Left Ventricular Entropy Is a Novel Predictor of Arrhythmic Events in Patients With Dilated Cardiomyopathy Receiving Defibrillators for Primary Prevention

Risk Stratification in Nonischemic Dilated Cardiomyopathy Using CMR Imaging: A Systematic Review and Meta-Analysis

Importance

Accurate risk stratification of nonischemic dilated cardiomyopathy (NIDCM) remains challenging.

Objective

To evaluate the association of cardiac magnetic resonance (CMR) imaging-derived measurements with clinical outcomes in NIDCM.

Data Sources

MEDLINE, Embase, Cochrane Library, and Web of Science Core Collection databases were systematically searched for articles from January 2005 to April 2023.

Study Selection

Prospective and retrospective nonrandomized diagnostic studies reporting on the association between CMR imaging-derived measurements and adverse clinical outcomes in NIDCM were deemed eligible.

Data Extraction and Synthesis

Prespecified items related to patient population, CMR imaging measurements, and clinical outcomes were extracted at the study level by 2 independent reviewers. Random-effects models were fitted using restricted maximum likelihood estimation and the method of Hartung, Knapp, Sidik, and Jonkman.

Main Outcomes and Measures

All-cause mortality, cardiovascular mortality, arrhythmic events, heart failure events, and major adverse cardiac events (MACE).

Results

A total of 103 studies including 29 687 patients with NIDCM were analyzed. Late gadolinium enhancement (LGE) presence and extent (per 1%) were associated with higher all-cause mortality (hazard ratio [HR], 1.81 [95% CI, 1.60-2.04]; P < .001 and HR, 1.07 [95% CI, 1.02-1.12]; P = .02, respectively), cardiovascular mortality (HR, 2.43 [95% CI, 2.13-2.78]; P < .001 and HR, 1.15 [95% CI, 1.07-1.24]; P = .01), arrhythmic events (HR, 2.69 [95% CI, 2.20-3.30]; P < .001 and HR, 1.07 [95% CI, 1.03-1.12]; P = .004) and heart failure events (HR, 1.98 [95% CI, 1.73-2.27]; P < .001 and HR, 1.06 [95% CI, 1.01-1.10]; P = .02). Left ventricular ejection fraction (LVEF) (per 1%) was not associated with all-cause mortality (HR, 0.99 [95% CI, 0.97-1.02]; P = .47), cardiovascular mortality (HR, 0.97 [95% CI, 0.94-1.00]; P = .05), or arrhythmic outcomes (HR, 0.99 [95% CI, 0.97-1.01]; P = .34). Lower risks for heart failure events (HR, 0.97 [95% CI, 0.95-0.98]; P = .002) and MACE (HR, 0.98 [95% CI, 0.96-0.99]; P < .001) were observed with higher LVEF. Higher native T1 relaxation times (per 10 ms) were associated with arrhythmic events (HR, 1.07 [95% CI, 1.01-1.14]; P = .04) and MACE (HR, 1.06 [95% CI, 1.01-1.11]; P = .03). Global longitudinal strain (GLS) (per 1%) was not associated with heart failure events (HR, 1.06 [95% CI, 0.95-1.18]; P = .15) or MACE (HR, 1.03 [95% CI, 0.94-1.14]; P = .43). Limited data precluded definitive analysis for native T1 relaxation times, GLS, and extracellular volume fraction (ECV) with respect to mortality outcomes.

Conclusion

The presence and extent of LGE were associated with various adverse clinical outcomes, whereas LVEF was not significantly associated with mortality and arrhythmic end points in NIDCM. Risk stratification using native T1 relaxation times, extracellular volume fraction, and global longitudinal strain requires further evaluation.

Cardiomyopathy and Sudden Cardiac Death: Bridging Clinical Practice with Cutting-Edge Research

Sudden cardiac death (SCD) prevention in cardiomyopathies such as hypertrophic (HCM), dilated (DCM), non-dilated left ventricular (NDLCM), and arrhythmogenic right ventricular cardiomyopathy (ARVC) remains a crucial but complex clinical challenge, especially among younger populations. Accurate risk stratification is hampered by the variability in phenotypic expression and genetic heterogeneity inherent in these conditions. This article explores the multifaceted strategies for preventing SCD across a spectrum of cardiomyopathies and emphasizes the integration of clinical evaluations, genetic insights, and advanced imaging techniques such as cardiac magnetic resonance (CMR) in assessing SCD risks. Advanced imaging, particularly CMR, not only enhances our understanding of myocardial architecture but also serves as a cornerstone for identifying at-risk patients. The integration of new research findings with current practices is essential for advancing patient care and improving survival rates among those at the highest risk of SCD. This review calls for ongoing research to refine risk stratification models and enhance the predictive accuracy of both clinical and imaging techniques in the management of cardiomyopathies.

Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias

The efficacy of an implantable cardioverter-defibrillator (ICD) in patients with a non-ischaemic cardiomyopathy for primary prevention of sudden cardiac death is increasingly debated. We developed a multimodal deep learning model for arrhythmic risk prediction that integrated late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI), electrocardiography (ECG) and clinical data. Short-axis LGE-MRI scans and 12-lead ECGs were retrospectively collected from a cohort of 289 patients prior to ICD implantation, across two tertiary hospitals. A residual variational autoencoder was developed to extract physiological features from LGE-MRI and ECG, and used as inputs for a machine learning model (DEEP RISK) to predict malignant ventricular arrhythmia onset. In the validation cohort, the multimodal DEEP RISK model predicted malignant ventricular arrhythmias with an area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval (CI) 0.71-0.96), a sensitivity of 0.98 (95% CI 0.75-1.00) and a specificity of 0.73 (95% CI 0.58-0.97). The models trained on individual modalities exhibited lower AUROC values compared to DEEP RISK [MRI branch: 0.80 (95% CI 0.65-0.94), ECG branch: 0.54 (95% CI 0.26-0.82), Clinical branch: 0.64 (95% CI 0.39-0.87)]. These results suggest that a multimodal model achieves high prognostic accuracy in predicting ventricular arrhythmias in a cohort of patients with non-ischaemic systolic heart failure, using data collected prior to ICD implantation.

The Prognostic Value of Left Ventricular Entropy From T1 Mapping in Patients With Hypertrophic Cardiomyopathy

Background

The prognostic value of left ventricular (LV) entropy in hypertrophic cardiomyopathy (HCM) is unclear.

Objectives

This study aimed to assess the prognostic value of LV entropy from T1 mapping in HCM.

Methods

A total of 748 participants with HCM, who underwent cardiovascular magnetic resonance (CMR), were consecutively enrolled. LV entropy was quantified by native T1 mapping. A competing risk analysis and a Cox proportional hazards regression analysis were performed to identify potential associations of LV entropy with sudden cardiac death (SCD) and cardiovascular death (CVD), respectively.

