Triage for coronary artery bypass graft surgery in Canada: Do patients agree on who should come first?

<p>Abstract</p> <p>Background</p> <p>The extent to which clinical and non-clinical factors impact on the waiting-list prioritization preferences of patients in the queue is unknown. Using a series of hypothetical scenarios, the objective of this study was to examine the extent to which clinical and non-clinical factors impacted on how patients would prioritize others relative to themselves in the coronary artery bypass surgical queue.</p> <p>Methods</p> <p>Ninety-one consecutive eligible patients awaiting coronary artery bypass grafting surgery at Sunnybrook Health Sciences Centre (median waiting-time duration prior to survey of 8 weeks) were given a self-administered survey consisting of nine scenarios in which clinical and non-clinical characteristic profiles of hypothetical patients (also awaiting coronary artery bypass surgery) were varied. For each scenario, patients were asked where in the queue such hypothetical patients should be placed relative to themselves.</p> <p>Results</p> <p>The eligible response rate was 65% (59/91). Most respondents put themselves marginally ahead of a hypothetical patient with identical clinical and non-clinical characteristics as themselves. There was a strong tendency for respondents to place patients of higher clinical acuity ahead of themselves in the queue (P < 0.0001). Social independence among young individuals was a positively valued attribute (P < 0.0001), but neither age per se nor financial status, directly impacted on patients waiting-list priority preferences.</p> <p>Conclusion</p> <p>While patient perceptions generally reaffirmed a bypass surgical triage process based on principals of equity and clinical acuity, the valuation of social independence may justify further debate with regard to the inclusion of non-clinical factors in waiting-list prioritization management systems in Canada, as elsewhere.</p

Weak pairwise correlations imply strongly correlated network states in a neural population

Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher order interactions among large groups of elements play an important role. In the vertebrate retina, we show that weak correlations between pairs of neurons coexist with strongly collective behavior in the responses of ten or more neurons. Surprisingly, we find that this collective behavior is described quantitatively by models that capture the observed pairwise correlations but assume no higher order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behavior. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah (http://cosyne.org

Risk-taking attitudes and their association with process and outcomes of cardiac care: a cohort study

<p>Abstract</p> <p>Background</p> <p>Prior research reveals that processes and outcomes of cardiac care differ across sociodemographic strata. One potential contributing factor to such differences is the personality traits of individuals within these strata. We examined the association between risk-taking attitudes and cardiac patients' clinical and demographic characteristics, the likelihood of undergoing invasive cardiac procedures and survival.</p> <p>Methods</p> <p>We studied a large inception cohort of patients who underwent cardiac catheterization between July 1998 and December 2001. Detailed clinical and demographic data were collected at time of cardiac catheterization and through a mailed survey one year post-catheterization. The survey included three general risk attitude items from the Jackson Personality Inventory. Patients' (n = 6294) attitudes toward risk were categorized as risk-prone versus non-risk-prone and were assessed for associations with baseline clinical and demographic characteristics, treatment received (i.e., medical therapy, coronary artery bypass graft (CABG) surgery, percutaneous coronary intervention (PCI)), and survival (to December 2005).</p> <p>Results</p> <p>2827 patients (45%) were categorized as risk-prone. Having risk-prone attitudes was associated with younger age (p < .001), male sex (p < .001), current smoking (p < .001) and higher household income (p < .001). Risk-prone patients were more likely to have CABG surgery in unadjusted (Odds Ratio [OR] = 1.21; 95% CI 1.08–1.36) and adjusted (OR = 1.18; 95% CI 1.02–1.36) models, but were no more likely to have PCI or any revascularization. Having risk-prone attitudes was associated with better survival in an unadjusted survival analysis (Hazard Ratio [HR] = 0.78 (95% CI 0.66–0.93), but not in a risk-adjusted analysis (HR = 0.92, 95% CI 0.77–1.10).</p> <p>Conclusion</p> <p>These exploratory findings suggest that patient attitudes toward risk taking may <b>contribute to </b>some of the documented differences in use of invasive cardiac procedures. An awareness of these associations could help healthcare providers as they counsel patients regarding cardiac care decisions.</p

