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

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.

Current Insights and Novel Cardiovascular Magnetic Resonance-Based Techniques in the Prognosis of Non-Ischemic Dilated Cardiomyopathy

Cardiac magnetic resonance (CMR) imaging has an important emerging role in the evaluation and management of patients with cardiomyopathies, especially in patients with dilated cardiomyopathy (DCM). It allows a non-invasive characterization of myocardial tissue, thus assisting early diagnosis and precise phenotyping of the different cardiomyopathies, which is an essential step for early and individualized treatment of patients. Using imaging techniques such as late gadolinium enhancement (LGE), standard and advanced quantification as well as quantitative mapping parameters, CMR-based tissue characterization is useful in the differential diagnosis of DCM and risk stratification. The purpose of this article is to review the utility of CMR in the diagnosis and management of idiopathic DCM, as well as risk prediction and prognosis based on standard and emerging CMR contrast and non-contrast techniques. This is consistent with current evidence and guidance moving beyond traditional prognostic markers such as ejection fraction.

Diagnostic Impact and Prognostic Value of Cardiac Magnetic Resonance in Patients With Ventricular Arrhythmias

BACKGROUND

Cardiac magnetic resonance (CMR) characterizes myocardial substrate relevant to sudden cardiac death (SCD). However, its clinical value in patients presenting with ventricular arrhythmias is still being defined.

OBJECTIVES

The authors sought to examine the diagnostic and prognostic value of multiparametric CMR in a cohort of consecutive patients referred for assessment of ventricular arrhythmias.

METHODS

Consecutive patients undergoing CMR for nonsustained ventricular tachycardia (NSVT) (n = 345) or sustained ventricular tachycardia (VT)/aborted SCD (n = 297) were followed over a median of 4.4 years. Major adverse cardiac events included death, recurrent VT/ventricular fibrillation requiring therapy, and hospitalization for congestive heart failure.

RESULTS

Of the 642 patients, 256 were women (40%), mean age was 54 ± 15 years, and median left ventricular ejection fraction was 58% (IQR: 49%-63%). A structurally abnormal heart by CMR assessment was detected in 40% of patients with NSVT and 66% in those with VT/SCD (P < 0.001). CMR assessment yielded a diagnostic change in 27% of NSVT patients vs 41% of those with VT/SCD (P < 0.001). During follow-up, 51 patients (15%) with NSVT and 104 patients (35%) with VT/SCD experienced major adverse cardiac events (MACE). An abnormal CMR was associated with a higher annual rate for MACE for both NSVT (0.7% vs 7.7%; P < 0.001) and VT/SCD (3.8% vs 13.3%; P < 0.001) patients. In a multivariate model including left ventricular ejection fraction, an abnormal CMR remained strongly associated with MACE in NSVT (HR: 5.23 [95% CI: 2.28-12.0]; P < 0.001) and VT/SCD (HR: 1.88 [95% CI: 1.07-3.30]; P = 0.03). Adding CMR assessment to the multivariable model for MACE yielded a significant improvement in the integrated discrimination improvement and an improvement in the C-statistic in the NSVT cohort.

CONCLUSIONS

In patients presenting with ventricular arrhythmias, multiparametric CMR assessment provides diagnostic clarification and effective risk stratification beyond current standard of care.

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.

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.

Arrhythmia in hypertrophic cardiomyopathy: Risk prediction using contrast enhanced MRI, T1 mapping, and personalized virtual heart technology

BACKGROUND

Hypertrophic cardiomyopathy (HCM), a disease with myocardial fibrosis manifestation, is a common cause of sudden cardiac death (SCD) due to ventricular arrhythmias (VA). Current clinical risk stratification criteria are inadequate in identifying patients who are at risk for VA and in need of an implantable cardioverter defibrillator (ICD) for primary prevention.

OBJECTIVE

We aimed to develop a risk prediction approach based on imaging biomarkers from the combination of late gadolinium contrast-enhanced (LGE) MRI and T1 mapping. We then aimed to compare the prediction to a virtual heart computational risk assessment approach based on LGE-T1 virtual heart models.

METHODS

The methodology involved combining short-axis LGE-MRI with post-contrast T1 maps to define personalized thresholds for diffuse and dense fibrosis. The combined LGE-T1 maps were used to evaluate imaging biomarkers for VA risk prediction. The risk prediction capability of the biomarkers was compared with that of the LGE-T1 virtual heart arrhythmia inducibility simulation. VA risk prediction performance from both approaches was compared to clinical outcome (presence of clinical VA).

RESULTS

Image-based biomarkers, including hypertrophy, signal intensity heterogeneity, and fibrotic border complexity, could not discriminate high vs low VA risk. LGE-T1 virtual heart technology outperformed all the image-based biomarker metrics and was statistically significant in predicting VA risk in HCM.

CONCLUSIONS

We combined two MR imaging techniques to analyze imaging biomarkers in HCM. Raw and processed image-based biomarkers cannot discriminate patients with VA from those without VA. Hybrid LGE-T1 virtual heart models could correctly predict VA risk for this cohort and may improve SCD risk stratification to better identify HCM patients for primary preventative ICD implantation.