Results

A total of 40 patients with HCM experienced SCD, and 65 experienced CVD during a median follow-up of 43 months. Participants with increased LV entropy (≥4.06) were more likely to experience SCD and CVD (all P < 0.05) in the entire study cohort or the subgroup with low late gadolinium enhancement (LGE) extent (<15%). After adjustment for the European Society of Cardiology predictors and the presence of high LGE extent (≥15%), LV mean entropy was an independent predictor for SCD (HR: 1.03; all P < 0.05) by the multivariable competing risk analysis and CVD (HR: 1.06; 95% CI: 1.03-1.09; P < 0.001) by multivariable Cox regression analysis.

Conclusions

LV mean entropy derived from native T1 mapping, reflecting myocardial tissue heterogeneity, was an independent predictor of SCD and CVD in participants with HCM. (Cardiac Magnetic Resonance Imaging Clinical Application Registration Study; ChiCTR1900024094).

Left ventricular entropy is a novel predictor of major adverse cardiac events (MACE) in patients with coronary atherosclerosis: a multi-center study

OBJECTIVES

To investigate the incremental prognostic value of left ventricular (LV) entropy in a large multi-center population with coronary atherosclerotic heart disease (CAD).

BACKGROUND

Current risk stratification of patients with CAD is imprecise and not accurate enough.

METHODS

A total of 314 CAD patients who underwent cardiovascular magnetic resonance (CMR) late gadolinium enhancement (LGE) at two medical centers in China between October 2015 and July 2022 were included in this study. Additionally, the 193 patients under 3.0-T field also underwent CMR T1 mapping. LV entropy and extracellular volume (ECV) were calculated from the LGE image of LV myocardium, and major adverse cardiac events (MACEs) were analyzed.

RESULTS

Among 314 patients, 110 experienced MACE during a median follow-up of 13 months. The risk of MACE was significantly increased in the high entropy group (log-rank p < 0.001). Entropy maintained an independent association with MACE in a multivariable model including left ventricular ejection fraction (LVEF) and LGE (HR = 1.78; p = 0.001). In addition, the primary endpoint events prognostic value was significantly improved by adding LV entropy to the baseline multivariable model (C-statistic improvement: 0.785-0.818, Delong test: p = 0.001). Similarly, among 193 3.0-T field patients, adding LV entropy to the multivariable baseline model significantly improved the prognostic value of the model for MACE (C-statistic improvement: 0.820-0.898, Delong test: p = 0.004).

CONCLUSION

CMR-assessed LV entropy is a powerful independent predictor of MACE in patients with CAD, incremental to common clinical and CMR risk factors, including LVEF, LGE, Native T1, and ECV.

CLINICAL RELEVANCE STATEMENT

Left ventricular entropy is a powerful independent predictor of major adverse cardiac events in patients with coronary atherosclerotic heart disease, incremental to common clinical and cardiac magnetic resonance risk factors.

KEY POINTS

• Left ventricular entropy, a novel cardiac magnetic resonance parameter of myocardial heterogeneity, demonstrated a robust prognostic association with major adverse cardiac events beyond guideline-based, clinical risk markers. • Entropy can have an important role in the primary prevention of major adverse cardiac events in patients with coronary atherosclerotic heart disease. • Compared with late gadolinium enhancement, extracellular volume, and native T1, entropy could be used to more comprehensively characterize the heterogeneity of left ventricular myocardium.

Cardiac Magnetic Resonance as Risk Stratification Tool in Non-Ischemic Dilated Cardiomyopathy Referred for Implantable Cardioverter Defibrillator Therapy-State of Art and Perspectives

Non-ischemic dilated cardiomyopathy (DCM) is a disease characterized by left ventricular dilation and systolic dysfunction. Patients with DCM are at higher risk for ventricular arrhythmias and sudden cardiac death (SCD). According to current international guidelines, left ventricular ejection fraction (LVEF) ≤ 35% represents the main indication for prophylactic implantable cardioverter defibrillator (ICD) implantation in patients with DCM. However, LVEF lacks sensitivity and specificity as a risk marker for SCD. It has been seen that the majority of patients with DCM do not actually benefit from the ICD implantation and, on the contrary, that many patients at risk of SCD are not identified as they have preserved or mildly depressed LVEF. Therefore, the use of LVEF as unique decision parameter does not maximize the benefit of ICD therapy. Multiple risk factors used in combination could likely predict SCD risk better than any single risk parameter. Several predictors have been proposed including genetic variants, electric indexes, and volumetric parameters of LV. Cardiac magnetic resonance (CMR) can improve risk stratification thanks to tissue characterization sequences such as LGE sequence, parametric mapping, and feature tracking. This review evaluates the role of CMR as a risk stratification tool in DCM patients referred for ICD.

Entropy as a novel predictor of cardiovascular events in patients with left ventricular noncompaction

BACKGROUND

The risk stratification of left ventricular noncompaction (LVNC) remains ambiguous. LV entropy derived from late gadolinium enhancement (LGE) in cardiac magnetic resonance (CMR) as a novel measurement of myocardial heterogeneity may serve as the substrate of major adverse cardiovascular events (MACEs). This retrospective study aimed to investigate the value of LV entropy for predicting MACEs in LVNC patients.

METHODS

Consecutive patients who underwent CMR and met the diagnosis criteria of LVNC were included. All patients were follow-up for MACEs (cardiac death, ventricular arrhythmia requiring therapy or heart failure hospitalization), and their LV entropy derived from the distribution of pixel signal intensities in the LGE of the LV myocardium was analyzed.

RESULTS

One hundred and forty-three patients (mean age 40 years, 64.3% male) were followed for a median of 3.2 years, and forty-two (29.4%) experienced MACEs. Presenting of symptoms, left ventricular end-diastolic diameter (LVEDD), LV end-diastolic volume (LVEDV) index, LV end-systolic volume (LVESV) index, LV ejection fraction (LVEF), LGE extent, and LV entropy showed association with MACEs. LV entropy maintained independent association with MACEs (HR: 4.76, 95%CI 3.68-5.15, p < 0.001) in multivariable analysis. Entropy was also strong independent predictor of MACEs in patients with and without LGE (HR: 5.89, 95% CI4.18-7.73, p < 0.001; HR: 3.06, 95% CI:1.53-4.80, p = 0.013, respectively).

CONCLUSIONS

LV entropy can predict MACEs in LVNC patients and provide incremental prognostic value on top of LVEF and LGE. Also, LV entropy may help risk stratification in LGE-negative LVNC patients.

Cardiac magnetic resonance imaging T1 mapping and late gadolinium enhancement entropy: Prognostic value in patients with systemic sclerosis

BACKGROUND

Systemic sclerosis (SSc) affects the myocardium, thereby resulting in a poor prognosis. Late gadolinium enhancement (LGE) entropy, derived from routine cardiac magnetic resonance (CMR) LGE images, is an index that reflects the complexity of the left ventricular myocardium. The aim of this study was to investigate whether LGE entropy can serve as a prognostic factor in patients with SSc.

