Prevalence and risk factors of methicillin-resistant Staphylococcus aureus in critically-ill hospitalized patients in a tertiary care center in Houston, Texas: An active surveillance pilot project

Objective. The objective of this study is to determine the prevalence of MRSA colonization in adult patients admitted to intensive care units at an urban tertiary care hospital in Houston, Texas and to evaluate the risk factors associated with colonization during a three month active-screening pilot project. Design. This study used secondary data from a small cross-sectional pilot project. Methods. All patients admitted to the seven specialty ICUs were screened for MRSA by nasal culture. Results were obtained utilizing the BD GeneOhm™ IDI-MRSA assay in vitro diagnostic test, for rapid MRSA detection. Statistical analysis was performed using the STATA 10, Epi Info, and JavaStat. Results . 1283/1531 (83.4%) adult ICU admissions were screened for nasal MRSA colonization. Of those screened, demographic and risk factor data was available for 1260/1283 (98.2%). Unresolved results were obtained for 73 patients. Therefore, a total of 1187/1531 (77.5%) of all ICU admissions during the three month study period are described in this analysis. Risk factors associated with colonization included the following: hospitalization within the last six months (odds ratio 2.48 [95% CI, 1.70-3.63], p=0.000), hospitalization within the last 12 months, (odds ratio 2.27 [95% CI, 1.57-3.80], p=0.000), and having diabetes mellitus (odds ratio 1.63 [95% CI, 1.14-2.32], p=0.007). Conclusion. Based on the literature, the prevalence of MRSA for this population is typical of other prevalence studies conducted in the United States and coincides with the continual increasing trend of MRSA colonization. Significant risk factors were similar to those found in previous studies. Overall, the active surveillance screening pilot project has provided valuable information on a population not widely addressed. These findings can aid in future interventions for the education, control, prevention, and treatment of MRSA. ^

The determinants of improvements in health outcomes and of cost reduction in hospital inpatient care

This study aims to address two research questions. First, ‘Can we identify factors that are determinants both of improved health outcomes and of reduced costs for hospitalized patients with one of six common diagnoses?’ Second, ‘Can we identify other factors that are determinants of improved health outcomes for such hospitalized patients but which are not associated with costs?’ The Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) database from 2003 to 2006 was employed in this study. The total study sample consisted of hospitals which had at least 30 patients each year for the given diagnosis: 954 hospitals for acute myocardial infarction (AMI), 1552 hospitals for congestive heart failure (CHF), 1120 hospitals for stroke (STR), 1283 hospitals for gastrointestinal hemorrhage (GIH), 979 hospitals for hip fracture (HIP), and 1716 hospitals for pneumonia (PNE). This study used simultaneous equations models to investigate the determinants of improvement in health outcomes and of cost reduction in hospital inpatient care for these six common diagnoses. In addition, the study used instrumental variables and two-stage least squares random effect model for unbalanced panel data estimation. The study concluded that a few factors were determinants of high quality and low cost. Specifically, high specialty was the determinant of high quality and low costs for CHF patients; small hospital size was the determinant of high quality and low costs for AMI patients. Furthermore, CHF patients who were treated in Midwest, South, and West region hospitals had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. Gastrointestinal hemorrhage and pneumonia patients who were treated in South region hospitals also had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. This study found that six non-cost factors were related to health outcomes for a few diagnoses: hospital volume, percentage emergency room admissions for a given diagnosis, hospital competition, specialty, bed size, and hospital region.^

DETERMINING THE GENOTYPE-PHENOTYPE CONNECTION IN SYNTHETIC INDUCIBLE GENE EXPRESSION SYSTEMS

Introduction Gene expression is an important process whereby the genotype controls an individual cell’s phenotype. However, even genetically identical cells display a variety of phenotypes, which may be attributed to differences in their environment. Yet, even after controlling for these two factors, individual phenotypes still diverge due to noisy gene expression. Synthetic gene expression systems allow investigators to isolate, control, and measure the effects of noise on cell phenotypes. I used mathematical and computational methods to design, study, and predict the behavior of synthetic gene expression systems in S. cerevisiae, which were affected by noise. Methods I created probabilistic biochemical reaction models from known behaviors of the tetR and rtTA genes, gene products, and their gene architectures. I then simplified these models to account for essential behaviors of gene expression systems. Finally, I used these models to predict behaviors of modified gene expression systems, which were experimentally verified. Results Cell growth, which is often ignored when formulating chemical kinetics models, was essential for understanding gene expression behavior. Models incorporating growth effects were used to explain unexpected reductions in gene expression noise, design a set of gene expression systems with “linear” dose-responses, and quantify the speed with which cells explored their fitness landscapes due to noisy gene expression. Conclusions Models incorporating noisy gene expression and cell division were necessary to design, understand, and predict the behaviors of synthetic gene expression systems. The methods and models developed here will allow investigators to more efficiently design new gene expression systems, and infer gene expression properties of TetR based systems.