Epicardial adipose tissue in obesity with heart failure with preserved ejection fraction: Cardiovascular magnetic resonance biomarker study.

BACKGROUND: Obesity has become a serious public health issue, significantly elevating the risk of various complications. It is a well-established contributor to Heart failure with preserved ejection fraction (HFpEF). Evaluating HFpEF in obesity is crucial. Epicardial adipose tissue (EAT) has emerged as a valuable tool for validating prognostic biomarkers and guiding treatment targets. Hence, assessing EAT is of paramount importance. Cardiovascular magnetic resonance (CMR) imaging is acknowledged as the gold standard for analyzing cardiac function and morphology. We hope to use CMR to assess EAT as a bioimaging marker to evaluate HFpEF in obese patients. AIM: To assess the diagnostic utility of CMR for evaluating heart failure with preserved ejection fraction [HFpEF; left ventricular (LV) ejection fraction ≥ 50%] by measuring the epicardial adipose tissue (EAT) volumes and EAT mass in obese patients. METHODS: Sixty-two obese patients were divided into two groups for a case-control study based on whether or not they had heart failure with HFpEF. The two groups were defined as HFpEF+ and HFpEF-. LV geometry, global systolic function, EAT volumes and EAT mass of all subjects were obtained using cine magnetic resonance sequences. RESULTS: Forty-five patients of HFpEF- group and seventeen patients of HFpEF+ group were included. LV mass index (g/m2) of HFpEF+ group was higher than HFpEF- group (P < 0.05). In HFpEF+ group, EAT volumes, EAT volume index, EAT mass, EAT mass index and the ratio of EAT/[left atrial (LA) left-right (LR) diameter] were higher compared to HFpEF- group (P < 0.05). In multivariate analysis, Higher EAT/LA LR diameter ratio was associated with higher odds ratio of HFpEF. CONCLUSION: EAT/LA LR diameter ratio is highly associated with HFpEF in obese patients. It is plausible that there may be utility in CMR for assessing obese patients for HFpEF using EAT/LA LR diameter ratio as a diagnostic biomarker. Further prospective studies, are needed to validate these proof-of-concept findings.

Towards Accurate Cardiac MRI Segmentation with Variational Autoencoder-Based Unsupervised Domain Adaptation.

Accurate myocardial segmentation is crucial in the diagnosis and treatment of myocardial infarction (MI), especially in Late Gadolinium Enhancement (LGE) cardiac magnetic resonance (CMR) images, where the infarcted myocardium exhibits a greater brightness. However, segmentation annotations for LGE images are usually not available. Although knowledge gained from CMR images of other modalities with ample annotations, such as balanced-Steady State Free Precession (bSSFP), can be transferred to the LGE images, the difference in image distribution between the two modalities (i.e., domain shift) usually results in a significant degradation in model performance. To alleviate this, an end-to-end Variational autoencoder based feature Alignment Module Combining Explicit and Implicit features (VAMCEI) is proposed. We first re-derive the Kullback-Leibler (KL) divergence between the posterior distributions of the two domains as a measure of the global distribution distance. Second, we calculate the prototype contrastive loss between the two domains, bringing closer the prototypes of the same category across domains and pushing away the prototypes of different categories within or across domains. Finally, a domain discriminator is added to the output space, which indirectly aligns the feature distribution and forces the extracted features to be more favorable for segmentation. In addition, by combining CycleGAN and VAMCEI, we propose a more refined multi-stage unsupervised domain adaptation (UDA) framework for myocardial structure segmentation. We conduct extensive experiments on the MSCMRSeg 2019, MyoPS 2020 and MM-WHS 2017 datasets. The experimental results demonstrate that our framework achieves superior performances than state-of-the-art methods.

Comparison of the cachexia index based on hand-grip strength (H-CXI) with the original CXI for the prediction of cancer cachexia and prognosis in patients who underwent radical colectomy for colorectal cancer.

