1887
Research Open Access
Like 0

Abstract

Background

Surveillance of hospital-acquired infections (HAI) often relies on point prevalence surveys (PPS) to detect major deviations in the occurrence of HAI, supplemented with incidence measurements when more detailed information is needed. In a 1,320-bed university medical centre in the Netherlands, we evaluated an electronically assisted surveillance system based on frequently performed computer-assisted PPS (CAPPS).

Aim

The primary goals were to evaluate the performance of this method to detect trends and to determine how adjustments in the frequency with which the CAPPS are performed would affect this performance. A secondary goal was to evaluate the performance of the algorithm (nosocomial infection index (Nii)) used.

Methods

We analysed the data of 77 hospital-wide PPS, performed over a 2-year period (2013 and 2014) and including 25,056 patients.

Results

Six trends with statistical significance were detected. The probability to detect such trends rapidly decreased when PPS are performed at a lower frequency. The Nii and its dynamics strongly correlated with the presence of HAI.

Conclusion

Performing computer-assisted, high frequency hospital-wide PPS, is a feasible method that will detect even subtle changes in HAI prevalence over time.

Loading

Article metrics loading...

/content/10.2807/1560-7917.ES.2019.24.13.1800177
2019-03-28
2019-08-23
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2019.24.13.1800177
Loading
Loading full text...

Full text loading...

/deliver/fulltext/eurosurveillance/24/13/eurosurv-24-13-2.html?itemId=/content/10.2807/1560-7917.ES.2019.24.13.1800177&mimeType=html&fmt=ahah

