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Validation of an Automated Surveillance Approach for Drain-Related Meningitis: A Multicenter Study

Published online by Cambridge University Press:  05 January 2015

Maaike S. M. van Mourik ,
Annet Troelstra ,
Jan Willem Berkelbach van der Sprenkel ,
Marischka C. E. van der Jagt-Zwetsloot ,
Jolande H. Nelson ,
Piet Vos ,
Mark P. Arts ,
Paul J. W. Dennesen ,
Karel G. M. Moons  and
Marc J. M. Bonten
Maaike S. M. van Mourik*
Affiliation:
Department of Medical Microbiology, University Medical Center Utrecht, The Netherlands
Annet Troelstra
Affiliation:
Department of Medical Microbiology, University Medical Center Utrecht, The Netherlands
Jan Willem Berkelbach van der Sprenkel
Affiliation:
Department of Neurosurgery, University Medical Center Utrecht, The Netherlands
Marischka C. E. van der Jagt-Zwetsloot
Affiliation:
Department of Hospital Hygiene and Infection Prevention, University Medical Center Utrecht, The Netherlands
Jolande H. Nelson
Affiliation:
Department of Infection Control and Prevention, St. Elisabeth Ziekenhuis, Tilburg, The Netherlands
Piet Vos
Affiliation:
Department of Intensive Care, St. Elisabeth Ziekenhuis, Tilburg, The Netherlands
Mark P. Arts
Affiliation:
Department of Neurosurgery, Medisch Centrum Haaglanden, The Hague, The Netherlands
Paul J. W. Dennesen
Affiliation:
Department of Intensive Care, Medisch Centrum Haaglanden, The Hague, The Netherlands
Karel G. M. Moons
Affiliation:
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands
Marc J. M. Bonten
Affiliation:
Department of Medical Microbiology, University Medical Center Utrecht, The Netherlands Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands
*
Address correspondence to Maaike S.M. van Mourik, MD, PhD, HP G04.614, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands (M.S.M.vanMourik-2@umcutrecht.nl).
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Abstract

OBJECTIVE

Manual surveillance of healthcare-associated infections is cumbersome and vulnerable to subjective interpretation. Automated systems are under development to improve efficiency and reliability of surveillance, for example by selecting high-risk patients requiring manual chart review. In this study, we aimed to validate a previously developed multivariable prediction modeling approach for detecting drain-related meningitis (DRM) in neurosurgical patients and to assess its merits compared to conventional methods of automated surveillance.

METHODS

Prospective cohort study in 3 hospitals assessing the accuracy and efficiency of 2 automated surveillance methods for detecting DRM, the multivariable prediction model and a classification algorithm, using manual chart review as the reference standard. All 3 methods of surveillance were performed independently. Patients receiving cerebrospinal fluid drains were included (2012–2013), except children, and patients deceased within 24 hours or with pre-existing meningitis. Data required by automated surveillance methods were extracted from routine care clinical data warehouses.

RESULTS

In total, DRM occurred in 37 of 366 external cerebrospinal fluid drainage episodes (12.3/1000 drain days at risk). The multivariable prediction model had good discriminatory power (area under the ROC curve 0.91–1.00 by hospital), had adequate overall calibration, and could identify high-risk patients requiring manual confirmation with 97.3% sensitivity and 52.2% positive predictive value, decreasing the workload for manual surveillance by 81%. The multivariable approach was more efficient than classification algorithms in 2 of 3 hospitals.

CONCLUSIONS

Automated surveillance of DRM using a multivariable prediction model in multiple hospitals considerably reduced the burden for manual chart review at near-perfect sensitivity.

Infect Control Hosp Epidemiol 2015;36(1): 65–75

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 36 , Issue 1 , January 2015 , pp. 65 - 75
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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References

