Level of pain and waiting time in the emergency department.

Pain is the leading reason individuals seek emergency care. Studies have concluded that acute pain conditions are underevaluated and undertreated in the emergency department (ED). There is a paucity of information about how the severity of pain influences the time spent in the ED before being seen by a physician. Therefore, this study focuses on what role pain plays regarding time to treatment in the ED, i.e., to examine the effects of patients' perceived level of pain on wait time in the ED. The CDC's National Hospital Ambulatory Medical Survey 2003 data were used in this study. The sample consisted of 12,860 caucasians and African Americans with a mean age of 44.52 years. Analysis of covariance was used to explore differences in length of waiting time in ED based on race, triage nurses rating of the immediacy of the need to be seen by a physician, and the level of pain the patient reported. The findings showed that patients' reports of pain had very little effect on the length of waiting time. Given the sample size, we feel there is adequate power to detect the effect of pain in determining the length of waiting time to see a physician if it were present. In addition, African Americans had a statistically significant longer wait (15.29 minutes) than Whites. The effect of race might be interpreted as another example of health disparities or could be a hospital-level effect which was not examined within this model.

Antiviral efficacy of abacavir in antiretroviral therapy-experienced adults harbouring HIV-1 with specific patterns of resistance to nucleoside reverse transcriptase inhibitors.

OBJECTIVE: To evaluate HIV-1 reverse transcriptase genotypic and phenotypic indicators of resistance to abacavir (ABC) as predictors of ABC antiviral efficacy. DESIGN: The study was a retrospective, combined analysis of five multicentre trials in which ABC was added as a single agent to background antiretroviral therapy in experienced adults. METHODS: Baseline HIV-1 genotype and phenotypic susceptibility to ABC were determined and the association of genotype and phenotype with virological response after addition of ABC was analysed. RESULTS: Overall, 68% of these therapy-experienced subjects had a virological response (>0.5 log10 or <400 copies/ml; 42% <400 copies/ml) 4 weeks after addition of ABC. Multivariable analyses revealed no significant difference in the response rate between subjects with wild-type virus and those carrying virus with 1-2 nucleoside reverse transcriptase inhibitor (NRTI)-associated mutations. At the 4-week time-point subjects harbouring virus with > or = 3 mutations associated with NRTI resistance were significantly less likely to respond to ABC than were subjects harbouring wild-type virus (P=0.015). However, at the last viral RNA measurement after addition of ABC (12-28 weeks), > or = 4 mutations were required to diminish virological response significantly (P=0.012). Phenotypic resistance was also predictive of antiviral response. Significant breakpoints were identified for virological responses for the PhenoSense HIV assay and the Antivirogram assay. CD4 responses generally paralleled the antiviral responses with a median increase of 55 cells/microl by weeks 12-28. CONCLUSIONS: Virological response to ABC may be diminished significantly by multiple NRTI-associated mutations and/or by reductions in phenotypic susceptibility to ABC. However, many subjects with baseline samples showing evidence of resistance to NRTIs respond to ABC.

Clinically validated genotype analysis: guiding principles and statistical concerns.

Whereas previously the output of HIV resistance tests has been based on therapeutically arbitrary criteria, there is now an ongoing move towards correlating test interpretation with virological outcomes on treatment. This approach is undeniably superior, in principle, for tests intended to guide drug choices. However the predictive accuracy of a given stratagem that links genotype or phenotype to drug response is strongly influenced by the study design, data capture and analytical methodology used to derive it. For genotyping, the most widely used resistance tool in clinical practice, these considerations are further complicated by the range of mutational patterns present in the treated population. There is no definitively superior methodology for generating a genotype-response association for use in interpreting a resistance test, and the various approaches used to date all have their strengths and weaknesses. This review discusses the processes involved in constructing such tools, with particular emphasis on establishing validated mutation score rules, and examines the key issues and confounding factors that influence predictive accuracy outside the originating dataset. Since the size of the sample is a key influence on the statistical power to determine an effect, it is hoped that a greater understanding of the influence of study design and methodology will assist the development of standardized outcome measures and reporting formats that allow data pooling at the international level.