Background and aims: The cachexia index (CXI) is a novel biomarker for estimating cancer cachexia. The cachexia index based on hand-grip strength (H-CXI) has been recently developed as a simple proxy for CXI. The present study aims to compare both the H-CXI and CXI for the prediction of cancer cachexia and postoperative outcomes in patients who underwent radical colectomy for colorectal cancer. Methods: Patients who underwent radical operations for colorectal cancer were included in this study. Cancer cachexia was diagnosed according to the international consensus outlined by Fearon et al. The cachexia index (CXI) was calculated as [skeletal muscle index (SMI) × serum albumin/neutrophil-to-lymphocyte ratio (NLR)]. The H-CXI was calculated as [hand-grip strength (HGS)/height2 × serum albumin/NLR]. The SMI was measured based on the preoperative CT images at the third lumbar vertebra (L3) level. HGS was measured before surgery. Results: From July 2014 to May 2021, a total of 1,411 patients were included in the present study, of whom 361 (25.6%) were identified as having cancer cachexia. Patients with cachexia had a lower CXI (p < 0.001) and lower H-CXI (p < 0.001) than those without cachexia. A low CXI but not low H-CXI independently predicted cancer cachexia in the multivariate analysis (OR 1.448, p = 0.024). Both a low CXI (HR 1.476, p < 0.001 for OS; HR 1.611, p < 0.001 for DFS) and low H-CXI (HR 1.369, p = 0.007 for OS; HR 1.642, p < 0.001 for DFS) were independent predictors for overall survival (OS) and disease-free survival (DFS) after adjusting for the same covariates. A low H-CXI but not low CXI was an independent risk factor for postoperative complications (OR 1.337, p = 0.044). No significant association was found between cancer cachexia and postoperative complications. Conclusion: The CXI and H-CXI exhibited better prognostic value than cancer cachexia for the prediction of postoperative outcomes in patients who underwent radical colectomy for colorectal cancer. The H-CXI was a superior index over the CXI in predicting short-term clinical outcomes, whereas the CXI demonstrated a closer correlation with Fearon's criteria for cancer cachexia. Ideal tools for the assessment of cancer cachexia should incorporate not only weight loss but also muscle mass, physical function, and inflammatory state.

Fractal analysis of left ventricular trabeculae in post-STEMI: from acute to chronic phase.

PURPOSE: The temporal evolution of ventricular trabecular complexity and its correlation with major adverse cardiovascular events (MACE) remain indeterminate in patients presenting with acute ST elevation myocardial infarction (STEMI). METHODS: This retrospective analysis enrolled patients undergoing primary percutaneous coronary intervention (pPCI) for acute STEMI, possessing cardiac magnetic resonance (CMR) data in the acute (within 7 days), subacute (1 month after pPCI), and chronic phases (6 months after pPCI) from January 2015 to January 2020 at the three participating sites. Fractal dimensions (FD) were measured for the global, infarct, and remote regions of left ventricular trabeculae during each phase. The potential association of FD with MACE was analyzed using multivariate Cox regression. RESULTS: Among the 200 analyzed patients (182 men; median age, 61 years; age range, 50-66 years), 37 (18.5%) encountered MACE during a median follow-up of 31.2 months. FD exhibited a gradual decrement (global FD at acute, subacute, and chronic phases: 1.253 ± 0.049, 1.239 ± 0.046, 1.230 ± 0.045, p < 0.0001), with a more pronounced decrease observed in patients subsequently experiencing MACE (p < 0.001). The global FD at the subacute phase correlated with MACE (hazard ratio 0.89 (0.82, 0.97), p = 0.01), and a global FD value below 1.26 was associated with a heightened risk. CONCLUSION: In patients post-STEMI, the global FD, serving as an indicator of left ventricular trabeculae complexity, independently demonstrated an association with subsequent major adverse cardiovascular events, beyond factors encompassing left ventricular ejection fraction, indexed left ventricular end-diastolic volume, infarct size, heart rate, NYHA class, and post-pPCI TIMI flow. CRITICAL RELEVANCE STATEMENT: In patients who have had an ST-segment elevation myocardial infarction, global fractal dimension, as a measure of left ventricular trabeculae complexity, provided independent association with subsequent major adverse cardiovascular event. KEY POINTS: • Global and regional FD decreased after STEMI, and more so in patients with subsequent MACE. • Lower global FD at the subacute phase and Δglobal FD from acute to subacute phase were associated with subsequent MACE besides clinical and CMR factors. • Global FD at the subacute phase independently correlated with MACE and global FD value below 1.26 was associated with higher risk.

Prognostic value of left ventricular trabeculae fractal analysis in patients with dilated cardiomyopathy.

