Author + information
- Received June 12, 2018
- Revision received December 12, 2018
- Accepted December 12, 2018
- Published online April 15, 2019.
- Alexander F.A. Androulakis, MDa,
- Katja Zeppenfeld, MD, PhDa,∗ (, )
- Elisabeth H.M. Paiman, MDb,
- Sebastiaan R.D. Piers, MD, PhDa,
- Adrianus P. Wijnmaalen, MD, PhDa,
- Hans-Marc J. Siebelink, MD, PhDa,
- Marek Sramko, MD, PhDa,
- Hildo J. Lamb, MD, PhDb,
- Rob J. van der Geest, PhDc,
- Marta de Riva, MDa and
- Qian Tao, PhDc,∗ ()
- aDepartment of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- bDepartment of Radiology, Leiden University Medical Center, Leiden, the Netherlands
- cDivision of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
- ↵∗Address for correspondence:
Dr. Katja Zeppenfeld, and Dr. Q. Tao, Leiden University Medical Center, Department of Cardiology, Department of Radiology (C-03-Q), P.O. Box 9600, 2300 RC Leiden, the Netherlands.
Objectives This study proposed entropy as a new late gadolinium enhanced cardiac magnetic resonance–derived parameter to evaluate tissue inhomogeneity, independent of signal intensity thresholds. This study hypothesized that entropy within the scar is associated with ventricular arrhythmias (VAs), whereas entropy of the entire left ventricular (LV) myocardium is associated with mortality.
Background In patients after myocardial infarction, the heterogeneity of fibrosis determines the substrate for VA. Fibrosis in remote areas has been associated with heart failure and mortality. Late gadolinium-enhanced cardiac magnetic resonance has been used to delineate fibrosis, but available methods depend on signal intensity thresholds and results have been inconsistent.
Methods Consecutive post–myocardial infarction patients undergoing late gadolinium enhanced cardiac magnetic resonance prior to implantable cardioverter-defibrillator implantation were included. From cardiac magnetic resonance imaging, total scar size, scar gray zone, scar transmurality, and tissue entropy were derived. Patients were followed for appropriate implantable cardioverter-defibrillator therapy and mortality.
Results A total of 154 patients (age 64 ± 10 years, 84% male, LV ejection fraction 29 ± 10%, 47% acute revascularization) were included. During a median follow-up of 56 (interquartile range: 40 to 73) months, appropriate implantable cardioverter-defibrillator therapy occurred in 46 patients (30%), and 41 patients (27%) died. From multivariable analysis, higher entropy of the scar (hazard ratio [HR]: 1.9; 95% confidence interval [CI]: 1.0 to 3.5; p = 0.042) was independently associated with VA, after adjusting for multivessel disease, acute revascularization, LV ejection fraction, scar gray zone, and transmurality. Entropy of the entire LV was independently associated with mortality (HR: 3.2; 95% CI: 1.1 to 9.9; p = 0.038).
Conclusions High entropy within the scar was associated with VA and may indicate an arrhythmogenic scar. High entropy of the entire LV was associated with mortality and may reflect a fibrosis pattern associated with adverse remodeling.
- cardiac magnetic resonance
- diffuse fibrosis
- late gadolinium enhancement
- magnetic resonance imaging
- sudden death
- ventricular arrhythmia
Patients after myocardial infarction (MI) are at risk for ventricular arrhythmias (VAs) and adverse left ventricular (LV) remodeling, both related to cardiac mortality (1,2). The implantable cardioverter-defibrillator (ICD) reduces mortality in patients considered at high risk for VA (1,3). However, during long-term follow-up, only 35% of post-MI patients who have received an ICD for primary prevention of sudden cardiac death experience appropriate therapy (4).
Although several noninvasive parameters have been suggested to identify patients at risk for VA, the ICD indication for primary prevention still relies on a reduced left ventricular ejection fraction (LVEF) (5). Of interest, the vast majority of VAs that prompt ICD therapy in post-MI patients implanted for primary prevention are monomorphic ventricular tachycardias (MVT), suggesting that MVT contributes significantly to arrhythmogenic death in patients without an ICD (6). MVT in post-MI patients are typically due to scar-related re-entry facilitated by areas of slow conduction. Histological hallmarks of the arrhythmogenic substrate are inhomogeneous areas within the scar, consisting of surviving myocyte bundles imbedded and interspersed by fibrous tissue (7).
