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Development of a Novel Electronic Surveillance System for Monitoring of Bloodstream Infections

Published online by Cambridge University Press:  02 January 2015

Jenine Leal ,
Daniel B. Gregson ,
Terry Ross ,
Ward W. Flemons ,
Deirdre L. Church  and
Kevin B. Laupland
Jenine Leal
Affiliation:
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada Division of Microbiology, Calgary Laboratory Services, Calgary, Alberta, Canada
Daniel B. Gregson
Affiliation:
Department of Medicine, University of Calgary, Calgary, Alberta, Canada Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, Alberta, Canada Division of Microbiology, Calgary Laboratory Services, Calgary, Alberta, Canada
Terry Ross
Affiliation:
Division of Microbiology, Calgary Laboratory Services, Calgary, Alberta, Canada Centre for Antimicrobial Resistance, University of Calgary, Alberta Health Services and Calgary Laboratory Services, Calgary, Alberta, Canada
Ward W. Flemons
Affiliation:
Department of Medicine, University of Calgary, Calgary, Alberta, Canada
Deirdre L. Church
Affiliation:
Department of Medicine, University of Calgary, Calgary, Alberta, Canada Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, Alberta, Canada Division of Microbiology, Calgary Laboratory Services, Calgary, Alberta, Canada
Kevin B. Laupland*
Affiliation:
Department of Medicine, University of Calgary, Calgary, Alberta, Canada Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, Alberta, Canada Centre for Antimicrobial Resistance, University of Calgary, Alberta Health Services and Calgary Laboratory Services, Calgary, Alberta, Canada
*
Alberta Health Services, Foothills Medical Centre, Room 719, North Tower, 1403-29th Street NW, Calgary, AB T2N 2T9, Canada ([email protected])
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Extract

Background.

Electronic surveillance systems (ESSs) that utilize existing information in databases are more efficient than conventional infection surveillance methods.

Objective.

To develop an ESS for monitoring bloodstream infections (BSIs) and assess whether data obtained from the ESS were in agreement with data obtained by traditional manual medical-record review.

Methods.

An ESS was developed by linking data from regional laboratory and hospital administrative databases. Definitions for excluding BSI episodes representing contamination and duplicate episodes were developed and applied. Infections were classified as nosocomial infections, healthcare-associated community-onset infections, or community-acquired infections. For a random sample of episodes, data in the ESS were compared with data obtained by independent medical chart review.

Results.

From the records of the 306 patients whose infections were selected for comparative review, the ESS identified 323 episodes of BSI, of which 107 (33%) were classified as healthcare-associated community-onset infections, 108 (33%) were classified as community-acquired infections, 107 (33%) were classified as nosocomial infections, and 1 (0.3%) could not be classified. In comparison, 310 episodes were identified by use of medical chart review, of which 116 (37%) were classified as healthcare-associated community-onset infections, 95 (31%) as community-acquired infections, and 99 (32%) as nosocomial infections. For 302 episodes of BSI, there was concordance between the findings of the ESS and those of traditional manual chart review. Of the additional 21 discordant episodes that were identified by use of the ESS, 17 (81%) were classified as representing isolation of skin contaminants, by use of chart review. Of the additional 8 discordant episodes further identified by use of chart review, most were classified as repeat or polymicrobial episodes of disease. There was an overall 85% agreement between the findings of the ESS and those of chart review (K = 0.78; standard error, K = 0.04) for classification according to location of acquisition.

Conclusion.

Our novel ESS allows episodes of BSI to be identified and classified with a high degree of accuracy. This system requires validation in other cohorts and settings.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 31 , Issue 7 , July 2010 , pp. 740 - 747
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2010

