The Role of Cardiac MRI in the Diagnosis and Risk Stratification of Hypertrophic Cardiomyopathy

Cardiac MRI in diagnosis, prognosis, and follow-up of hypertrophic cardiomyopathy in children: current perspectives

Hypertrophic Cardiomyopathy (HCM) is an inherited myocardial disease characterised by left ventricular hypertrophy, which carries an increased risk of life-threatening arrhythmias and sudden cardiac death. The age of presentation and the underlying aetiology have a significant impact on the prognosis and quality of life of children with HCM, as childhood-onset HCM is associated with high mortality risk and poor long-term outcomes. Accurate cardiac assessment and identification of the HCM phenotype are therefore crucial to determine the diagnosis, prognostic stratification, and follow-up. Cardiac magnetic resonance (CMR) is a comprehensive evaluation tool capable of providing information on cardiac morphology and function, flow, perfusion, and tissue characterisation. CMR allows to detect subtle abnormalities in the myocardial composition and characterise the heterogeneous phenotypic expression of HCM. In particular, the detection of the degree and extent of myocardial fibrosis, using late-gadolinium enhanced sequences or parametric mapping, is unique for CMR and is of additional value in the clinical assessment and prognostic stratification of paediatric HCM patients. Additionally, childhood HCM can be progressive over time. The rate, timing, and degree of disease progression vary from one patient to the other, so close cardiac monitoring and serial follow-up throughout the life of the diagnosed patients is of paramount importance. In this review, an update of the use of CMR in childhood HCM is provided, focussing on its clinical role in diagnosis, prognosis, and serial follow-up.

Role of Imaging in the Diagnosis, Evaluation, and Management of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is increasingly recognized and may benefit from the recent approval of new, targeted medical therapy. Successful management of HCM is dependent on early and accurate diagnosis. The lack of a definitive diagnostic test, the wide variation in phenotype and the commonness of phenocopy conditions, and the presence of normal or hyperdynamic left ventricular function in most patients makes HCM a condition that is highly dependent on imaging for all aspects of management including, diagnosis, classification, predicting risk of complications, detecting complications, identifying risk for ventricular arrhythmias, evaluating choice of therapy and monitoring therapy, intraprocedural guidance, and screening family members. Although echocardiographic imaging remains the mainstay in the diagnosis and subsequent management of HCM, this disease clearly requires multimethod imaging for various aspects of optimal patient care. Advances in echocardiography hardware and techniques, development and refinement of imaging with computed tomography, magnetic resonance, and nuclear scanning, and the emergence of very focused assessments such as diastology and fibrosis imaging have all advanced the diagnosis and management of HCM. In this review, we discuss the relative utility and evidence support for these imaging approaches to contribute to improve patient outcomes.

Impact of a Center of Excellence in Confirming or Excluding a Diagnosis of Hypertrophic Cardiomyopathy

Tertiary hospitals with expertise in hypertrophic cardiomyopathy (HCM) are assuming a greater role in confirming and correcting HCM diagnoses at referring centers. The objectives were to establish the frequency of alternate diagnoses from referring centers and identify predictors of accuracy of an HCM diagnosis from the referring centers. Imaging findings from echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging (CMR) in 210 patients referred to an HCM Center of Excellence between September 2020 and October 2022 were reviewed. Clinical and imaging characteristics from pre-referral studies were used to construct a model for predictors of ruling out HCM or confirming the diagnosis using machine learning methods (least absolute shrinkage and selection operator logistic regression). Alternative diagnoses were found in 38 of the 210 patients (18.1%) (median age 60 years, 50% female). A total of 17 of the 38 patients (44.7%) underwent a new CMR after their initial visit, and 14 of 38 patients (36.8%) underwent review of a previous CMR. Increased left ventricular end-diastolic volume, indexed, greater septal thickness measurements, greater left atrial size, asymmetric hypertrophy on echocardiography, and the presence of an implantable cardioverter-defibrillator were associated with higher odds ratios for confirming a diagnosis of HCM, whereas increasing age and the presence of diabetes were more predictive of rejecting a diagnosis of HCM (area under the curve 0.902, p <0.0001). In conclusion, >1 in 6 patients with presumed HCM were found to have an alternate diagnosis after review at an HCM Center of Excellence, and both clinical findings and imaging parameters predicted an alternate diagnosis.

Machine learning and physical based modeling for cardiac hypertrophy

Background and objective

Predicting the long-term expansion and remodeling of the left ventricle in patients is challenging task but it has the potential to be clinically very useful.

Methods

In our study, we present machine learning models based on random forests, gradient boosting, and neural networks, used to track cardiac hypertrophy. We collected data from multiple patients, and then the model was trained using the patient's medical history and present level of cardiac health. We also demonstrate a physical-based model, using the finite element procedure to simulate the development of cardiac hypertrophy.

Results

Our models were used to forecast the evolution of hypertrophy over six years. The machine learning model and finite element model provided similar results.

Conclusions

The finite element model is much slower, but it's more accurate compared to the machine learning model since it's based on physical laws guiding the hypertrophy process. On the other hand, the machine learning model is fast but the results can be less trustworthy in some cases. Both of our models, enable us to monitor the development of the disease. Because of its speed machine learning model is more likely to be used in clinical practice. Further improvements to our machine learning model could be achieved by collecting data from finite element simulations, adding them to the dataset, and retraining the model. This can result in a fast and more accurate model combining the advantages of physical-based and machine learning modeling.