Late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (CMR) is the current gold standard to visualize scarred myocardium (8). Intermediate signal intensity (SI) values are assumed to indicate a mixture of fibrotic and viable tissue, often referred to as gray zone (GZ). GZ delineation depends on particular SI thresholds from image analysis methods (9). These methods rely on operator-defined areas with maximum SI, areas with remote myocardium, or a combination of both as a reference. Prior studies have evaluated the association between the extent of the GZ and spontaneous and inducible VA, but with conflicting results (10–18). To quantify the tissue inhomogeneity while avoiding the inconsistency of zone definition, we propose a new LGE-CMR–derived metric: entropy. Entropy is a classical measure of uncertainty in information theory (19), and it measures the uncertainty of tissue composition as reflected by the uncertainty of SI. Instead of partitioning zones by threshold, the entropy is computed from all SI values in LGE-CMR.
Progressive heart failure, due to adverse LV remodeling and myocardial fibrosis in the (noninfarcted) myocardium, further contributes to cardiac mortality. Histological studies demonstrated a higher amount of fibrous tissue in noninfarcted myocardium of hearts from patients with end-stage heart failure with an ischemic cause compared with control hearts (obtained from autopsies in patients who died of a noncardiovascular cause) (20–23). Remote fibrosis may be quantified as increased extracellular volume fraction using T1 mapping, which has been associated with mortality independent from LVEF and MI size (2,24). However, T1 mapping is usually restricted to a limited number of pre-selected slices (2,24,25), and it is not a direct measure of tissue inhomogeneity.
In this work, we propose a new LGE-CMR–derived method to quantify tissue inhomogeneity by the entropy of the SI values and hypothesize the following: 1) entropy within the scar is a marker for inhomogeneous scar composition and therefore might be associated with MVT; and 2) entropy of the entire LV is a marker for overall inhomogeneous fibrosis in the LV and therefore might be associated with adverse remodeling and mortality.
Data of consecutive patients with prior MI who underwent LGE-CMR before ICD implantation for primary or secondary prevention between 2003 and 2012 were collected. Patients who underwent surgical LV reconstruction within 1 year after LGE-CMR and patients in whom the LGE-CMR quality was poor were excluded (Online Figure 1). The remaining patients constituted the final study population. The diagnosis of MI was based on the presence of subendocardial or transmural LGE areas in the perfusion territory of a significantly stenotic coronary artery (>70% stenosis on coronary angiogram).
Patient medical records were reviewed for baseline clinical characteristics. At the day of ICD implantation, serum creatinine was retrieved, and renal failure was defined as creatinine blood level ≥1.4 mg/dl. In addition, details on prior ischemic events, acute reperfusion therapy during the first MI (within 24 h from onset of symptoms), and elective revascularization strategies were collected. Patients with a single MI who underwent acute reperfusion therapy were categorized as “acute revascularized” patients. Multivessel disease was defined as a significant stenosis in ≥2 coronary arteries. Data on prior VA episodes were collected. For patients who had undergone ventricular tachycardia (VT)-ablation, all procedural reports were reviewed to determine procedural success.
The Dutch Central Committee on Human-Related Research permits use of anonymous data without prior approval of an institutional review board, if the data are obtained for patient care and if the data do not contain identifiers that could be traced back to the individual patient. All data used for this study were acquired based on clinical care; any identifying information was removed from the data.
All images were acquired by a 1.5-T Gyroscan magnetic resonance imaging scanner (Online Methods in the Online Appendix). Data analysis was performed as described earlier to evaluate the LVEF, LV mass, and myocardial scar (Online Methods) (10). The scar was automatically identified as myocardium with SI >35% of the maximum SI, the scar GZ as myocardium with SI >35% but <50% of the maximum SI, and scar core as myocardium with a SI ≥50% of the maximum SI (Figure 1B). Scar transmurality (ST) was calculated as the percentage of scar from the total LV myocardial wall in the radial direction (Figure 1C). The median value of ST (STmedian) was used as a measure of the overall ST for each patient. The STmedian was divided into 4 categories: 1) 0% < STmedian ≤25%; 2) 26% < STmedian ≤50%; 3) 51 < STmedian ≤75%; and 4) 76% < STmedian ≤100%.