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References

1.Laupland, K, Zygun, D, Doig, C, Bagshaw, S, Svenson, L, Fick, G. One-year mortality of bloodstream infection-associated sepsis and septic shock among patients presenting to a regional critical care system. Intensive Care Med 2005;31(2): 213–219.CrossRefGoogle ScholarPubMed
2.Laupland, KB, Ross, T, Church, DL, Gregson, DB. Population-based surveillance of invasive pyogenic streptococcal infection in a large Canadian region. Qin Microbiol Infect 2006;12(3): 224–230.CrossRefGoogle Scholar
3.German, RR, Lee, LM, Horan, JM, Milstein, RL, Pertowski, CA, Waller, MN; Guidelines Working Group for Centers for Disease Control and Protection. Updated guidelines for evaluating public health surveillance systems: recommendations from the Guidelines Working Group. MMWR Morb Mortal Wkly Rep 2001;50(RR-13):1–35.Google Scholar
4.Davies, H, McGeer, A, Schwartz, B, Green, K, Cann, D, Simor, A; Ontario Group A Streptococcal Study Group. Invasive group A streptococcal infections in Ontario, Canada. N Engl J Med 1996;335(8): 547–554.Google Scholar
5.Trick, WE, Zagorski, BM, Tokars, JI, et al. Computer algorithms to detect bloodstream infections. Emerg Infect Dis 2004;10(9): 1612–1620.CrossRefGoogle ScholarPubMed
6.Leal, J, Laupland, KB. Validity of electronic surveillance systems: a systematic review. J Hosp Infect 2008;69:220–229.Google Scholar
7.Wright, MO, Perencevich, EN, Novak, C, Hebden, JN, Standiford, HC, Harris, AD. Preliminary assessment of an automated surveillance system for infection control. Infect Control Hosp Epidemiol 2004;25(4): 325–332.Google Scholar
8.Baker, C, Luce, J, Chenoweth, C, Friedman, C. Comparison of case-finding methodologies for endometritis after cesarean section. Am J Infect Control 1995;23(1):27–33.CrossRefGoogle ScholarPubMed
9.Wurtz, R, Cameron, BJ. Electronic laboratory reporting for the infectious diseases physician and clinical microbiologist. Clin Infect Dis 2005;40(11): 1638–1643.CrossRefGoogle ScholarPubMed
10.Garner, JS, Jarvis, WR, Emori, TG, Horan, TC, Hughes, JM. CDC definitions for nosocomial infections, 1988. Am J Infect Control 1988;16(3): 128–140.Google Scholar
11.Friedman, ND, Kaye, KS, Stout, JE, et al. Health care-associated bloodstream infections in adults: a reason to change the accepted definition of community-acquired infections. Ann Intern Med 2002;137:791–797.CrossRefGoogle ScholarPubMed
12.Manns, BJ, Mortis, GP, Taub, KJ, McLaughlin, K, Donaldson, C, Ghali, WA. The Southern Alberta Renal Program database: a prototype for patient management and research initiatives. Clin Invest Med 2001;24(4): 164–170.Google ScholarPubMed
13.Laupland, KB, Gill, MJ, Schenk, L, Goodwin, D, Davies, HD. Outpatient parenteral antibiotic therapy: evolution of the Calgary adult home parenteral therapy program. Clin Invest Med 2002;25(5): 185–190.Google ScholarPubMed
14.Altman, DG. Practical statistics for medical research. London: Chapman & Hall, 1991.Google Scholar
15.Quan, H, Sundararajan, V, Halfon, P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005;43(11): 1130-1139.CrossRefGoogle ScholarPubMed
16.Canadian Institute for Health Information. Final report: the Canadian enhancement of ICD-10. Ottawa: Canadian Institute for Health Information, 2001.Google Scholar
17.Shorr, AF, Tabak, YP, Killian, AD, Gupta, V, Liu, LZ, Kollef, MH. Healthcareassociated bloodstream infection: a distinct entity? Insights from a large U.S. database. Crit Care Med 2006;34(10):2588–2595.Google Scholar
18.Lesens, O, Hansmann, Y, Brannigan, E, et al. Healthcare-associated Staphylococcus aureus bacteremia and the risk for methicillin resistance: is the Centers for Disease Control and Prevention definition for community-acquired bacteremia still appropriate? Infect Control Hosp Epidemiol 2005; 26:204–209.CrossRefGoogle ScholarPubMed
19.Johannes, RS. Epidemiology of early-onset bloodstream infection and implications for treatment. Am J Infect Control 2008;36(10): S171.e17-S171.e21.CrossRefGoogle ScholarPubMed
20.Kollef, MH, Shorr, A, Tabak, YP, Gupta, V, Lui, LZ, Johannes, RS. Epidemiology and outcomes of healthcare-associated pneumonia: results from a large US database of culture-positive pneumonia. Chest 2005; 128:3854–3862.Google Scholar
21.Yokoe, DS, Anderson, J, Chambers, R, et al. Simplified surveillance for nosocomial bloodstream infections. Infect Control Hosp Epidemiol 1998;19(9): 657–660.CrossRefGoogle ScholarPubMed
22.Pokorny, L, Rovira, A, Martin-Baranera, M, Gimeno, C, Alonso-Tarres, 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(5): 500–503.Google Scholar
23.Bax, R, Bywater, R, Cornaglia, G, et al. Surveillance of antimicrobial resistance—what, how and whither? Clin Microbiol Infect 2001;7:316–325.Google Scholar
24.Peterson, LR, Brossette, SE. Hunting health care-associated infections from the clinical microbiology laboratory: passive, active, and virtual surveillance. J Clin Microbiol 2002;40(1):1–4.CrossRefGoogle ScholarPubMed
25.Magee, JT. Effects of duplicate and screening isolates on surveillance of community and hospital antibiotic resistance. J Antimicrob Chemother 2004;54(1):155–162.Google Scholar
26.Shannon, KP, French, GL. Antibiotic resistance: effect of different criteria for classifying isolates as duplicates on apparent resistance frequencies. J Antimicrob Chemother 2002;49:201–204.CrossRefGoogle ScholarPubMed
27.Lee, SO, Cho, YK, Kim, SY, Lee, ES, Park, SY, Seo, YH. Comparison of trends of resistance rates over 3 years calculated from results for all isolates and for the first isolate of a given species from a patient. J Clin Microbiol 2004;42(10): 4776–4779.CrossRefGoogle ScholarPubMed
28.Reacher, M, Shah, A, Livermore, D, et al. Bacteremia and antibiotic resistance of its pathogens reported in England and Wales between 1990 and 1998: trend analysis. BMJ 2000;320(7229):213–216.CrossRefGoogle ScholarPubMed
29.Laupland, KB, Ross, T, Pitout, JDD, Church, DL, Gregson, DB. Investigation of sources of potential bias in laboratory surveillance for anti-microbial resistance. Clin Invest Med 2007;30(4): E159-E66.Google Scholar
30.Leal, J, Laupland, K. Validity of ascertainment of co-morbid illness using administrative databases: a systematic review. Clin Microbiol Infect 2009; 16(6):715–721.Google Scholar