Genetic Evaluation of A Nation-Wide Dutch Pediatric DCM Cohort: The Use of Genetic Testing in Risk Stratification.

BACKGROUND: This study aimed to describe the current practice and results of genetic evaluation in Dutch children with dilated cardiomyopathy and to evaluate genotype-phenotype correlations that may guide prognosis. METHODS: We performed a multicenter observational study in children diagnosed with dilated cardiomyopathy, from 2010 to 2017. RESULTS: One hundred forty-four children were included. Initial diagnostic categories were idiopathic dilated cardiomyopathy in 67 children (47%), myocarditis in 23 (16%), neuromuscular in 7 (5%), familial in 18 (13%), inborn error of metabolism in 4 (3%), malformation syndrome in 2 (1%), and "other" in 23 (16%). Median follow-up time was 2.1 years [IQR 1.0-4.3]. Hundred-seven patients (74%) underwent genetic testing. We found a likely pathogenic or pathogenic variant in 38 children (36%), most often in MYH7 (n = 8). In 1 patient initially diagnosed with myocarditis, a pathogenic LMNA variant was found. During the study, 39 patients (27%) reached study endpoint (SE: all-cause death or heart transplantation). Patients with a likely pathogenic or pathogenic variant were more likely to reach SE compared with those without (hazard ratio 2.8; 95% CI 1.3-5.8, P = 0.007), while transplant-free survival was significantly lower (P = 0.006). Clinical characteristics at diagnosis did not differ between the 2 groups. CONCLUSIONS: Genetic testing is a valuable tool for predicting prognosis in children with dilated cardiomyopathy, with carriers of a likely pathogenic or pathogenic variant having a worse prognosis overall. Genetic testing should be incorporated in clinical work-up of all children with dilated cardiomyopathy regardless of presumed disease pathogenesis.

Interatrial Block Predicts Life-Threatening Arrhythmias in Dilated Cardiomyopathy.

Background Interatrial block (IAB) has been associated with supraventricular arrhythmias and stroke, and even with sudden cardiac death in the general population. Whether IAB is associated with life-threatening arrhythmias (LTA) and sudden cardiac death in dilated cardiomyopathy (DCM) remains unknown. This study aimed to determine the association between IAB and LTA in ambulant patients with DCM. Methods and Results A derivation cohort (Maastricht Dilated Cardiomyopathy Registry; N=469) and an external validation cohort (Utrecht Cardiomyopathy Cohort; N=321) were used for this study. The presence of IAB (P-wave duration>120 milliseconds) or atrial fibrillation (AF) was determined using digital calipers by physicians blinded to the study data. In the derivation cohort, IAB and AF were present in 291 (62%) and 70 (15%) patients with DCM, respectively. LTA (defined as sudden cardiac death, justified shock from implantable cardioverter-defibrillator or anti-tachypacing, or hemodynamic unstable ventricular fibrillation/tachycardia) occurred in 49 patients (3 with no IAB, 35 with IAB, and 11 patients with AF, respectively; median follow-up, 4.4 years [2.1; 7.4]). The LTA-free survival distribution significantly differed between IAB or AF versus no IAB (both P<0.01), but not between IAB versus AF (P=0.999). This association remained statistically significant in the multivariable model (IAB: HR, 4.8 (1.4-16.1), P=0.013; AF: HR, 6.4 (1.7-24.0), P=0.007). In the external validation cohort, the survival distribution was also significantly worse for IAB or AF versus no IAB (P=0.037; P=0.005), but not for IAB versus AF (P=0.836). Conclusions IAB is an easy to assess, widely applicable marker associated with LTA in DCM. IAB and AF seem to confer similar risk of LTA. Further research on IAB in DCM, and on the management of IAB in DCM is warranted.

Candidate Plasma Biomarkers to Detect Anthracycline-Related Cardiomyopathy in Childhood Cancer Survivors: A Case Control Study in the Dutch Childhood Cancer Survivor Study.