Tissue inhomogeneity was quantified by the entropy of SI values within the tissue.
Introducing entropy to quantify tissue inhomogeneity is based on the assumption that areas with varying SI values in LGE represent tissue with different composition. The SI was normalized according to a predefined range between 0 and 1,024 for each patient. The scar and LV-entropy were automatically calculated (Matlab environment; MathWorks, Natick, Massachusetts) using the formula for irregularity as proposed by Shannon, allowing for an entropy range between 0 and 10 (19) (Online Methods), with 0 being a complete homogeneous distribution of SI values (consisting of only a single SI value) and 10 being the most inhomogeneous distribution of SI equally scattering in the SI range. The investigator who calculated entropy was blinded for patient outcome. Subsequently, the tissue entropy was quantified for both the scar region and the entire LV myocardium (Figure 2, Online Appendix).
ICD implantation and programming
Patients received an ICD or a cardiac resynchronization therapy defibrillator for primary or secondary prevention according to the guidelines of the European Society of Cardiology that were valid at the time of implantation (26,27). ICD were typically programmed to include 3 zones: monitor zone/VT1 zone (150 to 188 beats/min; no therapy/antitachycardia pacing [ATP] if indicated), VT2 zone (188 to 210 beats/min; ATP and shock), and ventricular fibrillation (VF) zone (>210 beats/min; if available ATP during charging, and shock). In case of secondary prevention, programming was adapted according to the clinical VT.
Patients were followed in the outpatient clinic 2 months after ICD implantation and every 3 to 6 months thereafter. Follow-up visits included clinical evaluation, device interrogation, and a 12-lead electrocardiogram. All episodes prompting ICD therapy were reviewed by an experienced physician for exclusion of inappropriate therapies and categorization of the VA episodes in MVT or VF. Appropriate device therapy was defined as any ATP or ICD shock delivered for an MVT or VF. MVT on ICD was defined as a VT with a morphologically stable far-field electrogram and a beat-to-beat cycle length variation ≤30 ms. VF/polymorphic VT was defined as any VA with beat-to-beat variations in both far-field electrogram morphology and cycle length. VA was defined as sustained when lasting >30 s or when treated with ATP or shock. All ICD recordings were analyzed by an experienced observer. In case of death, the medical records concerning cause of death were obtained. In the event of late LV reconstruction (>1 year post-ICD implantation), patient follow-up was censored at the operation date. Medical records were reviewed to assess cardiac/noncardiac mortality.
Continuous variables are presented as mean ± SD, and categorical data are summarized as frequencies and percentages. Differences in baseline characteristics between patients were analyzed using Student’s t-test or Fisher exact test, as appropriate.
Univariable and multivariable Cox proportional hazards regression models were constructed to study the relation between scar features and the 2 types of endpoints, namely, mortality and appropriate ICD therapy. Hazard ratios (HRs) were obtained after adjustment for pre-determined potential confounders based on clinical relevance (for appropriate therapy: multivessel disease, acute revascularization, LVEF, GZ, and STmedian; and for mortality: age, renal failure, New York Heart Association [NYHA] functional class >II, multivessel disease, prior coronary artery bypass graft, LVEF, scar size, and scar entropy). HR with 95% confidence intervals (CIs) are reported. All tests were 2-sided, and p < 0.05 was considered statistically significant. Spline curves were fitted for the univariable and multivariable associations between scar entropy and the HR (log scale) of ICD therapy and the univariable and multivariable associations between entropy of the entire LV and the HR (log scale) of mortality. Using Kaplan-Meier survival analyses, the cumulative incidence of appropriate ICD therapy during follow-up was compared between patients with entropy within the scar above versus below the median value. The cumulative mortality during follow-up was compared between patients with entropy in the entire LV above versus below the median value.