BACKGROUND: The prognostic value of left ventricular (LV) myocardial trabecular complexity on cardiovascular magnetic resonance (CMR) in dilated cardiomyopathy (DCM) remains unknown. This study aimed to evaluate the prognostic value of LV myocardial trabecular complexity using fractal analysis in patients with DCM. METHODS: Consecutive patients with DCM who underwent CMR between March 2017 and November 2021 at two hospitals were prospectively enrolled. The primary endpoints were defined as the combination of all-cause death and heart failure hospitalization. The events of cardiac death alone were defined as the secondary endpoints.LV trabeculae complexity was quantified by measuring the fractal dimension (FD) of the endocardial border based on fractal geometry on CMR. Cox proportional hazards regression and Kaplan-Meier survival analysis were used to examine the association between variables and outcomes. The incremental prognostic value of FD was assessed in nested models. RESULTS: A total of 403 patients with DCM (49.31 ± 14.68 years, 69% male) were recruited. After a median follow-up of 43 months (interquartile range, 28-55 months), 87 and 24 patients reached the primary and secondary endpoints, respectively. Age, heart rate, New York Heart Association functional class >II, N-terminal pro-B-type natriuretic peptide, LV ejection fraction, LV end-diastolic volume index, LV end-systolic volume index, LV mass index, presence of late gadolinium enhancement, global FD, LV mean apical FD, and LV maximal apical FD were univariably associated with the outcomes (all P < 0.05). After multivariate adjustment, LV maximal apical FD remained a significant independent predictor of outcome [hazard ratio = 1.179 (1.116, 1.246), P < 0.001]. The addition of LV maximal apical FD in the nested models added incremental prognostic value to other common clinical and imaging risk factors (all <0.001; C-statistic: 0.84-0.88, P < 0.001). CONCLUSION: LV maximal apical FD was an independent predictor of the adverse clinical outcomes in patients with DCM and provided incremental prognostic value over conventional clinical and imaging risk factors.

Prognostic value of right atrial strains in arrhythmogenic right ventricular cardiomyopathy.

OBJECTIVES: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiomyopathy characterized by progressive fibrofatty infiltration of atrial and ventricular myocardium resulting in adverse cardiac events. Atrial function has been increasingly recognized as prognostically important for cardiovascular disease. As the right atrial (RA) strain is a sensitive parameter to describe RA function, we aimed to analyze the prognostic value of the RA strain in ARVC. METHODS: RA strain parameters were derived from cardiac magnetic resonance (CMR) images of 105 participants with definite ARVC. The endpoint was defined as a combination of sudden cardiac death, survival cardiac arrest, and appropriate implantable cardioverter-defibrillator intervention. Cox regression and Kaplan-Meier survival analyses were performed to evaluate the association between RA strain parameters and endpoint. Concordance index (C index), net reclassification index (NRI), and integrated discrimination improvement (IDI) were calculated to assess the incremental value of RA strain in predicting the endpoint. RESULTS: After a median follow-up of 5 years, 36 (34.3%) reaching the endpoint displayed significantly reduced RA strain parameters. At Kaplan-Meier analysis, impaired RA reservoir (RARS) and booster strains (RABS) were associated with an increased risk of the endpoint. After adjusting for conventional risk factors, RARS (hazard ratio [HR], 0.956; p = 0.005) and RABS (HR, 0.906; p = 0.002) resulted as independent predictors for endpoint at Cox regression analyses. In addition, RARS and RABS improved prognostic value to clinical risk factors and CMR morphological and functional predictors (all p < 0.05). CONCLUSION: RARS and RABS were independent predictors for adverse cardiac events, which could provide incremental prognostic value for conventional predictors in ARVC. CRITICAL RELEVANCE STATEMENT: We evaluated the prognostic value of right atrial strain in ARVC patients and suggested cardiologists consider RA strain as a predictive parameter when evaluating the long-term outcome of ARVC patients in order to formulate better clinical therapy. KEY POINTS: • Patients with ARVC had significantly reduced RA strain and strain rates compared with healthy participants. • Participants with lower RA reservoir and booster stains were associated with a significantly higher risk of adverse cardiac events. • RA booster and reservoir strain provide incremental value to conventional parameters.

3D Fractal Dimension Analysis: Prognostic Value of Right Ventricular Trabecular Complexity in Participants with Arrhythmogenic Cardiomyopathy.