References

  1. van der Kooi TI, Manniën J, Wille JC, van Benthem BH. Prevalence of nosocomial infections in The Netherlands, 2007-2008: results of the first four national studies. J Hosp Infect. 2010;75(3):168-72.  https://doi.org/10.1016/j.jhin.2009.11.020  PMID: 20381910 
  2. Ustun C, Hosoglu S, Geyik MF, Parlak Z, Ayaz C. The accuracy and validity of a weekly point-prevalence survey for evaluating the trend of hospital-acquired infections in a university hospital in Turkey. Int J Infect Dis. 2011;15(10):e684-7.  https://doi.org/10.1016/j.ijid.2011.05.010  PMID: 21757384 
  3. Streefkerk RHRA, Moorman PW, Parlevliet GA, van der Hoeven C, Verbrugh HA, Vos MC, et al. An automated algorithm to preselect patients to be assessed individually in point prevalence surveys for hospital-acquired infections in surgery. Infect Control Hosp Epidemiol. 2014;35(7):886-7. PMID: 24915221 
  4. Streefkerk RHRA, Borsboom GJ, van der Hoeven CP, Vos MC, Verkooijen RP, Verbrugh HA. Evaluation of an algorithm for electronic surveillance of hospital-acquired infections yielding serial weekly point prevalence scores. Infect Control Hosp Epidemiol. 2014;35(7):888-90.  https://doi.org/10.1086/676869  PMID: 24915222 
  5. Streefkerk HR, Lede IO, Eriksson JL, Meijling MG, van der Hoeven CP, Wille JC, et al. Internal and external validation of a computer-assisted surveillance system for hospital-acquired infections in a 754-bed general hospital in the Netherlands. Infect Control Hosp Epidemiol. 2016;37(11):1355-60.  https://doi.org/10.1017/ice.2016.159  PMID: 27488723 
  6. PREZIES-team. Protocol en dataspecificaties PREZIES. Prevalentieonderzoek Ziekenhuizen – versie: Maart/oktober 2014. [Protocol and data specifications PREZIES. Hospital prevalence survey – version Mar/Oct 2014]. Document version 2.2. Bilthoven: National Institute for Public Health and the Environment (RIVM); 2014. Dutch. Available from: https://www.rivm.nl/documenten/protocol-en-ds-po-2014-def-versie-22
  7. Eilers PHC, Marx BD. Flexible smoothing with b-splines and penalties. Stat Sci. 1996;11(2):89-121.  https://doi.org/10.1214/ss/1038425655 
  8. R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2017. Available from: https://www.R-project.org/
  9. Petitti T, Sadun B, Dicuonzo G. Usefulness and accuracy of weekly point-prevalence surveys in active surveillance for healthcare-associated infections. Infect Control Hosp Epidemiol. 2005;26(4):335-6.  https://doi.org/10.1086/503510  PMID: 15865265 
  10. Rhame FS, Sudderth WD. Incidence and prevalence as used in the analysis of the occurrence of nosocomial infections. Am J Epidemiol. 1981;113(1):1-11.  https://doi.org/10.1093/oxfordjournals.aje.a113058  PMID: 7457475 
  11. Gastmeier P, Bräuer H, Sohr D, Geffers C, Forster DH, Daschner F, et al. Converting incidence and prevalence data of nosocomial infections: results from eight hospitals. Infect Control Hosp Epidemiol. 2001;22(1):31-4.  https://doi.org/10.1086/501821  PMID: 11198019 
  12. Meijs AP, Ferreira JA, DE Greeff SC, Vos MC, Koek MB. Incidence of surgical site infections cannot be derived reliably from point prevalence survey data in Dutch hospitals. Epidemiol Infect. 2017;145(5):970-80.  https://doi.org/10.1017/S0950268816003162  PMID: 28065193 
  13. King C, Aylin P, Holmes A. Converting incidence and prevalence data: an update to the rule. Infect Control Hosp Epidemiol. 2014;35(11):1432-3.  https://doi.org/10.1086/678435  PMID: 25333444 
  14. Broderick A, Mori M, Nettleman MD, Streed SA, Wenzel RP. Nosocomial infections: validation of surveillance and computer modeling to identify patients at risk. Am J Epidemiol. 1990;131(4):734-42.  https://doi.org/10.1093/oxfordjournals.aje.a115558  PMID: 2180283 
  15. Brossette SE, Hacek DM, Gavin PJ, Kamdar MA, Gadbois KD, Fisher AG, et al. A laboratory-based, hospital-wide, electronic marker for nosocomial infection: the future of infection control surveillance? Am J Clin Pathol. 2006;125(1):34-9.  https://doi.org/10.1309/502AUPR8VE67MBDE  PMID: 16482989 
  16. Brown C, Richards M, Galletly T, Coello R, Lawson W, Aylin P, et al. Use of anti-infective serial prevalence studies to identify and monitor hospital-acquired infection. J Hosp Infect. 2009;73(1):34-40.  https://doi.org/10.1016/j.jhin.2009.05.020  PMID: 19647890 
  17. Chang YJ, Yeh ML, Li YC, Hsu CY, Lin CC, Hsu MS, et al. Predicting hospital-acquired infections by scoring system with simple parameters. PLoS One. 2011;6(8):e23137.  https://doi.org/10.1371/journal.pone.0023137  PMID: 21887234 
  18. Du M, Xing Y, Suo J, Liu B, Jia N, Huo R, et al. Real-time automatic hospital-wide surveillance of nosocomial infections and outbreaks in a large Chinese tertiary hospital. BMC Med Inform Decis Mak. 2014;14(1):9.  https://doi.org/10.1186/1472-6947-14-9  PMID: 24475790 
  19. Evans RS, Burke JP, Classen DC, Gardner RM, Menlove RL, Goodrich KM, et al. Computerized identification of patients at high risk for hospital-acquired infection. Am J Infect Control. 1992;20(1):4-10.  https://doi.org/10.1016/S0196-6553(05)80117-8  PMID: 1554148 
  20. Evans RS, Larsen RA, Burke JP, Gardner RM, Meier FA, Jacobson JA, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA. 1986;256(8):1007-11.  https://doi.org/10.1001/jama.1986.03380080053027  PMID: 3735626 
/content/10.2807/1560-7917.ES.2019.24.13.1800177
Loading

Data & Media loading...

Supplementary data

Comment has been disabled for this content
Submit comment
Close
Comment moderation successfully completed
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error