METHODS

Twenty-four patients with SSc, who underwent CMR-T1 mapping and LGE to identify myocardial damage, were enrolled, and LGE entropy was measured. Extracellular volume (ECV) values were calculated using the same CMR-LGE images. The endpoint was major adverse cardiac events (MACEs), comprising all-cause death, hospitalization due to heart failure, and the onset of sustained ventricular tachycardia and ventricular fibrillation. The ability to predict MACE was assessed using receiver operating characteristic (ROC) analysis, and the predictability of LGE entropy was analyzed using Kaplan-Meier analysis.

RESULTS

The ROC curve analysis demonstrated a cut-off value of 7.39 for MACE with LGE entropy and had a sensitivity and specificity of 80 % and 79 %, respectively. Patients with LGE entropy ≥7.39 had a significantly higher MACE rate than those with LGE entropy <7.39 (p = 0.010). Moreover, LGE entropy ≥7.39 was a poor prognostic factor in patients without elevated ECV values.

CONCLUSIONS

LGE entropy can be used to predict MACE and allows for further risk stratification in addition to ECV determination.

Can left ventricular entropy by cardiac magnetic resonance late gadolinium enhancement be a prognostic predictor in patients with left ventricular non-compaction?

PURPOSE

Left ventricular non-compaction (LVNC) is considered rare; however, the use of cardiac magnetic resonance (CMR) has shown that its incidence is not uncommon, and its clinical presentation remains variable, with an uncertain prognosis. Risk stratification of major adverse cardiac events (MACE) in patients with LVNC remains complex. Therefore, this study aims to determine whether tissue heterogeneity from late gadolinium enhancement-derived entropy is associated with MACE in patients with LVNC.

METHODS

This study was registered in the Clinical Trial Registry (CTR2200062045). Consecutive patients who underwent CMR imaging and were diagnosed with LVNC were followed up for MACE, which was defined by heart failure, arrhythmias, systemic embolism, and cardiac death. The patients were divided into MACE and non-MACE groups. The CMR parameters included left ventricular (LV) entropy, LV ejection fraction (LVEF), LV end-diastolic volume, LV end-systolic volume (LVESV), and LV mass (LVM).

RESULTS

Eighty-six patients (age: 45.48 ± 16.64 years; female: 62.7%; LVEF: 42.58 ± 17.20%) were followed up for a median of 18 months and experienced 30 MACE events (34.9%). The MACE group showed higher LV entropy, LVESV, and LVM and lower LVEF than the non-MACE group. LV entropy [hazard ratio (HR): 1.710, 95% confidence interval (CI): 1.078-2.714, P = 0.023] and LVEF (HR: 0.961, 95% CI: 0.936-0.988, P = 0.004) were independent predictors of MACE (P <0.050) according to the Cox regression analysis. Receiver operating characteristic curve analysis revealed that the area under the curve of LV entropy was 0.789 (95% CI: 0.687-0.869, P < 0.001), LVEF was 0.804 (95% CI: 0.699-0.878, P < 0.001), and the combined model of LV entropy and LVEF was 0.845 (95% CI: 0.751-0.914, P < 0.050).

CONCLUSION

LGE-derived LV entropy and LVEF are independent risk indicators of MACE in patients with LVNC. The combination of the two factors was more conducive to improving the prediction of MACE.

Risk of Arrhythmic Death in Patients With Nonischemic Cardiomyopathy: JACC Review Topic of the Week

Nonischemic cardiomyopathy (NICM) is common and patients are at significant risk for early mortality secondary to ventricular arrhythmias. Current guidelines recommend implantable cardioverter-defibrillator (ICD) therapy to decrease sudden cardiac death (SCD) in patients with heart failure and reduced left ventricular ejection fraction. However, in randomized clinical trials comprised solely of patients with NICM, primary prevention ICDs did not confer significant mortality benefit. Moreover, left ventricular ejection fraction has limited sensitivity and specificity for predicting SCD. Therefore, precise risk stratification algorithms are needed to define those at the highest risk of SCD. This review examines mechanisms of sudden arrhythmic death in patients with NICM, discusses the role of ICD therapy and treatment of heart failure for prevention of SCD in patients with NICM, examines the role of cardiac magnetic resonance imaging and computational modeling for SCD risk stratification, and proposes new strategies to guide future clinical trials on SCD risk assessment in patients with NICM.

Late gadolinium enhancement entropy as a new measure of myocardial tissue heterogeneity for prediction of adverse cardiac events in patients with hypertrophic cardiomyopathy

OBJECTIVES

Entropy is a new late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR)-derived parameter that is independent of signal intensity thresholds. Entropy can be used to measure myocardial tissue heterogeneity by comparing full pixel points of tissue images. This study investigated the incremental prognostic value of left ventricular (LV) entropy in patients with hypertrophic cardiomyopathy (HCM).

METHODS

This study enrolled 337 participants with HCM who underwent 3.0-T CMR. The LV entropy was obtained by calculating the probability distribution of the LV myocardial pixel signal intensities of the LGE sequence. Patients who underwent CMR imaging were followed up for endpoints. The primary endpoint was defined as readmission to the hospital owing to heart failure. The secondary endpoint was the composite of the primary endpoint, sudden cardiac death and non-cardiovascular death.

RESULTS

During the median follow-up of 24 months ± 13 (standard deviation), 43 patients who reached the primary and secondary endpoints had a higher entropy (6.20 ± 0.45, p < 0.001). The patients with increased entropy (≥ 5.587) had a higher risk of the primary and secondary endpoints, compared with HCM patients with low entropy (p < 0.001 for both). In addition, Cox analysis showed that LV entropy provided significant prognostic value for predicting both primary and secondary endpoints (HR: 1.291 and 1.273, all p < 0.001). Addition of LV entropy to the multivariable model improved model performance and risk reclassification (p < 0.05).

CONCLUSION

LV entropy assessed by CMR was an independent predictor of primary and secondary endpoints. LV entropy assessment contributes to improved risk stratification in patients with HCM.

CRITICAL RELEVANCE STATEMENT

Myocardial heterogeneity reflected by entropy the derived parameter of LGE has prognostic value for adverse events in HCM. The measurement of LV entropy helped to identify patients with HCM who were at risk for heart failure and sudden cardiac death.

KEY POINTS

• Left ventricular entropy can reflect myocardial heterogeneity in HCM patients. • Left ventricular entropy was significantly higher in HCM patients who reached endpoint events. • Left ventricular entropy helps to predict the occurrence of heart failure and death in HCM patients.

T1 Map-Based Radiomics for Prediction of Left Ventricular Reverse Remodeling in Patients With Nonischemic Dilated Cardiomyopathy

OBJECTIVE

This study aimed to develop and validate models using radiomics features on a native T1 map from cardiac magnetic resonance (CMR) to predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy (NIDCM).