Background Plasma biomarkers may aid in the detection of anthracycline-related cardiomyopathy (ACMP). However, the currently available biomarkers have limited diagnostic value in long-term childhood cancer survivors. This study sought to identify diagnostic plasma biomarkers for ACMP in childhood cancer survivors. Methods and Results We measured 275 plasma proteins in 28 ACMP cases with left ventricular ejection fraction <45%, 29 anthracycline-treated controls with left ventricular ejection fraction ≥53% matched on sex, time after cancer, and anthracycline dose, and 29 patients with genetically determined dilated cardiomyopathy with left ventricular ejection fraction <45%. Multivariable linear regression was used to identify differentially expressed proteins. Elastic net model, including clinical characteristics, was used to assess discrimination of proteins diagnostic for ACMP. NT-proBNP (N-terminal pro-B-type natriuretic peptide) and the inflammatory markers CCL19 (C-C motif chemokine ligands 19) and CCL20, PSPD (pulmonary surfactant protein-D), and PTN (pleiotrophin) were significantly upregulated in ACMP compared with controls. An elastic net model selected 45 proteins, including NT-proBNP, CCL19, CCL20 and PSPD, but not PTN, that discriminated ACMP cases from controls with an area under the receiver operating characteristic curve (AUC) of 0.78. This model was not superior to a model including NT-proBNP and clinical characteristics (AUC=0.75; P=0.766). However, when excluding 8 ACMP cases with heart failure, the full model was superior to that including only NT-proBNP and clinical characteristics (AUC=0.75 versus AUC=0.50; P=0.022). The same 45 proteins also showed good discrimination between dilated cardiomyopathy and controls (AUC=0.89), underscoring their association with cardiomyopathy. Conclusions We identified 3 specific inflammatory proteins as candidate plasma biomarkers for ACMP in long-term childhood cancer survivors and demonstrated protein overlap with dilated cardiomyopathy.

Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks.

AIMS: While electrocardiogram (ECG) characteristics have been associated with life-threatening ventricular arrhythmias (LTVA) in dilated cardiomyopathy (DCM), they typically rely on human-derived parameters. Deep neural networks (DNNs) can discover complex ECG patterns, but the interpretation is hampered by their 'black-box' characteristics. We aimed to detect DCM patients at risk of LTVA using an inherently explainable DNN. METHODS AND RESULTS: In this two-phase study, we first developed a variational autoencoder DNN on more than 1 million 12-lead median beat ECGs, compressing the ECG into 21 different factors (F): FactorECG. Next, we used two cohorts with a combined total of 695 DCM patients and entered these factors in a Cox regression for the composite LTVA outcome, which was defined as sudden cardiac arrest, spontaneous sustained ventricular tachycardia, or implantable cardioverter-defibrillator treated ventricular arrhythmia. Most patients were male (n = 442, 64%) with a median age of 54 years [interquartile range (IQR) 44-62], and median left ventricular ejection fraction of 30% (IQR 23-39). A total of 115 patients (16.5%) reached the study outcome. Factors F8 (prolonged PR-interval and P-wave duration, P < 0.005), F15 (reduced P-wave height, P = 0.04), F25 (increased right bundle branch delay, P = 0.02), F27 (P-wave axis P < 0.005), and F32 (reduced QRS-T voltages P = 0.03) were significantly associated with LTVA. CONCLUSION: Inherently explainable DNNs can detect patients at risk of LTVA which is mainly driven by P-wave abnormalities.

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.

Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders.

Aims: Deep neural networks (DNNs) perform excellently in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction (EF). Despite these promising developments, implementation is hampered by the lack of trustworthy techniques to explain the algorithms to clinicians. Especially, currently employed heatmap-based methods have shown to be inaccurate. Methods and results: We present a novel pipeline consisting of a variational auto-encoder (VAE) to learn the underlying factors of variation of the median beat ECG morphology (the FactorECG), which are subsequently used in common and interpretable prediction models. As the ECG factors can be made explainable by generating and visualizing ECGs on both the model and individual level, the pipeline provides improved explainability over heatmap-based methods. By training on a database with 1.1 million ECGs, the VAE can compress the ECG into 21 generative ECG factors, most of which are associated with physiologically valid underlying processes. Performance of the explainable pipeline was similar to 'black box' DNNs in conventional ECG interpretation [area under the receiver operating curve (AUROC) 0.94 vs. 0.96], detection of reduced EF (AUROC 0.90 vs. 0.91), and prediction of 1-year mortality (AUROC 0.76 vs. 0.75). Contrary to the 'black box' DNNs, our pipeline provided explainability on which morphological ECG changes were important for prediction. Results were confirmed in a population-based external validation dataset. Conclusions: Future studies on DNNs for ECGs should employ pipelines that are explainable to facilitate clinical implementation by gaining confidence in artificial intelligence and making it possible to identify biased models.

A novel risk model for predicting potentially life-threatening arrhythmias in non-ischemic dilated cardiomyopathy (DCM-SVA risk).

