T1 Mapping Tissue Heterogeneity Provides Improved Risk Stratification for ICDs Without Needing Gadolinium in Patients With Dilated Cardiomyopathy

Cardiovascular Magnetic Resonance Radiomics to Identify Components of the Extracellular Matrix in Dilated Cardiomyopathy

BACKGROUND

Current cardiovascular magnetic resonance sequences cannot discriminate between different myocardial extracellular space (ECSs), including collagen, noncollagen, and inflammation. We sought to investigate whether cardiovascular magnetic resonance radiomics analysis can distinguish between noncollagen and inflammation from collagen in dilated cardiomyopathy.

METHODS

We identified data from 132 patients with dilated cardiomyopathy scheduled for an invasive septal biopsy who underwent cardiovascular magnetic resonance at 3 T. Cardiovascular magnetic resonance imaging protocol included native and postcontrast T1 mapping and late gadolinium enhancement (LGE). Radiomic features were computed from the midseptal myocardium, near the biopsy region, on native T1, extracellular volume (ECV) map, and LGE images. Principal component analysis was used to reduce the number of radiomic features to 5 principal radiomics. Moreover, a correlation analysis was conducted to identify radiomic features exhibiting a strong correlation (r>0.9) with the 5 principal radiomics. Biopsy samples were used to quantify ECS, myocardial fibrosis, and inflammation.

RESULTS

Four histopathological phenotypes were identified: low collagen (n=20), noncollagenous ECS expansion (n=49), mild to moderate collagenous ECS expansion (n=42), and severe collagenous ECS expansion (n=21). Noncollagenous expansion was associated with the highest risk of myocardial inflammation (65%). Although native T1 and ECV provided high diagnostic performance in differentiating severe fibrosis (C statistic, 0.90 and 0.90, respectively), their performance in differentiating between noncollagen and mild to moderate collagenous expansion decreased (C statistic: 0.59 and 0.55, respectively). Integration of ECV principal radiomics provided better discrimination and reclassification between noncollagen and mild to moderate collagen (C statistic, 0.79; net reclassification index, 0.83 [95% CI, 0.45-1.22]; P<0.001). There was a similar trend in the addition of native T1 principal radiomics (C statistic, 0.75; net reclassification index, 0.93 [95% CI, 0.56-1.29]; P<0.001) and LGE principal radiomics (C statistic, 0.74; net reclassification index, 0.59 [95% CI, 0.19-0.98]; P=0.004). Five radiomic features per sequence were identified with correlation analysis. They showed a similar improvement in performance for differentiating between noncollagen and mild to moderate collagen (native T1, ECV, LGE C statistic, 0.75, 0.77, and 0.71, respectively). These improvements remained significant when confined to a single radiomic feature (native T1, ECV, LGE C statistic, 0.71, 0.70, and 0.64, respectively).

CONCLUSIONS

Radiomic features extracted from native T1, ECV, and LGE provide incremental information that improves our capability to discriminate noncollagenous expansion from mild to moderate collagen and could be useful for detecting subtle chronic inflammation in patients with dilated cardiomyopathy.

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).

Assessing heterogeneity on cardiovascular magnetic resonance imaging: a novel approach to diagnosis and risk stratification in cardiac diseases

