Importance of Newer Cardiac Magnetic Resonance-Based Risk Markers for Sudden Death Prevention in Hypertrophic Cardiomyopathy:An International Multicenter Study

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach

BACKGROUND

Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic resonance (CMR) imaging to identify high-risk imaging features. However, CMR imaging is resource intensive and is not widely accessible worldwide.

OBJECTIVE

The purpose of this study was to develop electrocardiogram (ECG) deep-learning (DL) models for the identification of patients with HCM and high-risk imaging features.

METHODS

Patients with HCM evaluated at Tufts Medical Center (N = 1930; Boston, MA) were used to develop ECG-DL models for the prediction of high-risk imaging features: systolic dysfunction, massive hypertrophy (≥30 mm), apical aneurysm, and extensive late gadolinium enhancement. ECG-DL models were externally validated in a cohort of patients with HCM from the Amrita Hospital HCM Center (N = 233; Kochi, India).

RESULTS

ECG-DL models reliably identified high-risk features (systolic dysfunction, massive hypertrophy, apical aneurysm, and extensive late gadolinium enhancement) during holdout testing (c-statistic 0.72, 0.83, 0.93, and 0.76) and external validation (c-statistic 0.71, 0.76, 0.91, and 0.68). A hypothetical screening strategy using echocardiography combined with ECG-DL-guided selective CMR use demonstrated a sensitivity of 97% for identifying patients with high-risk features while reducing the number of recommended CMRs by 61%. The negative predictive value with this screening strategy for the absence of high-risk features in patients without ECG-DL recommendation for CMR was 99.5%.

CONCLUSION

In HCM, novel ECG-DL models reliably identified patients with high-risk imaging features while offering the potential to reduce CMR testing requirements in underresourced areas.

Canadian Cardiovascular Society Clinical Practice Update on Contemporary Management of the Patient With Hypertrophic Cardiomyopathy

Numerous guidelines on the diagnosis and management of hypertrophic cardiomyopathy (HCM) have been published, by learned societies, over the past decade. Although helpful they are often long and less adapted to nonexperts. This writing panel was challenged to produce a document that grew as much from years of practical experience as it did from the peer-reviewed literature. As such, rather than produce yet another set of guidelines, we aim herein to deliver a concentrate of our own experiential learning and distill for the reader the essence of effective and appropriate HCM care. This Clinical Practice Update on HCM is therefore aimed at general cardiologists and other cardiovascular practitioners rather than for HCM specialists. We set the stage with a description of the condition and its clinical presentation, discuss the central importance of "obstruction" and how to look for it, review the role of cardiac magnetic resonance imaging, reflect on the appropriate use of genetic testing, review the treatment options for symptomatic HCM-crucially including cardiac myosin inhibitors, and deal concisely with practical issues surrounding risk assessment for sudden cardiac death, and management of the end-stage HCM patient. Uniquely, we have captured the pediatric experience on our panel to discuss appropriate differences in the management of younger patients with HCM. We ask the reader to remember that this document represents expert consensus opinion rather than dogma and to use their best judgement when dealing with the HCM patient in front of them.

Scar architecture affects the electrophysiological characteristics of induced ventricular arrhythmias in hypertrophic cardiomyopathy

AIMS

Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) detects myocardial scarring, a risk factor for ventricular arrhythmias (VAs) in hypertrophic cardiomyopathy (HCM). The LGE-CMR distinguishes core, borderzone (BZ) fibrosis, and BZ channels, crucial components of re-entry circuits. We studied how scar architecture affects inducibility and electrophysiological traits of VA in HCM.