BACKGROUND: Arrhythmogenic cardiomyopathy (ACM) is characterized by progressive myocardial fibro-fatty infiltration accompanied by trabecular disarray. Traditionally, two-dimensional (2D) instead of 3D fractal dimension (FD) analysis has been used to evaluate trabecular disarray. However, the prognostic value of trabecular disorder assessed by 3D FD measurement remains unclear. PURPOSE: To investigate the prognostic value of right ventricular trabecular complexity in ACM patients using 3D FD analysis based on cardiac MR cine images. STUDY TYPE: Retrospective. POPULATION: 85 ACM patients (mean age: 45 ± 17 years, 52 male). FIELD STRENGTH/SEQUENCE: 3.0T/cine imaging, T2-short tau inversion recovery (T2-STIR), and late gadolinium enhancement (LGE). ASSESSMENT: Using cine images, RV (right ventricular) volumetric and functional parameters were obtained. RV trabecular complexity was measured with 3D fractal analysis by box-counting method to calculate 3D-FD. Cox and logistic regression models were established to evaluate the prognostic value of 3D-FD for major adverse cardiac events (MACE). STATISTICAL TESTS: Cox regression and logistic regression to explore the prognostic value of 3D-FD. C-index, time-dependent receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) to evaluate the incremental value of 3D-FD. Intraclass correlation coefficient for interobserver variability. P < 0.05 indicated statistical significance. RESULTS: 26 MACE were recorded during the 60 month follow-up (interquartile range: 48-67 months). RV 3D-FD significantly differed between ACM patients with MACE (2.67, interquartile range: 2.51 ~ 2.81) and without (2.52, interquartile range: 2.40 ~ 2.67) and was a significant independent risk factor for MACE (hazard ratio, 1.02; 95% confidence interval: 1.01, 1.04). In addition, prognostic model fitness was significantly improved after adding 3D-FD to RV global longitudinal strain, LV involvement, and 5-year risk score separately. DATA CONCLUSION: The myocardial trabecular complexity assessed through 3D FD analysis was found associated with MACE and provided incremental prognostic value beyond conventional ACM risk factors. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

Prognostic value of right ventricular trabecular complexity in patients with arrhythmogenic cardiomyopathy.

OBJECTIVES: The present study aimed to investigate the incremental prognostic value of the right ventricular fractal dimension (FD), a novel marker of myocardial trabecular complexity by cardiac magnetic resonance (CMR) in patients with arrhythmogenic cardiomyopathy (ACM). METHODS: Consecutive patients with ACM undergoing CMR were followed up for major cardiac events, including sudden cardiac death, aborted cardiac arrest, and appropriate implantable cardioverter defibrillator intervention. Prognosis prediction was compared by Cox regression analysis. We established a multivariable model supplemented with RV FD and evaluated its discrimination by Harrell's C-statistic. We compared the category-free, continuous net reclassification improvement (cNRI) and integrated discrimination index (IDI) before and after the addition of FD. RESULTS: A total of 105 patients were prospectively included from three centers and followed up for a median of 60 (48, 66) months; experienced 36 major cardiac events were recorded. Trabecular FD displayed a strong unadjusted association with major cardiac events (p < 0.05). In the multivariable Cox regression analysis, RV maximal apical FD maintained an independent association with major cardiac events (hazard ratio, 1.31 (1.11-1.55), p < 0.002). The Hosmer-Lemeshow goodness of fit test displayed good fit (X2 = 0.68, p = 0.99). Diagnostic performance was significantly improved after the addition of RV maximal apical FD to the multivariable baseline model, and the continuous net reclassification improvement increased 21% (p = 0.001), and the integrated discrimination index improved 16% (p = 0.045). CONCLUSIONS: In patients with ACM, CMR-assessed myocardial trabecular complexity was independently correlated with adverse cardiovascular events and provided incremental prognostic value. CLINICAL RELEVANCE STATEMENT: The application of FD values for assessing RV myocardial trabeculae may become an accessible and promising parameter in monitoring and early diagnosis of risk factors for adverse cardiovascular events in patients with ACM. KEY POINTS: • Ventricular trabecular morphology, a novel quantitative marker by CMR, has been explored for the first time to determine the severity of ACM. • Patients with higher maximal apical fractal dimension of RV displayed significantly higher cumulative incidence of major cardiac events. • RV maximal apical FD was independently associated with major cardiac events and provided incremental prognostic value in patients with ACM.

Fractal Analysis of Left Ventricular Trabeculae in Patients with End-Stage Renal Disease: A Random Survival Tree Analysis.