Cardiac disease affects the heart non-uniformly. Examples include focal septal or apical hypertrophy with reduced strain in hypertrophic cardiomyopathy, replacement fibrosis with akinesia in an infarct-related coronary artery territory, and a pattern of scarring in dilated cardiomyopathy. The detail and versatility of cardiovascular magnetic resonance (CMR) imaging mean it contains a wealth of information imperceptible to the naked eye and not captured by standard global measures. CMR-derived heterogeneity biomarkers could facilitate early diagnosis, better risk stratification, and a more comprehensive prediction of treatment response. Small cohort and case-control studies demonstrate the feasibility of proof-of-concept structural and functional heterogeneity measures. Detailed radiomic analyses of different CMR sequences using open-source software delineate unique voxel patterns as hallmarks of histopathological changes. Meanwhile, measures of dispersion applied to emerging CMR strain sequences describe variable longitudinal, circumferential, and radial function across the myocardium. Two of the most promising heterogeneity measures are the mean absolute deviation of regional standard deviations on native T1 and T2 and the standard deviation of time to maximum regional radial wall motion, termed the tissue synchronization index in a 16-segment left ventricle model. Real-world limitations include the non-standardization of CMR imaging protocols across different centres and the testing of large numbers of radiomic features in small, inadequately powered patient samples. We, therefore, propose a three-step roadmap to benchmark novel heterogeneity biomarkers, including defining normal reference ranges, statistical modelling against diagnosis and outcomes in large epidemiological studies, and finally, comprehensive internal and external validations.

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.

Association of epicardial and intramyocardial fat with ventricular arrhythmias

BACKGROUND

Among patients with ischemic cardiomyopathy (ICM) and nonischemic cardiomyopathy (NICM), myocardial fibrosis is associated with an increased risk for ventricular arrhythmia (VA). Growing evidence suggests that myocardial fat contributes to ventricular arrhythmogenesis. However, little is known about the volume and distribution of epicardial adipose tissue and intramyocardial fat and their relationship with VAs.

OBJECTIVE

The purpose of this study was to assess the association of contrast-enhanced computed tomography (CE-CT)-derived left ventricular (LV) tissue heterogeneity, epicardial adipose tissue volume, and intramyocardial fat volume with the risk of VA in ICM and NICM patients.

METHODS

Patients enrolled in the PROSE-ICD registry who underwent CE-CT were included. Intramyocardial fat volume (voxels between -180 and -5 Hounsfield units [HU]), epicardial adipose tissue volume (between -200 and -50 HU), and LV tissue heterogeneity were calculated. The primary endpoint was appropriate ICD shocks or sudden arrhythmic death.

RESULTS

Among 98 patients (47 ICM, 51 NICM), LV tissue heterogeneity was associated with VA (odds ratio [OR] 1.10; P = .01), particularly in the ICM cohort. In the NICM subgroup, epicardial adipose tissue and intramyocardial fat volume were associated with VA (OR 1.11, P = .01; and OR = 1.21, P = .01, respectively) but not in the ICM patients (OR 0.92, P =.22; and OR = 0.96, P =.19, respectively).

CONCLUSION

In ICM patients, increased fat distribution heterogeneity is associated with VA. In NICM patients, an increased volume of intramyocardial fat and epicardial adipose tissue is associated with a higher risk for VA. Our findings suggest that fat's contribution to VAs depends on the underlying substrate.

Impact of Cardiac Magnetic Resonance to Arrhythmic Risk Stratification in Nonischemic Cardiomyopathy

Left ventricular ejection fraction-based arrhythmic risk stratification in nonischemic cardiomyopathy (NICM) is insufficient and has led to the failure of primary prevention implantable cardioverter defibrillator trials, mainly due to the inability of selecting patients at high risk for sudden cardiac death (SCD). Cardiac magnetic resonance offers unique opportunities for tissue characterization and has gained a central role in arrhythmic risk stratification in NICM. The presence of myocardial scar, denoted by late gadolinium enhancement, is a significant, independent, and strong predictor of ventricular arrhythmias and SCD with high negative predictive value. T1 maps and extracellular volume fraction, which are able to quantify diffuse fibrosis, hold promise as complementary tools but need confirmatory results from large studies.