Utility of cardiac imaging in patients with ventricular tachycardia

Ventricular tachycardia (VT) is a life-threatening arrhythmia that may be idiopathic or result from structural heart disease. Cardiac imaging is critical in the diagnostic workup and risk stratification of patients with VT. Data gained from cardiac imaging provides information on likely mechanisms and sites of origin, as well as risk of intervention. Pre-procedural imaging can be used to plan access route(s) and identify patients where post-procedural intensive care may be required. Integration of cardiac imaging into electroanatomical mapping systems during catheter ablation procedures can facilitate the optimal approach, reduce radiation dose, and may improve clinical outcomes. Intraprocedural imaging helps guide catheter position, target substrate, and identify complications early. This review summarises the contemporary imaging modalities used in patients with VT, and their uses both pre-procedurally and intra-procedurally.

Alcohol Septal Ablation in Patients with Hypertrophic Obstructive Cardiomyopathy: A Contemporary Perspective

Alcohol septal ablation is a minimally invasive procedure for the treatment of left ventricular outflow tract (LVOT) obstruction in patients with hypertrophic obstructive cardiomyopathy (HOCM) who remain symptomatic despite optimal medical therapy. The procedure causes a controlled myocardial infarction of the basal portion of the interventricular septum by the injection of absolute alcohol with the aim of reducing LVOT obstruction and improving the patient's hemodynamics and symptoms. Numerous observations have demonstrated the efficacy and safety of the procedure, making it a valid alternative to surgical myectomy. In particular, the success of alcohol septal ablation depends on appropriate patient selection and the experience of the institution where the procedure is performed. In this review, we summarize the current evidence on alcohol septal ablation and highlight the importance of a multidisciplinary approach involving a team of clinical and interventional cardiologists and cardiac surgeons with high expertise in the management of HOCM patients-the Cardiomyopathy Team.

[The association between cardiac mr feature tracking strain and myocardial late gadolinium enhancement in patients with hypertrophic cardiomyopathy]

Aim    Hypertrophic cardiomyopathy (HCM) is a relatively common, heritable cardiomyopathy, and cardiac magnetic resonance (CMR) studies have been performed previously to evaluate different aspects of the disease. However, a comprehensive study, including all four cardiac chambers and analysis of left atrial (LA) function, is missing in the literature. The aim of this retrospective study was to analyze CMR-feature tracking (CMR-FT) strain parameters and atrial function of HCM patients and to investigate the association of these parameters with the amount of myocardial late gadolinium enhancement (LGE).Material and Methods    In this retrospective, cross-sectional study, we analyzed the CMR images (CMRI) of 58 consecutive patients, who from February 2020 to September 2022 were diagnosed with HCM at our tertiary cardiovascular center. Patients who were younger than 18 yrs or who had moderate or severe valvular heart disease, significant coronary artery disease, previous myocardial infarction, suboptimal image quality, or with contraindication to CMR were excluded. CMRI was performed at 1.5 T with a scanner, and all scans were assessed by an experienced cardiologist and then re-assessed by an experienced radiologist. SSFP 2-, 3- and 4‑chamber, short axis views were obtained and left ventricular (LV) end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and mass were measured. LGE images were obtained using a PSIR sequence. Native T1 and T2 mapping and post-contrast T1 map sequences were performed and each patient's myocardial extracellular volume (ECV) was calculated. LA volume index (LAVI), LA ejection fraction (LAEF), LA coupling index (LACI) were calculated. The complete CMR analysis of each patient was performed with CVI 42 software (Circle CVi, Calgary, Canada), off-line.Results    The patients were divided into two groups, HCM with LGE (n=37, 64 %) and HCM without LGE (n=21, 36 %). The average patient age in the HCM patients with LGE was 50.8±14 yrs and 47±12.9 yrs in the HCM patients without LGE. Maximum LV wall thickness and basal antero-septum thickness were significantly higher in the HCM with LGE group compared to the HCM without LGE group (14.8±3.5 mm vs 20.3±6.5 mm (p&lt;0.001), 14.2±3.2 mm vs 17.3±6.1 mm (p=0.015), respectively). LGE was 21.9±31.7 g and 15.7±13.4 % in the HCM with LGE group. LA area (22.2±6.1 vs 28.8±11.2 cm2; p=0.015) and LAVI (28.9±10.2 vs 45.6±23.1; p-0.004) were significantly higher in the HCM with LGE group. LACI was doubled in the HCM with LGE group (0.2±0.1 vs 0.4±0.2; p&lt;0.001). LA strain (30.4±13.2 vs 21.3±16.2; p-0.04) and LV strain (15.2±3 vs 12.2±4.5; p=0.012) were significantly decreased in the HCM with LGE group.Conclusion    This study sheds light on the CMR-FT differences between HCM with and without LGE. We found a greater burden of LA volume but significantly lower LA and LV strain in the LGE patients. These findings highlight further the LA and LV remodeling in HCM. Impaired LA function appears to have physiological significance, being associated with greater LGE. While our CMR-FT findings support the progressive nature of HCM, beginning with sarcomere dysfunction to eventual fibrosis, further studies are needed to validate these results in larger cohorts and to evaluate their clinical relevance.