BACKGROUND: The complexity of left ventricular (LV) trabeculae is related to the prognosis of several cardiovascular diseases. PURPOSE: To evaluate the prognostic value of LV trabecular complexity in patients with end-stage renal disease (ESRD). STUDY TYPE: Prospective outcome study. POPULATION: 207 participants on maintenance dialysis, divided into development (160 patients from 2 centers) and external validation (47 patients from a third center) cohorts, and 72 healthy controls. FIELD STRENGTH: 3.0T, steady-state free precession (SSFP) and modified Look-Locker imaging sequences. ASSESSMENT: All participants had their trabecular complexity quantified by fractal analysis using cine SSFP images. Patients were followed up every 2 weeks until April 2023, or endpoint events happened. Random Forest (RF) and Cox regression models including age, diabetes, LV mass index, mean basal fractal dimension (FD), and left atrial volume index, were developed to predict major adverse cardiac events (MACE). Patients were divided into low- and high-risk groups based on scores derived from the RF model and survival compared. STATISTICAL TESTS: Receiver operating characteristic curve analysis; Kaplan-Meier survival analysis with log rank tests; Harrel's C-index to assess model performance. A P value <0.05 was considered statistically significant. RESULTS: Fifty-five patients (26.57%) experienced MACE during a median follow-up time of 21.83 months. An increased mean basal FD (≥1.324) was associated with a significantly higher risk of MACE. The RF model (C-index: 0.81) had significantly better discrimination than the Cox regression model (C-index: 0.74). Participants of the external validation dataset classified into the high-risk group had a hazard of experiencing MACE increased by 12.29 times compared to those in the low-risk group. DATA CONCLUSION: LV basal FD was an independent predictor for MACE in patients with ESRD. Reliable risk stratification models could be generated based on LV basal FD and other MRI variables using RF analysis. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

A Cardiac-Targeted Nanozyme Interrupts the Inflammation-Free Radical Cycle in Myocardial Infarction.

Severe systemic inflammation following myocardial infarction (MI) is a major cause of patient mortality. MI-induced inflammation can trigger the production of free radicals, which in turn ultimately leads to increased inflammation in cardiac lesions (i.e., inflammation-free radicals cycle), resulting in heart failure and patient death. However, currently available anti-inflammatory drugs have limited efficacy due to their weak anti-inflammatory effect and poor accumulation at the cardiac site. Herein, a novel Fe-Cur@TA nanozyme is developed for targeted therapy of MI, which is generated by coordinating Fe3+ and anti-inflammatory drug curcumin (Cur) with further modification of tannic acid (TA). Such Fe-Cur@TA nanozyme exhibits excellent free radicals scavenging and anti-inflammatory properties by reducing immune cell infiltration, promoting macrophage polarization toward the M2-like phenotype, suppressing inflammatory cytokine secretion, and blocking the inflammatory free radicals cycle. Furthermore, due to the high affinity of TA for cardiac tissue, Fe-Cur@TA shows an almost tenfold greater in cardiac retention and uptake than Fe-Cur. In mouse and preclinical beagle dog MI models, Fe-Cur@TA nanozyme preserves cardiac function and reduces scar size, suggesting promising potential for clinical translation in cardiovascular disease.

Deep Learning for Discrimination of Hypertrophic Cardiomyopathy and Hypertensive Heart Disease on MRI Native T1 Maps.

BACKGROUND: Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learning (DL) is a promising technique in differential diagnosis. However, its feasibility for discriminating HCM and HHD has not been investigated. PURPOSE: To examine the feasibility of DL in differentiating HCM and HHD based on T1 images and compare its diagnostic performance with other methods. STUDY TYPE: Retrospective. POPULATION: 128 HCM patients (men, 75; age, 50 years ± 16) and 59 HHD patients (men, 40; age, 45 years ± 17). FIELD STRENGTH/SEQUENCE: 3.0T; Balanced steady-state free precession, phase-sensitive inversion recovery (PSIR) and multislice native T1 mapping. ASSESSMENT: Compare HCM and HHD patients baseline data. Myocardial T1 values were extracted from native T1 images. Radiomics was implemented through feature extraction and Extra Trees Classifier. The DL network is ResNet32. Different input including myocardial ring (DL-myo), myocardial ring bounding box (DL-box) and the surrounding tissue without myocardial ring (DL-nomyo) were tested. We evaluate diagnostic performance through AUC of ROC curve. STATISTICAL TESTS: Accuracy, sensitivity, specificity, ROC, and AUC were calculated. Independent t test, Mann-Whitney U-test and Chi-square test were adopted for HCM and HHD comparison. P < 0.05 was considered statistically significant. RESULTS: DL-myo, DL-box, and DL-nomyo models showed an AUC (95% confidential interval) of 0.830 (0.702-0.959), 0.766 (0.617-0.915), 0.795 (0.654-0.936) in the testing set. AUC of native T1 and radiomics were 0.545 (0.352-0.738) and 0.800 (0.655-0.944) in the testing set. DATA CONCLUSION: The DL method based on T1 mapping seems capable of discriminating HCM and HHD. Considering diagnostic performance, the DL network outperformed the native T1 method. Compared with radiomics, DL won an advantage for its high specificity and automated working mode. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

Left Ventricular Vertical Run-Length Nonuniformity MRI Adds Prognostic Value to MACE in Patients with End-Stage Renal Disease.