BACKGROUND: Non-ischemic dilated cardiomyopathy (DCM) can be complicated by sustained ventricular arrhythmias (SVA) and sudden cardiac death (SCD). By now, left-ventricular ejection fraction (LV-EF) is the main guideline criterion for primary prophylactic ICD implantation, potentially leading either to overtreatment or failed detection of patients at risk without severely impaired LV-EF. The aim of the European multi-center study DETECTIN-HF was to establish a clinical risk calculator for individualized risk stratification of DCM patients. METHODS: 1393 patients (68% male, mean age 50.7 ± 14.3y) from four European countries were included. The outcome was occurrence of first potentially life-threatening ventricular arrhythmia. The model was developed using Cox proportional hazards, and internally validated using cross validation. The model included seven independent and easily accessible clinical parameters sex, history of non-sustained ventricular tachycardia, history of syncope, family history of cardiomyopathy, QRS duration, LV-EF, and history of atrial fibrillation. The model was also expanded to account for presence of LGE as the eight8h parameter for cases with available cMRI and scar information. RESULTS: During a mean follow-up period of 57.0 months, 193 (13.8%) patients experienced an arrhythmic event. The calibration slope of the developed model was 00.97 (95% CI 0.90-1.03) and the C-index was 0.72 (95% CI 0.71-0.73). Compared to current guidelines, the model was able to protect the same number of patients (5-year risk ≥8.5%) with 15% fewer ICD implantations. CONCLUSIONS: This DCM-SVA risk model could improve decision making in primary prevention of SCD in non-ischemic DCM using easily accessible clinical information and will likely reduce overtreatment.

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.

Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks.

Standard reference terminology of diagnoses and risk factors is crucial for billing, epidemiological studies, and inter/intranational comparisons of diseases. The International Classification of Disease (ICD) is a standardized and widely used method, but the manual classification is an enormously time-consuming endeavor. Natural language processing together with machine learning allows automated structuring of diagnoses using ICD-10 codes, but the limited performance of machine learning models, the necessity of gigantic datasets, and poor reliability of terminal parts of these codes restricted clinical usability. We aimed to create a high performing pipeline for automated classification of reliable ICD-10 codes in the free medical text in cardiology. We focussed on frequently used and well-defined three- and four-digit ICD-10 codes that still have enough granularity to be clinically relevant such as atrial fibrillation (I48), acute myocardial infarction (I21), or dilated cardiomyopathy (I42.0). Our pipeline uses a deep neural network known as a Bidirectional Gated Recurrent Unit Neural Network and was trained and tested with 5548 discharge letters and validated in 5089 discharge and procedural letters. As in clinical practice discharge letters may be labeled with more than one code, we assessed the single- and multilabel performance of main diagnoses and cardiovascular risk factors. We investigated using both the entire body of text and only the summary paragraph, supplemented by age and sex. Given the privacy-sensitive information included in discharge letters, we added a de-identification step. The performance was high, with F1 scores of 0.76-0.99 for three-character and 0.87-0.98 for four-character ICD-10 codes, and was best when using complete discharge letters. Adding variables age/sex did not affect results. For model interpretability, word coefficients were provided and qualitative assessment of classification was manually performed. Because of its high performance, this pipeline can be useful to decrease the administrative burden of classifying discharge diagnoses and may serve as a scaffold for reimbursement and research applications.

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

AIMS: Dilated cardiomyopathy (DCM) is a complex disease where genetics interplay with extrinsic factors. This study aims to compare the phenotype, management, and outcome of familial DCM (FDCM) and non-familial (sporadic) DCM (SDCM) across Europe. METHODS AND RESULTS: Patients with DCM that were enrolled in the prospective ESC EORP Cardiomyopathy & Myocarditis Registry were included. Baseline characteristics, genetic testing, genetic yield, and outcome were analysed comparing FDCM and SDCM; 1260 adult patients were studied (238 FDCM, 707 SDCM, and 315 not disclosed). Patients with FDCM were younger (P < 0.01), had less severe disease phenotype at presentation (P < 0.02), more favourable baseline cardiovascular risk profiles (P ≤ 0.007), and less medication use (P ≤ 0.042). Outcome at 1 year was similar and predicted by NYHA class (HR 0.45; 95% CI [0.25-0.81]) and LVEF per % decrease (HR 1.05; 95% CI [1.02-1.08]. Throughout Europe, patients with FDCM received more genetic testing (47% vs. 8%, P < 0.01) and had higher genetic yield (55% vs. 22%, P < 0.01). CONCLUSIONS: We observed that FDCM and SDCM have significant differences at baseline but similar short-term prognosis. Whether modification of associated cardiovascular risk factors provide opportunities for treatment remains to be investigated. Our results also show a prevalent role of genetics in FDCM and a non-marginal yield in SDCM although genetic testing is largely neglected in SDCM. Limited genetic testing and heterogeneity in panels provides a scaffold for improvement of guideline adherence.

