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Matching Bacteriological and Medico-Administrative Databases Is Efficient for a Computer-Enhanced Surveillance of Surgical Site Infections: Retrospective Analysis of 4,400 Surgical Procedures in a French University Hospital

Published online by Cambridge University Press:  10 May 2016

Brice Leclère ,
Camille Lasserre ,
Céline Bourigault ,
Marie-Emmanuelle Juvin ,
Marie-Pierre Chaillet ,
Nicolas Mauduit ,
Jocelyne Caillon ,
Matthieu Hanf ,
Didier Lepelletier  and
SSI Study Group
Brice Leclère
Affiliation:
Department of Bacteriology and Infection Control, Nantes University Hospital, Nantes, France
Camille Lasserre
Affiliation:
Department of Bacteriology and Infection Control, Nantes University Hospital, Nantes, France EA3826 Thérapeutiques Cliniques et Expérimentales des Infections, Medical School, University of Nantes, Nantes, France
Céline Bourigault
Affiliation:
Department of Bacteriology and Infection Control, Nantes University Hospital, Nantes, France
Marie-Emmanuelle Juvin
Affiliation:
Department of Bacteriology and Infection Control, Nantes University Hospital, Nantes, France
Marie-Pierre Chaillet
Affiliation:
Department of Medical Information, Nantes University Hospital, Nantes, France
Nicolas Mauduit
Affiliation:
Department of Medical Information, Nantes University Hospital, Nantes, France
Jocelyne Caillon
Affiliation:
Department of Bacteriology and Infection Control, Nantes University Hospital, Nantes, France EA3826 Thérapeutiques Cliniques et Expérimentales des Infections, Medical School, University of Nantes, Nantes, France
Matthieu Hanf
Affiliation:
Clinical Investigation Center, Nantes University Hospital, Nantes, France
Didier Lepelletier*
Affiliation:
Department of Bacteriology and Infection Control, Nantes University Hospital, Nantes, France EA3826 Thérapeutiques Cliniques et Expérimentales des Infections, Medical School, University of Nantes, Nantes, France Members of the SSI Study Group are listed at the end of the text
SSI Study Group
Affiliation:
Department of Bacteriology and Infection Control, Nantes University Hospital, Nantes, France EA3826 Thérapeutiques Cliniques et Expérimentales des Infections, Medical School, University of Nantes, Nantes, France Members of the SSI Study Group are listed at the end of the text
*
Department of Bacteriology and Infection Control, Nantes University Hospital, Nantes, France (didier.lepelletier@chu-nantes.fr).
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Abstract

Objective.

Our goal was to estimate the performance statistics of an electronic surveillance system for surgical site infections (SSIs), generally applicable in French hospitals

Methods.

Three detection algorithms using 2 different data sources were tested retrospectively on 9 types of surgical procedures performed between January 2010 and December 2011 in the University Hospital of Nantes. The first algorithm was based on administrative codes, the second was based on bacteriological data, and the third used both data sources. For each algorithm, sensitivity, specificity, and positive and negative predictive values (PPV and NPV) were calculated. The reference method was the hospital’s routine surveillance: a comprehensive review of the computerized medical charts of the patients who underwent one of the targeted procedures during the study period.

Setting.

A 3,000-bed teaching hospital in western France.

Population.

We analyzed 4,400 targeted surgical procedures.

Results.

Sensitivity results varied significantly between the three algorithms, from 25% (95% confidence interval, 17–33) when using only administrative codes to 87% (80%–93%) with the bacteriological data and 90% (85%–96%) with the combined algorithm. Fewer variations were observed for specificity (91%–98%), PPV (21%–25%), and NPV (98% to nearly 100%). Overall, performance statistics were higher for deep SSIs than for superficial infections.

Conclusions.

A reliable computer-enhanced SSI surveillance can easily be implemented in French hospitals using common data sources. This should allow infection control professionals to spend more time on prevention and education duties. However, a multicenter study should be conducted to assess the generalizability of this method.

Infect Control Hosp Epidemiol 2014;35(11):1330–1335

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 35 , Issue 11 , November 2014 , pp. 1330 - 1335
Copyright
© 2014 by The Society for Healthcare Epidemiology of America. All rights reserved.

