Differences between familial and sporadic dilated cardiomyopathy: ESC EORP Cardiomyopathy & Myocarditis registry

Characterization of Non-Ischemic Dilated Cardiomyopathy in a Native Tanzanian Cohort: MOYO Study

Background

Non-ischemic dilated cardiomyopathy (NIDCM) is a common cause of heart failure with progressive tendency. The disease occurs in one in every 2,500 individuals in the developed world, with high morbidity and mortality. However, detailed data on the role of NIDCM in heart failure in Tanzania is lacking.

Aim

To characterize NIDCM in a Tanzanian cohort with respect to demographics, clinical profile, imaging findings and management.

Methods

Characterization of non-ischemic dilated cardioMyOpathY in a native Tanzanian cOhort (MOYO) is a prospective cohort study of NIDCM patients seen at the Jakaya Kikwete Cardiac Institute. Patients aged ≥18 years with a clinical diagnosis of heart failure, an ejection fraction of ≤45% on echocardiography and no evidence of ischemia were enrolled. Clinical data, echocardiography, electrocardiography (ECG), coronary angiography and stress ECG information were collected from February 2020 to March 2022.

Results

Of 402 patients, n = 220 (54.7%) were males with a median (IQR) age of 55.0 (41.0, 66.0) years. Causes of NIDCM were presumably hypertensive n = 218 (54.2%), idiopathic n = 116 (28.9%), PPCM n = 45 (11.2%), alcoholic n = 10 (2.5%) and other causes n = 13 (3.2%). The most common presenting symptoms were dyspnea n = 342 (85.1%), with the majority of patients presenting with New York Heart Association (NYHA) Class III n = 195 (48.5%). The mean (SD) left ventricular ejection fraction (LVEF) was 29.4% (±7.7), and severe systolic dysfunction (LVEF <30%) was common n = 208 (51.7%). Compared with other forms of DCM, idiopathic DCM patients were significantly younger, had more advanced NYHA class (p < 0.001) and presented more often with left bundle branch block on ECG (p = 0.0042). There was suboptimal use of novel guidelines recommended medications ARNI n = 10 (2.5%) and SGLT2 2-inhibitors n = 2 (0.5%).

Conclusions

In our Tanzanian cohort, the majority of patients with NIDCM have an identified underlying cause, and they present at late stages of the disease. Patients with idiopathic DCM are younger with more severe disease compared to other forms of NIDCM.

Genetic Characterization of Dilated Cardiomyopathy in Romanian Adult Patients

Dilated cardiomyopathy (DCM) represents a group of disorders affecting the structure and function of the heart muscle, leading to a high risk of heart failure and sudden cardiac death (SCD). DCM frequently involves an underlying genetic etiology. Genetic testing is valuable for risk stratification, treatment decisions, and family screening. Romanian population data on the genetic etiology of DCM are lacking. We aimed to investigate the genetic causes for DCM among Romanian adult patients at tertiary referral centers across the country. Clinical and genetic investigations were performed on adult patients presenting to tertiary hospitals in Romania. The genetic investigations used next-generation sequencing panels of disease-associated DCM genes. A total of 122 patients with DCM underwent genetic testing. The mean age at DCM diagnosis was 41.6 ± 12.4 years. The genetic investigations identified pathogenic or likely pathogenic variants in 50.8% of participants, while 25.4% had variants of unknown significance. Disease-causing variants in 15 genes were identified in people with DCM, with 31 previously unreported variants. Variants in TTN, LMNA, and DSP explained 75% of genetic causes for DCM. In total, 52.4% of patients had a family history of DCM/SCD. Left ventricular ejection fraction of <35% was observed in 41.9% of patients with disease-causing variants and 55% with negative or uncertain findings. Further genotype-phenotype correlations were explored in this study population. The substantial percentage (50.8%) of disease-causing variants identified in patients with DCM acknowledges the importance of genetic investigations. This study highlights the genetic landscape in genes associated with DCM in the Romanian population.

Genetic and functional variants of the TBX20 gene promoter in dilated cardiomyopathy

BACKGROUND

Dilated cardiomyopathy (DCM) is a major cause of heart failure and sudden cardiac death. As DCM is a genetically heterogeneous disease, genetic variants of cardiac transcription factor genes may play an important role. Transcription factor TBX20, an indispensable factor in normal heart development, is involved in the regulation of cardiac structure and function. Although the TBX20 gene is associated with the occurrence and development of DCM, the influence of genetic variants of the TBX20 gene promoter region on DCM has not been reported.

