Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-28T17:32:30.725Z Has data issue: false hasContentIssue false
  • English
  • Français

Discord among Performance Measures for Central Line—Associated Bloodstream Infection

Published online by Cambridge University Press:  02 January 2015

David M. Tehrani ,
Dana Russell ,
Jennifer Brown ,
Kim Boynton-Delahanty ,
Kathleen Quan ,
Laurel Gibbs ,
Geri Braddock ,
Teresa Zaroda ,
Marsha Koopman  and
Deborah Thompson
...Show all authors
David M. Tehrani*
Affiliation:
Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
Dana Russell
Affiliation:
Division of Infectious Diseases, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, California
Jennifer Brown
Affiliation:
Division of Infectious and Immunologic Diseases, University of California Davis Medical Center, Sacramento, California
Kim Boynton-Delahanty
Affiliation:
Division of Infectious Disease and Infection Prevention and Clinical Epidemiology Unit, University of California San Diego, San Diego, California
Kathleen Quan
Affiliation:
Epidemiology and Infection Prevention Program, University of California Irvine Health, Orange, California
Laurel Gibbs
Affiliation:
Department of Hospital Epidemiology and Infection Control, University of California San Francisco Medical Center, San Francisco, California
Geri Braddock
Affiliation:
Division of Infectious Diseases, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, California
Teresa Zaroda
Affiliation:
Division of Infectious Diseases, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, California
Marsha Koopman
Affiliation:
Division of Infectious and Immunologic Diseases, University of California Davis Medical Center, Sacramento, California
Deborah Thompson
Affiliation:
Epidemiology and Infection Prevention Program, University of California Irvine Health, Orange, California
Amy Nichols
Affiliation:
Department of Hospital Epidemiology and Infection Control, University of California San Francisco Medical Center, San Francisco, California
Eric Cui
Affiliation:
Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
Catherine Liu
Affiliation:
Division of Infectious Diseases, University of California San Francisco, San Francisco, California
Stuart Cohen
Affiliation:
Division of Infectious and Immunologic Diseases, University of California Davis Medical Center, Sacramento, California
Zachary Rubin
Affiliation:
Division of Infectious Diseases, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, California
David Pegues
Affiliation:
Division of Infectious Diseases, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, California
Francesca Torriani
Affiliation:
Division of Infectious Disease and Infection Prevention and Clinical Epidemiology Unit, University of California San Diego, San Diego, California
Rupak Datta
Affiliation:
Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
*
University of California Irvine School of Medicine, Division of Infectious Diseases and Health Policy Research Institute, 100 Theory Drive, Suite 110, Irvine, CA 92617 ([email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Background.

Central line-associated bloodstream infection (CLABSI) is a national target for mandatory reporting and a Centers for Medicare and Medicaid Services target for value-based purchasing. Differences in chart review versus claims-based metrics used by national agencies and groups raise concerns about the validity of these measures.

Objective.

Evaluate consistency and reasons for discordance among chart review and claims-based CLABSI events.

Methods.

We conducted 2 multicenter retrospective cohort studies within 6 academic institutions. A total of 150 consecutive patients were identified with CLABSI on the basis of National Healthcare Safety Network (NHSN) criteria (NHSN cohort), and an additional 150 consecutive patients were identified with CLABSI on the basis of claims codes (claims cohort). Ail events had full-text medical record reviews and were identified as concordant or discordant with the other metric.

Results.

In the NHSN cohort, there were 152 CLABSIs among 150 patients, and 73.0% of these cases were discordant with claims data. Common reasons for the lack of associated claims codes included coding omission and lack of physician documentation of bacteremia cause. In the claims cohort, there were 150 CLABSIs among 150 patients, and 65.3% of these cases were discordant with NHSN criteria. Common reasons for the lack of NHSN reporting were identification of non-CLABSI with bacteremia meeting Centers for Disease Control and Prevention (CDC) criteria for an alternative infection source.

Conclusion.

