Risk Stratification for Sudden Cardiac Death in Non-Ischaemic Dilated Cardiomyopathy

PURPOSE OF REVIEW: Non-ischaemic dilated cardiomyopathy (DCM) occurs in 1 in 2500 individuals in the general population and is associated with considerable morbidity and mortality. Studies involving large numbers of unselected DCM patients have led to consensus guidelines recommending implantable cardioverter-defibrillator (ICD) implantation for protection against sudden cardiac death (SCD) in those with LVEF ≤35%. The purpose of this article is to review the literature for other potential markers including serological, electrocardiographic, echocardiographic, cardiac magnetic resonance, ambulatory ECG and genetic data, to highlight other potential markers that may optimise risk stratification for SCD in this cohort and thereby allow a more personalized approach to ICD-implantation. RECENT FINDINGS: Recent studies including the Danish study to assess the efficacy of ICDs in patients with non-ischemic systolic heart failure on mortality (DANISH) trial have questioned the benefits of ICD implantation in this group of patients with no changes in all-cause mortality. Recent pooled cohorts of patients with genetic DCM and in particular in those with Lamin A/C (LMNA) mutations have identified patients at increased risk of SCD and allowed the creation of algorithms to prognosticate SCD risk in mutation carriers. Furthermore, genetic testing has identified other DCM-causing genes including filamin C (FLNC) and RBM20 which may be associated with higher rates of ventricular arrhythmia. To date, risk-stratification for SCD has been hampered by the utilisation of heterogenous subsets of idiopathic DCM patients and by use of static risk models where predictions are based on a single time point with a lack of consideration of disease progression. The current focus of personalised risk-stratification for SCD is shifting towards better characterisation of underlying DCM aetiology and the development of multi-parametric risk-stratification models that incorporate time-dependent disease characteristics and novel biomarkers.