METHODS

We conducted a case-control study consisting of 107 DCM patients and 210 healthy controls. Genetic variants within TBX20 gene promoter region were identified using sequencing techniques and were functionally analyzed by dual-luciferase reporting assay. Electrophoretic mobility shift assay (EMSA) was used to investigate DNA-protein interactions.

RESULTS

In this study cohort (n = 317), we identified eight variants within TBX20 gene promoter. One novel DNA sequence variants (DSV) (g.4275G>T) and four single-nucleotide polymorphisms (SNPs) [g.4169G>A (rs1263874255), g.4949C>T (rs1191745927), g.5114G>A (rs112076877), g.5252C>T (rs1356932911)] were identified in DCM patients, but in none of controls. Among them, the DSV (g.4275G>T) and three SNPs [g.4949C>T (rs1191745927), g.5114G>A (rs112076877) and g.5252C>T (rs1356932911)] significantly altered the transcription activity of TBX20 gene promoter by dual-luciferase reporting assay (p < 0.05). Further, EMSA assay indicated that the DSV (g.4275G>T) and three SNPs [g.4949C>T (rs1191745927), g.5114G>A (rs112076877) and g.5252C>T (rs1356932911)] affected the binding of transcription factors.

CONCLUSIONS

These data indicate that the DSV (g.4275G>T) and three SNPs [g.4949C>T (rs1191745927), g.5114G>A (rs112076877) and g.5252C>T (rs1356932911)] increase transcription activity of TBX20 gene promoter in both HEK-293 and neonatal rat cardiomyocytes (NRCMs) cell lines by affecting the binding of transcription factors. But the mechanism remains to be verified in vivo.

LMNA Cardiomyopathy: Important Considerations for the Heart Failure Clinician

BACKGROUND

A diagnosis of Lamin proteins A and C cardiomyopathy (LMNA-CM) not only impacts disease prognosis, but also leads to specific guideline-recommended treatment options for these patients. This etiology is fundamentally different from other genetic causes of dilated CM.

METHODS AND RESULTS

LMNA-CM often presents early in the third to fourth decades and there is an age-dependent penetrance of nearly 90% among those with a positive genotype for LMNA-CM. Oftentimes, electrical abnormalities with either conduction disturbances and/or either atrial or ventricular arrhythmias manifest before there is imaging evidence of left ventricular dysfunction. Given these subtle early findings, cardiac magnetic resonance provides helpful guidance regarding patterns of enhancement associated with LMNA-CM, often before there is significant left ventricular dilation and/or a decrease in the ejection fraction and could be used for further understanding of risk stratification and prognosis of asymptomatic genotype-positive individuals. Among symptomatic patients with LMNA-CM, approximately one-quarter of individuals progress to needing advanced heart failure therapies such as heart transplantation.

CONCLUSIONS

In the era of precision medicine, increased recognition of clinical findings associated with LMNA-CM and increased detection by genetic testing among patients with idiopathic nonischemic CM is of increasing importance. Not only does a diagnosis of LMNA-CM have implications for management and risk stratification, but new gene-based therapies continue to be evaluated for this group. Clinicians must be aware not only of the general indications for genetic testing in arrhythmogenic and dilated cardiomyopathies and of when to suspect LMNA-CM, but also of the clinical trials underway targeted toward the different genetic cardiomyopathies.

The Uncertain Benefit from Implantable Cardioverter-Defibrillators in Nonischemic Cardiomyopathy: How to Guide Clinical Decision-Making?

Life-threatening dysrhythmias remain a significant cause of mortality in patients with nonischemic cardiomyopathy (NICM). Implantable cardioverter-defibrillators (ICD) effectively reduce mortality in patients who have survived a life-threatening arrhythmic event. The evidence for survival benefit of primary prevention ICD for patients with high-risk NICM on guideline-directed medical therapy is not as robust, with efficacy questioned by recent studies. In this review, we summarize the data on the risk of life-threatening arrhythmias in NICM, the recommendations, and the evidence supporting the efficacy of primary prevention ICD, and highlight tools that may improve the identification of patients who could benefit from primary prevention ICD implantation.