During the study period, a total of 188 post-MI patients underwent LGE-CMR prior to ICD implantation. Twenty patients who underwent early LV reconstruction and 14 in whom only a poor quality CMR was available for analysis were excluded. The remaining 154 patients (age 64 ± 10 years, 84% male) constituted the study population. In 121 patients (79%), an ICD was implanted for primary prevention and in 33 patients (21%) for secondary prevention. In 77 of 121 patients receiving the ICD for primary prevention, the indication was established based on the 2003 European Society of Cardiology guidelines update (Class IIa recommendation if LVEF <30%) (26). The remaining 44 patients received the ICD according to the 2008 European Society of Cardiology guidelines (Class Ia recommendation if LVEF ≤35% and NYHA functional class ≥II despite optimal medical therapy) (27). Of the 33 patients who had an ICD implanted for secondary prevention, 17 had undergone VT ablation prior to ICD implantation. Of those, 6 were rendered noninducible for any VT after ablation. Three patients underwent VT-ablation prior to LGE-CMR. Baseline characteristics are shown in Table 1.
The LVEF of the study population was 29 ± 10%; the total infarct size was 47 ± 26 g; and the infarct GZ size was 19 ± 9 g. The most prevalent STmedian was 50% to 75%, which was observed in 66 patients (43%). The entropy was 7.8 ± 0.5 for the total infarct scar and 8.0 ± 0.7 for the entire LV. Detailed CMR data are shown in Table 2.
During a median follow-up of 56 (interquartile range [IQR]: 40 to 73) months, 46 patients (30%) received at least 1 appropriate ICD therapy (44 of 46 for MVT) and 41 patients (27%) died, among which 22 deaths (50%) were due to a cardiac cause. Seven patients (5%) were lost to follow-up after a median follow-up period of 42 months (IQR: 39 to 46 months).
Appropriate ICD therapy
Patients who received any appropriate ICD therapy during follow-up had less frequently undergone acute revascularization during the first MI (33% vs. 53%; p = 0.022) and had more often a secondary prevention indication (35% vs. 16%; p = 0.008). No other differences in baseline clinical characteristics were observed between patients with or without appropriate ICD therapies (Table 1).
LVEF, LV size, total infarct size, and GZ size were comparable between patients with or without appropriate therapies. The STmedian of 50% to 75% tended to be more prevalent in those who received appropriate therapy (54% vs. 38%; p = 0.06). In addition, patients receiving appropriate ICD therapy tended to have a higher entropy within the scar than did patients without appropriate therapy (7.94 ± 0.5 vs. 7.77 ± 0.6; p = 0.07) (Table 2).
Predictors of appropriate ICD therapy
Clinical variables associated with appropriate ICD therapy were presence of multivessel disease (HR: 2.1; 95% CI: 1.0 to 4.3; p = 0.036), acute revascularization during the first infarction (HR: 0.5; 95% CI: 0.3 to 0.9; p = 0.016), and VA prior to ICD implantation (HR: 2.6; 95% CI: 1.4 to 4.8; p = 0.002).
A higher entropy within the scar was the only CMR-derived parameter associated with appropriate ICD therapy (HR: 1.9 per unit entropy; 95% CI: 1.1 to 3.3; p = 0.029). The STmedian of 50% to 75% showed a trend (HR: 1.8; 95% CI: 1.0 to 3.1; p = 0.055). On multivariable analysis, the entropy within the scar remained the only CMR-derived parameter associated with appropriate ICD therapy (HR: 1.9 per unit entropy; 95% CI: 1.0 to 3.5; p = 0.042), independent of LVEF, acute revascularization, multivessel disease, GZ size, and the STmedian of 50% to 75% (Table 3, Figure 2B3). Of note, univariable and multivariable association between entropy and ICD therapy is approximately linear (Online Figure 2).
Patients who died during follow-up were of similar age at ICD implantation; more frequently had diabetes (34% vs. 16%; p = 0.014), renal failure (37% vs. 12%; p < 0.001), multivessel disease (85% vs. 59%; p = 0.001); and had a higher NYHA functional class (Online Table 1). In addition, these patients had a lower CMR-derived LVEF (24% vs. 31%; p < 0.001), a larger total scar (62 g vs. 42 g; p < 0.001), and a larger GZ (23 g vs. 17 g; p = 0.001). Of importance, the entropy within the scar and within the entire LV was significantly higher in deceased patients (8.05 ± 0.4 vs. 7.74 ± 0.6; p = 0.001 and 8.47 ± 0.6 vs. 7.96 ± 0.7; p < 0.001, respectively) (Table 4).