Leisure-time and competitive sport participation: a changing paradigm for HCM patients

HCM has long been considered the most frequent cause of death in athletes, and reason for disqualification from sport. However, our perception of the impact of sports on HCM is largely based on anecdotal evidence. In this review, we provide a reappraisal of current knowledge relative to 1) the impact of sport on LV remodeling, and 2) on the clinical outcome of HCM in athletes. 1) The limited available evidence argues against the hypothesis that intensive exercise conditioning may trigger and/or worsen the development of LV hypertrophy or cause changes in LV function in adult HCM athletes. 2) Recent observations challenge the concept of a detrimental effect of sport on HCM clinical course. The Reset-HCM study showed that 16-week moderate-intensity exercise resulted in a small, significant increase in exercise capacity and no adverse events. In a cohort of 88 low-risk HCM athletes followed for a 7-year period, survival analyses showed no difference in mortality between HCM who discontinued or pursued vigorous exercise programmes. Further reassurance was provided by the ICD Sports Safety Registry. Clinical implications: At present, patients' attitude to sport participation is highly variable, based on social and legal backgrounds surrounding medical practice in different countries. The shared-decision-making as suggested by current US and European guidelines allows the physician to deliver a tailored and more liberal advice. Physicians should be aware of the changing paradigm relative to exercise and sport prescription for HCM and promote active lifestyle as an integral component of modern management of HCM patients.

Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports

Background

Cardiac magnetic resonance (CMR) imaging is important for diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM) patients. However, collection of information from large numbers of CMR reports by manual review is time-consuming, error-prone and costly. Natural language processing (NLP) is an artificial intelligence method for automated extraction of information from narrative text including text in CMR reports in electronic health records (EHR). Our objective was to assess whether NLP can accurately extract diagnosis of HCM from CMR reports.

Methods

An NLP system with two tiers was developed for information extraction from narrative text in CMR reports; the first tier extracted information regarding HCM diagnosis while the second extracted categorical and numeric concepts for HCM classification. We randomly allocated 200 HCM patients with CMR reports from 2004 to 2018 into training (100 patients with 185 CMR reports) and testing sets (100 patients with 206 reports).

Results

NLP algorithms demonstrated very high performance compared to manual annotation. The algorithm to extract HCM diagnosis had accuracy of 0.99. The accuracy for categorical concepts included HCM morphologic subtype 0.99, systolic anterior motion of the mitral valve 0.96, mitral regurgitation 0.93, left ventricular (LV) obstruction 0.94, location of obstruction 0.92, apical pouch 0.98, LV delayed enhancement 0.93, left atrial enlargement 0.99 and right atrial enlargement 0.98. Accuracy for numeric concepts included maximal LV wall thickness 0.96, LV mass 0.99, LV mass index 0.98, LV ejection fraction 0.98 and right ventricular ejection fraction 0.99.

Conclusions

NLP identified and classified HCM from CMR narrative text reports with very high performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-02017-y.

Simultaneous multi-parametric acquisition and reconstruction techniques in cardiac magnetic resonance imaging: Basic concepts and status of clinical development

Simultaneous multi-parametric acquisition and reconstruction techniques (SMART) are gaining attention for their potential to overcome some of cardiovascular magnetic resonance imaging's (CMR) clinical limitations. The major advantages of SMART lie within their ability to simultaneously capture multiple "features" such as cardiac motion, respiratory motion, T1/T2 relaxation. This review aims to summarize the overarching theory of SMART, describing key concepts that many of these techniques share to produce co-registered, high quality CMR images in less time and with less requirements for specialized personnel. Further, this review provides an overview of the recent developments in the field of SMART by describing how they work, the parameters they can acquire, their status of clinical testing and validation, and by providing examples for how their use can improve the current state of clinical CMR workflows. Many of the SMART are in early phases of development and testing, thus larger scale, controlled trials are needed to evaluate their use in clinical setting and with different cardiac pathologies.

Artificial intelligence study on left ventricular function among normal individuals, hypertrophic cardiomyopathy and dilated cardiomyopathy patients using 1.5T cardiac cine MR images obtained by SSFP sequence

OBJECTIVES

To evaluate the performance of a deep learning-based method to automatically quantify left ventricular (LV) function from MR images in different cardiomyopathy.

METHODS

This retrospective study included MRI data sets from 2013 to 2020. Data on left ventricular function from patients with hypertrophic cardiomyopathy (HCM), patients with dilated cardiomyopathy (DCM), and healthy participants were analyzed. MRI data from a total of 388 patients were measured manually and automatically.The performance of Convolutional Neural Networks (CNNs) was evaluated based on the manual notes of two experienced observers: (a) LV segmentation accuracy, and (b) LV functional parameter accuracy. Bland-Altman analysis, Receiver operating Characteristic (ROC) curve analysis and Pearson correlation analysis were used to evaluate the consistency between fully automatic and manual diagnosis of HCM and DCM.

RESULTS

The deep-learning CNN performed best in HCM in evaluating LV function and worst in DCM. Compared with manual analysis, four parameters of LV function in the HCM group showed high correlation (r at least >0.901), and the correlation of DCM in all parameters was weaker than that of HCM, especially EF (r2 = 0.776) and SV (r2 = 0.645). ROC curve analysis indicated that at the optimal cut-off value, EF from automatic segmentation identified DCM and HCM patients with sensitivity of 92.31 and 78.05%, specificity of 82.96 and 54.07%, respectively.

CONCLUSION

Among different heart diseases, the analysis of cardiac function based on deep-learning CNN may have different performances, with DCM performing the worst and HCM the best and thus, special attention should be paid to DCM patients when assessing LV function through artificial intelligence method. LV function parameter obtained by artificial intelligence method may play an important role in the future AI diagnosis of HCM and DCM.