REFERENCES

1.Freeman, R, Moore, LS, Garcia, AL, Charlett, A, Holmes, A. Advances in electronic surveillance for healthcare-associated infections in the 21st century: a systematic review. J Hosp Infect 2013;84:106–119.CrossRefGoogle ScholarPubMed
2.van Mourik, MS, Troelstra, A, van Solinge, WW, Moons, KG, Bonten, MJ. Automated surveillance for healthcare-associated infections: opportunities for improvement. Clin Infect Dis 2013;57:85–93.Google Scholar
3.Trick, WE. Decision making during healthcare-associated infection surveillance: a rationale for automation. Clin Infect Dis 2013;57:434–440.Google Scholar
4.Tokars, JI, Richards, C, Andrus, M, et al. The changing face of surveillance for health care-associated infections. Clin Infect Dis 2004;39:1347–1352.Google Scholar
5.Haley, RW, Culver, DH, White, JW, et al. The efficacy of infection surveillance and control programs in preventing nosocomial infections in US hospitals. Am J Epidemiol 1985;121:182–205.Google Scholar
6.Haustein, T, Gastmeier, P, Holmes, A, et al. Use of benchmarking and public reporting for infection control in four high-income countries. Lancet Infect Dis 2011;11:471–481.Google Scholar
7.Geubbels, EL, Nagelkerke, NJ, Mintjes-De Groot, AJ, Vandenbroucke-Grauls, CM, Grobbee, DE, De Boer, AS. Reduced risk of surgical site infections through surveillance in a network. Int J Qual Health Care 2006;18:127–133.Google Scholar
8.Lin, MY, Hota, B, Khan, YM, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA 2010;304:2035–2041.Google Scholar
9.Klompas, M. Interobserver variability in ventilator-associated pneumonia surveillance. Am J Infect Control 2010;38:237–239.CrossRefGoogle ScholarPubMed
10.Mayer, J, Greene, T, Howell, J, et al. Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clin Infect Dis 2012;55:364–370.Google Scholar
11.Klompas, M. Eight initiatives that misleadingly lower ventilator-associated pneumonia rates. Am J Infect Control 2012;40:408–410.Google Scholar
12.Woeltje, KF. Moving into the future: electronic surveillance for healthcare-associated infections. J Hosp Infect 2013;84:103–105.CrossRefGoogle ScholarPubMed
13.Klompas, M, Yokoe, DS. Automated surveillance of health care-associated infections. Clin Infect Dis 2009;48:1268–1275.Google Scholar
14.van Mourik, MS, Moons, KG, van Solinge, WW, et al. Automated detection of healthcare associated infections: external validation and updating of a model for surveillance of drain-related meningitis. PLoS ONE 2012;7:e51509.Google Scholar
15.Lozier, AP, Sciacca, RR, Romagnoli, MF, Connolly, ES Jr.Ventriculostomy-related infections: a critical review of the literature. Neurosurgery 2002;51:170–181.Google Scholar
16.Scheithauer, S, Burgel, U, Bickenbach, J, et al. External ventricular and lumbar drainage-associated meningoventriculitis: prospective analysis of time-dependent infection rates and risk factor analysis. Infection 2010;38:205–209.Google Scholar
17.Lyke, KE, Obasanjo, OO, Williams, MA, O'Brien, M, Chotani, R, Perl, TM. Ventriculitis complicating use of intraventricular catheters in adult neurosurgical patients. Clin Infect Dis 2001;33:2028–2033.Google Scholar
18.Leverstein-Van Hall, MA, Hopmans, TEM, Van Der Sprenkel, JWB, et al. A bundle approach to reduce the incidence of external ventricular and lumbar drain-related infections: Clinical article. J Neurosurg 2010;112:345–353.Google Scholar
19.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;36:309–332.Google Scholar
20.Altman, DG, Vergouwe, Y, Royston, P, Moons, KG. Prognosis and prognostic research: validating a prognostic model. BMJ 2009;338:b605.Google Scholar
21.Justice, AC, Covinsky, KE, Berlin, JA. Assessing the generalizability of prognostic information. Ann Intern Med 1999;130:515–524.Google Scholar
22.ten Berg, MJ, Huisman, A, van den Bemt, PM, Schobben, AF, Egberts, AC, van Solinge, WW. Linking laboratory and medication data: new opportunities for pharmacoepidemiological research. Clin Chem Lab Med 2007;45:13–19.Google Scholar
23.van Mourik, MS, Groenwold, RH, Berkelbach van der Sprenkel, JW, van Solinge, WW, Troelstra, A, Bonten, MJ. Automated detection of external ventricular and lumbar drain-related meningitis using laboratory and microbiology results and medication data. PLoS One 2011;6:e22846.Google Scholar
24.Donders, AR, van der Heijden, GJ, Stijnen, T, Moons, KG. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol 2006;59:1087–1091.Google Scholar
25.Sterne, JA, White, IR, Carlin, JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 2009;338:b2393.Google Scholar
26.Steyerberg, EW. Clinical Prediction Models: A practical approach to development, validation, and updating. New York: Springer, 2009.Google Scholar
27.Keller, SC, Linkin, DR, Fishman, NO, Lautenbach, E. Variations in identification of healthcare-associated infections. Infect Control Hosp Epidemiol 2013;34:678–686.Google Scholar
28.Dixon-Woods, M, Leslie, M, Bion, J, Tarrant, C. What counts? An ethnographic study of infection data reported to a patient safety program. Milbank Q 2012;90:548–591.Google Scholar
29.Trick, WE. Building a data warehouse for infection control. Am J Infect Control 2008;36:S75–S81.Google Scholar
30.Tejedor, SC, Garrett, G, Jacob, JT, et al. Electronic documentation of central venous catheter-days: validation is essential. Infect Control Hosp Epidemiol 2013;34:900–907.CrossRefGoogle ScholarPubMed
31.Wright, MO, Fisher, A, John, M, Reynolds, K, Peterson, LR, Robicsek, A. The electronic medical record as a tool for infection surveillance: successful automation of device-days. Am J Infect Control 2009;37:364–370.Google Scholar
32.Janssen, KJ, Vergouwe, Y, Donders, AR, et al. Dealing with missing predictor values when applying clinical prediction models. Clin Chem 2009;55:994–1001.Google Scholar
33.Lin, MY, Bonten, MJ. The Dilemma of Assessment Bias in Infection Control Research. Clin Infect Dis 2012;54:1342–1347.Google Scholar
34.De Bruin, JS, Blacky, A, Koller, W, Adlassnig, KP. Validation of fuzzy sets in an automated detection system for intensive-care-unit-acquired central-venous-catheter-related infections. Stud Health Technol Inform 2013;192:215–218.Google Scholar