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References

1. Platt, R, Yokoe, DS, Sands, KE. Automated methods for surveillance of surgical site infections. Emerg Infect Dis 2001;7(2):212–216. doi:10.3201/eid0702.700212.Google Scholar
2. Leal, J, Laupland, KB. Validity of electronic surveillance systems: a systematic review. J Hosp Infect 2008;69(3):220–229. doi:10.1016/j.jhin.2008.04.030.Google Scholar
3. Freeman, R, Moore, LSP, García Álvarez, L, Charlett, A, Holmes, A. Advances in electronic surveillance for healthcare-associated infections in the 21st century: a systematic review. J Hosp Infect 2013;84(2):106–119. doi:10.1016/j.jhin.2012.11.031.Google Scholar
4. Astagneau, P, L’Hériteau, F, Daniel, F, et al. Reducing surgical site infection incidence through a network: results from the French ISO-RAISIN surveillance system. J Hosp Infect 2009;72(2):127–134. doi:10.1016/j.jhin.2009.03.005.Google Scholar
5. Agence Technique de l’Information sur l’Hospitalisation. CCAM Classification Commune des Actes Médicaux, V5. 2006. http://www.atih.sante.fr.Google Scholar
6. Horan, TC, Gaynes, RP, Martone, WJ, Jarvis, WR, Emori, TG. CDC definitions of nosocomial surgical site infections, 1992: a modification of CDC definitions of surgical wound infections. Infect Control Hosp Epidemiol 1992;13(10):606–608.Google Scholar
7. Woeltje, KF, Lautenbach, E. Informatics and epidemiology in infection control. Infect Dis Clin North Am 2011;25(1):261–270. doi:10.1016/j.idc.2010.11.013.Google Scholar
8. Wilson, APR, Gibbons, C, Reeves, BC, et al. Surgical wound infection as a performance indicator: agreement of common definitions of wound infection in 4773 patients. BMJ 2004;329(7468):720. doi:10.1136/bmj.38232.646227.DE.Google Scholar
9. Lepelletier, D, Ravaud, P, Baron, G, Lucet, J-C. Agreement among health care professionals in diagnosing case vignette-based surgical site infections. PLoS ONE 2012;7(4):e35131. doi:10.1371/journal.pone.0035131.Google Scholar
10. Birgand, G, Lepelletier, D, Baron, G, et al. Agreement among healthcare professionals in ten European countries in diagnosing case-vignettes of surgical-site infections. PLoS ONE 2013;8(7):e68618. doi:10.1371/journal.pone.0068618.Google Scholar
11. Landers, T, Apte, M, Hyman, S, Furuya, Y, Glied, S, Larson, E. A comparison of methods to detect urinary tract infections using electronic data. Jt Comm J Qual Patient Saf 2010;36(9):411–417.Google Scholar
12. Apte, M, Landers, T, Furuya, Y, Hyman, S, Larson, E. Comparison of two computer algorithms to identify surgical site infections. Surg Infect (Larchmt) 2011;12(6):459–464. doi:10.1089/sur.2010.109.Google Scholar
13. Chalfine, A, Cauet, D, Lin, WC, et al. Highly sensitive and efficient computer-assisted system for routine surveillance for surgical site infection. Infect Control Hosp Epidemiol 2006;27(8):794–801. doi:10.1086/506393.Google Scholar
14. Moro, ML, Morsillo, F. Can hospital discharge diagnoses be used for surveillance of surgical-site infections? J Hosp Infect 2004;56(3):239–241. doi:10.1016/j.jhin.2003.12.022.Google Scholar
15. Song, X, Cosgrove, SE, Pass, MA, Perl, TM. Using hospital claim data to monitor surgical site infections for inpatient procedures. Am J Infect Control 2008;36(3):S32–S36. doi:10.1016/j.ajic.2007.10.008.Google Scholar
16. Bolon, MK, Hooper, D, Stevenson, KB, et al. Improved surveillance for surgical site infections after orthopedic implantation procedures: extending applications for automated data. Clin Infect Dis 2009;48(9):1223–1229. doi:10.1086/597584.CrossRefGoogle ScholarPubMed
17. West, J, Khan, Y, Murray, DM, Stevenson, KB. Assessing specific secondary ICD-9-CM codes as potential predictors of surgical site infections. Am J Infect Control 2010;38(9):701–705. doi:10.1016/j.ajic.2010.03.015.CrossRefGoogle ScholarPubMed
18. Gerbier, S, Bouzbid, S, Pradat, E, et al. Use of the French medico-administrative database (PMSI) to detect nosocomial infections in the University hospital of Lyon. Rev Epidemiol Sante Publique 2011;59(1):3–14. doi:10.1016/j.respe.2010.08.003.Google Scholar
19. Grammatico-Guillon, L, Baron, S, Gaborit, C, Rusch, E, Astagneau, P. Quality assessment of hospital discharge database for routine surveillance of hip and knee arthroplasty–related infections. Infect Control Hosp Epidemiol 2014;35:646–651. doi:10.1086/676423.Google Scholar
20. Bouzbid, S, Gicquel, Q, Gerbier, S, et al. Automated detection of nosocomial infections: evaluation of different strategies in an intensive care unit 2000–2006. J Hosp Infect 2011;79(1):38–43. doi:10.1016/j.jhin.2011.05.006.Google Scholar
21. Petherick, ES, Dalton, JE, Moore, PJ, Cullum, N. Methods for identifying surgical wound infection after discharge from hospital: a systematic review. BMC Infect Dis 2006;6:170. doi:10.1186/1471-2334-6-170.Google Scholar
22. Miner, AL, Sands, KE, Yokoe, DS, et al. Enhanced identification of postoperative infections among outpatients. Emerg Infect Dis 2004;10(11):1931–1937. doi:10.3201/eid1011.040784.Google Scholar