【JAMA文献2010】医院获得性血流感染的传统监测质量

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Quality of Traditional Surveillance for Public Reporting of Nosocomial Bloodstream Infection Rates
出处：JAMA. 2010;304(18):2035-2041
作者LIST：
Michael Y. Lin, MD, MPH
Bala Hota, MD, MPH
Yosef M. Khan, MBBS, MPH
Keith F. Woeltje, MD, PhD
Tara B. Borlawsky, MA
Joshua A. Doherty, BS
Kurt B. Stevenson, MD, MPH
Robert A. Weinstein, MD
William E. Trick, MD
for the CDC Prevention Epicenter
Program

摘要：
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 Toassessinstitutionalvariationinperformanceoftraditionalcentralline–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 (=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.
JAMA. 2010;304(18):2035-2041 www.jama.com


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