ADVANCES IN KNOWLEDGE

These data for the first time objectively evaluate the performance of a commercially available deep learning-based method in cardiac function evaluation of different cardiomyopathy and point out its advantages and disadvantages in different cardiomyopathy. This work did not attempt to design the algorithm itself, but rather applied an already existing method to a test dataset of clinical data and evaluated the results for a limited number of cardiomyopathy.

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.

From Takotsubo to Yamaguchi

Our patient was a 56-year-old Caucasian female who had 34 emergency department visits to our center with recurrent chest pain, of which eleven were of cardiac etiology, involving cardiac causes over the period of seven years. Her chest pain was diagnosed as atypical during her previous visits. Chest CT revealed “ace-of-spades” in the cardiac transverse section. A transthoracic echocardiogram (TTE) demonstrated apical hypertrophy with end-systolic cavity obliteration and an ejection fraction (EF) of 65%-70%, seated amidst a normal-sized left ventricle, with normal wall thickness, indicating Yamaguchi syndrome. In the case report, we portray the need to widen the spectrum of differentials in an encounter with a patient presenting with chest pain.

Anatomical-MRI Correlations in Adults and Children with Hypertrophic Cardiomyopathy

Hypertrophic Cardiomyopathy (HCM) is the most frequent hereditary cardiovascular disease and the leading cause of sudden cardiac death in young individuals. Advancements in CMR imaging have allowed for earlier identification and more accurate prognosis of HCM. Interventions aimed at slowing or stopping the disease’s natural course may be developed in the future. CMR has been validated as a technique with high sensitivity and specificity, very few contraindications, a low risk of side effects, and is overall a good tool to be employed in the management of HCM patients. The goal of this review is to evaluate the magnetic resonance features of HCM, starting with distinct phenotypic variants of the disease and progressing to differential diagnoses of athlete’s heart, hypertension, and infiltrative cardiomyopathies. HCM in children has its own section in this review, with possible risk factors that are distinct from those in adults; delayed enhancement in children may play a role in risk stratification in HCM. Finally, a number of teaching points for general cardiologists who recommend CMR for patients with HCM will be presented.

Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning

BACKGROUND

Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD).

OBJECTIVE

Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period.

METHODS

The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method.

RESULTS

The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R² from 0.3 to 0.6.

CONCLUSIONS

By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets.

The Role of Cardiovascular Magnetic Resonance Imaging in the Evaluation of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disorder, affecting 1 out of 500 adults globally. It is a widely heterogeneous disorder characterized by a range of phenotypic expressions, and is most often identified by non-invasive imaging that includes echocardiography and cardiovascular magnetic resonance imaging (CMR). Within the last two decades, cardiac magnetic resonance imaging (MRI) has emerged as the defining tool for the characterization and prognostication of cardiomyopathies. With a higher image quality, spatial resolution, and the identification of morphological variants of HCM, CMR has become the gold standard imaging modality in the assessment of HCM. Moreover, it has been crucial in its management, as well as adding prognostic information that clinical history nor other imaging modalities may not provide. This literature review addresses the role and current applications of CMR, its capacity in evaluating HCM, and its limitations.

Cardiac magnetic resonance in the assessment of hypertrophic cardiomyopathy phenotypes and stages - pictorial review

The aim of this paper is to present recent advances in hypertrophic cardiomyopathy (HCM) diagnosis and treatment based on a literature review. Special emphasis has been placed on the role of cardiac magnetic resonance imaging (CMR) for the assessment of morphological and functional consequences of different stages of HCM including prognostication. The text is illustrated with the images and data of the HCM patients diagnosed with CMR study in our hospital. CMR is an important tool, particularly relevant in novel risk factors and LV dysfunction groups. The HCM group with overt left ventricular dysfunction is underrecognized, often labelled by clinicians as dilated cardiomyopathy. Advanced diagnostic and management strategies effectively influence the natural history of HCM.

Diagnosis of Cardiac Involvement in Amyloid A Amyloidosis by Cardiovascular Magnetic Resonance Imaging

Background: Diagnosis of cardiac involvement in amyloid A (AA) amyloidosis is challenging since AA amyloidosis is a rare disease and cardiac involvement even less frequent. The diagnostic yield of currently available non-invasive imaging methods is not well-studied and rather limited, and invasive endomyocardial biopsy (EMB) is rarely performed due to the potential risk of this procedure. Cardiovascular magnetic resonance (CMR)-based myocardial tissue characterization by late-gadolinium-enhancement (LGE) imaging and novel-mapping approaches may increase the diagnostic yield in AA amyloidosis.

Methods: Two patients with AA amyloidosis in whom cardiac involvement was suspected based on CMR findings and subsequently proven by biopsy work-up are presented. CMR studies were performed on a 1.5-T system and comprised a cine steady-state free precession pulse sequence for ventricular function and a late-gadolinium-enhancement (LGE) sequence for detection of myocardial pathology. Moreover, a modified Look-Locker inversion recovery (MOLLI) T1-mapping sequence was applied in basal, mid and apical short-axes prior to contrast agent administration and ~20 min thereafter to determine native T1 and ECV values.

Results: Both patients showed slightly dilated left ventricles (LV) with mild to moderate LV hypertrophy and preserved systolic function. Only a very subtle pattern of LGE was observed in both patients with AA amyloidosis. However, markedly elevated native T1 (max. 1,108 and 1,112 ms, respectively) and extracellular volume fraction (ECV) values (max. 39 and 48%, respectively) were measured in the myocardium suggesting the presence of cardiac involvement - with subsequent EMB-based proof of AA amyloidosis.