Diagnostic yield of genetic testing in a multinational heterogeneous cohort of 2088 DCM patients

Background

Familial dilated cardiomyopathy (DCM) causes heart failure and may lead to heart transplantation. DCM is typically a monogenic disorder with autosomal dominant inheritance. Currently disease-causing variants have been reported in over 60 genes that encode proteins in sarcomeres, nuclear lamina, desmosomes, cytoskeleton, and mitochondria. Over half of the patients undergoing comprehensive genetic testing are left without a molecular diagnosis even when patient selection follows strict DCM criteria.

Methods and results

This study was a retrospective review of patients referred for genetic testing at Blueprint Genetics due to suspected inherited DCM. Next generation sequencing panels included 23-316 genes associated with cardiomyopathies and other monogenic cardiac diseases. Variants were considered diagnostic if classified as pathogenic (P) or likely pathogenic (LP). Of the 2,088 patients 514 (24.6%) obtained a molecular diagnosis; 534 LP/P variants were observed across 45 genes, 2.7% (14/514) had two diagnostic variants in dominant genes. Nine copy number variants were identified: two multigene and seven intragenic. Diagnostic variants were observed most often in TTN (45.3%), DSP (6.7%), LMNA (6.7%), and MYH7 (5.2%). Clinical characteristics independently associated with molecular diagnosis were: a lower age at diagnosis, family history of DCM, paroxysmal atrial fibrillation, absence of left bundle branch block, and the presence of an implantable cardioverter-defibrillator.

Conclusions

Panel testing provides good diagnostic yield in patients with clinically suspected DCM. Causative variants were identified in 45 genes. In minority, two diagnostic variants were observed in dominant genes. Our results support the use of genetic panels in clinical settings in DCM patients with suspected genetic etiology.

Rare Variant Genetics and Dilated Cardiomyopathy Severity: The DCM Precision Medicine Study

BACKGROUND

Dilated cardiomyopathy (DCM) can lead to advanced disease, defined herein as necessitating a durable left ventricular assist device or a heart transplant (LVAD/HT). DCM is known to have a genetic basis, but the association of rare variant genetics with advanced DCM has not been studied.

METHODS

We analyzed clinical and genetic sequence data from patients enrolled between 2016 and 2021 in the US multisite DCM Precision Medicine Study, which was a geographically diverse, multiracial, multiethnic cohort. Clinical evaluation included standardized patient interview and medical record query forms. DCM severity was classified into 3 groups: patients with advanced disease with LVAD/HT; patients with an implantable cardioverter defibrillator (ICD) only; or patients with no ICD or LVAD/HT. Rare variants in 36 DCM genes were classified as pathogenic or likely pathogenic or variants of uncertain significance. Confounding factors we considered included demographic characteristics, lifestyle factors, access to care, DCM duration, and comorbidities. Crude and adjusted associations between DCM severity and rare variant genetic findings were assessed using multinomial models with generalized logit link.

RESULTS

Patients' mean (SD) age was 51.9 (13.6) years; 42% were of African ancestry, 56% were of European ancestry, and 44% were female. Of 1198 patients, 347 had LVAD/HT, 511 had an ICD, and 340 had no LVAD/HT or ICD. The percentage of patients with pathogenic or likely pathogenic variants was 26.2%, 15.9%, and 15.0% for those with LVAD/HT, ICD only, or neither, respectively. After controlling for sociodemographic characteristics and comorbidities, patients with DCM with LVAD/HT were more likely than those without LVAD/HT or ICD to have DCM-related pathogenic or likely pathogenic rare variants (odds ratio, 2.3 [95% CI, 1.5-3.6]). The association did not differ by ancestry. Rare variant genetic findings were similar between patients with DCM with an ICD and those without LVAD/HT or ICD.

CONCLUSIONS

Advanced DCM was associated with higher odds of rare variants in DCM genes adjudicated as pathogenic or likely pathogenic, compared with individuals with less severe DCM. This finding may help assess the risk of outcomes in management of patients with DCM and their at-risk family members.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT03037632.

DCM-PROGRESS: predicting end-stage heart failure in non-ischemic dilated cardiomyopathy patients

Aims

Patients with non-ischemic dilated cardiomyopathy (DCM) are at considerable risk for end-stage heart failure (HF), requiring close monitoring to identify early signs of disease. We aimed to develop a model to predict the 5-years risk of end-stage HF, allowing for tailored patient monitoring and management.