Predictors of mortality
In univariate Cox regression analysis, diabetes (HR: 2.5; 95% CI: 1.3 to 4.7; p = 0.007), renal failure (HR: 2.9; 95% CI: 1.6 to 5.6; p = 0.001), a higher NYHA functional class (>II: HR: 2.9; 95% CI: 1.5 to 5.4; p = 0.001), QRS >120 ms (HR: 2.4; 95% CI: 1.3 to 4.5; p = 0.006), presence of multivessel disease (HR: 4.6; 95% CI: 1.6 to 12.9; p = 0.004), prior coronary artery bypass graft (HR: 1.9; 95% CI: 1.0 to 3.6; p = 0.041), LVEF (HR: 0.5; 95% CI: 0.4 to 0.7; p < 0.001), LV mass (HR: 1.1; 95% CI: 1.0 to 1.2; p = 0.042), scar size (HR: 1.2; 95% CI: 1.1 to 1.3; p < 0.001), GZ size (HR: 1.5; 95% CI: 1.2 to 2.0; p = 0.002), entropy within scar (HR: 2.6; 95% CI: 1.4 to 4.9; p = 0.003), and LV entropy (HR: 2.4; 95% CI: 1.5 to 3.9; p < 0.001) were associated with mortality (Figure 2B3). A history of acute revascularization during the first MI was associated with lower mortality (HR: 0.3; 95% CI: 0.2 to 0.7; p = 0.004). In multivariable analysis, the entropy in the entire LV (HR: 3.2 per unit entropy; 95% CI: 1. 1 to 9.9; p = 0.038) and renal failure (HR: 2.4: 95% CI: 1.1 to 5.1; p = 0.032) remained independently associated with mortality (Table 5). Furthermore, the observed univariable and multivariable association between entropy in the entire LV and mortality is approximately linear (Online Figure 2).
In the present study, we have introduced entropy as a new parameter to assess tissue inhomogeneity from LGE-CMR, for both the scar and the entire LV myocardium. We found that in post-MI patients, the entropy within the scar was the only CMR-derived parameter associated with VA and that the entropy in the entire LV was independently associated with mortality.
Risk assessment for VA after MI
Current guidelines on ICD implantation for primary prevention in post-MI patients are based on a reduced LVEF (5). However, the majority of patients implanted for primary prevention do not benefit from the ICD (4). Multiple factors play a role in risk stratification, and prevention of death in this population has been shown to be a complex issue. The risk of sudden death in patients after MI has a biphasic temporal course. In the early phase after MI, VAs are common but ICD implantation does not improve patient survival because of the competing risks (28). Patients who have survived this early phase remain at risk, however, for scar-related re-entry MVT (29). Identification of patients at risk for late re-entrant VT is of upmost importance because these patients benefit from prophylactic ICD implantation. Accordingly, noninvasive parameters to predict the occurrence of VA are important. Of interest, the vast majority of reported arrhythmia episodes that prompt appropriate ICD therapy are MVT (6). In post-MI patients, MVT are typically due to scar-related re-entry dependent on areas of inhomogeneous tissue consisting of surviving myocytes imbedded and interspersed by fibrous tissue (7). Therefore, noninvasive identification of inhomogeneous scars is a logical and promising parameter for risk stratification.
CMR parameters and VA
The most extensively evaluated surrogate for scar inhomogeneity is the scar GZ (Online Table 2) (10–18). Delineation of the GZ requires predefined SI thresholds. Different methods have been applied to determine these SI thresholds, which either use the areas with maximum SI, areas with normal remote myocardium, or a combination of both (10–18). While some studies reported an association between the GZ size and occurrence of VA (10,11,13–15), others, including the current study, could not confirm such an association (15,17,18). This inconsistency remained, even when the same method for defining the GZ was applied (15–18) (Online Table 2).
