Clinical significance of discrepant mid-wall late gadolinium enhancement in patients with nonischemic dilated cardiomyopathy

Late Gadolinium-Enhanced Cardiac Magnetic Resonance for Predicting Left Ventricular Reverse Remodeling in Dilated Cardiomyopathy A Comprehensive Review and Meta-Analysis

BACKGROUND

There is currently a lack of evidence regarding the significance of late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) in predicting left ventricular (LV) reverse remodeling (RR) in pooled data. This study aimed to evaluate the predictive value of the presence and extent of LGE for LVRR in patients with dilated cardiomyopathy (DCM).

METHODS

Systematic searches were conducted in PubMed, Embase, Cochrane Library, and ClinicalTrials.gov from database inception to May 21, 2024. We estimated the overall effect sizes using the Mantel-Haenszel random-effects model. Subgroup analyses, meta-regression, and sensitivity analyses were performed to investigate potential sources of heterogeneity among studies.

RESULTS

A total of 1141 patients (LGE prevalence: 49.7%) from 13 studies (five prospective and eight retrospective) were included. After a median follow-up period of 15 months, 43.5% of patients achieved LVRR. The presence of LGE predicted LVRR with a pooled odds ratio (OR) of 0.23 (95% confidence interval [CI]: 0.14-0.38, P<0.01) with significant heterogeneity (I² = 68%). The pooled OR for LVRR per percent increase in the extent of LGE was 0.94 (95% CI: 0.90-0.98, P<0.01) with low heterogeneity (I² = 19%). Subgroup analysis based on follow-up duration demonstrated that the presence of LGE was more strongly inversely associated with LVRR in <12 months follow-up (OR 0.06, 95% CI: 0.03-0.13, P<0.01) compared to ≥ 12 months follow-up (OR 0.36, 95% CI: 0.24-0.54, P<0.01).

CONCLUSION

The presence and increase extent of LGE on CMR significantly diminish LVRR achievement in DCM patients, particularly in short-term follow-up (<12 months).

Late Gadolinium Enhancement Magnetic Resonance Imaging (MRI) for Predicting Left Ventricular Reverse Remodeling in Non-Ischemic Cardiomyopathy: A Systematic Review and Meta-Analysis

Background/Objectives: Late gadolinium enhancement (LGE)-MRI has proven utility in prognosticating outcomes in patients with non-ischemic cardiomyopathy (NICM). However, evidence regarding its ability to predict responsiveness to optimal medical therapy remains insufficient. This study conducted a meta-analysis to evaluate the predictive utility of LGE-MRI for left ventricular reverse remodeling (LVRR) in response to pharmacological therapy. Methods: Data from 1092 NICM patients across 13 studies were included in the analysis. To assess the predictive ability of LGE-MRI for LVRR following optimal medical therapy, a pooled odds ratio was calculated using an inverse variance random-effects meta-analysis. Subgroup analyses were performed by stratifying patients based on the presence or absence of left ventricular dilation and by LVEF (<30% vs. ≥30%). Results: The pooled odds ratio of the absence of LGE for predicting LVRR in NICM was 3.72 (95% CI: 2.83-4.90, I2 = 0, P for heterogeneity = 0.54). A comparison of pooled odds ratios between dilated cardiomyopathy (DCM) and NICM showed no significant difference (p = 0.16). A subgroup analysis in NICM based on the left ventricular ejection fraction (LVEF) demonstrated no significant difference in odds ratios between patients with LVEF <30% (OR: 2.96, 95% CI: 1.80-4.87) and those with LVEF ≥30% (OR: 3.97, 95% CI: 2.97-5.31), (p = 0.13). Conclusions: This meta-analysis suggested that LGE-MRI serves as a reliable predictor of LVRR in patients with NICM, regardless of left ventricular dilation or baseline LVEF classification.

Image-Based Biological Heart Age Estimation Reveals Differential Aging Patterns Across Cardiac Chambers

BACKGROUND

Biological heart age estimation can provide insights into cardiac aging. However, existing studies do not consider differential aging across cardiac regions.

PURPOSE

To estimate biological age of the left ventricle (LV), right ventricle (RV), myocardium, left atrium, and right atrium using magnetic resonance imaging radiomics phenotypes and to investigate determinants of aging by cardiac region.

STUDY TYPE

Cross-sectional.

POPULATION

A total of 18,117 healthy UK Biobank participants including 8338 men (mean age = 64.2 ± 7.5) and 9779 women (mean age = 63.0 ± 7.4).

FIELD STRENGTH/SEQUENCE

A 1.5 T/balanced steady-state free precession.

ASSESSMENT

An automated algorithm was used to segment the five cardiac regions, from which radiomic features were extracted. Bayesian ridge regression was used to estimate biological age of each cardiac region with radiomics features as predictors and chronological age as the output. The "age gap" was the difference between biological and chronological age. Linear regression was used to calculate associations of age gap from each cardiac region with socioeconomic, lifestyle, body composition, blood pressure and arterial stiffness, blood biomarkers, mental well-being, multiorgan health, and sex hormone exposures (n = 49).

STATISTICAL TEST

Multiple testing correction with false discovery method (threshold = 5%).

RESULTS

The largest model error was with RV and the smallest with LV age (mean absolute error in men: 5.26 vs. 4.96 years). There were 172 statistically significant age gap associations. Greater visceral adiposity was the strongest correlate of larger age gaps, for example, myocardial age gap in women (Beta = 0.85, P = 1.69 × 10-26 ). Poor mental health associated with large age gaps, for example, "disinterested" episodes and myocardial age gap in men (Beta = 0.25, P = 0.001), as did a history of dental problems (eg LV in men Beta = 0.19, P = 0.02). Higher bone mineral density was the strongest associate of smaller age gaps, for example, myocardial age gap in men (Beta = -1.52, P = 7.44 × 10-6 ).

DATA CONCLUSION

This work demonstrates image-based heart age estimation as a novel method for understanding cardiac aging.

EVIDENCE LEVEL

1.

TECHNICAL EFFICACY

Stage 1.

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.

Using Multiparametric Cardiac Magnetic Resonance to Phenotype and Differentiate Biopsy-Proven Chronic from Healed Myocarditis and Dilated Cardiomyopathy

(1) Objectives: To discriminate biopsy-proven myocarditis (chronic vs. healed myocarditis) and to differentiate from dilated cardiomyopathy (DCM) using cardiac magnetic resonance (CMR). (2) Methods: A total of 259 consecutive patients (age 51 ± 15 years; 28% female) who underwent both endomyocardial biopsy (EMB) and CMR in the years 2008−2021 were evaluated. According to right-ventricular EMB results, patients were divided into either chronic (n = 130, 50%) or healed lymphocytic myocarditis (n = 60, 23%) or DCM (n = 69, 27%). The CMR protocol included functional, strain, and late gadolinium enhancement (LGE) imaging, T2w imaging, and T2 mapping. (3) Results: Left-ventricular ejection fraction (LV-EF) was higher, and the indexed end-diastolic volume (EDV) was lower in myocarditis patients (chronic: 42%, median 96 mL/m²; healed: 49%, 86 mL/m²) compared to the DCM patients (31%, 120 mL/m²), p < 0.0001. Strain analysis demonstrated lower contractility in DCM patients vs. myocarditis patients, p < 0.0001. Myocarditis patients demonstrated a higher LGE prevalence (68% chronic; 59% healed) than the DCM patients (45%), p = 0.01. Chronic myocarditis patients showed a higher myocardial edema prevalence and ratio (59%, median 1.3) than healed myocarditis (23%, 1.3) and DCM patients (13%, 1.0), p < 0.0001. T2 mapping revealed elevated values more frequently in chronic (90%) than in healed (21%) myocarditis and DCM (23%), p < 0.0001. T2 mapping yielded an AUC of 0.89 (sensitivity 90%, specificity 76%) in the discrimination of chronic from healed myocarditis and an AUC of 0.92 (sensitivity 86%, specificity 91%) in the discrimination of chronic myocarditis from DCM, both p < 0.0001. (4) Conclusions: Multiparametric CMR imaging, including functional parameters, LGE and T2 mapping, may allow differentiation of chronic from healed myocarditis and DCM and therefore help to optimize patient management in this clinical setting.

Cardiac MRI in cardiomyopathies

Heart failure affects 1-2% of the adult population and one of the main contributors to its development is cardiomyopathy. Assessing a patient's risk for adverse events in heart failure is challenging and made more difficult by the heterogenous phenotypic expression of the disease. Cardiac MRI has long been a gold standard measure of myocardial function and anatomy due to its high spatial and temporal resolution. More recently, it has been posited to play a more critical role in the diagnosis and prognosis of cardiomyopathy-related heart failure. Given the limitations of more commonly used imaging modalities, increasing the clinical use of cardiac magnetic resonance imaging could potentially improve the prognosis of specific subgroups of patients at risk of adverse cardiac events.

Left ventricular midwall fibrosis as a predictor of sudden cardiac death in non-ischaemic dilated cardiomyopathy: a meta-analysis

Identification of patients with non‐ischaemic dilated cardiomyopathy (NICM) who are at risk of sudden cardiac death (SCD) and could benefit from an implantable cardioverter defibrillator (ICD) is challenging. The study aims to systematically assess the prognostic value of left ventricular (LV) midwall late gadolinium enhancement (LGE) pattern in patients with NICM and further explore its value on predicting SCD events. The study was prospectively registered in PROPSERO (CRD42019138468). We systematically searched PubMed, Ovid Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov to identify studies that evaluated the association between LV midwall LGE and clinical outcomes (all‐cause mortality, cardiovascular mortality, and SCD or aborted SCD endpoint) in NICM patients. A meta‐analysis was performed to determine pooled odds ratio (OR) for these adverse events. Seven studies including 1827 NICM patients over a mean follow‐up duration of 36.1 ± 19.3 months were included. The presence of LV midwall LGE pattern was observed in 562 (30.8%) patients. The pooled OR was 3.37 [95% confidence intervals (CIs): 1.35–8.42] for all‐cause mortality, 5.56 (95% CI: 1.23–25.22) for cardiovascular mortality, and 2.25 (95% CI: 1.16–3.16) for SCD or aborted SCD. In a subgroup analysis with mean ejection fraction cut‐off point of 35%, the pooled OR for SCD or aborted SCD was 2.06 (95% CI: 1.32–3.22) for LV ejection fraction (LVEF) > 35% and 2.49 (95% CI: 1.48–4.20) for LVEF ≤ 35%. In addition, our study indicated that LV midwall LGE showed an excellent negative predictive value in identifying high‐risk NICM patients and that the number needed to treat with ICD implantation in NICM patients with midwall LGE is 7. The presence of LV midwall on LGE is a significant prognosticator of adverse events in NICM patients. Additionally, patients with LV midwall LGE may be considered for ICD therapy irrespective of LVEF.