Native T1 high region and left ventricular ejection fraction recovery in patients with dilated cardiomyopathy

Native T1 mapping is used to assess myocardial tissue characteristics without gadolinium contrast agents. The focal T1 high-intensity region can indicate myocardial alterations. This study aimed to identify the association between the native T1 mapping including the native T1 high region and left ventricular ejection fraction (LVEF) recovery in patients with dilated cardiomyopathy (DCM). Patients with newly diagnosed DCM (LVEF of < 45%) who underwent cardiac magnetic resonance imaging with native T1 mapping were included in the analysis. Native T1 high region was defined as a signal intensity of > 5 SD in the remote myocardium. Recovered EF was defined as a follow-up LVEF of ≥ 45% and an LVEF increase of ≥ 10% after 2 years from baseline. Seventy-one patients met the inclusion criteria for this study. Forty-four patients (61.9%) achieved recovered EF. Logistic regression analysis showed that the native T1 value (OR: 0.98; 95% CI: 0.96-0.99; P = 0.014) and the native T1 high region (OR: 0.17; 95% CI: 0.05-0.55; P = 0.002), but not late gadolinium enhancement, were independent predictors of recovered EF. Compared with native T1 value alone, combined native T1 high region and native T1 value improved the area under the curve from 0.703 to 0.788 for predicting recovered EF. Myocardial damage, which was quantified using native T1 mapping and the native T1 high region were independently associated with recovered EF in patients with newly diagnosed DCM.

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.

The extent of late gadolinium enhancement predicts mortality, sudden death and major adverse cardiovascular events in patients with nonischaemic cardiomyopathy: a systematic review and meta-analysis

AIM

To conduct a systematic review and meta-analysis with the objective of evaluating the prognostic value of extent of myocardial fibrosis by late gadolinium-enhanced cardiac magnetic resonance imaging (CMR) in non-ischaemic dilated cardiomyopathy (NICM).

MATERIAL AND METHODS

The databases PubMed, EMBASE, and Google Scholar were searched for studies that investigated the prognostic value of quantification of late gadolinium enhancement (LGE) in patients with NICM. Unadjusted and adjusted hazard ratios (HRs) of uniformly defined predictors were pooled for meta-analysis.

RESULTS

Fourteen studies were retrieved from 884 publications for this systematic review and meta-analysis. In total, 4,336 patients (mean age 51.2 years; mean follow-up 35.1 months) were included in the analysis. Meta-analysis showed the extent of LGE was associated with an increased risk of all-cause mortality (HR: 1.07/1% LGE; 95% confidence interval [CI]: 1.03-1.11; p=0.0003), composite arrhythmic endpoint (HR: 1.09/1% LGE; 95% CI: 1.03-1.15; p=0.002) and major adverse cardiovascular events (MACE; HR: 1.06/1% LGE; 95% CI: 1.02-1.11; p=0.005). After adjusting for baseline characteristics, the higher extent of LGE remained associated with the risk of all-cause mortality (HR_adjusted: 1.07/1% LGE; 95% CI: 1.00-1.14; p=0.04), also strongly associated with the risk of composite arrhythmic endpoint (HR_adjusted: 1.07; 95% CI: 1.02-1.012; p=0.004) and MACE (HR_adjusted: 1.04; 95% CI: 1.01-1.08; p=0.005).

CONCLUSIONS

Extent of LGE in CMR predicts all-cause mortality, arrhythmic events, and MACE. Collectively, these findings emphasise that extent of LGE by CMR may have value for optimising current predictive models for clinical events or mortality in patients with NICM.

