Risk Stratification in Patients With Nonisquemic Dilated Cardiomyopathy. The Role of Genetic Testing

Incidence, risk assessment and prevention of sudden cardiac death in cardiomyopathies

Cardiomyopathies are a significant contributor to cardiovascular morbidity and mortality, mainly due to the development of heart failure and increased risk of sudden cardiac death (SCD). Despite improvement in survival with contemporary treatment, SCD remains an important cause of mortality in cardiomyopathies. It occurs at a rate ranging between 0.15% and 0.7% per year (depending on the cardiomyopathy), which significantly surpasses SCD incidence in the age- and sex-matched general population. The risk of SCD is affected by multiple factors including the aetiology, genetic basis, age, sex, physical exertion, the extent of myocardial disease severity, conduction system abnormalities, and electrical instability, as measured by various metrics. Over the past decades, the knowledge on the mechanisms and risk factors for SCD has substantially improved, allowing for a better-informed risk stratification. However, unresolved issues still challenge the guidance of SCD prevention in patients with cardiomyopathies. In this review, we aim to provide an in-depth discussion of the contemporary concepts pertinent to understanding the burden, risk assessment and prevention of SCD in cardiomyopathies (dilated, non-dilated left ventricular, hypertrophic, arrhythmogenic right ventricular, and restrictive). The review first focuses on SCD incidence in cardiomyopathies and then summarizes established and emerging risk factors for life-threatening arrhythmias/SCD. Finally, it discusses validated approaches to the risk assessment and evidence-based measures for SCD prevention in cardiomyopathies, pointing to the gaps in evidence and areas of uncertainties that merit future clarification.

Genetic Testing as a Guide for Treatment in Dilated Cardiomyopathies

PURPOSE OF REVIEW

Dilated cardiomyopathy (DCM) is one of the most prevalent primary cardiomyopathies and may be caused by genetic and non-genetic etiologies. DCM may also be the final common pathway of other cardiomyopathies such as hypertrophic, arrhythmogenic, or non-compaction cardiomyopathy. We review the main DCM genetic substrates, specific genotype-phenotype aspects, the role of genetic testing in risk stratification, and advances regarding genotype-based precision medicine.

RECENT FINDINGS

Performing a comprehensive genetic study could have a diagnostic yield up to 40% in DCM, and it is considered a cost-effective approach nowadays. The detection of a specific underlying genetic substrate explaining the disease can have important consequences for clinical management, especially for familial cascade screening, optimizing medical treatment, and improving the arrhythmic risk stratification. The identification of the genetic substrate underlying dilated cardiomyopathy makes possible the genotype-phenotype correlation analysis and a better understanding of the natural history of this disease. Nowadays, there are many promising targeting-gene therapies in different developing phases.

Genotype-Phenotype Correlation in Familial BAG3 Mutation Dilated Cardiomyopathy

We report the case of a 22-year-old male who visited a cardiologist after the first episode of atrial fibrillation (AF). Echocardiography and magnetic resonance imaging revealed decreased left ventricular (LV) systolic function with dilated LV. An intermittent second-degree AV (atrioventricular) block was detected during 24 h Holter monitoring. Genetic test revealed the pathogenic variant of the BAG3 (BLC2-associated athanogene 3) gene. Due to the high risk of heart failure (HF) progression and ventricular arrhythmias, an event recorder was implanted and a pathogenetic HF treatment was prescribed. The analysis of genealogy revealed that the patient’s father, at the age of 32, was diagnosed with dilated cardiomyopathy (DCM) and recurrent AF episodes. Genetic testing also confirmed a pathogenic variant of the BAG3 gene. Currently, with the optimal treatment of HF, the patient’s disease has been stable for three years and the condition is closely monitored on an outpatient basis. So, we demonstrate the importance of early detection for genetic testing and the unusual stability exhibited by the patient‘s optimal medical therapy for 3 years.

The Role of AI in Characterizing the DCM Phenotype

Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease. Emerging evidence suggests many patients remain vulnerable to major adverse outcomes despite clear therapeutic success of modern evidence-based heart failure therapy. In this era of personalized medical care, the conventional assessment of left ventricular ejection fraction falls short in fully predicting evolution and risk of outcomes in this heterogenous group of heart muscle disease, as such, a more refined means of phenotyping this disease appears essential. Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue characterization across different disease states and patient cohorts. Advanced tools are needed to leverage these sensitive metrics and integrate with clinical, genetic and biochemical information for personalized, and more clinically useful characterization of the dilated cardiomyopathy phenotype. Recent advances in artificial intelligence offers the unique opportunity to impact clinical decision making through enhanced precision image-analysis tasks, multi-source extraction of relevant features and seamless integration to enhance understanding, improve diagnosis, and subsequently clinical outcomes. Focusing particularly on deep learning, a subfield of artificial intelligence, that has garnered significant interest in the imaging community, this paper reviews the main developments that could offer more robust disease characterization and risk stratification in the Dilated Cardiomyopathy phenotype. Given its promising utility in the non-invasive assessment of cardiac diseases, we firstly highlight the key applications in CMR, set to enable comprehensive quantitative measures of function beyond the standard of care assessment. Concurrently, we revisit the added value of tissue characterization techniques for risk stratification, showcasing the deep learning platforms that overcome limitations in current clinical workflows and discuss how they could be utilized to better differentiate at-risk subgroups of this phenotype. The final section of this paper is dedicated to the allied clinical applications to imaging, that incorporate artificial intelligence and have harnessed the comprehensive abundance of data from genetics and relevant clinical variables to facilitate better classification and enable enhanced risk prediction for relevant outcomes.