Skip to main content Accessibility help
×
Home

National Automated Surveillance of Hospital-Acquired Bacteremia in Denmark Using a Computer Algorithm

  • Sophie Gubbels (a1), Jens Nielsen (a1), Marianne Voldstedlund (a1), Brian Kristensen (a2), Henrik C. Schønheyder (a3) (a4), Svend Ellermann-Eriksen (a5), Jørgen H. Engberg (a6), Jens K. Møller (a7), Christian Østergaard (a8) and Kåre Mølbak (a1)...

Abstract

BACKGROUND

In 2015, Denmark launched an automated surveillance system for hospital-acquired infections, the Hospital-Acquired Infections Database (HAIBA).

OBJECTIVE

To describe the algorithm used in HAIBA, to determine its concordance with point prevalence surveys (PPSs), and to present trends for hospital-acquired bacteremia

SETTING

Private and public hospitals in Denmark

METHODS

A hospital-acquired bacteremia case was defined as at least 1 positive blood culture with at least 1 pathogen (bacterium or fungus) taken between 48 hours after admission and 48 hours after discharge, using the Danish Microbiology Database and the Danish National Patient Registry. PPSs performed in 2012 and 2013 were used for comparison.

RESULTS

National trends showed an increase in HA bacteremia cases between 2010 and 2014. Incidence was higher for men than women (9.6 vs 5.4 per 10,000 risk days) and was highest for those aged 61–80 years (9.5 per 10,000 risk days). The median daily prevalence was 3.1% (range, 2.1%–4.7%). Regional incidence varied from 6.1 to 8.1 per 10,000 risk days. The microorganisms identified were typical for HA bacteremia. Comparison of HAIBA with PPS showed a sensitivity of 36% and a specificity of 99%. HAIBA was less sensitive for patients in hematology departments and intensive care units. Excluding these departments improved the sensitivity of HAIBA to 44%.

CONCLUSIONS

Although the estimated sensitivity of HAIBA compared with PPS is low, a PPS is not a gold standard. Given the many advantages of automated surveillance, HAIBA allows monitoring of HA bacteremia across the healthcare system, supports prioritizing preventive measures, and holds promise for evaluating interventions.

Infect Control Hosp Epidemiol 2017;38:559–566

Copyright

Corresponding author

Address correspondence to Sophie Gubbels, Department of Infectious Disease Epidemiology Statens Serum Institut, Artillerivej 5, 2300 Copenhagen S, Denmark (gub@ssi.dk).

Footnotes

Hide All

PREVIOUS PRESENTATION: A description of the algorithm, but not the comparison study, was presented at the European Scientific Conference on Applied Infectious Disease Epidemiology in Stockholm, Sweden, on November 11, 2015.

