Biomedical subjects

Robert Culverhouse

Publications and source records attributed to Robert Culverhouse.

7 recordsLinked to original sources

PubMed2006

Clinical classification schemes for predicting hemorrhage: results from the National Registry of Atrial Fibrillation (NRAF).

BACKGROUND: Although warfarin and other anticoagulants can prevent ischemic events, they can cause hemorrhage. Quantifying the rate of hemorrhage is crucial for determining the risks and net benefits of prescribing antithrombotic therapy. Our objective was to find a bleeding classification scheme that could quantify the risk of hemorrhage in elderly patients with atrial fibrillation. METHODS: We combined bleeding risk factors from existing classification schemes into a new scheme, HEMORR2HAGES, and validated all bleeding classification schemes. We scored HEMORR2HAGES by adding 2 points for a prior bleed and 1 point for each of the other risk factors: hepatic or renal disease, ethanol abuse, malignancy, older (age > 75 years), reduced platelet count or function, hypertension (uncontrolled), anemia, genetic factors, excessive fall risk, and stroke. We used data from quality improvement organizations representing 7 states to assemble a registry of 3791 Medicare beneficiaries with atrial fibrillation. RESULTS: There were 162 hospital admissions with an International Classification of Diseases, Ninth Revision, Clinical Modification code for hemorrhage. With each additional point, the rate of bleeding per 100 patient-years of warfarin increased: 1.9 for 0, 2.5 for 1, 5.3 for 2, 8.4 for 3, 10.4 for 4, and 12.3 for > or =5 points. In patients prescribed warfarin, HEMORR2HAGES had greater predictive accuracy (c statistic 0.67) than other bleed prediction schemes (P < .001). CONCLUSIONS: Adaptations of existing classification schemes, especially a new bleeding risk scheme, HEMORR2HAGES, can quantify the risk of hemorrhage and aid in the management of antithrombotic therapy.

Aged↗

PubMed2005

Long-term stability of alcohol and other substance dependence diagnoses and habitual smoking: an evaluation after 5 years.

CONTEXT: A major criterion to validate diagnoses is stability over time. OBJECTIVE: To examine the stability of several classification systems for lifetime diagnosis of alcohol dependence, to identify characteristics predicting stability of alcoholism, and to study stability of lifetime assessments of habitual smoking (1 pack per day for at least 6 months) and other drug dependence. DESIGN: Participants in the Collaborative Study on the Genetics of Alcoholism were interviewed using the Semi-Structured Assessment for the Genetics of Alcoholism and reevaluated 5 years later. Initial and follow-up interviews were available for 1728 individuals (641 index cases, 800 siblings, 287 controls) with lifetime diagnoses of alcohol dependence, other substance dependence (marijuana, cocaine, other stimulants, sedatives, opioids), or habitual smoking at first interview. The likelihood that an individual with a lifetime history of substance dependence or habitual smoking at the first interview retained this classification after 5 years was examined to assess stability of diagnosis. RESULTS: Stability of a lifetime diagnosis of alcohol dependence varied among the subject groups of index cases, siblings, and community-based controls. Alcohol dependence as defined by DSM-III-R criteria was highly stable in the index cases (90.5% women, 94.7% men) but much less stable in the community-based controls (27.5% women, 64.7% men). The most important characteristic associated with stability of diagnosis of alcohol dependence was severity, defined by the number of alcohol-related symptoms. Other DSM-III-R substance dependence disorders varied in the stability of diagnosis over a 5-year period. Lifetime history of habitual smoking was highly stable in all subject groups (96.0% overall). CONCLUSIONS: Stability of lifetime assessment of alcohol dependence varies depending on severity of illness. Severe cases of alcohol dependence are more likely to be stable, whereas general population cases of alcohol dependence are less likely to have stable diagnoses. The stability of diagnosis for other substance dependence varies from substance to substance.

Adolescent↗

PubMed2005

A mouse model for developing treatment for secondary liver tumors.