BACKGROUND: Vertical run-length nonuniformity (VRLN) is a texture feature representing heterogeneity within native T1 images and reflects the extent of cardiac fibrosis. In uremic cardiomyopathy, interstitial fibrosis was the major histological alteration. The prognostic value of VRLN in patients with end-stage renal disease (ESRD) remains unclear. PURPOSE: To evaluate the prognostic value of VRLN MRI in patients with ESRD. STUDY TYPE: Prospective. POPULATION: A total of 127 ESRD patients (30 participants in the major adverse cardiac events, MACE group). FIELD STRENGTH/SEQUENCE: 3.0 T/steady-state free precession sequence, modified Look-Locker imaging. ASSESSMENT: MRI image qualities were assessed by three independent radiologists. VRLN values were measured in the myocardium on the mid-ventricular short-axis slice of T1 mapping. Left ventricular (LV) mass, LV end-diastolic and end-systolic volume, as well as LV global strain cardiac parameters were measured. STATISTICAL TESTS: The primary endpoint was the incident of MACE from enrollment time to January 2023. MACE is a composite endpoint consisting of all-cause mortality, acute myocardial infarction, stroke, heart failure hospitalization, and life-threatening arrhythmia. Cox proportional-hazards regression was performed to test whether VRLN independently correlated with MACE. The intraclass correlation coefficients of VRLN were calculated to evaluate intraobserver and interobserver reproducibility. The C-index was computed to examine the prognostic value of VRLN. P-value <0.05 were considered statistically significant. RESULTS: Participants were followed for a median of 26 months. VRLN, age, LV end-systolic volume index, and global longitudinal strain remained significantly associated with MACE in the multivariable model. Adding VRLN to a baseline model containing clinical and conventional cardiac MRI parameters significantly improved the accuracy of the predictive model (C-index of the baseline model: 0.781 vs. the model added VRLN: 0.814). DATA CONCLUSION: VRLN is a novel marker for risk stratification toward MACE in patients with ESRD, superior to native T1 mapping and LV ejection fraction. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 2.

Right ventricular global strain in patients with hypertrophic cardiomyopathy with and without right ventricular hypertrophy.

PURPOSE: Regardless of whether there are morphological abnormalities of right ventricle in hypertrophic cardiomyopathy (HCM) patients, the exact contribution of right ventricular (RV) global strains remains unresolved. We aimed to study the prognostic value of RV global strains in HCM patients with and without RV hypertrophy (RVH). METHOD: A total of 358 HCM patients who underwent the CMR examination and carried out the follow-up were finally included in this retrospective study. The endpoint was a composite of all-cause mortality, aborted SCD, and heart failure readmission. RV hypertrophy (RVH) was defined as maximal RVWT ≥ 5 mm at end-diastole. RV global strains (RV global longitudinal strain (GLS) and RV global circumferential strain (GCS) were measured in HCM patients by cardiac MRI feature tracking technique. The intraobserver and interobserver reproducibility were evaluated. Receiver-operating characteristic curves and Kaplan-Meier curves, cox proportional hazards regression, Likelihood ratio test and Integrated Discrimination Improvement (IDI) analysis were performed. P-value were corrected for multiple testing when using many covariables by a false discovery rate adjustment. RESULTS: Over a median follow-up of 25 (range 3-54) months, 49 patients reached the composite endpoints. HCM patients were divided into the RVH group and non-RVH groups. In the multivariate cox proportional hazards regression, after adjusting multiple clinical and imaging variables, RV GLS and RV GCS were independently associated with the composite endpoints in the RVH group (HR: 1.123; 95 % CI: 1.048-1.205; P = 0.002) and non-RVH group (HR: 1.174; 95 % CI: 1.031-1.337; P = 0.015), respectively. And The IDI index of models improved when adding RV GLS (IDI = 0.030, p < 0.001) and RV GLS (IDI = 0.056, p = 0.020), respectively. CONCLUSIONS: RV GLS and RV GCS are independent predictors of HCM with RVH and without RVH, respectively. RV GLS in the RVH group and RV GCS in the non-RVH group provide additional values for predicting the risk of adverse events.

Assessment of the Prognostic Value of MRI Left Ventricular Global Function Index (LVGFI) in Patients With End-Stage Renal Disease Under Maintenance Dialysis.

