Assessment of ICD eligibility in non-ischaemic cardiomyopathy patients: a position statement by the Task Force of the Dutch Society of Cardiology.

International guidelines recommend implantation of an implantable cardioverter-defibrillator (ICD) in non-ischaemic cardiomyopathy (NICM) patients with a left ventricular ejection fraction (LVEF) below 35% despite optimal medical therapy and a life expectancy of more than 1 year with good functional status. We propose refinement of these recommendations in patients with NICM, with careful consideration of additional risk parameters for both arrhythmic and non-arrhythmic death. These additional parameters include late gadolinium enhancement on cardiac magnetic resonance imaging and genetic testing for high-risk genetic variants to further assess arrhythmic risk, and age, comorbidities and sex for assessment of non-arrhythmic mortality risk.

Impact of a Chronic Total Coronary Occlusion on the Incidence of Appropriate Implantable Cardioverter-Defibrillator Shocks and Mortality: A Substudy of the Dutch Outcome in ICD Therapy (DO-IT)) Registry.

Background: Chronic total coronary occlusions (CTO) substantially increase the risk for sudden cardiac death. Among patients with chronic ischemic heart disease at risk for sudden cardiac death, an implantable cardioverter defibrillator (ICD) is the favored therapy for primary prevention of sudden cardiac death. This study sought to investigate the impact of CTOs on the risk for appropriate ICD shocks and mortality within a nationwide prospective cohort.

Long-term reliability of the phospholamban (PLN) p.(Arg14del) risk model in predicting major ventricular arrhythmia: a landmark study.

Aims: Recently, a genetic variant-specific prediction model for phospholamban (PLN) p.(Arg14del)-positive individuals was developed to predict individual major ventricular arrhythmia (VA) risk to support decision-making for primary prevention implantable cardioverter defibrillator (ICD) implantation. This model predicts major VA risk from baseline data, but iterative evaluation of major VA risk may be warranted considering that the risk factors for major VA are progressive.

ECG-only explainable deep learning algorithm predicts the risk for malignant ventricular arrhythmia in phospholamban cardiomyopathy.

Background: Phospholamban (PLN) p.(Arg14del) variant carriers are at risk for development of malignant ventricular arrhythmia (MVA). Accurate risk stratification allows timely implantation of intracardiac defibrillators and is currently performed with a multimodality prediction model.

Deep neural network-based clustering of deformation curves reveals novel disease features in PLN pathogenic variant carriers.

Echocardiographic deformation curves provide detailed information on myocardial function. Deep neural networks (DNNs) may enable automated detection of disease features in deformation curves, and improve the clinical assessment of these curves. We aimed to investigate whether an explainable DNN-based pipeline can be used to detect and visualize disease features in echocardiographic deformation curves of phospholamban (PLN) p.

Exercise does not influence development of phenotype in PLN p.(Arg14del) cardiomyopathy.

Background: Endurance and frequent exercise are associated with earlier onset of arrhythmogenic right ventricular cardiomyopathy (ARVC) and ventricular arrhythmias (VA) in desmosomal gene variant carriers. Individuals with the pathogenic c.40_42del; p.

Exploring the Correlation Between Fibrosis Biomarkers and Clinical Disease Severity in PLN p.Arg14del Patients.

Pathogenic variants in (, like p. Arg14del), are found in patients diagnosed with arrhythmogenic (ACM) and dilated cardiomyopathy (DCM). Fibrosis formation in the heart is one of the hallmarks in p.

Sex-specific aspects of phospholamban cardiomyopathy: The importance and prognostic value of low-voltage electrocardiograms.

Background: A pathogenic variant in the gene encoding phospholamban (PLN), a protein that regulates calcium homeostasis of cardiomyocytes, causes PLN cardiomyopathy. It is characterized by a high arrhythmic burden and can progress to severe cardiomyopathy. Risk assessment guides implantable cardioverter-defibrillator therapy and benefits from personalization.

Optimal echocardiographic assessment of myocardial dysfunction for arrhythmic risk stratification in phospholamban mutation carriers.

Aims: Phospholamban (PLN) p.Arg14del mutation carriers are at risk of developing malignant ventricular arrhythmias (VAs) and/or heart failure. Currently, left ventricular ejection fraction (LVEF) plays an important role in risk assessment for VA in these individuals.

The β-angle can help guide clinical decisions in the diagnostic work-up of patients suspected of Brugada syndrome: a validation study of the β-angle in determining the outcome of a sodium channel provocation test.

Aims: In patients with Brugada syndrome (BrS) but without spontaneous Type-1 electrocardiogram, several electrocardiographic characteristics have been studied, including the β-angle. Previous studies suggested that the β-angle might be useful in distinguishing BrS-patients from patients with only suggestive repolarization patterns without performing sodium channel blocker provocation testing. In this study, we aimed to determine the diagnostic value of the β-angle in patients suspected of BrS.

Prediction of ventricular arrhythmia in phospholamban p.Arg14del mutation carriers-reaching the frontiers of individual risk prediction.

Aims: This study aims to improve risk stratification for primary prevention implantable cardioverter defibrillator (ICD) implantation by developing a new mutation-specific prediction model for malignant ventricular arrhythmia (VA) in phospholamban (PLN) p.Arg14del mutation carriers. The proposed model is compared to an existing PLN risk model.

Improving electrocardiogram-based detection of rare genetic heart disease using transfer learning: An application to phospholamban p.Arg14del mutation carriers.

The pathogenic mutation p.Arg14del in the gene encoding Phospholamban (PLN) is known to cause cardiomyopathy and leads to increased risk of sudden cardiac death. Automatic tools might improve the detection of patients with this rare disease.

Development and external validation of prediction models to predict implantable cardioverter-defibrillator efficacy in primary prevention of sudden cardiac death.

Aims: This study was performed to develop and externally validate prediction models for appropriate implantable cardioverter-defibrillator (ICD) shock and mortality to identify subgroups with insufficient benefit from ICD implantation.

Methods And Results: We recruited patients scheduled for primary prevention ICD implantation and reduced left ventricular function. Bootstrapping-based Cox proportional hazards and Fine and Gray competing risk models with likely candidate predictors were developed for all-cause mortality and appropriate ICD shock, respectively.

Early Mechanical Alterations in Phospholamban Mutation Carriers: Identifying Subclinical Disease Before Onset of Symptoms.

Objectives: This study aimed to explore echocardiographic characteristics of phospholamban (PLN) p.Arg14del mutation carriers to investigate whether structural and/or functional abnormalities could be identified before onset of symptoms.

Background: Carriers of the genetic PLN p.

Computer versus cardiologist: Is a machine learning algorithm able to outperform an expert in diagnosing a phospholamban p.Arg14del mutation on the electrocardiogram?

Background: Phospholamban (PLN) p.Arg14del mutation carriers are known to develop dilated and/or arrhythmogenic cardiomyopathy, and typical electrocardiographic (ECG) features have been identified for diagnosis. Machine learning is a powerful tool used in ECG analysis and has shown to outperform cardiologists.

Handling missing predictor values when validating and applying a prediction model to new patients.

Missing data present challenges for development and real-world application of clinical prediction models. While these challenges have received considerable attention in the development setting, there is only sparse research on the handling of missing data in applied settings. The main unique feature of handling missing data in these settings is that missing data methods have to be performed for a single new individual, precluding direct application of mainstay methods used during model development.