Conclusion: We recommend a multi-parametric CMR approach in patients with AA amyloidosis comprising both LGE-based contrast-imaging and T1-mapping-based ECV measurement of the myocardium for non-invasive work-up of suspected cardiac involvement. The respective CMR findings may be used as gatekeeper for additional invasive procedures (such as EMB) and as a non-invasive monitoring tool regarding assessment and modification of ongoing treatments.

CMR-based T1-mapping offers superior diagnostic value compared to longitudinal strain-based assessment of relative apical sparing in cardiac amyloidosis

Cardiac amyloidosis (CA) is an infiltrative disease. In the present study, we compared the diagnostic accuracy of cardiovascular magnetic resonance (CMR)-based T1-mapping and subsequent extracellular volume fraction (ECV) measurement and longitudinal strain analysis in the same patients with (a) biopsy-proven cardiac amyloidosis (CA) and (b) hypertrophic cardiomyopathy (HCM). N = 30 patients with CA, N = 20 patients with HCM and N = 15 healthy control patients without relevant cardiac disease underwent dedicated CMR studies. The CMR protocol included standard sequences for cine-imaging, native and post-contrast T1-mapping and late-gadolinium-enhancement. ECV measurements were based on pre- and post-contrast T1-mapping images. Feature-tracking analysis was used to calculate 3D left ventricular longitudinal strain (LV-LS) in basal, mid and apical short-axis cine-images and to assess the presence of relative apical sparing. Receiver-operating-characteristic analysis revealed an area-under-the-curve regarding the differentiation of CA from HCM of 0.984 for native T1-mapping (p < 0.001), of 0.985 for ECV (p < 0.001) and only 0.740 for the "apical-to-(basal + midventricular)"-ratio of LV-LS (p = 0.012). A multivariable logistical regression analysis showed that ECV was the only statistically significant predictor of CA when compared to the parameter LV-LS or to the parameter "apical-to-(basal + midventricular)" LV-RLS-ratio. Native T1-mapping and ECV measurement are both superior to longitudinal strain measurement (with assessment of relative apical sparing) regarding the appropriate diagnosis of CA.

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy

BACKGROUND

Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools.

METHOD

Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death.

RESULTS

The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively.

CONCLUSIONS

The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general.

Cardiovascular Magnetic Resonance Imaging and Heart Failure

Purpose of Review

The purpose of this review is to summarize the application of cardiac magnetic resonance (CMR) in the diagnostic and prognostic evaluation of patients with heart failure (HF).

Recent Findings

CMR is an important non-invasive imaging modality in the assessment of ventricular volumes and function and in the analysis of myocardial tissue characteristics. The information derived from CMR provides a comprehensive evaluation of HF. Its unique ability of tissue characterization not only helps to reveal the underlying etiologies of HF but also offers incremental prognostic information.

Summary

CMR is a useful non-invasive tool for the diagnosis and assessment of prognosis in patients suffering from heart failure.

Ventricular Arrhythmia in Septal and Apical Hypertrophic Cardiomyopathy: The French-Canadian Experience

Background: Apical hypertrophic cardiomyopathy (aHCM) is thought to have a more benign clinical course compared to septal HCM (sHCM), but most data have been derived from Asian cohorts. Comparative data on clinical outcome in Caucasian aHCM cohorts are scarce, and the results are conflicting. The aim of this study was to estimate the prevalence and outcome of aHCM in French-Canadians of Caucasian descent.

Methods and results: We conducted a retrospective, single-center cohort study. The primary endpoint was a composite of documented sustained ventricular arrhythmia (VA), appropriate ICD therapy, arrhythmogenic syncope, cardiac arrest, or all-cause mortality. A total of 301 HCM patients (65% males) were enrolled including 80/301 (27%) with aHCM and 221/301 (73%) with sHCM. Maximal wall thickness was similar in both groups. Left ventricular apical aneurysm was significantly more common in aHCM (10 vs. 0.5%; p < 0.001). The proportion of patients with myocardial fibrosis ≥ 15% of the left ventricular mass was similar between aHCM and sHCM (21 vs. 24%; p = 0.68). Secondary prevention ICDs were more often implanted in aHCM patients (16 vs. 7%; p = 0.02). The primary endpoint occurred in 26% of aHCM and 10.4% of sHCM patients (p = 0.001) and was driven by an increased incidence of sustained VA (10 vs. 2.3%; p = 0.01). Multivariate analysis identified apical aneurysm and a phenotype of aHCM as independent predictors of the primary endpoint and the occurrence of sustained ventricular tachycardia. Unexplained syncope and a family history of sudden cardiac death were additional predictors for sustained VA. Apical HCM was associated with an increased risk of ventricular arrhythmia even when excluding patients with apical aneurysm.

Conclusions: The phenotype of apical HCM is much more common in French-Canadians (27%) of Caucasian descent compared to other Caucasian HCM populations. Apical HCM in French-Canadians is associated with an increased risk for ventricular arrhythmia.

Late Gadolinium Enhancement-Dispersion Mapping: A New Magnetic Resonance Imaging Technique to Assess Prognosis in Patients With Hypertrophic Cardiomyopathy and Low-Intermediate 5-Year Risk of Sudden Death

BACKGROUND

Late gadolinium enhancement (LGE) is an important prognostic marker in hypertrophic cardiomyopathy and an extent >15% it is associated with high risk of sudden cardiac death. We proposed a novel method, the LGE-dispersion mapping, to assess heterogeneity of scar, and evaluated its prognostic role in patients with hypertrophic cardiomyopathy.

METHODS

One hundred eighty-three patients with hypertrophic cardiomyopathy and a low- or intermediate 5-year risk of sudden cardiac death underwent cardiac magnetic resonance imaging. A parametric map was generated from each LGE image. A score from 0 to 8 was assigned at every pixel of these maps, indicating the number of the surrounding pixels having different quality (nonenhancement, mild-enhancement, or hyperenhancement) from the central pixel. The Global Dispersion Score (GDS) was calculated as the average score of all the pixels of the images.

RESULTS

During a median follow-up time of 6 (25th-75th, 4-10) years, 22 patients had hard cardiac events (sudden cardiac death, appropriate implantable cardioverter-defibrillator therapy, resuscitated cardiac arrest, and sustained ventricular tachycardia). Kaplan-Meier analysis showed that patients with GDS>0.86 had worse prognosis than those with lower GDS (P<0.0001). GDS>0.86 was the only independent predictor of cardiac events (hazard ratio, 9.9 [95% CI, 2.9-34.6], P=0.0003). When compared with LGE extent >15%, GDS improved the classification of risk in these patients (net reclassification improvement, 0.39 [95% CI, 0.11-0.72], P<0.019).

CONCLUSIONS

LGE-dispersion mapping is a marker of scar heterogeneity and provides a better risk stratification than LGE presence and its extent in patients with hypertrophic cardiomyopathy and a low-intermediate 5-year risk of sudden cardiac death.

Diagnostic value of the novel CMR parameter "myocardial transit-time" (MyoTT) for the assessment of microvascular changes in cardiac amyloidosis and hypertrophic cardiomyopathy

BACKGROUND

Coronary microvascular dysfunction (CMD) is present in various non-ischemic cardiomyopathies and in particular in those with left-ventricular hypertrophy. This study evaluated the diagnostic value of the novel cardiovascular magnetic resonance (CMR) parameter "myocardial transit-time" (MyoTT) in distinguishing cardiac amyloidosis from other hypertrophic cardiomyopathies.

METHODS

N = 20 patients with biopsy-proven cardiac amyloidosis (CA), N = 20 patients with known hypertrophic cardiomyopathy (HCM), and N = 20 control patients without relevant cardiac disease underwent dedicated CMR studies on a 1.5-T MR scanner. The CMR protocol comprised cine and late-gadolinium-enhancement (LGE) imaging as well as first-pass perfusion acquisitions at rest for MyoTT measurement. MyoTT was defined as the blood circulation time from the orifice of the coronary arteries to the pooling in the coronary sinus (CS) reflecting the transit-time of gadolinium in the myocardial microvasculature.

RESULTS

MyoTT was significantly prolonged in patients with CA compared to both groups: 14.8 ± 4.1 s in CA vs. 12.2 ± 2.5 s in HCM (p = 0.043) vs. 7.2 ± 2.6 s in controls (p < 0.001). Native T1 and extracellular volume (ECV) were significantly higher in CA compared to HCM and controls (p < 0.001). Both parameters were associated with a higher diagnostic accuracy in predicting the presence of CA compared to MyoTT: area under the curve (AUC) for native T1 = 0.93 (95% confidence interval (CI) = 0.83-1.00; p < 0.001) and AUC for ECV = 0.95 (95% CI = 0.88-1.00; p < 0.001)-compared to the AUC for MyoTT = 0.76 (95% CI = 0.60-0.92; p = 0.008). In contrast, MyoTT performed better than all other CMR parameters in differentiating HCM from controls (AUC for MyoTT = 0.93; 95% CI = 0.81-1.00; p = 0.003 vs. AUC for native T1 = 0.69; 95% CI = 0.44-0.93; p = 0.20 vs. AUC for ECV = 0.85; 95% CI = 0.66-1.00; p = 0.017).

CONCLUSION

The relative severity of CMD (measured by MyoTT) in relationship to extracellular changes (measured by native T1 and/or ECV) is more pronounced in HCM compared to CA-in spite of a higher absolute MyoTT value in CA patients. Hence, MyoTT may improve our understanding of the interplay between extracellular/intracellular and intravasal changes that occur in the myocardium during the disease course of different cardiomyopathies.

Myocardial tissue characterization by gadolinium-enhanced cardiac magnetic resonance imaging for risk stratification of adverse events in hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a genetic cardiomyopathy with a wide spectrum of clinical manifestations. Patients can be asymptomatic or suffer major adverse events including sudden cardiac death, ventricular arrhythmias, and heart failure. Identification of individuals with HCM who are at risk for these complications remains challenging. While echocardiography remains the mainstay of diagnostic evaluation, cardiac magnetic resonance imaging (CMR) is an important adjunctive diagnostic modality with emerging applications for risk-stratification of adverse events in the HCM population. Although not included in current guidelines for HCM management, there is increasing evidence to support the use of CMR for routine prognostic assessment of HCM patients. In this review we discuss the use of CMR techniques, including late gadolinium enhancement, T1 mapping, and quantification of extracellular volume fraction, for the risk stratification of three major adverse events in HCM: sudden cardiac death, ventricular arrhythmias, and congestive heart failure.

Aortic regurgitation is common in hypertrophic cardiomyopathy: An echocardiography and cardiovascular magnetic resonance study

PURPOSE

To investigate the incidence, mechanism, and risk factors of aortic regurgitation (AR) in patients with hypertrophic cardiomyopathy (HCM) by using echocardiography and cardiac magnetic resonance (CMR).

