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Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): PSIDER (ZOn-MW) PREDICT2 (Hartstichting) Background: The PLN p.Arg14del risk model is a mutation-specific risk model developed to predict individual malignant ventricular arrhythmia (VA) risk to inform decision-making for primary prevention implantable cardioverter defibrillator (ICD) implantation. While the risk model has proven to be valid, two main points of criticisms remain to be discussed: first, the risk model is only validated at baseline and second, appropriate ICD therapy and sustained ventricular tachycardia (VT) Purpose: To evaluate the diagnostic performance of the PLN p.Arg14del risk model longitudinal at 3 year follow-up and to reevaluate the predictors regarding SCD. Methods: Clinical data of 278 PLN p.Arg14del mutation carriers with no history of malignant VA at both baseline and 3 year follow-up were collected. During a median follow-up of 4 years (Interquartile range (IQR) 1.8-6.5), 31 (11%) carriers experienced malignant VA, defined as sustained VA, appropriate ICD intervention, or (aborted) SCD. The reevaluation of the predictors with the 3 year follow up data revealed hazard ratio’s (HR) that were similar to the baseline PLN p.Arg14del risk model for left ventricular ejection fraction (HR 1.10 [95% CI, 1.06-1.12], p To reevaluate the predictive performance of the PLN p.Arg14del risk model for a more strict surrogate of SCD we redefined the outcome as VT >250 beats per minute sustained (lasting ≥30 seconds) or terminated by ICD, ventricular fibrillation, aborted SCD and SCD. We included 669 PLN p.Arg14del carriers with no presentation or history of sustained VA or (aborted) SCD. During a median follow-up of 4.75 years (IQR 1.9-7.9), 33 (5%) carriers experienced the outcome. Results of the univariable analysis are shown in table 1. The PLN p.Arg14del risk model yielded a C-statistic of 0.75 (95% CI 0.70-0.80). Adding risk factors sex, age and sustained VT Conclusion: The PLN p.Arg14del risk model was validated longitudinal at 3 year follow-up and it held its performance with a more strict definition of SCD.

Background/Introduction Recently, a variant-specific prediction model for PLN p.Arg14del variant carriers was developed to predict individual malignant ventricular arrhythmia (VA) risk to inform decision-making for primary prevention implantable cardioverter defibrillator (ICD) implantation. This model predicts malignant VA risk from data at diagnosis, but iterative evaluation of malignant VA risk may be warranted considering that the risk factors for malignant VA are progressive. Purpose To evaluate the diagnostic performance of the PLN p.Arg14del risk model. Methods/Results Date were collected of 278 PLN p.Arg14del variant carriers at 3 year follow-up. This was considered the new baseline for the survival analysis. Patients with history of malignant VA at both baseline and during the first 3 years after baseline were excluded. At 3 year patients were aged 40.1±18.0 year and 40.7% was male. Median left ventricular ejection fraction (LVEF) was 53% and percentage with microvoltages was 10.3. During a median follow-up of 4 years (Interquartile range 1.8–6.5) 31 (11%) carriers experienced malignant VA, defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. Reevaluation of the predictors with the 3 year follow-up data revealed hazard rates that were similar to those in the original PLN p.Arg14del risk model; LVEF per 1% decrease (hazard ratio (HR) 1.10 [95% confidence interval (CI), 1.06–1.12]; p Conclusion The PLN p.Arg14del risk model is valid at 3 year follow-up in PLN p.Arg14del variant carriers with no history of malignant VA and can therefore be used to inform decision-making for primary prevention ICD implantation not merely at diagnosis, but also during follow-up and can be seen as a type of validation in a cohort where no other large cohort is present to perform external validation. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): 1. PSIDER: From pluripotent stem cells to prime editing gene therapy for inheritedcardiomyopathies. ZOn-MW.2. PREDICT2: Predicting sudden cardiac arrest. The Dutch Heart Foundation.

Background Genetic heterogeneity is common in inherited cardiac diseases. Next-generation sequencing gene panels are therefore suitable for genetic diagnosis. We describe the results of implementation of cardiomyopathy and arrhythmia gene panels in clinical care. Methods We present detection rates for variants with unknown (class 3), likely (class 4), and certain (class 5) pathogenicity in cardiogenetic gene panels since their introduction into diagnostics. Results In 936 patients tested on the arrhythmia panel, likely pathogenic and pathogenic variants were detected in 8.8% (4.6% class 5; 4.2% class 4), and one or multiple class 3 variants in 34.8%. In 1970 patients tested on the cardiomyopathy panel, likely pathogenic and pathogenic variants were detected in 19.8% (12.0% class 5; 7.9% class 4), and one or multiple class 3 variants in 40.8%. Detection rates of all different classes of variants increased with the increasing number of genes on the cardiomyopathy gene panel. Multiple variants were detected in 11.7% and 28.5% of patients on the arrhythmia and cardiomyopathy panels respectively. In more recent larger versions of the cardiomyopathy gene panel the detection rate of likely pathogenic and pathogenic variants only slightly increased, but was associated with a large increase of class 3 variants. Conclusion Overall detection rates (class 3, 4, and 5 variants) in a diagnostic setting are 44% and 61% for the arrhythmia and cardiomyopathy gene panel respectively, with only a small minority of likely pathogenic and pathogenic variants (8.8% and 19.8% respectively). Larger gene panels can increase the detection rate of likely pathogenic and pathogenic variants, but mainly increase the frequency of variants of unknown pathogenicity. Electronic supplementary material The online version of this article (10.1007/s12471-019-1250-5) contains supplementary material, which is available to authorized users.

In relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy, early detection of disease onset is essential to prevent sudden cardiac death and facilitate early treatment of heart failure. However, the optimal screening interval and combination of diagnostic techniques are unknown. The clinical course of disease in index patients and their relatives is variable due to incomplete and age-dependent penetrance. Several biomarkers, electrocardiographic and imaging (echocardiographic deformation imaging and cardiac magnetic resonance imaging) techniques are promising non-invasive methods for detection of subclinical cardiomyopathy. However, these techniques need optimisation and integration into clinical practice. Furthermore, determining the optimal interval and intensity of cascade screening may require a personalised approach. To address this, the CVON-eDETECT (early detection of disease in cardiomyopathy mutation carriers) consortium aims to integrate electronic health record data from long-term follow-up, diagnostic data sets, tissue and plasma samples in a multidisciplinary biobank environment to provide personalised risk stratification for heart failure and sudden cardiac death. Adequate risk stratification may lead to personalised screening, treatment and optimal timing of implantable cardioverter defibrillator implantation. In this article, we describe non-invasive diagnostic techniques used for detection of subclinical disease in relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy.

Background/Introduction The founder mutation p.Arg14del in the gene encoding phospholamban (PLN) is a known cause of arrhythmogenic cardiomyopathy (ACM) with distinct clinical features, such as microvoltages on the ECG and right or left ventricular dysfunction or both. At present, risk stratification for ICD implantation in carriers of this mutation is based on left ventricular ejection fraction and previous ventricular arrhythmia's, which are not specific risk factors for the PLN p.Arg14del cardiomyopathy. Purpose Our goal is to develop a mutation specific prediction model for incident malignant ventricular arrhythmia to guide ICD implantation. Methods Data were collected from p.Arg14del carriers with no history of malignant VA at baseline, identified between September 2009 and June 2018 in three Dutch university hospitals. Genetic analysis of PLN was performed in a clinical setting in index patients with clinical signs of DCM/ACM, or in family members of p.Arg14del carriers. We collected clinical data from the first cardiac evaluation and follow-ups. Malignant VAs were defined as sustained VA, appropriate ICD intervention or (aborted) sudden cardiac death. A prediction model was developed using Cox Proportional Hazard regression. Candidate baseline predictors were pre specified based on literature and clinical expertise. Age, sex, proband status, sudden cardiac death (SCD) in 1st degree relative, repolarization abnormalities, microvoltages, premature ventricular complexes (PVC) burden on 24hrs Holter monitoring, LVEF and non-sustained ventricular arrhythmias (NSVT) were considered. The multivariable model was fitted using stepwise backward selection based on Akaike's Information Criterion. Results We included 440 p.Arg14del carriers with a mean age of 41±18 years and 41% males. During a median follow-up of 4.7 years (IQR 1.7–7.3), 44 incident malignant VA occurred, 20 sustained VAs and 24 appropriate ICD therapies. The multivariable HR's of selected predictors were: 2.2 for minor and 4.5 for major repolarization abnormalities vs no abnormalities (p value respectively 0.06 and 0.01), 2.2 for LVEF 500PVC/24hrs (p value 0.003). Carriers with and without events could be accurately distinguished with this model, with an optimism corrected C-statistic of 0.81. The model calibrated well, with agreement of observed and predicted 5 year malignant VA risk. Risk groups were split into quintiles of predicted risk, figure 1 shows the Kaplan Meier curve of incident malignant VA per risk group. The 5 year risk of incident malignant VA in the lowest 3 quintiles was 0%, 9% in the 4th quintile and 25.2% in the highest quintile. Conclusion We created a PLN p.Arg14del mutation specific prediction model to estimate risk of incident malignant VA. With this model a clear distinction between high risk and low risk patients can be made and can be used to guide ICD implantation for primary prevention.

Congenital hydrocephalus is a common and often disabling disorder. The etiology is very heterogeneous. Little is known about the genetic causes of congenital hydrocephalus.A retrospective survey was performed including patients with primary congenital hydrocephalus referred to the Department of Clinical Genetics between 1985 and 2010 by perinatologists, (child) neurologists or pediatricians. Patients with hydrocephalus secondary to other pathology were excluded from this survey. We classified patients with primary congenital hydrocephalus into two main groups: non-syndromic hydrocephalus (NSH) and syndromic hydrocephalus (SH). Seventy-five individuals met the inclusion criteria, comprising 36% (27/75) NSH and 64% (48/75) SH. In 11% (8/75) hydrocephalus was familial. The cause of hydrocephalus was unknown in 81% (61/75), including all patients with NSH. The male-female ratio in this subgroup was 2.6:1, indicating an X-linked factor other than the L1CAM gene. In the group of SH patients, 29% (14/48) had a known cause of hydrocephalus including chromosomal abnormalities, L1 syndrome, Marden-Walker syndrome, Walker-Warburg syndrome and hemifacial microsomia.We performed this survey in order to evaluate current knowledge on the genetic etiology of primary congenital hydrocephalus and to identify new candidate genes or regulatory pathways for congenital hydrocephalus. Recommendations were made concerning the evaluation and genetic workup of patients with primary congenital hydrocephalus. We conclude that further molecular and functional analysis is needed to identify new genetic forms of congenital hydrocephalus. (C) 2011 Elsevier Masson SAS. All rights reserved.