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

Epicardial Adipose Tissue and Heterogeneity Parameters Combined with Inflammatory Cells to Predict the Value of Heart Failure with Preserved Ejection Fraction Patients Post Myocardial Infarction

BACKGROUND AND PURPOSE

Epicardial adipose tissue (EAT) comprises three distinct lipid components, each exerting differential effects on cardiovascular diseases. During disease progression, dynamic alterations in lipid composition and spatial distribution contribute to the inherent heterogeneity of EAT. The excessive activation of inflammatory cells may contribute to chronic inflammation, promoting atherosclerosis and cardiac diseases. However, the role of EAT in patients with myocardial infarction (MI) who develop heart failure with preserved ejection fraction (HFpEF) remains unclear. This study aims to quantify the overall and perivascular volumes of EAT using cardiac magnetic resonance (CMR) imaging and assess its heterogeneity, exploring the predictive value of EAT heterogeneity and different EAT volumes combined with inflammatory cells for the occurrence of HFpEF in MI patients with normal left ventricular ejection fraction (LVEF).

METHODS

This retrospective cohort study enrolled patients diagnosed with MI with preserved LVEF via clinical assessment and CMR at the Second Affiliated Hospital of Kunming Medical University between January 2015 and July 2023. Patients who did not undergo percutaneous coronary intervention (PCI) were followed, with the incidence of HFpEF serving as the primary endpoint. The cohort was stratified into two groups: those without HFpEF and those who developed HFpEF.Cardiac structure, function, EAT volume, and infarct volume parameters were obtained using the CMR post-processing software CVI-42, while EAT heterogeneity parameters entropy were derived using Python software. Independent sample t-tests, non-parametric tests, and chi-square tests were employed to analyze the differences in clinical baseline data and CMR metrics between the two groups. Spearman's rank correlation was utilized to analyze the associations between EAT parameters and inflammatory cells, inflammatory markers, and diastolic dysfunction indicators. Furthermore, we conducted univariate and multivariate Cox regression analyses to determine the predictive value of each parameter for the development of HFpEF in MI patients. Time-dependent ROC curves were generated to evaluate the efficacy of each parameter in predicting HFpEF, the AIC values of each parameter and the final model were calculated to evaluate the predictive performance. The optimal cut-off values were identified using time-dependent ROC curves in R software, and Kaplan-Meier event-survival curves were plotted to illustrate the event-free rates based on these optimal thresholds.The median follow-up time was calculated using the reverse Kaplan-Meier method.

RESULTS

A total of 203 MI patients with normal LVEF were included, with 74 in the HFpEF group and 129 in the non-HFpEF group. No significant differences were observed between the two groups regarding age, sex, and infarct volume; however, significant statistical differences were noted in BMI, diabetes, renal failure, leukocytes, neutrophils, monocytes, total EAT, EAT entropy, left ventricular EAT (LV EAT), left atrial end-systolic volume (LAESV), triglycerides, NHR, MHR and LACI(Left atrioventricular coupling index) (P < 0.05). Both overall and local EAT volumes showed a positive correlation with leukocytes and monocytes,as well as with the inflammatory markers MHR and SIRI. Furthermore, EAT volume exhibited a positive correlation with the LACI, a marker of diastolic dysfunction. Univariate and multivariate Cox regression analyses indicated that BMI, diabetes, monocyte, LV EAT, and EAT entropy are independent risk factors for HFpEF. And the AIC value of the multivariate regression model was the smallest.Further time-dependent ROC analysis revealed that the maximum AUC for BMI was 0.67, while the AUC for LV EAT was 0.63, and EAT entropy was 0.60, the maximum AUC for monocyte was 0.70, and the combined prediction of LV EAT and EAT entropy had a maximum AUC of 0.70. After a median follow-up of 34 months, Kaplan-Meier survival curves demonstrated that LV EAT greater than 21.23 mL was associated with the occurrence of HFpEF, whereas EAT entropy was not.

CONCLUSIONS

In patients with chronic MI, normal LVEF, and no prior PCI, the occurrence of HFpEF is not correlated with infarct volume; however, BMI, diabetes, monocyte, LV EAT, and EAT entropy are independent risk factors for HFpEF with significant predictive value, with the highest predictive efficacy observed monocyte and when combining EAT entropy and LV EAT. Additionally, both overall and local EAT volumes exhibit a moderate positive correlation with leukocytes,monocytes and inflammatory markers, and were also positively correlated with diastolic dysfunction. This suggests that, in clinical practice, beyond traditional indicators, there should be an increased focus on EAT heterogeneity and perivascular EAT in MI patients with normal LVEF who have not undergone PCI to to reduce the incidence of HFpEF.

Diagnostic efficacy of complexity metrics from cardiac MRI myocardial segmental motion curves in detecting late gadolinium enhancement in myocardial infarction patients

Background

Myocardial segmental motion is associated with cardiovascular pathology, often assessed through myocardial strain features. The stability of the motion can be influenced by myocardial fibrosis. This research aimed to explore the complexity metrics (CM) of myocardial segmental motion curves, observe their correlation with late gadolinium enhancement (LGE) transmural extension (TE), and assess diagnostic efficacy combined with segmental strains in different TE segments.

Methods

We included 42 myocardial infarction patients, dividing images into 672 myocardial segments (208 remote, 384 viable, and 80 unviable segments based on TE). Radial and circumferential segmental strain, along with CM for motion curves, were extracted. Correlation between CM and LGE, as well as the potential distinguishing role of CM, was evaluated using Pearson correlation, univariate linear regression (F-test), multivariate regression analysis (T-test), area under curve (AUC), machine learning models, and DeLong test.

Results

All CMs showed significant linear correlation with TE (P < 0.001). Six CMs were correlated with TE (r > 0.3), with radial frequency drift (FD) displayed the strongest correlation (r = 0.496, P < 0.001). Radial and circumferential FD significantly differed in higher TE myocardium than in remote segments (P < 0.05). Radial FD had practical diagnostic efficacy (remote vs. unviable AUC = 0.89, viable vs. unviable AUC = 0.77, remote vs. viable AUC = 0.65). Combining CM with segmental strain features boosted diagnostic efficacy than models using only segmental strain features (DeLong test, P < 0.05).

Conclusions

The CM of myocardial motion curves has been associated with LGE infarction, and combining CM with strain features improves the diagnosis of different myocardial LGE infarction degrees.

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.

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.

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.