MATERIALS AND METHODS

Data from 274 patients with NIDCM who underwent CMR imaging with T1 mapping at Severance Hospital between April 2012 and December 2018 were retrospectively reviewed. Radiomic features were extracted from the native T1 maps. LVRR was determined using echocardiography performed ≥ 180 days after the CMR. The radiomics score was generated using the least absolute shrinkage and selection operator logistic regression models. Clinical, clinical + late gadolinium enhancement (LGE), clinical + radiomics, and clinical + LGE + radiomics models were built using a logistic regression method to predict LVRR. For internal validation of the result, bootstrap validation with 1000 resampling iterations was performed, and the optimism-corrected area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI) was computed. Model performance was compared using AUC with the DeLong test and bootstrap.

RESULTS

Among 274 patients, 123 (44.9%) were classified as LVRR-positive and 151 (55.1%) as LVRR-negative. The optimism-corrected AUC of the radiomics model in internal validation with bootstrapping was 0.753 (95% CI, 0.698-0.813). The clinical + radiomics model revealed a higher optimism-corrected AUC than that of the clinical + LGE model (0.794 vs. 0.716; difference, 0.078 [99% CI, 0.003-0.151]). The clinical + LGE + radiomics model significantly improved the prediction of LVRR compared with the clinical + LGE model (optimism-corrected AUC of 0.811 vs. 0.716; difference, 0.095 [99% CI, 0.022-0.139]).

CONCLUSION

The radiomic characteristics extracted from a non-enhanced T1 map may improve the prediction of LVRR and offer added value over traditional LGE in patients with NIDCM. Additional external validation research is required.

Machine learning to predict left ventricular reverse remodeling by guideline-directed medical therapy by utilizing texture feature of extracellular volume fraction in patients with non-ischemic dilated cardiomyopathy

Extracellular volume fraction (ECV) by cardiac magnetic resonance (CMR) allows for the non-invasive quantification of diffuse myocardial fibrosis. Texture analysis and machine learning are now gathering attention in the medical field to exploit the ability of diagnostic imaging for various diseases. This study aimed to investigate the predictive value of texture analysis of ECV and machine learning for predicting response to guideline-directed medical therapy (GDMT) for patients with non-ischemic dilated cardiomyopathy (NIDCM). A total of one-hundred and fourteen NIDCM patients [age: 63 ± 12 years, 91 (81%) males] were retrospectively analyzed. We performed texture analysis of ECV mapping of LV myocardium using dedicated software. We calculated nine histogram-based features (mean, standard deviation, maximum, minimum, etc.) and five gray-level co-occurrence matrices. Five machine learning techniques and the fivefold cross-validation method were used to develop prediction models for LVRR by GDMT based on 14 texture parameters on ECV mapping. We defined the LVRR as follows: LVEF increased ≥ 10% points and decreased LVEDV ≥ 10% on echocardiography after GDMT > 12 months. Fifty (44%) patients were classified as non-responders. The area under the receiver operating characteristics curve for predicting non-responder was 0.82 for eXtreme Gradient Boosting, 0.85 for support vector machine, 0.76 for multi-layer perception, 0.81 for Naïve Bayes, 0.77 for logistic regression, respectively. Mean ECV value was the most critical factor among texture features for differentiating NIDCM patients with LVRR and those without (0.28 ± 0.03 vs. 0.36 ± 0.06, p < 0.001). Machine learning analysis using the support vector machine may be helpful in detecting high-risk NIDCM patients resistant to GDMT. Mean ECV is the most crucial feature among texture features.

Entropy of left ventricular late gadolinium enhancement and its prognostic value in hypertrophic cardiomyopathy a new CMR assessment method

PURPOSE

As a novel metric, entropy generated from late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) can be utilized to assess tissue heterogeneity. However, it is unknown if it can be utilized for risk stratification in hypertrophic cardiomyopathy (HCM). In addition, it is unknown if LGE entropy correlates with LGE mass%, which is commonly utilized for fibrosis assessment. This research was done to investigate these issues.

MATERIALS AND METHODS

Patients with HCM who underwent 3.0-T CMR between January 2015 and January 2020 were prospectively enrolled and classified into low- and high-risk groups according to the AHA/ACC risk stratification guideline for 2020. The LGE entropy was automatically estimated using a generic Python package algorithm. On CMR imaging, the LGE mass% was determined using the CVI 42 software. Endpoint events included sudden cardiac death (SCD), hospital readmission owing to heart failure, and implantable cardioverter defibrillator (ICD) treatment for ventricular arrhythmias.

RESULTS

A total of 109 HCM participants (70 males) were included. During the follow-up (23 ± 7 months), the patients in the high-risk group had higher LGE entropy (p < 0.001) and LGE mass% (p < 0.001) than those in the low-risk group, and patients with endpoint events had higher LGE entropy (p < 0.001) and LGE mass% (p < 0.001) than those without endpoint events. In all participants, there was a link between LGE entropy and LGE mass%, according to the Spearman rank correlation analysis (p < 0.001; r = 0.667). In ROC analysis, the area under the curve (AUC) of LGE entropy was 0.893 (95% CI, 0.794-0.993; P<0.001), AUC of LGE mass% was 0.826 (95% CI, 0.737-0.914; P<0.001), AUC of LVEF was 0.610 (95% CI, 0.473-0.748; P = 0.117) and AUC of 2020 AHA/ACC guideline for risk stratification was 0.716 (95% CI, 0.617-0.815; P = 0.002). According to Kaplan-Meier curves, HCM with a higher LGE entropy (≥cutoff value (<5.873) or ≥ thied tertile (5.540)) were more likely to experience the endpoint events. Following adjustment for the 2020 AHA/ACC guideline for risk categorization, LGE mass%, or decreased LVEF, Cox analysis showed that LGE entropy was independently linked with endpoint events.

CONCLUSIONS

The variability and extent of LGE pictures can be reflected by LGE entropy, which is a reliable, usable, and repeatable metric for risk classification in HCM. It is a prognostic indicator of endpoint events that is independent of other risk indicators.

Prognostic Value of Entropy Derived from Late Gadolinium Enhancement Images to Adverse Cardiac Events in Post-Myocardial Infarction Patients

RATIONALE AND OBJECTIVES

To explore the prognostic value of entropy derived from late gadolinium enhancement images on cardiac magnetic resonance (CMR) for major adverse cardiac events (MACE) in post-myocardial infarction (MI) patients.

MATERIALS AND METHODS

Participants with MI underwent 3.0T CMR were retrospectively enrolled. CMR parameters, including the entropy of infarct core (IC), peri-infarct border zone (BZ), and infarct core and peri-infarct border zone (IBZ) were analyzed. Patients were divided into the No-MACE group and the MACE group according to the absence or presence of MACE during the follow-up period.

