Familial Atrial Septal Defect and Sudden Cardiac Death:Identification of a Novel <i>NKX2-5</i> Mutation and a Review of the Literature

OBJECTIVE: Atrial septal defect (ASD) is the second most common congenital heart defect (CHD) and is observed in families as an autosomal dominant trait as well as in nonfamilial CHD. Mutations in the NKX2‐5 gene, located on chromosome 5, are associated with ASD, often combined with conduction disturbances, cardiomyopathies, complex CHD, and sudden cardiac death as well. Here, we show that NKX2‐5 mutations primarily occur in ASD patients with conduction disturbances and heritable ASD. Furthermore, these families are at increased risk of sudden cardiac death. RESULTS: We screened 39 probands with familial CHD for mutations in NKX2‐5 and discovered a novel mutation in one family (2.5%) with ASD and atrioventricular block. A review of the literature revealed 59 different NKX2‐5 mutations in 202 patients. Mutations were significantly more common in familial cases compared to nonfamilial cases (P = 7.1 × 10(−9)). The majority of patients (74%) had ASD with conduction disturbance. Nineteen patients (15%) of 120 with familial ASD and conduction disturbance died from sudden cardiac death of which nine (8%) were confirmed mutation carriers, and 10 were possible carriers. CONCLUSIONS: NKX2‐5 mutations mainly occur in familial CHD, the signature phenotype is ASD with conduction disturbances and mutation carriers are at increased risk of sudden cardiac death. We suggest that familial ASD patients should be screened for NKX2‐5 mutations and, if they are mutation carriers, implantation of an implantable cardioverter‐defibrillator should be considered in these patients

Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients

A key prerequisite for precision medicine is the estimation of disease progression from the current patient state. Disease correlations and temporal disease progression (trajectories) have mainly been analysed with focus on a small number of diseases or using large-scale approaches without time consideration, exceeding a few years. So far, no large-scale studies have focused on defining a comprehensive set of disease trajectories. Here we present a discovery-driven analysis of temporal disease progression patterns using data from an electronic health registry covering the whole population of Denmark. We use the entire spectrum of diseases and convert 14.9 years of registry data on 6.2 million patients into 1,171 significant trajectories. We group these into patterns centred on a small number of key diagnoses such as chronic obstructive pulmonary disease (COPD) and gout, which are central to disease progression and hence important to diagnose early to mitigate the risk of adverse outcomes. We suggest such trajectory analyses may be useful for predicting and preventing future diseases of individual patients