Epidemiology of atrial fibrillation in the general population

Atrial fibrillation is a common disease of the heart, characterized by an irregular and, if not treated, mostly fast heart rhythm. The origin of the disease is the atrial part of the heart. It is a rare disease before the age of 55, but the prevalence is sharply increasing with age. In many cases patients do not even notice that they have a rhythm disorder. But if they have complaints, they mention dyspnoea, chest pain, palpitations, dizziness, and sometimes syncope. For many years, atrial fibrillation has been considered as an innocent bystander of old age, but it is nowadays generally accepted that atrial fibrillation is associated with impaired quality of life and increased morbidity and mortality. Treatment regimens evolved as a consequence, unfortunately sometimes introducing new potential harms to the patients with this disease. Atrial fibrillation is involved in pathological processes through the whole body and has through that inspired numerous investigators in internal medicine, cardiology, neurology, pathology, pharmacology, physiology and epidemiology. The disease concept has evolved as science evolved and often has been in the centre of intense dispute

Non-steroidal anti-inflammatory drugs and the risk of atrial fibrillation: A population-based follow-up study

Objective: To investigate the association of non-steroidal anti-inflammatory drugs (NSAIDs) and the risk of atrial fibrillation in a prospective community-based follow-up study of elderly individuals with uniform case assessment and data on potential confounders. Design: Data came from the population-based follow-up study, the Rotterdam Study. Participants: The study comprised 8423 participants without atrial fibrillation at baseline. Main outcome measures: Atrial fibrillation was ascertained from ECG assessments as well as medical records. Use of NSAIDs was obtained from automated prescription records by linkage with participating pharmacies. We used Cox proportional hazards models to study the association between NSAID drug use and atrial fibrillation. Use of NSAIDs was included in the model as a time-varying variable. Results: At baseline, the mean age of the study population was 68.5 years (SD: 8.7) and 58% were women. During a mean follow-up of 12.9 years, 857 participants developed atrial fibrillation. Current use of NSAIDs was associated with increased risk compared with never-use (HR 1.76, 95% CI 1.07 to 2.88). Also, recent use (within 30 days after discontinuation of NSAIDs) was associated with an increased risk of atrial fibrillation compared with never-use (HR 1.84, 95% CI 1.34 to 2.51) adjusted for age, sex and several potential confounders. Conclusions: In this study, use of NSAIDs was associated with an increased risk of atrial fibrillation. Further studies are needed to investigate the underlying mechanisms behind this association

Methods of data collection and definitions of cardiac outcomes in the Rotterdam Study

The prevalence of cardiovascular diseases is rising. Therefore, adequate risk prediction and identification of its determinants is increasingly important. The Rotterdam Study is a prospective population-based cohort study ongoing since 1990 in the city of Rotterdam, The Netherlands. One of the main targets of the Rotterdam Study is to identify the determinants and prognosis of cardiovascular diseases. Case finding in epidemiological studies is strongly depending on various sources of followup and clear outcome definitions. The sources used for collection of data in the Rotterdam Study are diverse and the definitions of outcomes in the Rotterdam Study have changed due to the introduction of novel diagnostics and therapeutic interventions. This article gives the methods for data collection and the up-to-date definitions of the cardiac outcomes based on international guidelines, including the recently adopted cardiovascular disease mortality definitions. In all, detailed description of cardiac outcome definitions enhances the possibility to make comparisons with other studies in the field of cardiovascular research and may increase the strength of collaborations

Deep Networks are Reproducing Kernel Chains

Identifying an appropriate function space for deep neural networks remains a key open question. While shallow neural networks are naturally associated with Reproducing Kernel Banach Spaces (RKBS), deep networks present unique challenges. In this work, we extend RKBS to chain RKBS (cRKBS), a new framework that composes kernels rather than functions, preserving the desirable properties of RKBS. We prove that any deep neural network function is a neural cRKBS function, and conversely, any neural cRKBS function defined on a finite dataset corresponds to a deep neural network. This approach provides a sparse solution to the empirical risk minimization problem, requiring no more than $N$ neurons per layer, where $N$ is the number of data points