RESULTS

Eighty-four patients were included, among whom 51 patients without MACE and 33 patients with MACE. The MACE group showed higher IC mass, IBZ mass, IC entropy, BZ entropy, IBZ entropy, and LV entropy and lower LVEF than those of the NO-MACE group. LVEF, BZ entropy, and IBZ entropy were independent predictors of MACE (p < 0.05). Receiver operating characteristic curve revealed that the predictive values of BZ entropy with AUC of 0.860, IBZ entropy with AUC of 0.930, the combined model of LVEF and BZ entropy with AUC of 0.923, and the combined model of LVEF and IBZ entropy with AUC of 0.954 were higher than that of LVEF with AUC of 0.797. Delong test illustrated there was no significant difference in AUC among the three models with AUC > 0.900 (p > 0.05).

CONCLUSION

BZ entropy and IBZ entropy were noninvasive parameters for better risk stratification of post-MI patients. MI Patients with MACE showed higher BZ entropy and IBZ entropy than patients without MACE.

Association of Late-Gadolinium Enhancement in Cardiac Magnetic Resonance with Mortality, Ventricular Arrhythmias, and Heart Failure in Patients with Non-Ischemic Cardiomyopathy: A Systematic Review and Meta-Analysis

Background

Late gadolinium enhancement (LGE) on cardiac magnetic resonance is a predictor of adverse events in patients with nonischemic cardiomyopathy (NICM).

Objective

This meta-analysis evaluated the correlation between LGE and mortality, ventricular arrhythmias (VAs) and sudden cardiac death (SCD), and heart failure (HF) outcomes.

Methods

A literature search was conducted for studies reporting the association between LGE in NICM and the study endpoints. The primary endpoint was mortality. Secondary endpoints included VA and SCD, HF hospitalization, improvement in left ventricular ejection fraction (LVEF) to >35%, and heart transplantation referral. The search was not restricted to time or publication status. The minimum follow-up duration was 1 year.

Results

A total of 46 studies and 10,548 NICM patients (4610 with LGE, 5938 without LGE) were included; mean follow-up was 3 years (range 13-71 months). LGE was associated with increased mortality (odds ratio [OR] 2.9; 95% confidence interval [CI] 2.3-3.8; P < .01) and VA and SCD (OR 4.6; 95% CI 3.5-6.0; P < .01). LGE was associated with an increased risk of HF hospitalization (OR 3.4; 95% CI 2.3-5.0; P < .01), referral for transplantation (OR 5.1; 95% CI 2.5-10.4; P < .01), and decreased incidence of LVEF improvement to >35% (OR 0.2; 95% CI 0.03-0.85; P = .03).

Conclusion

LGE in NICM patients is associated with increased mortality, VA and SCD, and HF hospitalization and heart transplantation referral during long-term follow up. Given these competing risks of mortality and HF progression, prospective randomized controlled trials are required to determine if LGE is useful for guiding prophylactic implantable cardioverter-defibrillator placement in NICM patients.

A detailed analysis in thoracic aorta by means of the entropy generation rate: Prediction of the atherosclerotic lesion

A detailed numerical analysis is carried out in a real human thoracic aorta by means of the Computational Fluid Dynamics (CFD) for the prediction of the atherosclerosis lesion. Common hemodynamics parameters, such as, the oscillatory shear index (OSI) and the time average wall shear stress (TAWSS) are used for the prediction of the atherosclerosis lesion. Furthermore, the entropy generation rate is considered to obtain the main irreversibilities that occurs inside the thoracic aorta for the prediction of the atherosclerosis lesion. The model considers the blood flow inside the thoracic aorta in an unsteady state. The results show contours of velocity, streams lines, velocity profiles and the comparison of the hemodynamics parameters OSI versus TAWSS. Moreover, contours of the entropy generation rate are showed inside the aorta. The time averaged entropy generation rate (TAEGR) is obtained as a result of the entropy generation analysis. Finally, TAEGR index is compared and discussed with the common hemodynamics parameters, OSI and TAWSS. The accuracy to detect prone locations to atherosclerotic development in the real aorta using the TAEGR in comparison to the OSI and the TAWSS is in good agreement.

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.

Left ventricular shape predicts arrhythmic risk in fibrotic dilated cardiomyopathy

AIMS

Remodelling of the left ventricular (LV) shape is one of the hallmarks of non-ischaemic dilated cardiomyopathy (DCM) and may contribute to ventricular arrhythmias and sudden cardiac death. We sought to investigate a novel three dimensional (3D) shape analysis approach to quantify LV remodelling for arrhythmia prediction in DCM.

METHODS AND RESULTS

We created 3D LV shape models from end-diastolic cardiac magnetic resonance images of 156 patients with DCM and late gadolinium enhancement (LGE). Using the shape models, principle component analysis, and Cox-Lasso regression, we derived a prognostic LV arrhythmic shape (LVAS) score which identified patients who reached a composite arrhythmic endpoint of sudden cardiac death, aborted sudden cardiac death, and sustained ventricular tachycardia. We also extracted geometrical metrics to look for potential prognostic markers. During a follow-up period of up to 16 years (median 7.7, interquartile range: 3.9), 25 patients met the arrhythmic endpoint. The optimally prognostic LV shape for predicting the time-to arrhythmic event was a paraboloidal longitudinal profile, with a relatively wide base. The corresponding LVAS was associated with arrhythmic events in univariate Cox regression (hazard ratio = 2.0 per quartile; 95% confidence interval: 1.3-2.9), in univariate Cox regression with propensity score adjustment, and in three multivariate models; with LV ejection fraction, New York Heart Association Class III/IV (Model 1), implantable cardioverter-defibrillator receipt (Model 2), and cardiac resynchronization therapy (Model 3).

CONCLUSION

Biomarkers of LV shape remodelling in DCM can help to identify the patients at greatest risk of lethal ventricular arrhythmias.

Entropy as a Measure of Myocardial Tissue Heterogeneity in Patients With Ventricular Arrhythmias

OBJECTIVES

The authors investigated the incremental prognostic value of entropy, a novel measure of myocardial tissue heterogeneity by cardiac magnetic resonance (CMR) imaging in patients presenting with ventricular arrhythmias (VAs).

BACKGROUND

CMR can characterize myocardial areas serving as arrhythmogenic substrate.

METHODS

Consecutive patients undergoing CMR imaging for VAs were followed for major adverse cardiac events (MACEs) defined by all-cause death, incident VAs requiring therapy, or heart failure hospitalization. Entropy was derived from the probability distribution of pixel signal intensities of the left ventricular (LV) myocardium.

RESULTS

A total of 583 patients (age 54 ± 15 years, female 39%, left ventricular ejection fraction [LVEF] 54 ± 13%) were followed for a median of 4.4 years and experienced 141 MACEs. Entropy showed strong unadjusted association with MACE (HR: 1.88; 95% CI: 1.63-2.17; P < 0.001). In a multivariable model including LVEF, QRS duration, late gadolinium enhancement, and presenting arrhythmia, entropy maintained independent association with MACE (HR: 1.61; 95% CI: 1.32-1.96; P < 0.001). Entropy was further significantly associated with MACE in patients without myocardial scar (HR: 2.43; 95% CI: 1.55-3.82; P < 0.001) and in those presenting with nonsustained VAs (HR: 2.16; 95% CI: 1.43-3.25; P < 0.001). Addition of LV entropy to the baseline multivariable model significantly improved model performance (C-statistic improvement: 0.725 to 0.754; P = 0.003) and risk reclassification.

CONCLUSIONS

In patients with VAs, CMR-assessed LV entropy was independently associated with MACE and provided incremental prognostic value, on top of LVEF and late gadolinium enhancement. LV entropy assessment may help risk stratification in patients with absence of myocardial scar or with nonsustained VAs.

Radiomics Analysis Derived From LGE-MRI Predict Sudden Cardiac Death in Participants With Hypertrophic Cardiomyopathy

Objectives: To identify significant radiomics features derived from late gadolinium enhancement (LGE) images in participants with hypertrophic cardiomyopathy (HCM) and assess their prognostic value in predicting sudden cardiac death (SCD) endpoint.

Method: The 157 radiomic features of 379 sequential participants with HCM who underwent cardiovascular magnetic resonance imaging (MRI) were extracted. CoxNet (Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net) and Random Forest models were applied to optimize feature selection for the SCD risk prediction and cross-validation was performed.

Results: During a median follow-up of 29 months (interquartile range, 20–42 months), 27 participants with HCM experienced SCD events. Cox analysis revealed that two selected features, local binary patterns (LBP) (19) (hazard ratio (HR), 1.028, 95% CI: 1.032–1.134; P = 0.001) and Moment (1) (HR, 1.212, 95%CI: 1.032–1.423; P = 0.02) provided significant prognostic value to predict the SCD endpoints after adjustment for the clinical risk predictors and late gadolinium enhancement. Furthermore, the univariately significant risk predictor was improved by the addition of the selected radiomics features, LBP (19) and Moment (1), to predict SCD events (P < 0.05).

Conclusion: The radiomics features of LBP (19) and Moment (1) extracted from LGE images, reflecting scar heterogeneity, have independent prognostic value in identifying high SCD risk patients with HCM.

Prognostic Value of Late Enhanced Cardiac Magnetic Resonance Imaging Derived Texture Features in Dilated Cardiomyopathy Patients With Severely Reduced Ejection Fractions

Background: Late enhanced cardiac magnetic resonance (CMR) images of the left ventricular myocardium contain an enormous amount of information that could provide prognostic value beyond that of late gadolinium enhancements (LGEs). With computational postprocessing and analysis, the heterogeneities and variations of myocardial signal intensities can be interpreted and measured as texture features. This study aimed to evaluate the value of texture features extracted from late enhanced CMR images of the myocardium to predict adverse outcomes in patients with dilated cardiomyopathy (DCM) and severe systolic dysfunction.

Methods: This single-center study retrospectively enrolled patients with DCM with severely reduced left ventricular ejection fractions (LVEFs &lt; 35%). Texture features were extracted from enhanced late scanning images, and the presence and extent of LGEs were also measured. Patients were followed-up for clinical endpoints composed of all-cause deaths and cardiac transplantation. Cox proportional hazard regression and Kaplan–Meier analyses were used to evaluate the prognostic value of texture features and conventional CMR parameters with event-free survival.

Results: A total of 114 patients (37 women, median age 47.5 years old) with severely impaired systolic function (median LVEF, 14.0%) were followed-up for a median of 504.5 days. Twenty-nine patients experienced endpoint events, 12 died, and 17 underwent cardiac transplantations. Three texture features from a gray-level co-occurrence matrix (GLCM) (GLCM_contrast, GLCM_difference average, and GLCM_difference entropy) showed good prognostic value for adverse events when analyzed using univariable Cox hazard ratio regression (p = 0.007, p = 0.011, and p = 0.007, retrospectively). When each of the three features was analyzed using a multivariable Cox regression model that included the clinical parameter (systolic blood pressure) and LGE extent, they were found to be independently associated with adverse outcomes.

Conclusion: Texture features related LGE heterogeneities and variations (GLCM_contrast, GLCM_difference average, and GLCM_difference entropy) are novel markers for risk stratification toward adverse events in DCM patients with severe systolic dysfunction.

Mean Scar Entropy by Late Gadolinium Enhancement Cardiac Magnetic Resonance Is Associated With Ventricular Arrhythmias Events in Hypertrophic Cardiomyopathy

Background: Ventricular arrhythmias are associated with sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM). Previous studies have found the late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) was independently associated with ventricular arrhythmia (VA) in HCM. The risk stratification of VA remains complex and LGE is present in the majority of HCM patients. This study was conducted to determine whether the scar heterogeneity from LGE-derived entropy is associated with the VAs in HCM patients.

Materials and Methods: Sixty-eight HCM patients with scarring were retrospectively enrolled and divided into VA (31 patients) and non-VA (37 patients) groups. The left ventricular ejection fraction (LVEF) and percentage of the LGE (% LGE) were evaluated. The scar heterogeneity was quantified by the entropy within the scar and left ventricular (LV) myocardium.

Results: Multivariate analyses showed that a higher scar [hazard ratio (HR) 2.682; 95% CI: 1.022–7.037; p = 0.039] was independently associated with VA, after the adjustment for the LVEF, %LGE, LV maximal wall thickness (MWT), and left atrium (LA) diameter.

Conclusion: Scar entropy and %LGE are both independent risk indicators of VA. A high scar entropy may indicate an arrhythmogenic scar, an identification of which may have value for the clinical status assessment of VAs in HCM patients.

Spatial dispersion analysis of LGE-CMR for prediction of ventricular arrhythmias in patients with cardiac sarcoidosis

BACKGROUND

Patients with cardiac sarcoidosis (CS) are at increased risk of life-threatening ventricular arrhythmias (VA). Current approaches to risk stratification have limited predictive value.

OBJECTIVES

To assess the utility of spatial dispersion analysis of late gadolinium enhancement cardiac magnetic resonance (LGE-CMR), as a quantitative measure of myocardial tissue heterogeneity, in risk stratifying patients with CS for VA and death.

METHODS

Sixty two patients with CS underwent LGE-CMR. LGE images were segmented and dispersion maps of the left and right ventricles were generated as follows. Based on signal intensity (SI), each pixel was categorized as abnormal (SI ≥3SD above the mean), intermediate (SI 1-3 SD above the mean) or normal (SI <1SD above the mean); and each pixel was then assigned a value of 0 to 8 based on the number of adjacent pixels of a different category. Average dispersion score was calculated for each patient. The primary endpoint was VA during follow up. The composite of VA or death was assessed as a secondary endpoint.

RESULTS

During 4.7 ± 3.5 years of follow up, six patients had VA, and five without documented VA died. Average dispersion score was significantly higher in patients with VA versus those without (0.87 ± 0.08 vs. 0.71 ± 0.16; p = .002) and in patients with events versus those without (0.83 ± 0.08 vs. 0.70 ± 0.16; p = .003). Patients at higher tertiles of dispersion score had a higher incidence of VA (p = .03) and the composite of VA or death (p = .01).