BACKGROUND: Left ventricular global function index (LVGFI) integrates LV volumetric and functional parameters. In patients with end-stage renal disease (ESRD), cardiac injury manifests as LV hypertrophy and dysfunction. However, the prognostic value of LVGFI in this population remains unclear. PURPOSE: To investigate the association of LVGFI with major adverse cardiac events (MACE) in patients with ESRD. STUDY TYPE: Prospective. POPULATION: One hundred fifty-eight ESRD patients (mean age: 54.1 ± 14.4 years; 105 male) on maintenance dialysis. FILED STRENGTH/SEQUENCE: 3.0 T, balanced steady-state free precession (bSSFP) cine and modified Look-Locker inversion recovery (MOLLI) sequences. ASSESSMENT: LV volumetric and functional parameters were determined from bSSFP images. LVGFI was calculated as the ratio of stroke volume to global volume and native T1 was determined from MOLLI T1 maps. MACE was recorded on follow up. Models were developed to predict MACE from conventional risk factors combined with LVGFI, GLS, native T1, and LV mass index (LVMI), respectively. Subgroup analyses were further performed in participants with LVEF above median. STATISTICAL TESTS: Cox proportional hazard regression and log-rank test were used to investigate the association between LVGFI and MACE. The predictive models were evaluated and compared using Harrell's C-statistics and DeLong tests. A P value <0.05 was considered statistically significant. RESULTS: Thirty-four MACE occurred during the median follow-up period of 26 months. The hazard of MACE increased by 114% for each 10% decrease in LVGFI in univariable analysis. The predictive model consisting of LVGFI (C-statistic: 0.724) had significantly better predictive performance than the others (all P < 0.001). These results were consistent in patients (N = 79) with LVEF > median (63.54%). DATA CONCLUSION: LVGFI is a novel marker for MACE risk stratification in patients with ESRD and was better able to predict MACE than native T1 mapping and GLS. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Fractal analysis: Left ventricular trabecular complexity cardiac MRI adds independent risks for heart failure with preserved ejection fraction in participants with end-stage renal disease.

PURPOSE: To measure left ventricular (LV) trabecular complexity by fractal dimension (FD) in patients with end-stage renal disease (ESRD), and assess whether FD was an independent risk factor for heart failure with preserved ejection fraction (HFpEF), or a significant predictor for adverse outcome in this population. METHODS: The study retrospectively enrolled 104 participants with ESRD who underwent 3.0 T cardiac magnetic resonance imaging (MRI) from June 2018 to November 2020. LV trabeculation was quantified with fractal analysis of short-axis cine slices to estimate the FD. Logistic regression analyses were used to evaluate FD and cardiac MRI parameters and to find independent risk predictors. Cox proportional hazard regression was used to investigate the association between FD and MACE. RESULTS: LV FD was higher in in the HFpEF group than those in the non-HFpEF group, with the greatest difference near the base of the ventricle. Age, minimum left atrial volume index, and LV mean basal FD were independent predictors for HFpEF in patients with ESRD. Combining the mean basal FD with typical predictive factors resulted in a C-index (0.902 vs 0.921), which was not significantly higher. Same improvements were found for net reclassification improvement [0.642; 95% confidence interval (CI), 0.254-1.029] and integrated discrimination index (0.026; 95% CI, 0.008-0.061). Participants with a LV global FD above the cutoff value (1.278) had higher risks of MACE in ESRD patients. CONCLUSIONS: LV trabecular complexity measured by FD was an independent risk factor for HFpEF, and a significant predictor for MACE among patients with ESRD.

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.

A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data.

OBJECTIVE: The current study aimed to explore a deep convolutional neural network (DCNN) model that integrates multidimensional CMR data to accurately identify LV paradoxical pulsation after reperfusion by primary percutaneous coronary intervention with isolated anterior infarction. METHODS: A total of 401 participants (311 patients and 90 age-matched volunteers) were recruited for this prospective study. The two-dimensional UNet segmentation model of the LV and classification model for identifying paradoxical pulsation were established using the DCNN model. Features of 2- and 3-chamber images were extracted with 2-dimensional (2D) and 3D ResNets with masks generated by a segmentation model. Next, the accuracy of the segmentation model was evaluated using the Dice score and classification model by receiver operating characteristic (ROC) curve and confusion matrix. The areas under the ROC curve (AUCs) of the physicians in training and DCNN models were compared using the DeLong method. RESULTS: The DCNN model showed that the AUCs for the detection of paradoxical pulsation were 0.97, 0.91, and 0.83 in the training, internal, and external testing cohorts, respectively (p < 0.001). The 2.5-dimensional model established using the end-systolic and end-diastolic images combined with 2-chamber and 3-chamber images was more efficient than the 3D model. The discrimination performance of the DCNN model was better than that of physicians in training (p < 0.05). CONCLUSIONS: Compared to the model trained by 2-chamber or 3-chamber images alone or 3D multiview, our 2.5D multiview model can combine the information of 2-chamber and 3-chamber more efficiently and obtain the highest diagnostic sensitivity. CLINICAL RELEVANCE STATEMENT: A deep convolutional neural network model that integrates 2-chamber and 3-chamber CMR images can identify LV paradoxical pulsation which correlates with LV thrombosis, heart failure, ventricular tachycardia after reperfusion by primary percutaneous coronary intervention with isolated anterior infarction. KEY POINTS: • The epicardial segmentation model was established using the 2D UNet based on end-diastole 2- and 3-chamber cine images. • The DCNN model proposed in this study had better performance for discriminating LV paradoxical pulsation accurately and objectively using CMR cine images after anterior AMI compared to the diagnosis of physicians in training. • The 2.5-dimensional multiview model combined the information of 2- and 3-chamber efficiently and obtained the highest diagnostic sensitivity.