Cardiac MRI to Predict Sudden Cardiac Death Risk in Dilated Cardiomyopathy

Background Sudden cardiac death (SCD) is one of the leading causes of death in individuals with nonischemic dilated cardiomyopathy (DCM). However, the risk stratification of SCD events remains challenging in clinical practice. Purpose To determine whether myocardial tissue characterization with cardiac MRI could be used to predict SCD events and to explore a SCD stratification algorithm in nonischemic DCM. Materials and Methods In this prospective single-center study, adults with nonischemic DCM who underwent cardiac MRI between June 2012 and August 2020 were enrolled. SCD-related events included SCD, appropriate implantable cardioverter-defibrillator shock, and resuscitation after cardiac arrest. Competing risk regression analysis and Kaplan-Meier analysis were performed to identify the association of myocardial tissue characterization with outcomes. Results Among the 858 participants (mean age, 48 years; age range, 18-83 years; 603 men), 70 (8%) participants experienced SCD-related events during a median follow-up of 33.0 months. In multivariable competing risk analysis, late gadolinium enhancement (LGE) (hazard ratio [HR], 1.87; 95% CI: 1.07, 3.27; P = .03), native T1 (per 10-msec increase: HR, 1.07; 95% CI: 1.04, 1.11; P < .001), and extracellular volume fraction (per 3% increase: HR, 1.26; 95% CI: 1.11, 1.44; P < .001) were independent predictors of SCD-related events after adjustment of systolic blood pressure, atrial fibrillation, and left ventricular ejection fraction. An SCD risk stratification category was developed with a combination of native T1 and LGE. Participants with a native T1 value 4 or more SDs above the mean (1382 msec) had the highest annual SCD-related events rate of 9.3%, and participants with a native T1 value 2 SDs below the mean (1292 msec) and negative LGE had the lowest rate of 0.6%. This category showed good prediction ability (C statistic = 0.74) and could be used to discriminate SCD risk and competing heart failure risk. Conclusion Myocardial tissue characteristics derived from cardiac MRI were independent predictors of sudden cardiac death (SCD)-related events in individuals with nonischemic dilated cardiomyopathy and could be used to stratify participants according to different SCD risk categories. Clinical trial registration no. ChiCTR1800017058 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sakuma in this issue.

Native T1 heterogeneity for predicting reverse remodeling in patients with non-ischemic dilated cardiomyopathy

A recent study has shown that the heterogeneity of native T1 mapping may be a new prognostic factor for patients with non-ischemic dilated cardiomyopathy (NIDCM). This study aimed to investigate the predictive value of native T1 heterogeneity of the left ventricular (LV) myocardium, as assessed by pixel-wise histogram analysis, for predicting left ventricular reverse remodeling (LVRR) by medical therapy in patients with NIDCM. A total of one hundred and thirteen NIDCM patients (mean age: 63 ± 12 years; 91 males and 22 females; mean LV ejection fraction (EF): 37 ± 10%) were retrospectively analyzed. T1 mapping images were acquired using a modified look-locker inversion recovery (MOLLI) sequence. We performed histogram analysis of native T1 mapping of LV myocardium, mean (T1-mean) and standard deviation (T1-STD) of native T1 time from each pixel were calculated. Extracellular volume fraction (ECV) was also evaluated. LVRR was defined as LVEF increased ≥ 10% points and decrease in LV end-diastolic volume ≥ 10% at 12 months from initiation of medical therapy. Cutoff value of T1-mean and T1-STD was set as median value of each parameter. Sixty (53%) NIDCM patients reached LVRR. Area under the receiver-operating characteristics curve for predicting LVRR was 0.763 (95% confidence interval (CI) 0.679-0.847) for %LGE, 0.757 (95% CI 0.663-0.850) for T1-mean, 0.724 (95% CI 0.625-0.823) for T1-STD, 0.800 (95% CI 0.717-0.882) for ECV, respectively. Proportion of LVRR was significantly lower in NIDCM patients with high T1-mean and high T1-STD (12%) compared to NIDCM with high T1-mean and low T1-STD (65%) (p < 0.001). Adding T1-STD to T1-mean improved AUC from 0.757 to 0.806, comparable to AUC of ECV. Combination of T1-mean and T1-STD, a parameter of heterogeneity of native T1 of the LV myocardium, may be a useful for prediction of LVRR by medical therapy without use of gadolinium contrast for patients with NIDCM.

Imaging for risk stratification of sudden cardiac death

Sudden cardiac death (SCD) can be effectively prevented with the use of implantable cardioverter-defibrillator (ICD). Current guidelines advocate an ICD for primary prevention in the presence of an left ventricular ejection fraction (LVEF) ≤ 35%. The majority of individuals that experience SCD, however, have an LVEF > 35%. Multimodality cardiac imaging has the ability to visualize the three factors responsible for arrhythmia-mediated SCD, namely substrate, trigger and modulator. Advances in cardiac imaging techniques have allowed improved SCD risk stratification, especially in the group of patients with an LVEF > 35%. However, clinical integration of cardiac imaging for SCD risk stratification will require more comparative data between modalities and parameters, as well as evidence of an impact on outcomes. The current review represents an update on the use of multimodality imaging techniques for SCD risk stratification.

