Bala Hota

When Staphylococcus aureus is isolated in urine, it is thought to usually represent hematogenous spread. Because such spread might have special clinical significance, we evaluated predictors and outcomes of S. aureus bacteriuria among patients with S. aureus bacteremia.

Objective.To develop prediction algorithms for the presence of a central vascular catheter in hospitalized patients with use of data present in an electronic health record. Such algorithms could be used for measurement of device utilization rates and for clinical decision support rules.Design.Criterion standard.Setting.John H. Stroger, Jr, Hospital of Cook County, a 464-bed public hospital in Chicago, Illinois.Participants.Patients admitted to the medical intensive care unit from May 31, 2005 through June 26, 2006 (derivation data set, May 31, 2005-September 28, 2005; validation data set, September 29, 2005-June 28, 2006).Methods.Covariates were collected from the electronic medical record for each patient; the outcome variable was presence of a central vascular device. Multivariate models were developed using the derivation set and the generalized estimating equation. Three models, each with increasing database requirements, were validated using the validation set. Device utilization ratios and performance characteristics were calculated.Results.Although Charlson score and duration of intensive care unit stay were significant predictors in all models, factors that indicated use or presence of a central line were also important. Device utilization rates derived from the algorithmic models were as accurate as those obtained using manual sampling.Conclusions.Automated calculation of central vascular catheter use is both feasible and accurate, providing estimates statistically similar to those obtained using manual surveillance. Prediction modeling of central vascular catheter use may enable automated surveillance of bloodstream infections and enhance important prevention interventions, such as timely removal of unnecessary central lines.

Objective To formulate a model for translating manual infection control surveillance methods to automated, algorithmic approaches.Design We propose a

Recent studies have suggested that community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) infections are encroaching upon nosocomial settings. We assessed the performance characteristics of a rule using the antimicrobial phenotype to predict genotype. This rule could be applied for epidemiologic purposes to describe the trend in CA-MRSA infections over time.

Since 1998, community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) infections have emerged among patient groups with risk factors unassociated with health care, including sports exposure,^1-5 incarceration,^6-10 intravenous drug use,¹¹ overcrowded housing,^12-16 tattooing,^17,18 and poor hygiene.^11-13,19 An understanding of factors promoting acquisition and emergence of CA-MRSA may aid in the development of prevention strategies. For some infectious diseases, such as sexually transmitted infections, transmission can occur via infected core groups that contribute disproportionately to new cases.²⁰ Surveillance of the geographic distribution and secular trends of CA-MRSA infection may help identify specific high-risk community settings and groups.

The burden of infectious disease due to methicillin-resistant Staphylococcus aureus (MRSA) has increased since MRSA was first implicated as a nosocomial pathogen 4 decades ago [1, 2]. Data from the SENTRY Antimicrobial Surveillance Program estimate that ∼40% of S. aureus isolates recovered in intensive care units (ICUs) have been resistant to methicillin [2]. Risk factors for nosocomial infection with MRSA include exposure to antimicrobials and cross-colonization by health care workers from colonized or infected patients or from the contaminated inanimate environment (e.g., in ICUs or burn units) [3, 4]. Control of MRSA within hospitals generally relies on proper infection-control practice, as reflected by the statement that “the rate of MRSA transmission in a hospital reflects the effectiveness of infection control practices in that facility” [3, p. 11S].

Rising rates of fluoroquinolone resistance in bacteria have been associated with increased fluoroquinolone use. In vitro data show differences in potency among fluoroquinolone antibiotics against gram-negative bacteria and have led to the hypothesis that rates of selection of resistant microorganisms may be affected by the choice of the specific fluoroquinolone. Because clinical data to prove this hypothesis are lacking, the aim of the present study was to determine rates of acquisition of quinolone-resistant gram-negative bacilli (QRGNB) in the fecal flora of medical intensive care unit patients before and after a formulary change from ciprofloxacin to levofloxacin. Unadjusted rate ratios for acquisition of QRGNB were 1.09 (95%CI, 1.02–1.16) for each day of ciprofloxacin use and 1.01 (95%CI, 0.87–1.17) for each day of levofloxacin use. Following adjustment for other antibiotic use, enteral feeding, APACHE II score, and nursing home admission, neither ciprofloxacin nor levofloxacin use was associated with acquisition of QRGNB. In conclusion, a formulary change from ciprofloxacin to levofloxacin was not significantly associated with an increased risk of acquisition of QRGNB.

Despite documentation that the inanimate hospital environment (e.g., surfaces and medical equipment) becomes contaminated with nosocomial pathogens, t