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Measurement of the Impact of Risk Adjustment for Central Line–Days on Interpretation of Central Line–Associated Bloodstream Infection Rates

Published online by Cambridge University Press:  02 January 2015

Jerome I. Tokars ,
R. Monina Klevens ,
Jonathan R. Edwards  and
Teresa C. Horan
Jerome I. Tokars*
Affiliation:
Centers for Disease Control and Prevention, National Center for Infectious Diseases, Division of Healthcare Quality Promotion, Atlanta, Georgia
R. Monina Klevens
Affiliation:
Centers for Disease Control and Prevention, National Center for Infectious Diseases, Division of Healthcare Quality Promotion, Atlanta, Georgia
Jonathan R. Edwards
Affiliation:
Centers for Disease Control and Prevention, National Center for Infectious Diseases, Division of Healthcare Quality Promotion, Atlanta, Georgia
Teresa C. Horan
Affiliation:
Centers for Disease Control and Prevention, National Center for Infectious Diseases, Division of Healthcare Quality Promotion, Atlanta, Georgia
*
Centers for Disease Control and Prevention, 1600 Clifton Rd., MS D-45, Atlanta, GA 30333 ([email protected])
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Abstract

Objective.

To describe methods to assess the practical impact of risk adjustment for central line-days on the interpretation of central line–associated bloodstream infection (BSI) rates, because collecting these data is often burdensome.

Methods.

We analyzed data from 247 hospitals that reported to the adult and pediatric intensive care unit component of the National Nosocomial Infections Surveillance System from 1995 through 2003. For each unit each year, we calculated the percentile error as the absolute value of the difference between the percentile based on a risk-adjusted or more-sophisticated measure (eg, the central line–day rate) and the percentile based on a crude or less-sophisticated measure (eg, the patient-day rate). Using rate per central line–day as the “gold standard,” we calculated performance characteristics (eg, sensitivity and predictive values) of rate per patient-day for finding central line–associated BSI rates higher or lower than the mean. Greater impact of risk adjustment is indicated by higher values for percentile error and lower values for performance characteristics.

Results.

The median percentile error was ± 7 (ie, the percentile based on central line-days could be 7% higher or lower than the percentile based on patient-days). This error was less than 10 percentile points for 62% of the unit-years, was between 10 and 19 percentile points for 22% of the unit-years, and was 20 percentile points or more for 15% of the unit-years. Use of the rate based on patient-days had a sensitivity of 76% and a positive predictive value of 61% for detecting a significantly high or low central line–associated BSI rate.

Conclusions.

We found that risk adjustment for central line–days has an important impact on the calculated central line–associated BSI percentile for some units. Similar methods can be used to evaluate the impact of other risk adjustment methods. Our results support current recommendations to use central line–days for surveillance of central line–associated BSI when comparisons are made among facilities.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 28 , Issue 9 , September 2007 , pp. 1025 - 1029
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2007

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References

1. Horan, T, Gaynes, R. Surveillance of nosocomial infections. In: Mayhall, CG, ed. Hospital Epidemiology and Infection Control. 4th ed. Philadelphia, PA: Lippincott Williams and Wilkins; 2004:1659–1702.Google Scholar
2. Jarvis, WR, Edwards, JR, Culver, DH, et al. The National Nosocomial Infections Surveillance (NNIS) System: nosocomial infection rates in adult and pediatric intensive care units in the United States. Am J Med 1991;91(S 3B):185S–191S.Google Scholar
3. Khuri, SF, Daley, J, Henderson, W, et al. The Department of Veterans Affairs' NSQIP: the first national, validated, outcome-based, risk-adjusted, and peer-controlled program for the measurement and enhancement of the quality of surgical care. Ann Surg 1998;228:491–507.CrossRefGoogle ScholarPubMed
4. Brandt, C, Sohr, D, Behnke, M, Ruden, H, Gastmeier, P. To stratify or not to stratify surgical site infection rates according to risk factors: an analysis of data from the German National Nosocomial Infection Surveillance System. In: Program and abstracts of the 13th Annual Scientific Meeting of the Society for Healthcare Epidemiology of America (Alexandria, VA): 78. Abstract 110.Google Scholar
5. Brandt, C, Hansen, S, Sohr, D, Daschner, F, Ruden, H, Gastmeier, P. Finding a method for optimizing risk adjustment when comparing surgical-site infection rates. Infect Control Hosp Epidemiol 2004;25:313–318.CrossRefGoogle ScholarPubMed
6. Department of Health and Human Services, Agency for Healthcare Research and Quality. AHRQ Quality Indicators: Guide to Patient Safety Indicators. Rockville, MD: Agency for Healthcare Research and Quality; 2003. AHRQ publication 03–R203. Available at: http://www.qualityindicators.ahrq.gov/psi_download.htm. Accessed May 23, 2005.Google Scholar
7. Klevens, RM, Edwards, J, Tokars, II, Horan, TC; National Nosocomial Infections Surveillance (NNIS) System. Sampling for collection of central line-day denominators in surveillance of healthcare-associated bloodstream infections. Infect Control Hosp Epidemiol 2006;27:338–342.Google Scholar
8. National Nosocomial Infections Surveillance System. National Nosocomial Infections Surveillance (NNIS) System report, data summary from January 1992 through June 2003, issued August 2003. Am J Infect Control 2003;31:481–498.CrossRefGoogle Scholar
9. Fleis, JL. Statistical Methods for Rates and Proportion. New York, NY: John Wiley and Sons; 1981.Google Scholar
10. Alonso-Echanove, J, Edwards, JR, Richards, MJ, et al. Effect of nurse staffing and antimicrobial-impregnated central venous catheters on the risk for bloodstream infections in intensive care units. Infect Control Hosp Epidemiol 2003;24:916–925.CrossRefGoogle ScholarPubMed
11. Platt, R, Yokoe, DS, Sands, KE; CDC Eastern Massachusetts Prevention Epicenter Investigators. Automated methods for surveillance of surgical site infections. Emerg Infect Dis 2001;7:212–216.Google Scholar