METHODS

105 HCM patients, 52 hypertension (HTN) patients and 50 healthy controls (HC) were retrospectively recruited. HCM patients were divided into 38 with AR (HCMAR) subject and 67 without AR. The subaortic complex, D1 (the largest distance of the interventricular septum that protruded into the LVOT) and D3 (the LVOT effective width) were assessed and compared between the two groups of HCM patients.

RESULTS

AR was more common in HCM than in HTN and HC (36 %, 17 %, and 10 %, respectively, P = 0.001). HCM patients with AR were older (58 ± 11 vs. 45 ± 16 years, P < 0.001) and had a higher incidence of hypertension (55 % vs. 33 %, P = 0.03). D1 was greater (13.5 ± 4.4 vs. 10.6 ± 4.0 mm, P = 0.001), and D3 was shorter in the HCMAR group (10.2 ± 5.3 vs. 13.7 ± 5.9 mm, P = 0.003). Anterior mitral leaflet length and left atrial diameter were greater in HCMAR group (all P < 0.05). On multivariable logistic regression analysis, the independent risk factors of AR in HCM patients were LVOTO and age.

CONCLUSIONS

This study demonstrated that AR is a common comorbidity of HCM, especially in patients with LVOTO. LVOTO and age were independent risk factors of AR in HCM patient.

Phenotypes of hypertrophic cardiomyopathy: genetics, clinics, and modular imaging

Hypertrophic cardiomyopathy (HCM) is the most common cardiovascular disease with genetic transmission, characterized by the hypertrophy of any segment of the left ventricle (LV), not totally explained by improper loading conditions, with LV systolic function preserved, increased, or reduced. The histopathological mechanism involved in HCM refers to the primary injury of the myocardium, as follows: disorganized array of myocytes, extracellular matrix modification, microvascular dysfunction, with subsequent appearance of myocardial fibrosis. Multiple sarcomere proteins mutations are responsible for HCM, but two of them are involved in 70% of the cases of HCM: β-myosin heavy chain (MYH7) and myosin-binding protein C (MYBPC3). The development of new genetic techniques involving genome editing is promising to discover a gene therapy for patients with HCM. Clinical presentation may differ from asymptomatic to sudden cardiac death (SCD), the last one targeting younger adults. In this case, the diagnosis and evaluation of SCD risk factors is extremely important. The common method of diagnosis is transthoracic echocardiography, but cardiac magnetic resonance (CMR) imaging represents "gold standard" in the evaluation of HCM patients. Treatment includes pharmacological therapy, surgery, alcohol ablation, and not least SCD prevention.

Risk stratification in hypertrophic cardiomyopathy

Sudden cardiac death (SCD) is the most devastating complication of hypertrophic cardiomyopathy (HCM). The greatest challenge in the management of HCM is identifying those at increased risk, since an implantable cardioverter-defibrillator (ICD) is a potentially life-saving therapy. We sought to summarize the available data on SCD in HCM and provide a clinical perspective on the current differing and somewhat conflicting data on risk stratification, with balanced guidance regarding rational clinical decision-making. Additionally, we sought to determine the status of the current implementation of guidelines compiled by HCM experts worldwide. The HCM Risk-SCD model helps improve the risk stratification of HCM patients for primary prevention of SCD by calculating an individual risk estimate that contributes to the clinical decision-making process. Improved risk stratification is important for decision-making before ICD implantation for the primary prevention of SCD.

Cardiovascular Magnetic Resonance Imaging of Inherited Heart Conditions

Imaging modalities are central to diagnosis and prognostication of confirmed or suspected inherited cardiomyopathies. The availability and use of cardiovascular magnetic resonance imaging (CMR) to supplement traditional modalities has increased substantially and has several advantages over traditional imaging techniques. CMR is unique in its ability to easily acquire images in any plane. Moreover, advances in CMR sequences have begun to enable characterisation of the myocardium without the need for invasive biopsy and has provided a major step forward in the understanding of inherited heart disease pathology and genotype-phenotype interactions. This review summarises the current role of CMR in inherited cardiomyopathies depending on their genotype and phenotype status, using arrhythmogenic right ventricular dysplasia/cardiomyopathy and hypertrophic cardiomyopathy as prototypical examples.

The Role of Contrast-Enhanced Cardiac Magnetic Resonance in the Assessment of Patients with Malignant Ventricular Arrhythmias

Cardiac magnetic resonance (CMR) imaging has gained significant traction as an imaging modality of choice in the evaluation of individuals with, or at risk for, heart failure. Ventricular arrhythmias, often malignant, may be sequelae of heart failure and arise from fibrosis. Late gadolinium enhancement evaluation by CMR has become a preferred modality to assess individuals at risk for malignant ventricular arrhythmias. A spectrum of various pathologies that predispose individuals to malignant ventricular arrhythmias, as well as the usefulness of CMR in their identification and prognostication, are reviewed.