METHODS AND RESULTS

We correlated scar composition with programmed ventricular stimulation-inducible VA features using LGE intensity maps. Thirty consecutive patients were enrolled. Thirteen (43%) were non-inducible, 6 (20%) had inducible non-sustained, and 11 (37%) had inducible sustained mono (MMVT)- or polymorphic VT/VF (PVT/VF). Of 17 induced VA, 13 (76%) were MMVT that either ended spontaneously, persisted as sustained monomorphic, or degenerated into PVT/VF. Twenty-seven patients (90%) had LGE. Of these, 17 (57%) had non-sustained or sustained inducible VA. Scar mass significantly increased (P = 0.002) from non-inducible to inducible non-sustained and sustained VA patients in both the BZ and core components. Borderzone channels were found in 23%, 67%, and 91% of non-inducible, inducible non-sustained, and inducible sustained VA patients (P = 0.003). All 13 patients induced with MMVT or monomorphic-initiated PVT/VF had LGE. The origin of 10/13 of these VTs matched scar location, with 8/10 of these LGE regions showing BZ channels. During follow-up (20 months, interquartile range: 7-37), one patient with BZ channels and inducible PVT had an ICD shock for VF.

CONCLUSION

Scar architecture determines inducibility and electrophysiological traits of VA in HCM. Larger studies should explore the role of complex LGE patterns in refining risk assessment in HCM patients.

Cardiac magnetic resonance biomarkers as surrogate endpoints in cardiovascular trials for myocardial diseases

Cardiac magnetic resonance offers multiple facets in the diagnosis, risk stratification, and management of patients with myocardial diseases. Particularly, its feature to precisely monitor disease activity lends itself to quantify response to novel therapeutics. This review critically appraises the value of cardiac magnetic resonance imaging biomarkers as surrogate endpoints for prospective clinical trials. The primary focus is to comprehensively outline the value of established cardiac magnetic resonance parameters in myocardial diseases. These include heart failure, cardiac amyloidosis, iron overload cardiomyopathy, hypertrophic cardiomyopathy, cardio-oncology, and inflammatory cardiomyopathies like myocarditis and sarcoidosis.

High-risk imaging characteristics in left ventricular apex for the life-threatening arrhythmic events in Japanese hypertrophic cardiomyopathy patients

Left ventricular (LV) apical aneurysm is known to be associated with the life-threatening arrhythmic events in hypertrophic cardiomyopathy (HCM). However, the current 2014 ESC guideline has not included apical aneurysm as a major risk factor for sudden cardiac death and 2018 JCS guideline includes it only as a modulator, while it has been included as a new major risk marker in 2020 AHA/ACC guideline. Therefore, we sought to identify high-risk imaging characteristics in LV apex which is associated with a higher occurrence of ventricular tachycardia/fibrillation (VT/VF). In 99 consecutive Japanese HCM patients (median age, 65 years; 59 males) undergoing implantable cardioverter-defibrillator (ICD) implantation for primary prevention following cardiac magnetic resonance including late gadolinium enhancement (LGE), the occurrence of appropriate ICD interventions for VT/VF was evaluated for 6.2 (median) years after ICD implantation. Overall, appropriate ICD interventions occurred in 43% with annual rates of 7.0% for appropriate interventions. Kaplan-Meier analysis demonstrated that the presence of LV apical aneurysm was significantly associated with a higher occurrence of appropriate interventions (annual rates 18.9% vs. 6.4%, P = 0.013). Similarly, patients with high LV mid-to-apex pressure gradient (annual rates 14.9% vs. 6.2%, P = 0.022) and presence of apical LGE (annual rates 10.9% vs. 4.0%, P = 0.001) experienced appropriate interventions more frequently. An aneurysm, high-pressure gradient, and LGE in an apex are associated with VT/VF. These characteristics in apex should be kept in mind when implanting ICD in Japanese HCM patients as a primary prevention.

Imaging cardiac hypertrophy in hypertrophic cardiomyopathy and its differential diagnosis

PURPOSE OF REVIEW

The aim of this study was to review imaging of myocardial hypertrophy in hypertrophic cardiomyopathy (HCM) and its phenocopies. The introduction of cardiac myosin inhibitors in HCM has emphasized the need for careful evaluation of the underlying cause of myocardial hypertrophy.

