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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 ([email protected]).
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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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