Methods and results

Derivation data were available from a Dutch cohort of 293 DCM patients, with external validation available from a Czech Republic cohort of 235 DCM patients. Candidate predictors spanned patient and family histories, ECG and echocardiogram measurements, and biochemistry. End-stage HF was defined as a composite of death, heart transplantation, or implantation of a ventricular assist device. Lasso and sigmoid kernel support vector machine (SVM) algorithms were trained using cross-validation. During follow-up 65 (22%) of Dutch DCM patients developed end-stage HF, with 27 (11%) cases in the Czech cohort. Out of the two considered models, the lasso model (retaining NYHA class, heart rate, systolic blood pressure, height, R-axis, and TAPSE as predictors) reached the highest discriminative performance (testing c-statistic of 0.85, 95%CI 0.58; 0.94), which was confirmed in the external validation cohort (c-statistic of 0.75, 95%CI 0.61; 0.82), compared to a c-statistic of 0.69 for the MAGGIC score. Both the MAGGIC score and the DCM-PROGRESS model slightly over-estimated the true risk, but were otherwise appropriately calibrated.

Conclusion

We developed a highly discriminative risk-prediction model for end-stage HF in DCM patients. The model was validated in two countries, suggesting the model can meaningfully improve clinical decision-making.

Application of next generation sequencing in cardiology: current and future precision medicine implications

Inherited cardiovascular diseases are highly heterogeneous conditions with multiple genetic loci involved. The application of advanced molecular tools, such as Next Generation Sequencing, has facilitated the genetic analysis of these disorders. Accurate analysis and variant identification are required to maximize the quality of the sequencing data. Therefore, the application of NGS for clinical purposes should be limited to laboratories with a high level of technological expertise and resources. In addition, appropriate gene selection and variant interpretation can result in the highest possible diagnostic yield. Implementation of genetics in cardiology is imperative for the accurate diagnosis, prognosis and management of several inherited disorders and could eventually lead to the realization of precision medicine in this field. However, genetic testing should also be accompanied by an appropriate genetic counseling procedure that clarifies the significance of the genetic analysis results for the proband and his family. In this regard, a multidisciplinary collaboration among physicians, geneticists, and bioinformaticians is imperative. In the present review, we address the current state of knowledge regarding genetic analysis strategies employed in the field of cardiogenetics. Variant interpretation and reporting guidelines are explored. Additionally, gene selection procedures are accessed, with a particular emphasis on information concerning gene-disease associations collected from international alliances such as the Gene Curation Coalition (GenCC). In this context, a novel approach to gene categorization is proposed. Moreover, a sub-analysis is conducted on the 1,502,769 variation records with submitted interpretations in the Clinical Variation (ClinVar) database, focusing on cardiology-related genes. Finally, the most recent information on genetic analysis's clinical utility is reviewed.

Comparison of Clinical Course and Outcomes between Dilated and Hypokinetic Non-Dilated Cardiomyopathy

BACKGROUND

By definition, dilated cardiomyopathy (DCM) is characterized by enlargement of the left ventricular (LV) cavity, and systolic dysfunction. However, in 2016 ESC introduced a new clinical entity - hypokinetic non-dilated cardiomyopathy (HNDC). HNDC is defined as LV systolic dysfunction without LV dilatation. However, the diagnosis of HNDC has so far rarely been made by a cardiologist, and it is unknown whether "classic" DCM differs from HNDC in terms of clinical course and outcomes.

OBJECTIVES

Comparison of heart failure profiles and outcomes between patients with "classic" dilated (DCM) and HNDCs.

METHOD

We retrospectively analysed 785 DCM patients, defined as impaired left ventricle (LV) systolic function (ejection fraction [LVEF] <45%) in the absence of coronary artery disease, valve disease, congenital heart disease, and severe arterial hypertension. "Classic" DCM was diagnosed when LV dilatation was present (LV end-diastolic diameter >52 mm/58 mm in women/men); otherwise, HNDC was diagnosed. After 47 ± 31 months, the all-cause mortality and composite endpoint (all-cause mortality, heart transplant - HTX, left ventricle assist device implantation - LVAD) were assessed.