Arrhythmias as Presentation of Genetic Cardiomyopathy

There is increasing evidence regarding the prevalence of genetic cardiomyopathies, for which arrhythmias may be the first presentation. Ventricular and atrial arrhythmias presenting in the absence of known myocardial disease are often labelled as idiopathic, or lone. While ventricular arrhythmias are well-recognized as presentation for arrhythmogenic cardiomyopathy in the right ventricle, the scope of arrhythmogenic cardiomyopathy has broadened to include those with dominant left ventricular involvement, usually with a phenotype of dilated cardiomyopathy. In addition, careful evaluation for genetic cardiomyopathy is also warranted for patients presenting with frequent premature ventricular contractions, conduction system disease, and early onset atrial fibrillation, in which most detected genes are in the cardiomyopathy panels. Sudden death can occur early in the course of these genetic cardiomyopathies, for which risk is not adequately tracked by left ventricular ejection fraction. Only a few of the cardiomyopathy genotypes implicated in early sudden death are recognized in current indications for implantable cardioverter defibrillators which otherwise rely upon a left ventricular ejection fraction ≤0.35 in dilated cardiomyopathy. The genetic diagnoses impact other aspects of clinical management such as exercise prescription and pharmacological therapy of arrhythmias, and new therapies are coming into clinical investigation for specific genetic cardiomyopathies. The expansion of available genetic information and implications raises new challenges for genetic counseling, particularly with the family member who has no evidence of a cardiomyopathy phenotype and may face a potentially negative impact of a genetic diagnosis. Discussions of risk for both probands and relatives need to be tailored to their numeric literacy during shared decision-making. For patients presenting with arrhythmias or cardiomyopathy, extension of genetic testing and its implications will enable cascade screening, intervention to change the trajectory for specific genotype-phenotype profiles, and enable further development and evaluation of emerging targeted therapies.

Myocardial tissue imaging with cardiovascular magnetic resonance

Alteration in myocardial tissue, such as myocardial fibrosis, edema, inflammation, or accumulation with amyloid, lipids, or iron, has an important role in the cardiac remodeling that leads to diastolic and/or systolic dysfunction and the development of chronic heart failure, increasing the risk of adverse cardiovascular events. Thus, the early detection of changes at myocardial tissue level has great diagnostic and prognostic potential. The gold standard technique to assess these myocardial alterations is endomyocardial biopsy. However, this has been limited to a few patients due to the invasive nature, sampling errors, and its inability to assess the entire myocardium. Cardiovascular magnetic resonance (CMR) has emerged as the gold standard imaging not only for assessing cardiac volume, function quantification, and viability but also for noninvasive myocardial tissue characterization over the past decade. Its ability to characterize myocardial tissue composition is unique among noninvasive imaging modalities in cardiovascular disease. Currently, multi-parametric myocardial characterization with T1, T2, and extracellular volume has the potential to identify and track diffuse pathology in various diseases. In this review article, we present the role of established and emerging CMR techniques in myocardial tissue characterization, with an emphasis on T1 and T2 mapping, in clinical practice.

