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Probabilistic Measurement of Central Line–Associated Bloodstream Infections

Published online by Cambridge University Press:  14 December 2015

Bala Hota ,
Paul Malpiedi ,
Scott K. Fridkin ,
John Martin  and
William Trick
Bala Hota*
Affiliation:
Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois
Paul Malpiedi
Affiliation:
Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
Scott K. Fridkin
Affiliation:
Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
John Martin
Affiliation:
Premier Research Institute, Washington, DC
William Trick
Affiliation:
Collaborative Research Unit, Cook County Health and Hospitals System, Chicago, Illinois
*
Address correspondence to Bala Hota, MD, MPH, Internal Medicine, Rush University Medical Center, Office 364, 1700 W Van Buren St, Chicago, IL 60612 ([email protected]).
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Abstract

OBJECTIVE

To develop a probabilistic method for measuring central line–associated bloodstream infection (CLABSI) rates that reduces the variability associated with traditional, manual methods of applying CLABSI surveillance definitions.

DESIGN

Multicenter retrospective cohort study of bacteremia episodes among patients hospitalized in adult patient-care units; the study evaluated presence of CLABSI.

SETTING

Hospitals that used SafetySurveillor software system (Premier) and who also reported to the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN).

PATIENTS

Patients were identified from a stratified sample from all eligible blood culture isolates from all eligible hospital units to generate a final set with an equal distribution (ie, 20%) from each unit type. Units were divided a priori into 5 major groups: medical intensive care unit, surgical intensive care unit, medical-surgical intensive care unit, hematology unit, or general medical wards.

INTERVENTIONS

Episodes were reviewed by 2 experts, and a selection of discordant reviews were re-reviewed. Data were joined with NHSN data for hospitals for in-plan months. A predictive model was created; model performance was assessed using the c statistic in a validation set and comparison with NHSN reported rates for in-plan months.

RESULTS

A final model was created with predictors of CLABSI. The c statistic for the final model was 0.75 (0.68–0.80). Rates from regression modeling correlated better with expert review than NHSN-reported rates.

CONCLUSIONS

The use of a regression model based on the clinical characteristics of the bacteremia outperformed traditional infection preventionist surveillance compared with an expert-derived reference standard.

Infect. Control Hosp. Epidemiol. 2016;37(2):149–155

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 37 , Supplement 2 , February 2016 , pp. 149 - 155
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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