CONCLUSIONS

Increased substrate heterogeneity, quantified by spatial dispersion analysis of LGE-CMR, may be helpful in risk-stratifying patients with CS for adverse events, including life-threatening arrhythmias.

The prognostic role of late gadolinium enhancement on cardiac magnetic resonance in patients with nonischemic cardiomyopathy and reduced ejection fraction, implanted with cardioverter defibrillators for primary prevention. A systematic review and meta-analysis

BACKGROUND

Previous studies suggest that late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) is associated with arrhythmic events in patients with nonischemic cardiomyopathy (NICM), while others have questioned the role of left ventricular ejection fraction (LVEF) as a sole predictor of future events.

OBJECTIVES

To evaluate the role of LGE on CMR in identifying patients with NICM and reduced LVEF for whom a benefit from defibrillator implantation for primary prevention is not anticipated, thus they are mainly exposed to potential risks.

METHODS

Major electronic databases were searched for studies reporting the incidence of appropriate device therapy (ADT), sudden cardiac death (SCD), and cardiac death based on the presence of LGE on CMR, among patients with NICM and reduced LVEF, implanted with a cardioverter defibrillator for primary prevention.

RESULTS

Eleven studies (1652 patients, 947 with LGE) were included in the final analysis. LGE presence was strongly associated with ADT (logOR: 1.95, 95%CI: 1.21-2.69) and cardiac death (logOR: 0.91, 95%CI: 0.14-1.68), but not with SCD (logOR: 0.26, 95%CI: -1.09-1.6). Diagnostic accuracy analysis demonstrated that contrast enhancement is a sensitive marker of future ADT and cardiac death (93%, 95%CI: 85.8-96.7%; 82.9%, 95%CI: 70.6-90.7%; respectively), with moderate specificity ( 44%, 95%CI: 27.2-62.6%; 37.7%, 95%CI: 23.4-54.6%; respectively).

CONCLUSION

LGE is a highly sensitive predictor of ADT and cardiac death in NICM patients implanted with a defibrillator for primary prevention. However, due to moderate specificity, derivation of a cutoff with adequate predictive values and probably a multifactorial approach are needed to improve discrimination of patients who will not benefit from ICDs.

Late-Gadolinium Enhancement Interface Area and Electrophysiological Simulations Predict Arrhythmic Events in Patients With Nonischemic Dilated Cardiomyopathy

OBJECTIVES

This study sought to investigate whether shape-based late gadolinium enhancement (LGE) metrics and simulations of re-entrant electrical activity are associated with arrhythmic events in patients with nonischemic dilated cardiomyopathy (NIDCM).

BACKGROUND

The presence of LGE predicts life-threatening ventricular arrhythmias in NIDCM; however, risk stratification remains imprecise. LGE shape and simulations of electrical activity may be able to provide additional prognostic information.

METHODS

Cardiac magnetic resonance (CMR)-LGE shape metrics were computed for a cohort of 156 patients with NIDCM and visible LGE and tested retrospectively for an association with an arrhythmic composite endpoint of sudden cardiac death and ventricular tachycardia. Computational models were created from images and used in conjunction with simulated stimulation protocols to assess the potential for re-entry induction in each patient's scar morphology. A mechanistic analysis of the simulations was carried out to explain the associations.

RESULTS

During a median follow-up of 1,611 (interquartile range: 881 to 2,341) days, 16 patients (10.3%) met the primary endpoint. In an inverse probability weighted Cox regression, the LGE-myocardial interface area (hazard ratio [HR]: 1.75; 95% confidence interval [CI]: 1.24 to 2.47; p = 0.001), number of simulated re-entries (HR: 1.40; 95% CI: 1.23 to 1.59; p < 0.01) and LGE volume (HR: 1.44; 95% CI: 1.07 to 1.94; p = 0.02) were associated with arrhythmic events. Computational modeling revealed repolarization heterogeneity and rate-dependent block of electrical wavefronts at the LGE-myocardial interface as putative arrhythmogenic mechanisms directly related to the LGE interface area.

CONCLUSIONS

The area of interface between scar and surviving myocardium, as well as simulated re-entrant activity, are associated with an elevated risk of major arrhythmic events in patients with NIDCM and LGE and represent novel risk predictors.

Prognostic value of SPECT myocardial perfusion entropy in high-risk type 2 diabetic patients

PURPOSE

Risk stratification of patients with type 2 diabetes mellitus (T2D) remains suboptimal. We hypothesized that myocardial perfusion entropy (MPE) quantified from SPECT myocardial perfusion images may provide incremental prognostic value in T2D patients independently from myocardial ischemia.

METHODS

T2D patients with very high and high cardiovascular risk were prospectively included (n = 166, 65 ± 12 years). Stress perfusion defect was quantified by visual evaluation of SPECT MPI. SPECT MPI was also used for the quantification of rest and stress MPE. The primary end point was major adverse cardiac events (MACEs) defined as cardiac death, myocardial infarction (MI), and myocardial revascularization > 3 months after SPECT.

RESULTS

Forty-four MACEs were observed during a 4.6-year median follow-up. Significant differences in stress MPE were observed between patients with and without MACEs (4.19 ± 0.46 vs. 3.93 ± 0.40; P ≤ .01). By Kaplan-Meier analysis, the risk of MACEs was significantly higher in patients with higher stress MPE (log-rank P ≤ 01). Stress MPE and stress perfusion defect (SSS ≥ 4) were significantly associated with the risk of MACEs (hazard ratio 2.77 and 2.06, respectively, P < .05 for both) after adjustment for clinical and imaging risk predictors as identified from preliminary univariate analysis. MPE demonstrated incremental prognostic value over clinical risk factors, stress test EKG and SSS as evidenced by nested models showing improved Akaike information criterion (AIC), reclassification (global continuous net reclassification improvement [NRI]: 63), global integrated discrimination improvement (IDI: 6%), and discrimination (change in c-statistic: 0.66 vs 0.74).

CONCLUSIONS

Stress MPE provided independent and incremental prognostic information for the prediction of MACEs in diabetic patients.

TRIAL REGISTRATION NUMBER

NCT02316054 (12/12/2014).

Sudden Cardiac Death Prediction in Non-ischemic Dilated Cardiomyopathy: a Multiparametric and Dynamic Approach

PURPOSE OF REVIEW

Sudden cardiac death is recognised as a devastating consequence of non-ischaemic dilated cardiomyopathy. Although implantable cardiac defibrillators offer protection against some forms of sudden death, the identification of patients in this population most likely to benefit from this therapy remains challenging and controversial. In this review, we evaluate current guidelines and explore established and novel predictors of sudden cardiac death in patients with non-ischaemic dilated cardiomyopathy.