RECENT FINDINGS

Advances in imaging of myocardial hypertrophy have focused on improving precision, diagnosis, and predicting prognosis. From improved assessment of myocardial mass and function, to assessing myocardial fibrosis without the use of gadolinium, imaging continues to be the primary tool in understanding myocardial hypertrophy and its downstream effects. Advances in differentiating athlete's heart from HCM are noted, and the increasing rate of diagnosis in cardiac amyloidosis using noninvasive approaches is especially highlighted due to the implications on treatment approach. Finally, recent data on Fabry disease are shared as well as differentiating other phenocopies from HCM.

SUMMARY

Imaging hypertrophy in HCM and ruling out other phenocopies is central to the care of patients with HCM. This space will continue to rapidly evolve, as disease-modifying therapies are under investigation and being advanced to the clinic.

Deep learning-based measurement of echocardiographic data and its application in the diagnosis of sudden cardiac death

This study aimed to evaluate the potential of deep learning applied to the measurement of echocardiographic data in patients with sudden cardiac death (SCD). 320 SCD patients who met the inclusion and exclusion criteria underwent clinical evaluation, including age, sex, BMI, hypertension, diabetes, cardiac function classification, and echocardiography. The diagnostic value of deep learning model was observed by dividing the patients into two groups: training group (n=160) and verification group (n=160), as well as two groups of healthy volunteers (n=200 for each group) during the same period. Logistic regression analysis showed that MLVWT, LVEDD, LVEF, LVOT-PG, LAD, E/e' were all risk factors for SCD. Subsequently, a deep learning-based model was trained using the collected images of the training group. The optimal model was selected based on the identification accuracy of the validation group and showed an accuracy of 91.8%, sensitivity of 80.00%, and specificity of 91.90% in the training group. The AUC value of the ROC curve of the model was 0.877 for the training group and 0.995 for the validation groups. This approach demonstrates high diagnostic value and accuracy in predicting SCD, which is clinically important for the early detection and diagnosis of SCD.

Differing strategies for sudden death prevention in hypertrophic cardiomyopathy

Sudden death (SD) has traditionally been the most visible and feared complication of hypertrophic cardiomyopathy (HCM). Substantial progress in reducing the occurrence of these catastrophic events represents a new paradigm in disease management. Prevention of SD in HCM has resulted from introduction of primary prevention ICDs that reliably terminate life-threatening ventricular tachyarrhythmias, as well as a matured risk stratification algorithm capable of reliably identifying those patients at highest risk. This initiative has been a major determinant of reducing HCM-related mortality to a low rate of 0.5%/year. In such a heterogeneous heart disease as HCM, no perfect risk stratification strategy is possible, and available approaches differ in terms of sensitivity and specificity for identifying patients with SD risk. Major cardiovascular societies, American Heart Association/American College of Cardiology in the USA and European Society of Cardiology in Europe have promoted different risk stratification guidelines creating the potential for judging SD risk in a given HCM patient differently based on commitment to a particular societal guideline or country of residence. In this review, we provide a critical but balanced assessment of these two divergent SD prevention strategies with regard to their respective strengths and weaknesses, as a guide to clinicians directly engaged in this important management issue.

Magnetic Resonance Left Ventricle Mass-Index/Fibrosis: Long-Term Predictors for Ventricular Arrhythmia in Hypertrophic Cardiomyopathy—A Retrospective Registry

Objective: We aimed to study the long-term association of LV mass index (LVMI) and myocardial fibrosis with ventricular arrhythmia (VA) in a population of patients with confirmed hypertrophic cardiomyopathy (HCM) using cardiac magnetic resonance imaging (CMR). Methods: We retrospectively analyzed the data in consecutive HCM patients confirmed on CMR referred to an HCM clinic between January 2008 and October 2018. Patients were followed up yearly following diagnosis. Baseline demographics, risk factors and clinical outcomes from cardiac monitoring and an implanted cardioverter defibrillator (ICD) were analyzed for association of LVMI and LV late gadolinium enhancement (LVLGE) with VA. Patients were then allocated to one of two groups according to the presence of VA (Group A) or absence of VA (Group B) during the follow-up period. The transthoracic echocardiogram (TTE) and CMR parameters were compared between the two groups. Results: A total of 247 patients with confirmed HCM (age 56.2 ± 16.6, male = 71%) were studied over the follow-up period of 7 ± 3.3 years (95% CI = 6.6–7.4 years). LVMI derived from CMR was higher in Group A (91.1 ± 28.1 g/m2 vs. 78.8 ± 28.3 g/m2, p = 0.003) when compared to Group B. LVLGE was higher in Group A (7.3 ± 6.3% vs. 4.7 ± 4.3%, p = 0.001) when compared to Group B. Multivariable Cox regression analysis showed LVMI (hazard ratio (HR) = 1.02, 95% CI = 1.001–1.03, p = 0.03) and LVLGE (HR = 1.04, 95% CI = 1.001–1.08, p = 0.04) to be independent predictors for VA. Receiver operative curves showed higher LVMI and LVLGE with a cut-off of 85 g/m2 and 6%, respectively, to be associated with VA. Conclusions: LVMI and LVLGE are strongly associated with VA over long-term follow-up. LVMI requires more thorough studies to consider it as a risk stratification tool in patients with HCM.