Text-mining in electronic healthcare records can be used as efficient tool for screening and data collection in cardiovascular trials: a multicenter validation study.

OBJECTIVE: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of an international trial. STUDY DESIGN AND SETTING: In three medical centers with different EHR vendors, EHR-based text-mining was used to automatically screen patients for trial eligibility and extract baseline data on nineteen characteristics. First, the yield of screening with automated EHR text-mining search was compared with manual screening by research personnel. Second, the accuracy of extracted baseline data by EHR text mining was compared to manual data entry by research personnel. RESULTS: Of the 92,466 patients visiting the out-patient cardiology departments, 568 (0.6%) were enrolled in the trial during its recruitment period using manual screening methods. Automated EHR data screening of all patients showed that the number of patients needed to screen could be reduced by 73,863 (79.9%). The remaining 18,603 (20.1%) contained 458 of the actual participants (82.4% of participants). In trial participants, automated EHR text-mining missed a median of 2.8% (Interquartile range [IQR] across all variables 0.4-8.5%) of all data points compared to manually collected data. The overall accuracy of automatically extracted data was 88.0% (IQR 84.7-92.8%). CONCLUSION: Automatically extracting data from EHRs using text-mining can be used to identify trial participants and to collect baseline information.

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology.

The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.

Predicting sustained ventricular arrhythmias in dilated cardiomyopathy: a meta-analysis and systematic review.

AIMS: Patients with non-ischaemic dilated cardiomyopathy (DCM) are at increased risk of sudden cardiac death. Identification of patients that may benefit from implantable cardioverter-defibrillator implantation remains challenging. In this study, we aimed to determine predictors of sustained ventricular arrhythmias in patients with DCM. METHODS AND RESULTS: We searched MEDLINE/Embase for studies describing predictors of sustained ventricular arrhythmias in patients with DCM. Quality and bias were assessed using the Quality in Prognostic Studies tool, articles with high risk of bias in ≥2 areas were excluded. Unadjusted hazard ratios (HRs) of uniformly defined predictors were pooled, while all other predictors were evaluated in a systematic review. We included 55 studies (11 451 patients and 3.7 ± 2.3 years follow-up). Crude annual event rate was 4.5%. Younger age [HR 0.82; 95% CI (0.74-1.00)], hypertension [HR 1.95; 95% CI (1.26-3.00)], prior sustained ventricular arrhythmia [HR 4.15; 95% CI (1.32-13.02)], left ventricular ejection fraction on ultrasound [HR 1.45; 95% CI (1.19-1.78)], left ventricular dilatation (HR 1.10), and presence of late gadolinium enhancement [HR 5.55; 95% CI (4.02-7.67)] were associated with arrhythmic outcome in pooled analyses. Prior non-sustained ventricular arrhythmia and several genotypes [mutations in Phospholamban (PLN), Lamin A/C (LMNA), and Filamin-C (FLNC)] were associated with arrhythmic outcome in non-pooled analyses. Quality of evidence was moderate, and heterogeneity among studies was moderate to high. CONCLUSIONS: In patients with DCM, the annual event rate of sustained ventricular arrhythmias is approximately 4.5%. This risk is considerably higher in younger patients with hypertension, prior (non-)sustained ventricular arrhythmia, decreased left ventricular ejection fraction, left ventricular dilatation, late gadolinium enhancement, and genetic mutations (PLN, LMNA, and FLNC). These results may help determine appropriate candidates for implantable cardioverter-defibrillator implantation.

Dilated Cardiomyopathy Due to BLC2-Associated Athanogene 3 (BAG3) Mutations.

BACKGROUND: The BAG3 (BLC2-associated athanogene 3) gene codes for an antiapoptotic protein located on the sarcomere Z-disc. Mutations in BAG3 are associated with dilated cardiomyopathy (DCM), but only a small number of cases have been reported to date, and the natural history of BAG3 cardiomyopathy is poorly understood. OBJECTIVES: This study sought to describe the phenotype and prognosis of BAG3 mutations in a large multicenter DCM cohort. METHODS: The study cohort comprised 129 individuals with a BAG3 mutation (62% males, 35.1 ± 15.0 years of age) followed at 18 European centers. Localization of BAG3 in cardiac tissue was analyzed in patients with truncating BAG3 mutations using immunohistochemistry. RESULTS: At first evaluation, 57.4% of patients had DCM. After a median follow-up of 38 months (interquartile range: 7 to 95 months), 68.4% of patients had DCM and 26.1% who were initially phenotype-negative developed DCM. Disease penetrance in individuals >40 years of age was 80% at last evaluation, and there was a trend towards an earlier onset of DCM in men (age 34.6 ± 13.2 years vs. 40.7 ± 12.2 years; p = 0.053). The incidence of adverse cardiac events (death, left ventricular assist device, heart transplantation, and sustained ventricular arrhythmia) was 5.1% per year among individuals with DCM. Male sex, decreased left ventricular ejection fraction. and increased left ventricular end-diastolic diameter were associated with adverse cardiac events. Myocardial tissue from patients with a BAG3 mutation showed myofibril disarray and a relocation of BAG3 protein in the sarcomeric Z-disc. CONCLUSIONS: DCM caused by mutations in BAG3 is characterized by high penetrance in carriers >40 years of age and a high risk of progressive heart failure. Male sex, decreased left ventricular ejection fraction, and enlarged left ventricular end-diastolic diameter are associated with adverse outcomes in patients with BAG3 mutations.