Of importance, catheter mapping studies, using real-time integration of LGE-CMR–derived scar characteristics, demonstrated that only 29% to 55% of all VT-related sites in post-MI patients were located within the GZ area, suggesting that this parameter may not be sufficient to identify the arrhythmogenic substrate (30,31). Of interest, the mean scar transmurality at the VT-related sites was reported to be 75 ± 22% and 73 ± 21%, respectively (30,32), which is in line with our observation that a STmedian of 50% to 75% tended to be associated with VA.
Entropy of the infarct scar
The present study proposes a novel CMR-derived parameter that directly assesses the tissue inhomogeneity by entropy, which describes uncertainty in LGE signal (19). We assume that with the current LGE resolution and quality, the signal intensity distribution in the scar and myocardium, as quantified by the entropy, can differentiate the tissue composition to a certain extent that is clinically relevant. In contrast to GZ quantification, computation of entropy does not require subdividing the scar region into 2 zones, therefore avoiding thresholds. In addition, the entropy is richer in information than the GZ size is because entropy utilizes the entire SI distribution, potentially capturing subtle variations in tissue composition beyond the size of a particular zone.
In our study, we found a statistically significant association between entropy within the scar and occurrence of VA. In multivariable analysis, the entropy within the scar remained the only CMR-derived parameter associated with VA. As such, entropy calculated from 2-dimensional LGE MR imaging seems to be a promising parameter to indicate the presence of an arrhythmogenic scar.
Entropy of the entire LV
Patients with prior MI are also at risk for heart failure due to progressive adverse remodeling not only within the scar area but also within remote noninfarcted myocardium. In patients with end-stage heart failure due to coronary artery disease, explanted/post-mortem hearts showed an increased amount of fibrous tissue in the noninfarcted myocardium (20–23). T1 mapping has been suggested to noninvasively determine the extracellular volume fraction as a surrogate for diffuse fibrosis in noninfarcted myocardium (25,33). Increased ECV quantified by T1 mapping has been associated with progressive heart failure, mortality, or combined endpoint (2,24,33) However, in current clinical practice, T1 mapping is usually restricted to a limited number of pre-selected slices, and its precision is affected by motion artifacts (2,24,25). In contrast, whole-heart LGE scan is more accessible. It requires less acquisition time than T1/extracellular volume fraction mapping, with higher spatial resolution and precision (i.e., free of T1-fitting error). The current clinical resolution of LGE scans is not yet sufficient to detect homogeneous distributed (and potentially reversible) interstitial fibrosis on a microscopic level (34); nevertheless, patchy fibrosis and thicker strands of fibrosis, typical for (irreversible) replacement fibrosis, may well be detectable as inhomogeneous tissue by LV entropy. Hence, in the current study, entropy of the entire LV has been chosen as measure of global tissue inhomogeneity, and was associated with mortality independent of age, renal failure, multivessel disease, prior coronary artery bypass graft, LVEF, and entropy in the scar. The association between LV entropy and mortality may reflect adverse and irreversible, inhomogeneous remodeling of the post-infarct LV.
This study has a relatively limited sample size and a retrospective single-center study design. All LGE images were acquired with the same MR equipment and protocol. The generalizability of the metric to multicenter, multivendor data requires further investigation. Our study includes 17 patients who underwent VT ablation prior to ICD implantation, which can influence consecutive VT events. Three patients underwent VT ablation prior to LGE-CMR. However, the number of patients with complete procedural success defined as noninducibility after VT-ablation was low. In the present study, the LGE-CMR images were acquired as 2-dimensional short-axis images by a 1.5-T MR imaging scanner, and our results are only applicable to 2-dimensional LGE techniques with the same MR protocol. Furthermore, our results need to be validated in a prospective group. Recently, more advanced MR imaging protocols with a higher resolution have been developed, which potentially yield entropy measures of higher sensitivity in future studies. Studies to validate the histological basis of entropy are warranted.
The entropy, a newly proposed LGE-CMR–derived parameter, can be used to quantify tissue inhomogeneity. In post-MI patients, the entropy within the scar was the only LGE-CMR–derived parameter independently associated with VA and therefore seems to be a promising marker for an inhomogeneous and arrhythmogenic scar. Entropy in the entire LV was independently associated with mortality, indicating the presence of adverse and perhaps irreversible remodeling.