Multimodality Imaging in Sarcomeric Hypertrophic Cardiomyopathy: Get It Right…on Time

Hypertrophic cardiomyopathy (HCM) follows highly variable paradigms and disease-specific patterns of progression towards heart failure, arrhythmias and sudden cardiac death. Therefore, a generalized standard approach, shared with other cardiomyopathies, can be misleading in this setting. A multimodality imaging approach facilitates differential diagnosis of phenocopies and improves clinical and therapeutic management of the disease. However, only a profound knowledge of the progression patterns, including clinical features and imaging data, enables an appropriate use of all these resources in clinical practice. Combinations of various imaging tools and novel techniques of artificial intelligence have a potentially relevant role in diagnosis, clinical management and definition of prognosis. Nonetheless, several barriers persist such as unclear appropriate timing of imaging or universal standardization of measures and normal reference limits. This review provides an overview of the current knowledge on multimodality imaging and potentialities of novel tools, including artificial intelligence, in the management of patients with sarcomeric HCM, highlighting the importance of specific "red alerts" to understand the phenotype-genotype linkage.

Comparison of Demographic, Clinical, Biochemical, and Imaging Findings in Hypertrophic Cardiomyopathy Prognosis: A Network Meta-Analysis

BACKGROUND

Despite hypertrophic cardiomyopathy (HCM) being the most common inherited heart disease and conferring increased risk for heart failure (HF) and sudden cardiac death (SCD), risk assessment in HCM patients is still largely unresolved.

OBJECTIVES

This study aims to synthesize and compare the prognostic impact of demographic, clinical, biochemical, and imaging findings in patients with HCM.

METHODS

The authors searched PubMed, Embase, and Cochrane Library for studies published from 1955 to November 2020, and the endpoints were: 1) all-cause death; 2) an arrhythmic endpoint including SCD, sustained ventricular tachycardia, ventricular fibrillation, or aborted SCD; and 3) a composite endpoint including (1) or (2) plus hospitalization for HF or cardiac transplantation. The authors performed a pairwise meta-analysis obtaining the pooled estimate separately for the association between baseline variables and study endpoints. A random-effects network meta-analysis was subsequently used to comparatively assess the prognostic value of outcome associates.

RESULTS

A total of 112 studies with 58,732 HCM patients were included. Among others, increased brain natriuretic peptide/N-terminal pro-B-type natriuretic peptide, late gadolinium enhancement (LGE), positive genotype, impaired global longitudinal strain, and presence of apical aneurysm conferred increased risk for the composite endpoint. At network meta-analysis, LGE showed the highest prognostic value for all endpoints and was superior to all other associates except New York Heart Association functional class >class II. A multiparametric imaging-based model was superior in predicting the composite endpoint compared to a prespecified model based on conventional risk factors.

CONCLUSIONS

This network meta-analysis supports the development of multiparametric risk prediction algorithms, including advanced imaging markers additively to conventional risk factors, for refined risk stratification in HCM. (Long-term prognosis of hypertrophic cardiomyopathy according to genetic, clinical, biochemical and imaging findings: a systemic review and meta-analysis; CRD42020185219).

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.