Predicting arrhythmic risk in arrhythmogenic right ventricular cardiomyopathy: A systematic review and meta-analysis.

While many studies evaluate predictors of ventricular arrhythmias in arrhythmogenic right ventricular cardiomyopathy (ARVC), a systematic review consolidating this evidence is currently lacking. Therefore, we searched MEDLINE and Embase for studies analyzing predictors of ventricular arrhythmias (sustained ventricular tachycardia/fibrillation (VT/VF), appropriate implantable cardioverter-defibrillator therapy, or sudden cardiac death) in patients with definite ARVC, patients with borderline ARVC, and ARVC-associated mutation carriers. In the case of multiple publications on the same cohort, the study with the largest population was included. This yielded 45 studies with a median cohort size of 70 patients (interquartile range 60 patients) and a median follow-up of 5.0 years (interquartile range 3.3 - 6.7 years). The average proportion of arrhythmic events observed was 10.6%/y in patients with definite ARVC, 10.0%/y in patients with borderline ARVC, and 3.7%/y with mutation carriers. Predictors of ventricular arrhythmias were population dependent: consistently predictive risk factors in patients with definite ARVC were male sex, syncope, T-wave inversion in lead >V3, right ventricular dysfunction, and prior (non)sustained VT/VF; in patients with borderline ARVC, 2 additional predictors-inducibility during electrophysiology study and strenuous exercise-were identified; and with mutation carriers, all aforementioned predictors as well as ventricular ectopy, multiple ARVC-related pathogenic mutations, left ventricular dysfunction, and palpitations/presyncope determined arrhythmic risk. Most evidence originated from small observational cohort studies, with a moderate quality of evidence. In conclusion, the average risk of ventricular arrhythmia ranged from 3.7 to 10.6%/y depending on the population with ARVC. Male sex, syncope, T-wave inversion in lead >V3, right ventricular dysfunction, and prior (non)sustained VT/VF consistently predict ventricular arrhythmias in all populations with ARVC.

A systematic analysis of genetic dilated cardiomyopathy reveals numerous ubiquitously expressed and muscle-specific genes.

AIMS: Despite considerable progress being made in genetic diagnostics for dilated cardiomyopathy (DCM) using panels of the most prevalent genes, the cause remains unsolved in a substantial percentage of patients. We hypothesize that several previously described DCM genes with low or unknown prevalence have been neglected, which, if catalogued, could increase the yield of diagnostic DCM testing. The aim of this study is to catalogue all genetic evidence on DCM comprehensively. METHODS AND RESULTS: We have conducted a systematic literature search on PubMed, Embase, and OMIM to find genes implicated in syndromic and non-syndromic DCM and peripartum cardiomyopathy (PPCM). Our search yielded 110 nuclear protein-coding genes and 24 mitochondrial DNA genes. For nuclear genes, in addition to 42 genes sufficiently reviewed previously (group A), we provide a comprehensive annotation of the level of genetic evidence for the remaining 68 genes (group B). Next, we investigated the tissue specificity of the collected genes using public RNA sequencing data. We show that genes primarily expressed in heart and skeletal muscle are more likely to result in DCM with possible skeletal myopathies, while genes expressed ubiquitously cause DCM with extramuscular manifestations. CONCLUSION: This comprehensive analysis of DCM-associated genes revealed a much higher number of genes than currently screened in diagnostics. Since most genes in group B have only been found mutated in single DCM patients or families, their importance for DCM genetic diagnostics needs to be validated in large cohorts. Targeted sequencing of validated DCM-implicated protein-coding genes and mitochondrial DNA, together with consideration of the tissue specificity of mutated genes, may facilitate further genotype-phenotype studies in DCM.