Trends in left ventricular assist device use and outcomes among Medicare beneficiaries, 2004–2011

Objective: To characterise the trends in the left ventricular assist device (LVAD) implantation rates and outcomes between 2004 and 2011 in the Medicare population. Since the approval of the HeartMate II in 2008, the use of LVADs has steadily climbed. Given the increase in LVAD use, issues around discharge disposition, post-implant hospitalisations and costs require further understanding. Methods: We examined LVAD implantation rates and short-term and long-term outcomes among Medicare fee-for-service beneficiaries hospitalised for LVAD implantation. We also conducted analyses among survivors 1-year post-discharge to examine rehospitalisation rates. Lastly, we reported Centers for Medicare & Medicaid Services (CMS) payments for both index hospitalisation and rehospitalisations 1 year post-discharge. Results: A total of 2152 LVAD implantations were performed with numbers increasing from 107 in 2004 to 612 in 2011. The 30-day mortality rate decreased from 52% to 9%, and 1-year mortality rate decreased from 69% to 31%. We observed no change in overall length of stay, but post-procedure length of stay increased. We also found an increase in home discharge dispositions from 26% to 53%. Between 2004 and 2010, the rehospitalisation rate increased and the number of hospital days decreased. The adjusted CMS payment for the index hospitalisation increased from $188 789 to$225 697 over time but decreased for rehospitalisation from $60 647 to$53 630. Conclusions: LVAD implantations increased over time. We found decreasing 30-day and 1-year mortality rates and increasing home discharge disposition. The proportion of patients rehospitalised among 1-year survivors remained high with increasing index hospitalisation cost, but decreasing post-implantation costs over time

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

StrainGE: A toolkit to track and characterize low-abundance strains in complex microbial communities

Human-associated microbial communities comprise not only complex mixtures of bacterial species, but also mixtures of conspecific strains, the implications of which are mostly unknown since strain level dynamics are underexplored due to the difficulties of studying them. We introduce the Strain Genome Explorer (StrainGE) toolkit, which deconvolves strain mixtures and characterizes component strains at the nucleotide level from short-read metagenomic sequencing with higher sensitivity and resolution than other tools. StrainGE is able to identify strains at 0.1x coverage and detect variants for multiple conspecific strains within a sample from coverages as low as 0.5x

Features of mammalian microRNA promoters emerge from polymerase II chromatin immunoprecipitation data

Background: MicroRNAs (miRNAs) are short, non-coding RNA regulators of protein coding genes. miRNAs play a very important role in diverse biological processes and various diseases. Many algorithms are able to predict miRNA genes and their targets, but their transcription regulation is still under investigation. It is generally believed that intragenic miRNAs (located in introns or exons of protein coding genes) are co-transcribed with their host genes and most intergenic miRNAs transcribed from their own RNA polymerase II (Pol II) promoter. However, the length of the primary transcripts and promoter organization is currently unknown. Methodology: We performed Pol II chromatin immunoprecipitation (ChIP)-chip using a custom array surrounding regions of known miRNA genes. To identify the true core transcription start sites of the miRNA genes we developed a new tool (CPPP). We showed that miRNA genes can be transcribed from promoters located several kilobases away and that their promoters share the same general features as those of protein coding genes. Finally, we found evidence that as many as 26% of the intragenic miRNAs may be transcribed from their own unique promoters. Conclusion: miRNA promoters have similar features to those of protein coding genes, but miRNA transcript organization is more complex. © 2009 Corcoran et al

Effect of Size and Heterogeneity of Samples on Biomarker Discovery: Synthetic and Real Data Assessment

MOTIVATION: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the number of features); 2) heterogeneity of the disease; 3) heterogeneity of experimental protocols and computational pipelines employed in the analysis. In this paper, we focus on the first two issues and assess, both on simulated (through an in silico regulation network model) and real clinical datasets, the consistency of candidate biomarkers provided by a number of different methods. METHODS: We extensively simulated the effect of heterogeneity characteristic of complex diseases on different sets of microarray data. Heterogeneity was reproduced by simulating both intrinsic variability of the population and the alteration of regulatory mechanisms. Population variability was simulated by modeling evolution of a pool of subjects; then, a subset of them underwent alterations in regulatory mechanisms so as to mimic the disease state. RESULTS: The simulated data allowed us to outline advantages and drawbacks of different methods across multiple studies and varying number of samples and to evaluate precision of feature selection on a benchmark with known biomarkers. Although comparable classification accuracy was reached by different methods, the use of external cross-validation loops is helpful in finding features with a higher degree of precision and stability. Application to real data confirmed these results