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The Effect of Adding Comorbidities to Current Centers for Disease Control and Prevention Central-Line–Associated Bloodstream Infection Risk-Adjustment Methodology

Published online by Cambridge University Press:  03 July 2017

Sarah S. Jackson ,
Surbhi Leekha ,
Laurence S. Magder ,
Lisa Pineles ,
Deverick J. Anderson ,
William E. Trick ,
Keith F. Woeltje ,
Keith S. Kaye ,
Kristen Stafford  and
Kerri Thom
...Show all authors
Sarah S. Jackson*
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Surbhi Leekha
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Laurence S. Magder
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Lisa Pineles
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Deverick J. Anderson
Affiliation:
Duke Center for Antimicrobial Stewardship and Infection Prevention, Duke University Medical Center, Durham, NorthCarolina
William E. Trick
Affiliation:
Collaborative Research Unit, Cook County Health and Hospitals Systems, Chicago, Illinois
Keith F. Woeltje
Affiliation:
Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St Louis, Missouri
Keith S. Kaye
Affiliation:
Division of Infectious Diseases, Department of Clinical Research, University of Michigan Medical School, Ann Arbor, Michigan
Kristen Stafford
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Kerri Thom
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Timothy J. Lowe
Affiliation:
Premier, Inc, Charlotte, North Carolina
Anthony D. Harris
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
*
Address correspondence to Sarah S. Jackson, MPH, 685 West Baltimore St, MSTF 362A, Baltimore, MD 21201 (ssjackson@umaryland.edu).
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Abstract

BACKGROUND

Risk adjustment is needed to fairly compare central-line–associated bloodstream infection (CLABSI) rates between hospitals. Until 2017, the Centers for Disease Control and Prevention (CDC) methodology adjusted CLABSI rates only by type of intensive care unit (ICU). The 2017 CDC models also adjust for hospital size and medical school affiliation. We hypothesized that risk adjustment would be improved by including patient demographics and comorbidities from electronically available hospital discharge codes.

METHODS

Using a cohort design across 22 hospitals, we analyzed data from ICU patients admitted between January 2012 and December 2013. Demographics and International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) discharge codes were obtained for each patient, and CLABSIs were identified by trained infection preventionists. Models adjusting only for ICU type and for ICU type plus patient case mix were built and compared using discrimination and standardized infection ratio (SIR). Hospitals were ranked by SIR for each model to examine and compare the changes in rank.

RESULTS

Overall, 85,849 ICU patients were analyzed and 162 (0.2%) developed CLABSI. The significant variables added to the ICU model were coagulopathy, paralysis, renal failure, malnutrition, and age. The C statistics were 0.55 (95% CI, 0.51–0.59) for the ICU-type model and 0.64 (95% CI, 0.60–0.69) for the ICU-type plus patient case-mix model. When the hospitals were ranked by adjusted SIRs, 10 hospitals (45%) changed rank when comorbidity was added to the ICU-type model.

CONCLUSIONS

Our risk-adjustment model for CLABSI using electronically available comorbidities demonstrated better discrimination than did the CDC model. The CDC should strongly consider comorbidity-based risk adjustment to more accurately compare CLABSI rates across hospitals.

Infect Control Hosp Epidemiol 2017;38:1019–1024

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 38 , Issue 9 , September 2017 , pp. 1019 - 1024
Copyright
© 2017 by The Society for Healthcare Epidemiology of America. All rights reserved 

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