Role of cardiac imaging in Anderson-Fabry cardiomyopathy

The Anderson-Fabry disease (AFD, or simply Fabry Disease, FD; MIM #301500) is a rare X-linked lysosomal storage disorder (Xq22.1) characterized by progressive renal failure, leading to morbidity through cardio- and cerebro-vascular involvement. Despite the classic phenotype, only cardiac involvement (cardiac variant of AFD; MIM 301500) is frequent in about 40% of male and 28% of female AFD patients, as reported by the Fabry Registry (https://www.registrynxt.com). Morphologically, the cardiac characteristic of the disease, occurs as left ventricular hypertrophy, is accompanied by myocardial fibrosis. Cardiologists may come across these patients during clinical and instrumental evaluation in individuals with non-specific symptoms such as chest pain and arrhythmias, or after instrumental evidence of left ventricular hypertrophy/hypertrophic cardiomyopathy (HCM; MIM 192600). A comprehensive cardiological work-up, including a cardiological visit, a baseline electrocardiogram (ECG) and imaging by both echocardiography (ECHO) and magnetic resonance (MRI) enables identification of the cardiac involvement in patients with a proven diagnosis of AFD. The heart involvement is present in up to 75% of AFD patients irrespective of their sex. Involvement includes ECG and echocardiography features which suggest AFD and not HCM. Cardiac imaging plays an important role in detecting this sub-type of cardiomyopathy, which, since 2001, has benefited from the introduction of the enzyme replacement therapy (ERT) in symptomatic and pre-symptomatic patients.

Prevalence and Prognostic Significance of Right Ventricular Dysfunction in Patients With Hypertrophic Cardiomyopathy

Few data are available regarding the prevalence and clinical significance of right ventricular systolic dysfunction (RVSD) in hypertrophic cardiomyopathy (HC) patients. This study aimed to evaluate right ventricular (RV) systolic function by cardiovascular magnetic resonance and explore the prevalence and prognostic significance of RVSD in HC patients. A total of 226 patients with HC assessed by cardiovascular magnetic resonance were included in this retrospective study. RVSD was defined by RV ejection fraction (RVEF) ≤45% and was present in 26 (11.5%) patients. Association between RVSD, clinical characteristics, and outcomes were analyzed. RVEF was significantly lower in patients with RVSD than without RVSD (36.2 ± 7.0% vs 60.5 ± 7.4%, p < 0.001). There was a positive correlation between RVEF and left ventricular ejection fraction (r = 0.45; p < 0.001). During a mean follow-up of 30.5 ± 23.9 months, there were 22 (9.7 %) all-cause mortality, including 12 (5.3%) cardiovascular death. Kaplan-Meier analysis showed a significantly higher risk for cardiovascular mortality in patients with RVSD (p = 0.026), but no significant difference in all-cause mortality (p = 0.118) and heart failure related rehospitalization (p = 0.485). On multivariate Cox regression analysis, RVSD (hazard ratio 5.36; confidence interval 1.39 to 20.77; p = 0.015) and RVEF (hazard ratio 0.94; confidence interval 0.89 to 0.98; p = 0.011) were independent predictors of cardiovascular mortality. In conclusion, RVSD is a common phenotype and a strong independent predictor of cardiovascular mortality in HC patients.

Phenotypes of hypertrophic cardiomyopathy. An illustrative review of MRI findings

Objective

The purpose of this article is to review how cardiac MRI provides the clinician with detailed information about the hypertrophic cardiomyopathy (HCM) phenotypes, assessing its morphological and functional consequences.

Conclusion

An understanding of cardiac MRI manifestations of HCM phenotypes will aid early diagnosis recognition and its functional consequences.

Teaching Points

• The phenotypic variability of HCM expands beyond myocardial hypertrophy, to include morphological and functional manifestations, ranging from subtle anomalies to remodelling of the LV with progressive dilatation and thinning of its wall.

• The stages of HCM, which are based on the clinical evidence of disease progression, include subclinical HCM, the classic HCM phenothype, adverse remodelling and overt dysfunction, or end-stage HCM.

• Cardiac MRI provides the clinician with detailed information regarding the HCM phenotypes and enables the assessment of its functional consequences.

Late Gadolinium Enhancement for Prediction of Mutation-Positive Hypertrophic Cardiomyopathy on the Basis of Panel-Wide Sequencing

BACKGROUND

Cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) revealed a substantial variation in the extent of myocardial scarring, a pathological hallmark of hypertrophic cardiomyopathy (HCM). However, few data exist regarding the relationship between the presence of gene mutations and the extent of LGE. Therefore, we aimed to investigate whether variations in the extent of LGE in HCM patients can be explained by the presence or absence of disease-causing mutations.Methods and Results:We analyzed data from 82 unrelated HCM patients who underwent both LGE-CMR and next-generation sequencing. We identified disease-causing sarcomere gene mutations in 44 cases (54%). The extent of LGE on CMR was an independent factor for predicting mutation-positive HCM (odds ratio 2.12 [95% confidence interval 1.51-3.83], P<0.01). The area under the curve of %LGE was greater than that of the conventional Toronto score for predicting the presence of a mutation (0.96 vs. 0.69, P<0.01). Sensitivity, specificity, positive predictive value, and negative predictive value of %LGE (cutoff >8.1%) were 93.2%, 89.5%, 91.1%, and 91.9%, respectively.

CONCLUSIONS

The results demonstrated that %LGE clearly discriminated mutation-positive from mutation-negative HCM in a clinically affected HCM population. HCM with few or no myocardial scars may be genetically different from HCM with a higher incidence of myocardial scars.

Management of an asymptomatic patient with the apical variant of hypertrophic cardiomyopathy

Healthcare professionals are faced with challenging decisions regarding patient evaluation and management on a daily basis. Once a diagnosis is made, additional challenges include how to proceed with the management. Here, we present an eighty-two-year-old female who was incidentally diagnosed with the apical variant of hypertrophic cardiomyopathy on a transthoracic echocardiogram. She was found to have newly diagnosed atrial fibrillation, but was otherwise asymptomatic from a cardiomyopathy standpoint. No specific guidelines exist for this patient population. Therefore, how does one proceed with the management of an asymptomatic patient with the apical variant of hypertrophic cardiomyopathy?