Segmentation of Pericardial Adipose Tissue in CMR Images: A Benchmark Dataset MRPEAT and a Triple-Stage Network 3SUnet.

Increased pericardial adipose tissue (PEAT) is associated with a series of cardiovascular diseases (CVDs) and metabolic syndromes. Quantitative analysis of PEAT by means of image segmentation is of great significance. Although cardiovascular magnetic resonance (CMR) has been utilized as a routine method for non-invasive and non-radioactive CVD diagnosis, segmentation of PEAT in CMR images is challenging and laborious. In practice, no public CMR datasets are available for validating PEAT automatic segmentation. Therefore, we first release a benchmark CMR dataset, MRPEAT, which consists of cardiac short axis (SA) CMR images from 50 hypertrophic cardiomyopathy (HCM), 50 acute myocardial infarction (AMI), and 50 normal control (NC) subjects. We then propose a deep learning model, named as 3SUnet, to segment PEAT on MRPEAT to tackle the challenges that PEAT is relatively small and diverse and its intensities are hard to distinguish from the background. The 3SUnet is a triple-stage network, of which the backbones are all Unet. One Unet is used to extract a region of interest (ROI) for any given image with ventricles and PEAT being contained completely using a multi-task continual learning strategy. Another Unet is adopted to segment PEAT in ROI-cropped images. The third Unet is utilized to refine PEAT segmentation accuracy guided by an image adaptive probability map. The proposed model is qualitatively and quantitatively compared with the state-of-the-art models on the dataset. We obtain the PEAT segmentation results through 3SUnet, assess the robustness of 3SUnet under different pathological conditions, and identify the imaging indications of PEAT in CVDs. The dataset and all source codes are available at https://dflag-neu.github.io/member/csz/research/.

Prognostic significance of non-infarcted myocardium correlated with microvascular impairment evaluated dynamically by native T1 mapping.

OBJECTIVES: This study aimed to investigate the influence of microvascular impairment on myocardial characteristic alterations in remote myocardium at multiple time points, and its prognostic significance after acute ST-segment elevation myocardial infarction (STEMI). METHODS: Patients were enrolled prospectively and performed CMR at baseline, 30 days, and 6 months. The primary endpoint was major adverse cardiac events (MACE): death, myocardial reinfarction, malignant arrhythmia, and hospitalization for heart failure. Cox proportional hazards regression modeling was analyzed to estimate the correlation between T1 mapping of remote myocardium and MACE in patients with and without microvascular obstruction (MVO). RESULTS: A total of 135 patients (mean age 60.72 years; 12.70% female, median follow-up 510 days) were included, of whom 86 (63.70%) had MVO and 26 (19.26%) with MACE occurred in patients. Native T1 values of remote myocardium changed dynamically. At 1 week and 30 days, T1 values of remote myocardium in the group with MVO were higher than those without MVO (p = 0.030 and p = 0.001, respectively). In multivariable cox regression analysis of 135 patients, native1w T1 (HR 1.03, 95%CI 1.01-1.04, p = 0.002), native30D T1 (HR 1.05, 95%CI 1.03-1.07, p < 0.001) and LGE (HR 1.10, 95%CI 1.05-1.15, p < 0.001) were joint independent predictors of MACE. In multivariable cox regression analysis of 86 patients with MVO, native30D T1 (HR 1.05, 95%CI 1.04-1.07, p < 0.001) and LGE (HR 1.10, 95%CI 1.05-1.15, p < 0.001) were joint independent predictors of MACE. CONCLUSIONS: The evolution of native T1 in remote myocardium was associated with the extent of microvascular impairment after reperfusion injury. In patients with MVO, native30D T1 and LGE were joint independent predictors of MACE.