RECENT FINDINGS

Current international guidelines for primary prevention implantable defibrillator therapy do not result in improved longevity for many patients with non-ischemic cardiomyopathy and severe left ventricular dysfunction. More precise methods for identifying higher-risk patients that derive true prognostic benefit from this therapy are required. Dynamic and multi-parametric characterization of myocardial, electrical, serological and genetic substrate offers novel strategies for predicting major arrhythmic risk. Balancing the risk of non-sudden death offers an opportunity to personalize therapy and avoid unnecessary device implantation for those less likely to derive benefit.

High mean entropy calculated from cardiac MRI texture analysis is associated with antitachycardia pacing failure

BACKGROUND

Antitachycardia pacing (ATP), which may avoid unnecessary implantable cardioverter-defibrillator (ICD) shocks, does not always terminate ventricular arrhythmias (VAs). Mean entropy calculated using cardiac magnetic resonance texture analysis (CMR-TA) has been shown to predict appropriate ICD therapy. We examined whether scar heterogeneity, quantified by mean entropy, is associated with ATP failure and explore potential mechanisms using computer modeling.

METHODS

A subanalysis of 114 patients undergoing CMR-TA where the primary endpoint was delivery of appropriate ICD therapy (ATP or shock therapy) was performed. Patients receiving appropriate ICD therapy (n = 33) were dichotomized into "successful ATP" versus "shock therapy" groups. In silico computer modeling was used to explore underlying mechanisms.

RESULTS

A total of 16 of 33 (48.5%) patients had successful ATP to terminate VA, and 17 of 33 (51.5%) patients required shock therapy. Mean entropy was significantly higher in the shock versus successful ATP group (6.1 ± 0.5 vs 5.5 ± 0.7, P = .037). Analysis of patients receiving ATP (n = 22) showed significantly higher mean entropy in the six of 22 patients that failed ATP (followed by rescue ICD shock) compared to 16 of 22 that had successful ATP (6.3 ± 0.7 vs 5.5 ± 0.7, P = .048). Computer modeling suggested inability of the paced wavefront in ATP to successfully propagate from the electrode site through patchy fibrosis as a possible mechanism of failed ATP.

CONCLUSIONS

Our findings suggest lower scar heterogeneity (mean entropy) is associated with successful ATP, whereas higher scar heterogeneity is associated with more aggressive VAs unresponsive to ATP requiring shock therapy that may be due to inability of the paced wavefront to propagate through scar and terminate the VA circuit.

Left Ventricular Geometry and Replacement Fibrosis Detected by cMRI Are Associated with Major Adverse Cardiovascular Events in Nonischemic Dilated Cardiomyopathy

To investigate the relationship between left ventricular (LV) long-axis strain (LAS) and LV sphericity index (LVSI) and outcomes in patients with nonischemic dilated cardiomyopathy (NIDCM) and myocardial replacement fibrosis confirmed by late gadolinium enhancement (LGE) using cardiac magnetic resonance imaging (cMRI), we conducted a prospective study on 178 patients (48 ± 14.4 years; 25.2% women) with first NIDCM diagnosis. The evaluation protocol included ECG monitoring, echocardiography and cMRI. LAS and LVSI were cMRI-determined. Major adverse cardiovascular events (MACEs) were defined as a composite outcome including heart failure (HF), ventricular arrhythmias (VAs) and sudden cardiac death (SCD). After a median follow-up of 17 months, patients with LGE+ had increased risk of MACEs. Kaplan-Meier curves showed significantly higher rate of MACEs in patients with LGE+ (p < 0.001), increased LVSI (p < 0.01) and decreased LAS (p < 0.001). In Cox analysis, LAS (HR = 1.32, 95%CI (1.54–9.14), p = 0.001), LVSI [HR = 1.17, 95%CI (1.45–7.19), p < 0.01] and LGE+ (HR = 1.77, 95%CI (2.79–12.51), p < 0.0001) were independent predictors for MACEs. In a 4-point risk scoring system based on LV ejection fraction (LVEF) < 30%, LGE+, LAS > −7.8% and LVSI > 0.48%, patients with 3 and 4 points had a significantly higher risk for MACEs. LAS and LVSI are independent predictors of MACEs and provide incremental value beyond LVEF and LGE+ in patients with NIDCM and myocardial fibrosis.

Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy

BACKGROUND

Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-dimensional spatial complexity of grayscale patterns on late gadolinium enhanced cardiac magnetic resonance images to predict VAs in patients with ischemic cardiomyopathy.

METHODS

One hundred twenty-two consecutive ischemic cardiomyopathy patients with left ventricular ejection fraction ≤35% without prior history of VAs underwent late gadolinium enhanced cardiac magnetic resonance images. From raw grayscale data, we generated graphs encoding the 3-dimensional geometry of the left ventricle. A novel technique, adapted to these graphs, assessed global regularity of signal intensity patterns using Fourier-like analysis and generated a substrate spatial complexity profile for each patient. A machine learning statistical algorithm was employed to discern which substrate spatial complexity profiles correlated with VA events (appropriate implantable cardioverter-defibrillator firings and arrhythmic sudden cardiac death) at 5 years of follow-up. From the statistical machine learning results, a complexity score ranging from 0 to 1 was calculated for each patient and tested using multivariable Cox regression models.

RESULTS

At 5 years of follow-up, 40 patients had VA events. The machine learning algorithm classified with 81% overall accuracy and correctly classified 86% of those without VAs. Overall negative predictive value was 91%. Average complexity score was significantly higher in patients with VA events versus those without (0.5±0.5 versus 0.1±0.2; P<0.0001) and was independently associated with VA events in a multivariable model (hazard ratio, 1.5 [1.2-2.0]; P=0.002).

CONCLUSIONS

Substrate spatial complexity analysis of late gadolinium enhanced cardiac magnetic resonance images may be helpful in refining VA risk in patients with ischemic cardiomyopathy, particularly to identify low-risk patients who may not benefit from prophylactic implantable cardioverter-defibrillator therapy. Visual Overview: A visual overview is available for this article.

Scar shape analysis and simulated electrical instabilities in a non-ischemic dilated cardiomyopathy patient cohort

This paper presents a morphological analysis of fibrotic scarring in non-ischemic dilated cardiomyopathy, and its relationship to electrical instabilities which underlie reentrant arrhythmias. Two dimensional electrophysiological simulation models were constructed from a set of 699 late gadolinium enhanced cardiac magnetic resonance images originating from 157 patients. Areas of late gadolinium enhancement (LGE) in each image were assigned one of 10 possible microstructures, which modelled the details of fibrotic scarring an order of magnitude below the MRI scan resolution. A simulated programmed electrical stimulation protocol tested each model for the possibility of generating either a transmural block or a transmural reentry. The outcomes of the simulations were compared against morphological LGE features extracted from the images. Models which blocked or reentered, grouped by microstructure, were significantly different from one another in myocardial-LGE interface length, number of components and entropy, but not in relative area and transmurality. With an unknown microstructure, transmurality alone was the best predictor of block, whereas a combination of interface length, transmurality and number of components was the best predictor of reentry in linear discriminant analysis.