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Validation of a Sampling Method to Collect Exposure Data for Central-Line–Associated Bloodstream Infections

Published online by Cambridge University Press:  13 January 2016

Naïma Hammami
Affiliation:
Public Health and Surveillance Department, Scientific Institute of Public Health, Brussels, Belgium
Karl Mertens
Affiliation:
Public Health and Surveillance Department, Scientific Institute of Public Health, Brussels, Belgium
Rosanna Overholser
Affiliation:
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
Els Goetghebeur
Affiliation:
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
Boudewijn Catry
Affiliation:
Public Health and Surveillance Department, Scientific Institute of Public Health, Brussels, Belgium
Marie-Laurence Lambert*
Affiliation:
Public Health and Surveillance Department, Scientific Institute of Public Health, Brussels, Belgium
*
Address correspondence to Marie-Laurence Lambert, Public Health and Surveillance Department, Scientific Institute of Public Health, Rue Juliette Wytsmanstraat 14, 1050 Brussels, Belgium (Marie-Laurence.Lambert@wiv-isp.be).

Abstract

OBJECTIVE

Surveillance of central-line–associated bloodstream infections requires the labor-intensive counting of central-line days (CLDs). This workload could be reduced by sampling. Our objective was to evaluate the accuracy of various sampling strategies in the estimation of CLDs in intensive care units (ICUs) and to establish a set of rules to identify optimal sampling strategies depending on ICU characteristics.

DESIGN

Analyses of existing data collected according to the European protocol for patient-based surveillance of ICU-acquired infections in Belgium between 2004 and 2012.

SETTING AND PARTICIPANTS

CLD data were reported by 56 ICUs in 39 hospitals during 364 trimesters.

METHODS

We compared estimated CLD data obtained from weekly and monthly sampling schemes with the observed exhaustive CLD data over the trimester by assessing the CLD percentage error (ie, observed CLDs – estimated CLDs/observed CLDs). We identified predictors of improved accuracy using linear mixed models.

RESULTS

When sampling once per week or 3 times per month, 80% of ICU trimesters had a CLD percentage error within 10%. When sampling twice per week, this was >90% of ICU trimesters. Sampling on Tuesdays provided the best estimations. In the linear mixed model, the observed CLD count was the best predictor for a smaller percentage error. The following sampling strategies provided an estimate within 10% of the actual CLD for 97% of the ICU trimesters with 90% confidence: 3 times per month in an ICU with >650 CLDs per trimester or each Tuesday in an ICU with >480 CLDs per trimester.

CONCLUSION

Sampling of CLDs provides an acceptable alternative to daily collection of CLD data.

Infect Control Hosp Epidemiol 2016;37:549–554

Type
Original Articles
Copyright
© 2016 by The Society for Healthcare Epidemiology of America. All rights reserved 

