Hepatic gene expression during treatment with peginterferon and ribavirin: Identifying molecular pathways for treatment response.

UNLABELLED: The reasons for hepatitis C treatment failure remain unknown but may be related to different host responses to therapy. In this study, we compared hepatic gene expression in patients prior to and during peginterferon and ribavirin therapy. In the on-treatment group, patients received either ribavirin for 72 hours prior to peginterferon alpha-2a injection or peginterferon alpha-2a for 24 hours, prior to biopsy. The patients were grouped into rapid responders (RRs) with a greater than 2-log drop and slow responders (SRs) with a less than 2-log drop in hepatitis C virus RNA by week 4. Pretreatment biopsy specimens were obtained from a matched control group. The pretreatment patients were grouped as RRs or SRs on the basis of the subsequent treatment response. Gene expression profiling was performed with Affymetrix microarray technology. Known interferon-stimulated genes (ISGs) were induced in treated patients. In the pretreatment group, future SRs had higher pretreatment ISG expression than RRs. On treatment, RRs and SRs had similar absolute ISG expression, but when it was corrected for the baseline expression with the pretreatment group, RRs showed a greater fold change in ISGs, whereas SRs showed a greater change in interferon (IFN)-inhibitory pathways. The patients pretreated with ribavirin had heightened induction of IFN-related genes and down-regulation of genes involved in IFN inhibition and hepatic stellate cell activation. CONCLUSION: These data suggest that ISG inducibility is important for the treatment response and that ribavirin may improve outcomes by enhancing hepatic gene responses to peginterferon. Collectively, these mechanisms may provide a molecular basis for the improved efficacy of combination therapy.

Public health education for emergency medicine residents.

Emergency medicine (EM) has an important role in public health, but the ideal approach for teaching public health to EM residents is unclear. As part of the national Regional Public Health-Medicine Education Centers-Graduate Medical Education initiative from the CDC and the American Association of Medical Colleges, three EM programs received funding to create public health curricula for EM residents. Curricula approaches varied by residency. One program used a modular, integrative approach to combine public health and EM clinical topics during usual residency didactics, one partnered with local public health organizations to provide real-world experiences for residents, and one drew on existing national as well as departmental resources to seamlessly integrate more public health-oriented educational activities within the existing residency curriculum. The modular and integrative approaches appeared to have a positive impact on resident attitudes toward public health, and a majority of EM residents at that program believed public health training is important. Reliance on pre-existing community partnerships facilitated development of public health rotations for residents. External funding for these efforts was critical to their success, given the time and financial restraints on residency programs. The optimal approach for public health education for EM residents has not been defined.

Forecasting models of emergency department crowding.

OBJECTIVES: The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison. METHODS: From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaike's Information Criterion (AIC). The accuracies of 4- and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well. RESULTS: The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4- and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days. CONCLUSIONS: Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours.