsathidkiet, P. , … Chung, E. H. (2017). Baseline fragmented QRS increases the risk of major arrhythmic events in Brugada syndrome: Systematic review and meta‐analysis. Annals of Noninvasive Electrocardiology, 23(2), e12507 10.1111/anec.12507 10.1111/anec.12507 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Roger, V. L. , Weston, S. A. , Redfield, M. M. , Hellermann‐Homan, J. P. , Killian, J. , Yawn, B. P. , & Jacobsen, S. J. (2004). Trends in heart failure incidence and survival in a community‐based population. Journal of the American Medical Association, 292(3), 344–350. 10.1001/jama.292.3.344 10.1001/jama.292.3.344 [DOI] [PubMed] [Google Scholar]
  22. Sadeghi, R. , Dabbagh, V. R. , Tayyebi, M. , Zakavi, S. R. , & Ayati, N. (2016). Diagnostic value of fragmented QRS complex in myocardial scar detection: Systematic review and meta‐analysis of the literature. Kardiologia Polska, 74(4), 331–337. 10.5603/KP.a2015.0193 [DOI] [PubMed] [Google Scholar]
  23. Sha, J. , Zhang, S. , Tang, M. , Chen, K. , Zhao, X. , & Wang, F. (2011). Fragmented QRS is associated with all‐cause mortality and ventricular arrhythmias in patient with idiopathic dilated cardiomyopathy. Annals of Noninvasive Electrocardiology, 16(3), 270–275. 10.1111/j.1542-474X.2011.00442.x 10.1111/j.1542-474X.2011.00442.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Sinha, S. K. , Bhagat, K. , Asif, M. , Singh, K. , Sachan, M. , Mishra, V. , … Pandey, U. (2016). Fragmented QRS as a marker of electrical dyssynchrony to predict inter‐ventricular conduction defect by subsequent echocardiographic assessment in symptomatic patients of non‐ischemic dilated cardiomyopathy. Cardiology Research, 7(4), 140–145. 10.14740/cr495w 10.14740/cr495w [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Vandenberk, B. , Robyns, T. , Goovaerts, G. , Van Soest, S. , Flore, V. , Garweg, C. , & Willems, R. (2017). Inferior and anterior QRS fragmentation have different prognostic value in patients who received an implantable defibrillator in primary prevention of sudden cardiac death. International Journal of Cardiology, 243, 223–228. 10.1016/j.ijcard.2017.02.131 10.1016/j.ijcard.2017.02.131 [DOI] [PubMed] [Google Scholar]
  26. Xu, Y. , Qiu, Z. , Xu, Y. , Bao, H. , Gao, S. , & Cheng, X. (2015). The role of fQRS in coronary artery disease. A meta‐analysis of observational studies. Herz, 40(Suppl. 1), 8–15. 10.1007/s00059-014-4155-5 10.1007/s00059-014-4155-5 [DOI] [PubMed] [Google Scholar]
  27. Yancy, C. W. , Jessup, M. , Bozkurt, B. , Butler, J. , Casey, D. E. Jr , Drazner, M. H. , … Wilkoff, B. L. (2013). 2013 ACCF/AHA guideline for the management of heart failure: A report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Journal of the American College of Cardiology, 62(16), e147–e239. 10.1016/j.jacc.2013.05.019 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

 

Click here for additional data file. (18.8KB, docx)

Articles from Annals of Noninvasive Electrocardiology : The Official Journal of the International Society for Holter and Noninvasive Electrocardiology, Inc are provided here courtesy of International Society for Holter and Noninvasive Electrocardiology, Inc. and Wiley Periodicals, Inc.

RESOURCES