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References

REFERENCES

1. Siempos, II, Kopterides, P, Tsangaris, I, Dimopoulou, I, Armaganidis, AE. Impact of catheter-related bloodstream infections on the mortality of critically ill patients: a meta-analysis. Crit Care Med 2009;37:22832289.Google Scholar
2. Mertens, K. Surveillance national acquises dans les unités de soins intensifs—rapport annuel 2011. Scientific Institute of Public Health Public Health & Surveillance Healthcare Associated Infections (NSIH) website. http://www.nsih.be/download/NSIH_ICU_Rapport2011b_fr.pdf. Published 2012. Accessed April 15, 2015.Google Scholar
3. Mertens, K, Morales, I, Catry, B. Infections acquired in intensive care units: results of national surveillance in Belgium, 1997–2010. J Hosp Infect 2013;84:120125.CrossRefGoogle ScholarPubMed
4. Pronovost, P, Needham, D, Berenholtz, S, et al. An intervention to decrease catheter-related bloodstream infections in the ICU. N Engl J Med 2006;355:27252732.CrossRefGoogle ScholarPubMed
5. Pronovost, PJ, Goeschel, CA, Colantuoni, E, et al. Sustaining reductions in catheter related bloodstream infections in Michigan intensive care units: observational study. BMJ 2010;340:c309.Google Scholar
6. Kallen, AJ, Patel, PR, O’Grady, NP. Preventing catheter-related bloodstream infections outside the intensive care unit: expanding prevention to new settings. Clin Infect Dis 2010;51:335341.Google Scholar
7. Eggimann, P, Pittet, D. Overview of catheter-related infections with special emphasis on prevention based on educational programs. Clin Microbiol Infect 2002;8:295309.Google Scholar
8. Berenholtz, SM, Pronovost, PJ, Lipsett, PA, et al. Eliminating catheter-related bloodstream infections in the intensive care unit. Crit Care Med 2004;32:20142020.Google Scholar
9. Palomar, M, Alvarez-Lerma, F, Riera, A, et al. Impact of a national multimodal intervention to prevent catheter-related bloodstream infection in the ICU: the Spanish experience. Crit Care Med 2013;41:23642372.Google Scholar
10. Shelly, MA, Concannon, C, Dumyati, G. Device use ratio measured weekly can reliably estimate central line-days for central line-associated bloodstream infection rates. Infect Control Hosp Epidemiol 2011;32:727730.Google Scholar
11. Klevens, RM, Tokars, JI, Edwards, J, Horan, T. Sampling for collection of central line-day denominators in surveillance of healthcare-associated bloodstream infections. Infect Control Hosp Epidemiol 2006;27:338342.CrossRefGoogle ScholarPubMed
12. Thompson, ND, Edwards, JR, Bambert, W, et al. Evaluating the accuracy of sampling to estimate central line-days: simplification of the national healthcare safety network surveillance methods. Infect Control Hosp Epidemiol 2013;34:221228.Google Scholar
13. Thompson, ND, Edwards, JR, Bamberg, W, et al. Estimating central line-associated bloodstream infection incidence rates by sampling of denominator data: a prospective, multicenter evaluation. Am J Infect Control 2015;43:853856.CrossRefGoogle Scholar
14. Crescendo Stat-Gent. Statistical report—validation of a sampling method to collect exposure data for central line-associated bloodstream infections. Scientific Institute of Public Health Public Health & Surveillance Healthcare Associated Infections (NSIH) website. http://www.nsih.be/surv_sep/docs/Statisticalreport_CVCsampling_01122015.pdf. Published 2015. Accessed December 1, 2015.Google Scholar
15. Stone, PW, Pogorzelska-Maziarz, M, Herzig, CT, et al. State of infection prevention in US hospitals enrolled in the National Health and Safety Network. Am J Infect Control 2014;42:9499.Google Scholar
16. Bloodstream Infection Event (Central Line-Associated Bloodstream Infection and Non-central line-associated Bloodstream Infection). Centers for Disease Control and Prevention website. http://www.cdc.gov/nhsn/PDFs/pscManual/4PSC_CLABScurrent.pdf. Published 2015. Accessed February 3, 2015.Google Scholar
17. European surveillance of healthcare-associated infections in intensive care units. HAIICU protocol v1.01—Standard and light. European Centre for Disease Prevention and Control website. http://www.ecdc.europa.eu/en/aboutus/calls/Procurement%20Related%20Documents/5_ECDC_HAIICU_protocol_v1_1.pdf. Published 2010. Accessed February 3, 2015.Google Scholar
18. Climo, M, Diekema, D, Warren, DK, et al. Prevalence of the use of central venous access devices within and outside of the intensive care unit: results of a survey among hospitals in the prevention epicenter program of the Centers for Disease Control and Prevention. Infect Control Hosp Epidemiol 2003;24:942945.CrossRefGoogle ScholarPubMed
19. Wright, MO, Kharasch, M, Beaumont, JL, Peterson, LR, Robicsek, A. Reporting catheter-associated urinary tract infections: denominator matters. Infect Control Hosp Epidemiol 2011;32:635640.Google Scholar
20. Pittet, D, Wenzel, RP. Nosocomial bloodstream infections. Secular trends in rates, mortality, and contribution to total hospital deaths. Arch Intern Med 1995;155:11771184.Google Scholar
21. Eggimann, P, Harbarth, S, Constantin, MN, Touveneau, S, Chevrolet, JC, Pittet, D. Impact of a prevention strategy targeted at vascular-access care on incidence of infections acquired in intensive care. Lancet 2000;355:18641868.CrossRefGoogle ScholarPubMed
22. Horstman, MJ, Li, YF, Almenoff, PL, Freyberg, RW, Trautner, BW. Denominator doesn’t matter: standardizing healthcare-associated infection rates by bed days or device days. Infect Control Hosp Epidemiol 2015;36:710716.Google Scholar