RESULTS

There were 617 (79%) patients with LV dilatation. Patients with "classic" DCM differed from HNDC in terms of clinically relevant parameters [hypertension (47% vs. 64%, p = 0.008), ventricular tachyarrhythmias (29% vs. 15%, p = 0.007), NYHA class (2.5 ± 0.9 vs. 2.2 ± 0.8, p = 0.003)], had lower cholesterol (LDL: 2.9 ± 1.0 vs. 3.2 ± 1.1 mmol/L, p = 0.049), and higher N-terminal pro-brain natriuretic peptide (3,351 ± 5,415 vs. 2,563 ± 8584 pg/mL, p = 0.0001) and required higher diuretics dosages (57.8 ± 89.5 vs. 33.7 ± 48.7 mg/day, p ≤ 0.0001). All of their chambers were larger (LVEDd: 68.3 ± 4.5 vs. 52.7 ± 3.5 mm, p < 0.0001) and they had lower LVEF (25.2 ± 9.4 vs. 36.6 ± 11.7%, p < 0.0001). During the follow-up, there were 145 (18%) composite endpoints ("classic" DCM vs. HNDC: 122 [20%] vs. 26 [18%], p = 0.22): deaths (97 [16%] vs. 24 [14%], p = 0.67), HTX (17 [4%] vs. 4 [4%], p = 0.97) and LVAD (19 [5%] vs. 0 [0%], p = 0.03). Both groups did not differ in terms of all-cause mortality (p = 0.70), cardiovascular (CV) mortality (p = 0.37) and composite endpoint (p = 0.26).

CONCLUSIONS

LV dilatation was absent in more than one-fifth of DCM patients. HNDC patients had less severe heart failure symptoms, less advanced cardiac remodelling, and required lower diuretics dosages. On the other hand, "classic" DCM and HNDC patients did not differ in terms of all-cause mortality, CV mortality, and composite endpoint.

Spotlight on the 2022 ESC guideline management of ventricular arrhythmias and prevention of sudden cardiac death: 10 novel key aspects

Sudden cardiac death and ventricular arrhythmias are a global health issue. Recently, a new guideline for the management of ventricular arrhythmias and prevention of sudden cardiac death has been published by the European Society of Cardiology that serves as an update to the 2015 guideline on this topic. This review focuses on 10 novel key aspects of the current guideline: As new aspects, public basic life support and access to defibrillators are guideline topics. Recommendations for the diagnostic evaluation of patients with ventricular arrhythmias are structured according to frequently encountered clinical scenarios. Management of electrical storm has become a new focus. In addition, genetic testing and cardiac magnetic resonance imaging significantly gained relevance for both diagnostic evaluation and risk stratification. New algorithms for antiarrhythmic drug therapy aim at improving safe drug use. The new recommendations reflect increasing relevance of catheter ablation of ventricular arrhythmias, especially in patients without structural heart disease or stable coronary artery disease with only mildly impaired ejection fraction and haemodynamically tolerated ventricular tachycardias. Regarding sudden cardiac death risk stratification, risk calculators for laminopathies, and long QT syndrome are now considered besides the already established risk calculator for hypertrophic cardiomyopathy. Generally, 'new' risk markers beyond left ventricular ejection fraction are increasingly considered for recommendations on primary preventive implantable cardioverter defibrillator therapy. Furthermore, new recommendations for diagnosis of Brugada syndrome and management of primary electrical disease have been included. With many comprehensive flowcharts and practical algorithms, the new guideline takes a step towards a user-oriented reference book.

VEZF1 loss-of-function mutation underlying familial dilated cardiomyopathy

Dilated cardiomyopathy (DCM), characteristic of left ventricular or biventricular dilation with systolic dysfunction, is the most common form of cardiomyopathy, and a leading cause of heart failure and sudden cardiac death. Aggregating evidence highlights the underlying genetic basis of DCM, and mutations in over 100 genes have been causally linked to DCM. Nevertheless, due to pronounced genetic heterogeneity, the genetic defects underpinning DCM in most cases remain obscure. Hence, this study was sought to identify novel genetic determinants of DCM. In this investigation, whole-exome sequencing and bioinformatics analyses were conducted in a family suffering from DCM, and a novel heterozygous mutation in the VEZF1 gene (coding for a zinc finger-containing transcription factor critical for cardiovascular development and structural remodeling), NM_007146.3: c.490A > T; p.(Lys164*), was identified. The nonsense mutation was validated by Sanger sequencing and segregated with autosome-dominant DCM in the family with complete penetrance. The mutation was neither detected in another cohort of 200 unrelated DCM patients nor observed in 400 unrelated healthy individuals nor retrieved in the Single Nucleotide Polymorphism database, the Human Gene Mutation Database and the Genome Aggregation Database. Biological analyses by utilizing a dual-luciferase reporter assay system revealed that the mutant VEZF1 protein failed to transactivate the promoters of MYH7 and ET1, two genes that have been associated with DCM. The findings indicate VEZF1 as a new gene responsible for DCM, which provides novel insight into the molecular pathogenesis of DCM, implying potential implications for personalized precisive medical management of the patients affected with DCM.