COMPETENCY IN MEDICAL KNOWLEDGE: Entropy as a newly proposed LGE-CMR–based measure for tissue composition has potentially important clinical implications. The association between high entropy within the scar and VA in post-MI patients suggests that scar entropy may serve as an additional parameter for risk stratification. The association of a higher entropy of the entire LV with mortality suggests that this LGE-CMR–derived parameter for tissue composition may be used to monitor disease progression, and perhaps for early identification of patients with adverse LV remodeling and unfavorable outcome.
TRANSLATIONAL OUTLOOK: Further studies are needed to validate the association between entropy and VA/mortality, including the application of advanced high-resolution 3-dimensional LGE-CMR image acquisition.
This study was funded by NOW (Nederlandse Organisatie voor Wetenschappelijk Onderzoek–Dutch organisation for Scientific Research) Domain Applied and Engineering Sciences grant no. 12899. The Department of Cardiology (Leiden University Medical Center) has received unrestricted research grants from Edwards Lifesciences, Medtronic, Biotronik, and Boston Scientific. Dr. Zeppenfeld has received funding from a research grant awarded to the Department of Cardiology (Leiden University Medical Center) from Biosense Webster. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
All authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the JACC: Clinical Electrophysiology author instructions page.
- Abbreviations and Acronyms
- antitachycardia pacing
- confidence interval
- cardiac magnetic resonance imaging
- gray zone
- hazard ratio
- implantable cardioverter-defibrillator
- interquartile range
- late gadolinium enhancement
- left ventricle/ventricular
- left ventricular ejection fraction
- myocardial infarction
- monomorphic ventricular tachycardia
- New York Heart Association
- signal intensity
- scar transmurality
- median value of scar transmurality
- ventricular arrhythmia
- ventricular fibrillation
- ventricular tachycardia
- Received June 12, 2018.
- Revision received December 12, 2018.
- Accepted December 12, 2018.
- 2019 American College of Cardiology Foundation
- Schelbert E.B.,
- Piehler K.M.,
- Zareba K.M.,
- et al.
- Moss A.J.,
- Greenberg H.,
- Case R.B.,
- et al.,
- for the MADIT-II Research Group
- Priori S.G.,
- Blomstrom-Lundqvist C.,
- Mazzanti A.,
- et al.
- Daubert J.P.,
- Zareba W.,
- Hall W.J.,
- et al.,
- for the MADIT II Study Investigators
- de Bakker J.M.,
- van Capelle F.J.,
- Janse M.J.,
- et al.
- Kim R.J.,
- Fieno D.S.,
- Parrish T.B.,
- et al.
- Roes S.D.,
- Borleffs C.J.,
- van der Geest R.J.,
- et al.
- Schmidt A.,
- Azevedo C.F.,
- Cheng A.,
- et al.
- Wu K.C.,
- Gerstenblith G.,
- Guallar E.,
- et al.
- Robbers L.F.,
- Delewi R.,
- Nijveldt R.,
- et al.
- Rayatzadeh H.,
- Tan A.,
- Chan R.H.,
- et al.
- Gao P.,
- Yee R.,
- Gula L.,
- et al.
- de Haan S.,
- Meijers T.A.,
- Knaapen P.,
- Beek A.M.,
- van Rossum A.C.,
- Allaart C.P.
- Klem I.,
- Weinsaft J.W.,
- Bahnson T.D.,
- et al.
- Beltrami C.A.,
- Finato N.,
- Rocco M.,
- et al.
- Volders P.G.,
- Willems I.E.,
- Cleutjens J.P.,
- Arends J.W.,
- Havenith M.G.,
- Daemen M.J.
- Wong T.C.,
- Piehler K.,
- Meier C.G.,
- et al.
- Kammerlander A.A.,
- Marzluf B.A.,
- Zotter-Tufaro C.,
- et al.
- Dickstein K.,
- Cohen-Solal A.,
- Filippatos G.,
- et al.
- Piers S.R.,
- Tao Q.,
- de Riva Silva M.,
- et al.
- Sasaki T.,
- Miller C.F.,
- Hansford R.,
- et al.
- Iles L.,
- Pfluger H.,
- Phrommintikul A.,
- et al.
- Schelbert E.B.,
- Hsu L.Y.,
- Anderson S.A.,
- et al.