Substantial discordance between NHSN and claims-based CLABSI indicators persists. Compared with standardized CDC chart review criteria, claims data often had both coding omissions and misclassification of non-CLABSI infections as CLABSI. Additionally, claims did not identify any additional CLABSIs for CDC reporting. NHSN criteria are a more consistent interhospital standard for CLABSI reporting.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 34 , Issue 2 , February 2013 , pp. 176 - 183
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Kennedy, E, Enzi, M, Dingell, J. Health-Care-Associated Infections in Hospitals: Number Associated with Medical Deveices Unknown, but Experts Report Provider Practices as a Significant Factor. Washington, DC: US Government Accountability Office, 2008.Google Scholar
2. Centers for Disease Control and Prevention. Public health focus: surveillance, prevention, and control of nosocomial infections. MMWR Morb Mortal Wkly Rep 1992;41(42):783–787.Google Scholar
3. Roy, MC, Perl, TM. Basics of surgical-site infection surveillance. Infect Control Hosp Epidemiol 1997;18(9):659–668.Google Scholar
4. Misset, B, Timsit, JF, Dumay, MF, et al. A continuous quality-improvement program reduces nosocomial infection rates in the ICU. Intensive Care Medicine 2004;30(3):395–400.Google Scholar
5. Edwards, JR, Peterson, KD, Mu, Y, et al. National Healthcare Safety Network (NHSN) report: data summary for 2006 through 2008, issued December 2009. Am J Infect Control 2009;37(10): 783–805.Google Scholar
6. National Healthcare Safety Network, http://www.cdc.gov/nhsn/. Accessed June 16, 2012.Google Scholar
7. Leapfrog Group. Importance of public reporting in reducing ICU infections: Leapfrog responds to new CDC infection report. http://www.leapfroggroup.org/policy_leadership/leapfrog_news/4805797. Accessed June 28, 2012.Google Scholar
8. Association for Professionals in Infection Control and Epidemiology. Hospitals appear to be heeding mandates to reduce and report preventable infections, but long-term impact still to be determined. http://www.apic.org/For-Media/News-Releases/Article?id = 7dcel641-e014-fle-a89f-f614e055864c. Accessed June 28, 2012.Google Scholar
9. Consumer Reports. How we rate hospitals. http://www.consumerreports.org/health/doctors-hospitals/how-we-rate-hospitals/the-basics/the-basics.htm. Accessed June 28, 2012.Google Scholar
10. Horan, TC, Emori, TG. Definitions of key terms used in the NNIS system. Am J Infect Control 1997;25(2):112–116.Google Scholar
11. Yokoe, DS, Classen, D. Improving patient safety through infection control: a new healthcare imperative. Infect Control Hosp Epidemiol 2008;29(Suppl 1):S3–S11.Google Scholar
12. Stone, PW, Horan, TC, Shih, HC, Mooney-Kane, C, Larson, E. Comparisons of health care-associated infections identification using two mechanisms for public reporting. Am J Infect Control 2007;35(3):145–149.Google Scholar
13. Horan, TC, Lee, TB. Surveillance: into the next millennium. Am J Infect Control 1997;25(2):73–76.Google Scholar
14. Stone, PW, Glied, SA, McNair, PD, et al. CMS changes in reimbursement for HAIs: setting a research agenda. Medical Care 2010;48(5):433–439.Google Scholar
15. Agency for Healthcare Research and Quality, http://www.ahrq.gov/. Accessed June 28, 2012.Google Scholar
16. McDonald, KM, Romano, PS, Geppert, J, et al. Measures of Patient Safety Based on Hospital Administrative Data: The Patient. Rock-ville, MD: Agency for Healthcare Research and Quality, 2002.Google Scholar
17. University HealthSystem Consortium, https://www.uhc.edu/. Accessed June 28, 2012.Google Scholar
18. Stevenson, KB, Khan, Y, Dickman, J, et al. Administrative coding data, compared with CDC/NHSN criteria, are poor indicators of health care-associated infections. Am J Infect Control 2008; 36(3):155–164.Google Scholar
19. Zrelak, PA, Sadeghi, B, Utter, GH, et al. Positive predictive value of the Agency for Healthcare Research and Quality Patient Safety Indicator for central line-related bloodstream infection (“selected infections due to medical care”). J Healthc Qual 2011; 33(2):29–36.Google Scholar
20. Sherman, ER, Heydon, KH, St John, KH, et al. Administrative data fail to accurately identify cases of healthcare-associated infection. Infect Control Hosp Epidemiol 2006;27(4):332–337.Google Scholar
21. Platt, R. Toward better benchmarking. Infect Control Hosp Epidemiol 2005;26(5):433–434.Google Scholar
22. California Office of Statewide Health Planning and Development. http://www.oshpd.ca.gov/. Accessed June 16, 2012.Google Scholar
23. 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.Google Scholar
24. Emori, TG, Edwards, JR, Culver, DH, et al. Accuracy of reporting nosocomial infections in intensive-care-unit patients to the National Nosocomial Infections Surveillance System: a pilot study. Infect Control Hosp Epidemiol 1998;19(5):308–316.Google Scholar
25. McBryde, ES, Brett, J, Russo, PL, Worth, LJ, Bull, AL, Richards, MJ. Validation of statewide surveillance system data on central line-associated bloodstream infection in intensive care units in Australia. Infect Control Hosp Epidemiol 2009;30(11):1045–1049.Google Scholar
26. Rubin, MA, Mayer, J, Greene, T, et al. An agent-based model for evaluating surveillance methods for catheter-related bloodstream infection. AMM Annu Symp Proc 2008:631–635.Google Scholar
27. Thompson, N. Patient safety component protocol changes for 2013: update on changes to CLABSI definitions. In: Program and abstracts of the National Health and Safety Network Members Meeting at the Association for Professionals in Infection Control and Epidemiology 2012; San Antonio, TX; June 3, 2012.Google Scholar
28. Department of Health and Human Services. Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals and the Long-Term Care Hospital Prospective Payment System and Fiscal Year 2013 Rates; Hospitals' Resident Caps for Graduate Medical Education Payment Purposes; Qual ity Reporting Requirements for Specific Providers and for Ambulatory Surgical Centers. 77 Federal Register 53257 (2012). Document number 2012-19079.Google Scholar
29. Cardo, D, Dennehy, PH, Halverson, P, et al. Moving toward elimination of healthcare-associated infections: a call to action. Infect Control Hosp Epidemiol 2010;31(11):1101–1105.Google Scholar