Non-ischaemic dilated cardiomyopathy: recognising the genetic links

The landscape of genetically related cardiac disease continues to evolve. Heritable genetic variants can be a primary cause of familial or sporadic dilated cardiomyopathy (DCM). There is also increasing recognition that genetic variation is an important determinant of susceptibility to acquired causes of DCM. Genetic forms of DCM can show a wide variety of phenotypic manifestations. Identifying patients who are most likely to benefit from genetic testing is paramount. The objective of this review is to highlight the importance of recognising genetic DCM, key genotype-phenotype correlations and the value of genetic testing in clinical management for both the individual and their family. This is likely to become more relevant as management strategies continue to be refined with genotype-specific recommendations and disease-modifying therapies.

Automatic Identification of Patients With Unexplained Left Ventricular Hypertrophy in Electronic Health Record Data to Improve Targeted Treatment and Family Screening

Background

Unexplained Left Ventricular Hypertrophy (ULVH) may be caused by genetic and non-genetic etiologies (e.g., sarcomere variants, cardiac amyloid, or Anderson-Fabry's disease). Identification of ULVH patients allows for early targeted treatment and family screening.

Aim

To automatically identify patients with ULVH in electronic health record (EHR) data using two computer methods: text-mining and machine learning (ML).

Methods

Adults with echocardiographic measurement of interventricular septum thickness (IVSt) were included. A text-mining algorithm was developed to identify patients with ULVH. An ML algorithm including a variety of clinical, ECG and echocardiographic data was trained and tested in an 80/20% split. Clinical diagnosis of ULVH was considered the gold standard. Misclassifications were reviewed by an experienced cardiologist. Sensitivity, specificity, positive, and negative likelihood ratios (LHR+ and LHR–) of both text-mining and ML were reported.

Results

In total, 26,954 subjects (median age 61 years, 55% male) were included. ULVH was diagnosed in 204/26,954 (0.8%) patients, of which 56 had amyloidosis and two Anderson-Fabry Disease. Text-mining flagged 8,192 patients with possible ULVH, of whom 159 were true positives (sensitivity, specificity, LHR+, and LHR– of 0.78, 0.67, 2.36, and 0.33). Machine learning resulted in a sensitivity, specificity, LHR+, and LHR– of 0.32, 0.99, 32, and 0.68, respectively. Pivotal variables included IVSt, systolic blood pressure, and age.

Conclusions

Automatic identification of patients with ULVH is possible with both Text-mining and ML. Text-mining may be a comprehensive scaffold but can be less specific than machine learning. Deployment of either method depends on existing infrastructures and clinical applications.

Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics

Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of patients has identified genotype-phenotype associations and enhanced evaluation of at-risk relatives leading to better patient prognosis. The field is now ripe to explore opportunities to improve personalised risk assessments. Multivariable risk models presented as "risk calculators" can incorporate a multitude of clinical variables and predict outcome (such as heart failure hospitalisations or LTVA). In addition, genetic risk scores derived from genome/exome-wide association studies can estimate an individual's lifetime genetic risk of developing DCM. The use of clinically granular investigations, such as late gadolinium enhancement on cardiac magnetic resonance imaging, is warranted in order to increase predictive performance. To this end, constructing big data infrastructures improves accessibility of data by using electronic health records, existing research databases, and disease registries. By applying methods such as machine and deep learning, we can model complex interactions, identify new phenotype clusters, and perform prognostic modelling. This review aims to provide an overview of the evolution of DCM definitions as well as its clinical work-up and considerations in the era of genomics. In addition, we present exciting examples in the field of big data infrastructures, personalised prognostic assessment, and artificial intelligence.