Footnotes

References

Hide All
1. Goto, M, Al-Hasan, MN. Overall burden of bloodstream infection and nosocomial bloodstream infection in North America and Europe. Clin Microbiol Infect 2013;19:501509.
2. ECDC Surveillance report—Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals 2011–2012. European Centre for Disease Prevention and Control website. http://ecdc.europa.eu/en/publications/Publications/healthcare-associated-infections-antimicrobial-use-PPS.pdf. Published 2013. Accessed November 24, 2016.
3. Department of Microbiology and Infection Control, Landsprævalensundersøgelsen. Statens Serum Institute website. http://www.ssi.dk/Smitteberedskab/Infektionshygiejne/Overvaagning/Praevalensundersogelser/Data%20fra%20Praevalensundersogelser.aspx. Published 2015. Accessed November 24, 2016.
4. Lin, MY, Hota, B, Khan, YM, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA 2010;304:20352041.
5. Mayer, J, Greene, T, Howell, J, et al. Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clin Infect Dis 2012;55:364370.
6. Leth, RA, Møller, JK. Surveillance of hospital-acquired infections based on electronic hospital registries. J Hosp Infect 2006;62:7179.
7. Schønheyder, HC, Søgaard, M. Existing data sources for clinical epidemiology: The North Denmark Bacteremia Research Database. Clin Epidemiol 2010;2:171178.
8. Gradel, KO, Knudsen, JD, Arpi, M, Ostergaard, C, Schønheyder, HC, Søgaard, M. Danish Collaborative Bacteraemia Network (DACOBAN). Classification of positive blood cultures: computer algorithms versus physicians’ assessment—development of tools for surveillance of bloodstream infection prognosis using population-based laboratory databases. BMC Med Res Methodol 2012;12:139.
9. Redder, JD, Leth, RA, Møller, JK. Incidence rates of hospital-acquired urinary tract and bloodstream infections generated by automated compilation of electronically available healthcare data. J Hosp Infect 2015;91:231236.
10. Leal, J, Laupland, KB. Validity of electronic surveillance systems: a systematic review. J Hosp Infect 2008;69:220229.
11. Hospital-Acquired Infections Database (HAIBA). Sundhedsdatastyrelsen website. http://www.esundhed.dk/sundhedskvalitet/HAIBA/Sider/HAIBA_report.aspx. Accessed November 24, 2016.
12. Gubbels, S, Nielsen, J, Voldstedlund, M, et al. Utilization of blood cultures in Danish hospitals: a population-based descriptive analysis. Clin Microbiol Infect 2015;21:344.e13e21.
13. Voldstedlund, M, Haarh, M, Molbak, K, MiBa Board of Representatives. The Danish Microbiology Database (MiBa) 2010 to 2013. Euro Surveill 2014;19.
14. Lynge, E, Sandegaard, JL, Rebolj, M. The Danish National Patient Register. Scand J Public Health 2011;39:3033.
15. Gubbels, S, Nielsen, KS, Sandegaard, J, Mølbak, K, Nielsen, J. The development and use of a new methodology to reconstruct courses of admission and ambulatory care based on the Danish National Patient Registry. Int J Med Inf 2016;95:4959.
16. Ostrowsky, B. Chapter 1: Epidemiology of Healthcare-Associated Infections. In: Jarvis WR, ed Bennett & Brachman’s Hospital Infections. 5th ed. Philadelpia, PA: Lippincott Williams & Wilkins; 2007:324.
17. Horan, TC, Andrus, M, Dudeck, MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control 2008;36:309332.
18. Definitioner på de fire hyppigste infektioner til brug ved prævalensundersøgelser - modificeret efter CDC’s definitioner til danske forhold. Statens Serum Institut website. http://www.ssi.dk/~/media/Indhold/DK%20-%20dansk/Smitteberedskab/Infektionshygiejne/Praevalensundersogelser/Foraar%202011/Definitioner%20for%20infektioner_jan2011.ashx. Published 2011. Accessed November 24, 2016.
19. Ballard, MS, Schønheyder, HC, Knudsen, JD, et al. The changing epidemiology of group B streptococcus bloodstream infection: a multi-national population-based assessment. Infect Dis Lond Engl 2016;48:386391.
20. McGowan, JE, Barnes, MW, Finland, M. Bacteremia at Boston City Hospital: occurrence and mortality during 12 selected years (1935–1972), with special reference to hospital-acquired cases. J Infect Dis 1975;132:316335.
21. CDC/NHSN surveillance definitions for specific types of infections. Centers for Disease Control and Prevention website. http://www.cdc.gov/nhsn/PDFs/pscManual/17pscNosInfDef_current.pdf. Published 2016. Accessed November 24, 2016.
22. Woeltje, KF, McMullen, KM, Butler, AM, Goris, AJ, Doherty, JA. Electronic surveillance for healthcare-associated central line-associated bloodstream infections outside the intensive care unit. Infect Control Hosp Epidemiol 2011;32:10861090.
23. Leal, J, Gregson, DB, Ross, T, Flemons, WW, Church, DL, Laupland, KB. Development of a novel electronic surveillance system for monitoring of bloodstream infections. Infect Control Hosp Epidemiol 2010;31:740747.
24. Pokorny, L, Rovira, A, Martín-Baranera, M, Gimeno, C, Alonso-Tarrés, C, Vilarasau, J. Automatic detection of patients with nosocomial infection by a computer-based surveillance system: a validation study in a general hospital. Infect Control Hosp Epidemiol 2006;27:500503.
25. de Kraker, MEA, Jarlier, V, Monen, JCM, Heuer, OE, van de Sande, N, Grundmann, H. The changing epidemiology of bacteraemias in Europe: trends from the European Antimicrobial Resistance Surveillance System. Clin Microbiol Infect 2013;19:860868.
26. Gubbels, S, Schultz Nielsen, K, Sandegaard, J, Mølbak, K, Nielsen, J. The development and use of a new methodology to reconstruct courses of admission and ambulatory care based on the Danish National Patient Registry. Int J Med Inform 2016;95:4959.
27. Uslan, DZ, Crane, SJ, Steckelberg, JM, et al. Age- and sex-associated trends in bloodstream infection: a population-based study in Olmsted County, Minnesota. Arch Intern Med 2007;167:834839.
28. Cohen, B, Choi, YJ, Hyman, S, Furuya, EY, Neidell, M, Larson, E. Gender differences in risk of bloodstream and surgical site infections. J Gen Intern Med 2013;28:13181325.
29. Moon, H-W, Ko, YJ, Park, S, Hur, M, Yun, Y-M. Analysis of community- and hospital-acquired bacteraemia during a recent 5-year period. J Med Microbiol 2014;63:421426.
30. Krieger, JN, Kaiser, DL, Wenzel, RP. Urinary tract etiology of bloodstream infections in hospitalized patients. J Infect Dis 1983;148:5762.
31. Saint, S, Kaufman, SR, Rogers, MAM, Baker, PD, Boyko, EJ, Lipsky, BA. Risk factors for nosocomial urinary tract-related bacteremia: a case-control study. Am J Infect Control 2006;34:401407.
32. Greene, MT, Chang, R, Kuhn, L, et al. Predictors of hospital-acquired urinary tract-related bloodstream infection. Infect Control Hosp Epidemiol 2012;33:10011007.
33. Conway, LJ, Carter, EJ, Larson, EL. Risk factors for nosocomial bacteremia secondary to urinary catheter-associated bacteriuria: a systematic review. Urol Nurs 2015;35:191203.
34. Harris, AD, McGregor, JC. The importance of case-mix adjustment for infection rates and the need for more research. Infect Control Hosp Epidemiol 2008;29:693694.
35. Møller, JK, Jensen, TG, Prag, J, et al. Elektronisk svar og rekvisition i klinisk mikrobiologi - kodeværk for prøvebeskrivelse. MADS website. http://www.madsonline.dk/MDS-koder/MDS_Rapport.pdf. Accessed November 24, 2016.
Type Description Title
WORD
Supplementary materials

Gubbels supplementary material
Supplementary Table

 Word (18 KB)
18 KB

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed