Seasonal variations of ventricular arrhythmia clusters in defibrillator recipients

BACKGROUND: Clustering ventricular arrhythmias are the consequence of acute ventricular electrical instability and represent a challenge in the management of the growing number of patients with an implantable cardioverter-defibrillator (ICD). Triggering factors can rarely be identified. OBJECTIVES: Several studies have revealed seasonal variations in the frequency of cardiovascular events and life-threatening arrhythmias, and we sought to establish whether seasonal factors may exacerbate ventricular electrical instability leading to arrhythmia clusters and electrical storm. METHODS: Two hundred and fourteen consecutive defibrillator recipients were followed-up during 3.3 +/- 2.2 years. Arrhythmia cluster was defined as the occurrence of three or more arrhythmic events triggering appropriate defibrillator therapies within 2 weeks. Time intervals between two clusters were calculated for each month and each season, and were compared using Kruskal-Wallis test and Wilcoxon-Mann-Whitney test with Bonferroni adjustment. RESULTS: During a follow-up of 698 patient years, 98 arrhythmia clusters were observed in 51 patients; clustering ventricular arrhythmias were associated with temporal variables; they occurred more frequently in the winter and spring months than during the summer and fall. Accordingly, the time intervals between two clusters were significantly shorter during winter and spring (median and 95% CI): winter 16 (5-19), spring 11.5 (7-25), summer 34.5 (15-55), fall 50.5 (19-65), P = 0.0041. CONCLUSION: There are important seasonal variations in the incidence of arrhythmia clusters in ICD recipients. Whether these variations are related to environmental factors, change in physical activity, or psychological factors requires further study.

Problems in the classical calculus of variations

This dissertation concerns convergence analysis for nonparametric problems in the calculus of variations and sufficient conditions for weak local minimizer of a functional for both nonparametric and parametric problems. Newton's method in infinite-dimensional space is proved to be well-defined and converges quadratically to a weak local minimizer of a functional subject to certain boundary conditions. Sufficient conditions for global converges are proposed and a well-defined algorithm based on those conditions is presented and proved to converge. Finite element discretization is employed to achieve an implementable line-search-based quasi-Newton algorithm and a proof of convergence of the discretization of the algorithm is included. This work also proposes sufficient conditions for weak local minimizer without using the language of conjugate points. The form of new conditions is consistent with the ones in finite-dimensional case. It is believed that the new form of sufficient conditions will lead to simpler approaches to verify an extremal as local minimizer for well-known problems in calculus of variations.

Variations of the FEAST Eigenvalue Algorithm

FEAST is a recently developed eigenvalue algorithm which computes selected interior eigenvalues of real symmetric matrices. It uses contour integral resolvent based projections. A weakness is that the existing algorithm relies on accurate reasoned estimates of the number of eigenvalues within the contour. Examining the singular values of the projections on moderately-sized, randomly-generated test problems motivates orthogonalization-based improvements to the algorithm. The singular value distributions provide experimentally robust estimates of the number of eigenvalues within the contour. The algorithm is modified to handle both Hermitian and general complex matrices. The original algorithm (based on circular contours and Gauss-Legendre quadrature) is extended to contours and quadrature schemes that are recursively subdividable. A general complex recursive algorithm is implemented on rectangular and diamond contours. The accuracy of different quadrature schemes for various contours is investigated.

Detection of cryptic pathogenic copy number variations and constitutional loss of heterozygosity using high resolution SNP microarray analysis in 117 patients referred for cytogenetic analysis and impact on clinical practice

BACKGROUND: Microarray genome analysis is realising its promise for improving detection of genetic abnormalities in individuals with mental retardation and congenital abnormality. Copy number variations (CNVs) are now readily detectable using a variety of platforms and a major challenge is the distinction of pathogenic from ubiquitous, benign polymorphic CNVs. The aim of this study was to investigate replacement of time consuming, locus specific testing for specific microdeletion and microduplication syndromes with microarray analysis, which theoretically should detect all known syndromes with CNV aetiologies as well as new ones. METHODS: Genome wide copy number analysis was performed on 117 patients using Affymetrix 250K microarrays. RESULTS: 434 CNVs (195 losses and 239 gains) were found, including 18 pathogenic CNVs and 9 identified as "potentially pathogenic". Almost all pathogenic CNVs were larger than 500 kb, significantly larger than the median size of all CNVs detected. Segmental regions of loss of heterozygosity larger than 5 Mb were found in 5 patients. CONCLUSIONS: Genome microarray analysis has improved diagnostic success in this group of patients. Several examples of recently discovered "new syndromes" were found suggesting they are more common than previously suspected and collectively are likely to be a major cause of mental retardation. The findings have several implications for clinical practice. The study revealed the potential to make genetic diagnoses that were not evident in the clinical presentation, with implications for pretest counselling and the consent process. The importance of contributing novel CNVs to high quality databases for genotype-phenotype analysis and review of guidelines for selection of individuals for microarray analysis is emphasised.