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Multicenter Evaluation of Computer Automated versus Traditional Surveillance of Hospital-Acquired Bloodstream Infections

Published online by Cambridge University Press:  10 May 2016

Michael Y. Lin*
Affiliation:
Department of Medicine, Rush University Medical Center, Chicago, Illinois
Keith F. Woeltje
Affiliation:
Department of Medicine, Washington University School of Medicine, St Louis, Missouri
Yosef M. Khan
Affiliation:
Department of Medicine, Ohio State University Medical Center, Columbus, Ohio Present affiliation: Division of Quality and Health Information Technology, American Heart Association–National Center, Dallas Texas
Bala Hota
Affiliation:
Department of Medicine, Rush University Medical Center, Chicago, Illinois Department of Medicine, Cook County Health and Hospitals System, Chicago, Illinois
Joshua A. Doherty
Affiliation:
Department of Medicine, Washington University School of Medicine, St Louis, Missouri
Tara B. Borlawsky
Affiliation:
Department of Medicine, Ohio State University Medical Center, Columbus, Ohio
Kurt B. Stevenson
Affiliation:
Department of Medicine, Ohio State University Medical Center, Columbus, Ohio
Scott K. Fridkin
Affiliation:
Centers for Disease Control and Prevention, Atlanta, Georgia
Robert A. Weinstein
Affiliation:
Department of Medicine, Rush University Medical Center, Chicago, Illinois Department of Medicine, Cook County Health and Hospitals System, Chicago, Illinois
William E. Trick
Affiliation:
Department of Medicine, Rush University Medical Center, Chicago, Illinois Department of Medicine, Cook County Health and Hospitals System, Chicago, Illinois
*
600 South Paulina Street, Suite 143, Chicago, IL 60612 (michael_lin@rush.edu).

Abstract

Objective.

Central line–associated bloodstream infection (BSI) rates are a key quality metric for comparing hospital quality and safety. Traditional BSI surveillance may be limited by interrater variability. We assessed whether a computer-automated method of central line–associated BSI detection can improve the validity of surveillance.

Design.

Retrospective cohort study.

Setting.

Eight medical and surgical intensive care units (ICUs) in 4 academic medical centers.

Methods.

Traditional surveillance (by hospital staff) and computer algorithm surveillance were each compared against a retrospective audit review using a random sample of blood culture episodes during the period 2004–2007 from which an organism was recovered. Episode-level agreement with audit review was measured with κ statistics, and differences were assessed using the test of equal κ coefficients. Linear regression was used to assess the relationship between surveillance performance (κ) and surveillance-reported BSI rates (BSIs per 1,000 central line–days).

Results.

We evaluated 664 blood culture episodes. Agreement with audit review was significantly lower for traditional surveillance (κ [95% confidence interval (CI)] = 0.44 [0.37–0.51]) than computer algorithm surveillance (κ [95% CI] [0.52–0.64]; P = .001). Agreement between traditional surveillance and audit review was heterogeneous across ICUs (P = .001); furthermore, traditional surveillance performed worse among ICUs reporting lower (better) BSI rates (P = .001). In contrast, computer algorithm performance was consistent across ICUs and across the range of computer-reported central line–associated BSI rates.

Conclusions.

Compared with traditional surveillance of bloodstream infections, computer automated surveillance improves accuracy and reliability, making interfacility performance comparisons more valid.

Infect Control Hosp Epidemiol 2014;35(12):1483–1490

Type
Original Article
Copyright
© 2014 by The Society for Healthcare Epidemiology of America. All rights reserved.

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