Extracellular volume is an independent predictor of arrhythmic burden in dilated cardiomyopathy

The current stratification of arrhythmic risk in dilated cardiomyopathy (DCM) is sub-optimal. Cardiac fibrosis is involved in the pathology of arrhythmias; however, the relationship between cardiovascular magnetic resonance (CMR) derived extracellular volume (ECV) and arrhythmic burden (AB) in DCM is unknown. This study sought to evaluate the presence and extent of replacement and interstitial fibrosis in DCM and to compare the degree of fibrosis between DCM patients with and without AB. This is a prospective, single-center, observational study. Between May 2019 and September 2020, 102 DCM patients underwent CMR T1 mapping. 99 DCM patients (88 male, mean age 45.2 ± 11.8 years, mean EF 29.7 ± 10%) composed study population. AB was defined as the presence of VT or a high burden of PVCs. There were 41 (41.4%) patients with AB and 58 (58.6%) without AB. Replacement fibrosis was assessed with late gadolinium enhancement (LGE), whereas interstitial fibrosis with ECV. Overall, LGE was identified in 41% of patients. There was a similar distribution of LGE (without AB 50% vs. with AB 53.7%; p = 0.8) and LGE extent (without AB 4.36 ± 5.77% vs. with AB 4.68 ± 3.98%; p = 0.27) in both groups. ECV at nearly all myocardial segments and a global ECV were higher in patients with AB (global ECV: 27.9 ± 4.9 vs. 30.3 ± 4.2; p < 0.02). Only indexed left ventricular end-diastolic diameter (HR 1.1, 95%CI 1.0-1.2; p < 0.02) and global ECV (HR 1.12, 95%CI 1.0-1.25; p < 0.02) were independently associated with AB. The global ECV cut-off value of 31.05% differentiated both groups (AUC 0.713; 95%CI 0.598-0.827; p < 0.001). Neither qualitative nor quantitative LGE-based assessment of replacement fibrosis allowed for the stratification of DCM patients into low or high AB. Interstitial fibrosis, expressed as ECV, was an independent predictor of AB in DCM. Incorporation of CMR parametric indices into decision-making processes may improve arrhythmic risk stratification in DCM.

Incremental Values of T1 Mapping in the Prediction of Sudden Cardiac Death Risk in Hypertrophic Cardiomyopathy: A Comparison With Two Guidelines

Background: MRI native T1 mapping and extracellular volume fraction (ECV) are quantitative values that could reflect various myocardial tissue characterization. The role of these parameters in predicting the risk of sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM) is still poorly understood.

Aim: This study aims to investigate the ability of native T1 mapping and ECV values to predict major adverse cardiovascular events (MACE) in HCM, and its incremental values over the 2014 European Society of Cardiology (ESC) and enhanced American College of Cardiology/American Heart Association (ACC/AHA) guidelines.

Methods: Between July 2016 and October 2020, HCM patients and healthy individuals with sex and age matched who underwent cardiac MRI were prospectively enrolled. The native T1 and ECV parameters were measured. The SCD risk was evaluated by the 2014 ESC guidelines and enhanced ACC/AHA guidelines. MACE included cardiac death, transplantation, heart failure admission, and implantable cardioverter-defibrillator implantation.

Results: A total of 203 HCM patients (54.2 ± 14.9 years) and 101 healthy individuals (53.2 ± 14.7 years) were evaluated. During a median follow-up of 15 months, 25 patients (12.3%) had MACE. In multivariate Cox regression analysis, global native T1 mapping (hazard ratio (HR): 1.446; 95% confidence interval (CI): 1.195–1.749; P < 0.001) and non-sustained ventricular tachycardia (NSVT) (HR: 4.949; 95% CI, 2.033–12.047; P < 0.001) were independently associated with MACE. Ten of 86 patients (11.6%) with low SCD risk assessed by the two guidelines had MACE. In this subgroup of patients, multivariate Cox regression analysis showed that global native T1 mapping was independently associated with MACE (HR: 1.532; 95% CI: 1.221–1.922; P < 0.001). In 85 patients with conflicting results assessed by the two guidelines, end-stage systolic dysfunction was independently associated with MACE (HR: 7.942, 95% CI: 1.322–47.707, P = 0.023). In 32 patients with high SCD risk assessed by the two guidelines, NSVT was independently associated with MACE (HR: 9.779, 95% CI: 1.953–48.964, P = 0.006).

Conclusion: The global native T1 mapping could provide incremental values and serve as potential supplements to the current guidelines in the prediction of MACE.