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Lin MY, Hota B, Khan YM, et al. Quality of Traditional Surveillance for Public Reporting of Nosocomial Bloodstream Infection Rates. JAMA. 2010;304(18):2035–2041. doi:10.1001/jama.2010.1637
Author Affiliations: Section of Infectious Diseases (Drs Lin, Hota, and Weinstein) and Department of Medicine (Drs Lin, Hota, Weinstein, and Trick), Rush University Medical Center, and Division of Infectious Diseases (Drs Hota and Weinstein) and Department of Medicine (Drs Hota, Weinstein, and Trick), John H. Stroger, Jr, Hospital of Cook County, Chicago, Illinois; Division of Infectious Diseases, Department of Medicine,Washington University School of Medicine (Dr Woeltje), and Department of Medical Informatics, BJC Healthcare (Mr Doherty), St Louis, Missouri; Department of Biomedical Informatics (Ms Borlawsky) and Division of Infectious Diseases, College of Medicine (Drs Stevenson and Khan), The Ohio State University, Columbus.
Context Central line–associated bloodstream infection (BSI) rates, determined by infection preventionists using the Centers for Disease Control and Prevention (CDC) surveillance definitions, are increasingly published to compare the quality of patient care delivered by hospitals. However, such comparisons are valid only if surveillance is performed consistently across institutions.
Objective To assess institutional variation in performance of traditional central line–associated BSI surveillance.
Design, Setting, and Participants We performed a retrospective cohort study of 20 intensive care units among 4 medical centers (2004-2007). Unit-specific central line–associated BSI rates were calculated for 12-month periods. Infection preventionists, blinded to study participation, performed routine prospective surveillance using CDC definitions. A computer algorithm reference standard was applied retrospectively using criteria that adapted the same CDC surveillance definitions.
Main Outcome Measures Correlation of central line-associated BSI rates as determined by infection preventionist vs the computer algorithm reference standard. Variation in performance was assessed by testing for institution-dependent heterogeneity in a linear regression model.
Results Forty-one unit-periods among 20 intensive care units were analyzed, representing 241 518 patient-days and 165 963 central line–days. The median infection preventionist and computer algorithm central line–associated BSI rates were 3.3 (interquartile range [IQR], 2.0-4.5) and 9.0 (IQR, 6.3-11.3) infections per 1000 central line–days, respectively. Overall correlation between computer algorithm and infection preventionist rates was weak (ρ = 0.34), and when stratified by medical center, point estimates for institution-specific correlations ranged widely: medical center A: 0.83; 95% confidence interval (CI), 0.05 to 0.98; P = .04; medical center B: 0.76; 95% CI, 0.32 to 0.93; P = .003; medical center C: 0.50, 95% CI, −0.11 to 0.83; P = .10; and medical center D: 0.10; 95% CI −0.53 to 0.66; P = .77. Regression modeling demonstrated significant heterogeneity among medical centers in the relationship between computer algorithm and expected infection preventionist rates (P < .001). The medical center that had the lowest rate by traditional surveillance (2.4 infections per 1000 central line–days) had the highest rate by computer algorithm (12.6 infections per 1000 central line–days).
Conclusions Institutional variability of infection preventionist rates relative to a computer algorithm reference standard suggests that there is significant variation in the application of standard central line–associated BSI surveillance definitions across medical centers. Variation in central line–associated BSI surveillance practice may complicate interinstitutional comparisons of publicly reported central line–associated BSI rates.
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