Understanding Liver Health Using the National Center for Health Statistics

The National Center for Health Statistics (NCHS) is the principal health statistics agency for the United States. It seeks to provide accurate, relevant, and timely data on health status and utilization of health care. As such, the NCHS represents a tremendous repository of behavioral, biological, and clinical data that can be employed to identify issues and effect change in public policy related to liver health and disease. By providing an understanding of the rich, publicly available data systems within the NCHS, investigators may capitalize on an efficient means to shape current knowledge of liver disease

Missing value imputation for microarray gene expression data using histone acetylation information

<p>Abstract</p> <p>Background</p> <p>It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages.</p> <p>Results</p> <p>The paper explores the feasibility of doing missing value imputation with the help of gene regulatory mechanism. An imputation framework called histone acetylation information aided imputation method (HAIimpute method) is presented. It incorporates the histone acetylation information into the conventional KNN(<it>k</it>-nearest neighbor) and LLS(local least square) imputation algorithms for final prediction of the missing values. The experimental results indicated that the use of acetylation information can provide significant improvements in microarray imputation accuracy. The HAIimpute methods consistently improve the widely used methods such as KNN and LLS in terms of normalized root mean squared error (NRMSE). Meanwhile, the genes imputed by HAIimpute methods are more correlated with the original complete genes in terms of Pearson correlation coefficients. Furthermore, the proposed methods also outperform GOimpute, which is one of the existing related methods that use the functional similarity as the external information.</p> <p>Conclusion</p> <p>We demonstrated that the using of histone acetylation information could greatly improve the performance of the imputation especially at high missing percentages. This idea can be generalized to various imputation methods to facilitate the performance. Moreover, with more knowledge accumulated on gene regulatory mechanism in addition to histone acetylation, the performance of our approach can be further improved and verified.</p

Using Pre-existing Microarray Datasets to Increase Experimental Power: Application to Insulin Resistance

Although they have become a widely used experimental technique for identifying differentially expressed (DE) genes, DNA microarrays are notorious for generating noisy data. A common strategy for mitigating the effects of noise is to perform many experimental replicates. This approach is often costly and sometimes impossible given limited resources; thus, analytical methods are needed which increase accuracy at no additional cost. One inexpensive source of microarray replicates comes from prior work: to date, data from hundreds of thousands of microarray experiments are in the public domain. Although these data assay a wide range of conditions, they cannot be used directly to inform any particular experiment and are thus ignored by most DE gene methods. We present the SVD Augmented Gene expression Analysis Tool (SAGAT), a mathematically principled, data-driven approach for identifying DE genes. SAGAT increases the power of a microarray experiment by using observed coexpression relationships from publicly available microarray datasets to reduce uncertainty in individual genes' expression measurements. We tested the method on three well-replicated human microarray datasets and demonstrate that use of SAGAT increased effective sample sizes by as many as 2.72 arrays. We applied SAGAT to unpublished data from a microarray study investigating transcriptional responses to insulin resistance, resulting in a 50% increase in the number of significant genes detected. We evaluated 11 (58%) of these genes experimentally using qPCR, confirming the directions of expression change for all 11 and statistical significance for three. Use of SAGAT revealed coherent biological changes in three pathways: inflammation, differentiation, and fatty acid synthesis, furthering our molecular understanding of a type 2 diabetes risk factor. We envision SAGAT as a means to maximize the potential for biological discovery from subtle transcriptional responses, and we provide it as a freely available software package that is immediately applicable to any human microarray study

Serum Metabolomics Reveals Higher Levels of Polyunsaturated Fatty Acids in Lepromatous Leprosy: Potential Markers for Susceptibility and Pathogenesis

Leprosy is an infectious disease caused by the obligate intracellular bacterium Mycobacterium leprae. M. leprae infects the skin and nerves, leading to disfigurement and nerve damage, with the severity of the disease varying widely. We believe there are multiple factors (genetic, bacterial, nutritional and environmental), which may explain the differences in clinical manifestations of the disease. We studied the metabolites in the serum of infected patients to search for specific molecules that may contribute to variations in the severity of disease seen in leprosy. We found that there were variations in levels of certain lipids in the patients with different bacterial loads. In particular, we found that three polyunsaturated fatty acids (PUFAs) involved in the inhibition of inflammation were more abundant in the serum of patients with higher bacterial loads. However, we do not know whether these PUFAs originated from the host or the bacteria. The variations in the metabolite profile that we observed provide a foundation for future research into the explanations of how leprosy causes disease