Metastatic colorectal cancer is the second most common cause of cancer mortality. The liver is a common site of metastasis and only a minority of patients with liver metastases are candidates for potentially curative surgical resection. The treatment of patients with unresectable liver tumors is a major clinical problem and survival remains low. Many animal models of hepatic metastasis do not result in disease which resembles the advanced cancer setting. The purpose of this study was to establish a murine model for use in the evaluation of therapy for secondary liver cancer. Human colon cancer cells were injected directly into the portal vein of nude mice. Magnetic resonance imaging (MRI), performed weekly, was used to follow the time-course and characteristics of tumor growth. As expected, tumor size increased proportionately with time, following inoculation, and the MRI images correlated well with gross pathology findings at necropsy with respect to both tumor size and location. The tumors retained important morphology and biological characteristics of human colon cancer. Mice bearing liver metastasis were treated with irinotecan or drug vehicle. MRI evaluation pre/post-therapy gave an objective measure of therapeutic response. Irinotecan therapy was able to double the survival (median 76-93 days) compared to vehicle alone (median 43-46 days). This murine model is reproducible, rapid, inexpensive and has an excellent success rate for the development of liver metastasis (100%). When used in conjunction with small animal MRI, this model allows the efficient evaluation of the therapeutics of liver metastasis without the use of repeated laparotomy or splenectomy and without requiring large numbers of animals undergoing terminal experiments.

Animals↗

PubMed2004

Detecting epistatic interactions contributing to quantitative traits.

The restricted partition method (RPM) is a partitioning algorithm for examining multi-locus genotypes as (potentially non-additive) predictors of a quantitative trait. The motivating application was to develop a robust method to examine quantitative phenotypes for epistasis (gene-gene interactions), but the method can be applied without modification to gene-environment interactions. Simulation results indicate that the method provides an efficient way to identify loci contributing epistatically to a quantitative trait, even if the loci have no single locus effects. Statistical significance can be estimated through permutation testing. An example using real data involving the metabolism of a chemotherapy drug is included for illustration. Although the examples in this article involve 2-locus interactions, the RPM is computationally feasible for the analysis of more than two loci or factors.

Algorithms↗

PubMed2004

Single nucleotide polymorphism profiling across the methotrexate pathway in normal subjects and patients with rheumatoid arthritis.

Methotrexate (MTX) is a commonly used disease-modifying antirheumatic drug in rheumatoid arthritis (RA). Polymorphisms occur in several genes encoding key enzymes in the folic acid pathway, which is influenced by MTX, but have not been evaluated in patients with RA. The effect of race on allele frequency has also not been evaluated. In this study, the allele frequencies of polymorphisms in six key enzymes in the MTX-folate pathway in patients with RA and healthy controls, including several common racial groups were studied. European- and African-American patients with RA and European and African healthy controls were genotyped for 22 genetic loci in six genes in the MTX cellular pathway. Differences in genotype distributions between the different racial groups were evaluated using chi(2) tests. Allele frequencies were significantly different (p < 0.001) for eight single nucleotide polymorphisms between the European and African controls. The allele frequencies of two polymorphisms showed significant differences (p < 0.001) between the African- and European-American patients with RA. Thus, racial differences exist between the allele frequencies of several polymorphisms in enzymes in the MTX-folate pathway in patients with RA and healthy controls. Whether such differences contribute to a differential response to MTX in patients with RA deserves to be investigated.

Black or African American↗

PubMed2003

Analyzing microarray data using cluster analysis.

As pharmacogenetics researchers gather more detailed and complex data on gene polymorphisms that effect drug metabolizing enzymes, drug target receptors and drug transporters, they will need access to advanced statistical tools to mine that data. These tools include approaches from classical biostatistics, such as logistic regression or linear discriminant analysis, and supervised learning methods from computer science, such as support vector machines and artificial neural networks. In this review, we present an overview of another class of models, cluster analysis, which will likely be less familiar to pharmacogenetics researchers. Cluster analysis is used to analyze data that is not a priori known to contain any specific subgroups. The goal is to use the data itself to identify meaningful or informative subgroups. Specifically, we will focus on demonstrating the use of distance-based methods of hierarchical clustering to analyze gene expression data.

Cluster Analysis↗

PubMed2002

A perspective on epistasis: limits of models displaying no main effect.

The completion of a draft sequence of the human genome and the promise of rapid single-nucleotide-polymorphism-genotyping technologies have resulted in a call for the abandonment of linkage studies in favor of genome scans for association. However, there exists a large class of genetic models for which this approach will fail: purely epistatic models with no additive or dominance variation at any of the susceptibility loci. As a result, traditional association methods (such as case/control, measured genotype, and transmission/disequilibrium test [TDT]) will have no power if the loci are examined individually. In this article, we examine this class of models, delimiting the range of genetic determination and recurrence risks for two-, three-, and four-locus purely epistatic models. Our study reveals that these models, although giving rise to no additive or dominance variation, do give rise to increased allele sharing between affected sibs. Thus, a genome scan for linkage could detect genomic subregions harboring susceptibility loci. We also discuss some simple multilocus extensions of single-locus analysis methods, including a conditional form of the TDT.

Alleles↗