title: Validation of an automated surveillance approach for drain-related meningitis: a multicenter study.
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.
authors: 1Department of Medical Microbiology,University Medical Center Utrecht,The Netherlands.
authors: Maaike S M
authors: MS
authors: van Mourik
authors: 1Department of Medical Microbiology,University Medical Center Utrecht,The Netherlands.
authors: Annet
authors: A
authors: Troelstra
authors: 2Department of Neurosurgery,University Medical Center Utrecht,The Netherlands.
authors: Jan Willem
authors: JW
authors: Berkelbach van der Sprenkel
authors: 3Department of Hospital Hygiene and Infection Prevention,University Medical Center Utrecht,The Netherlands.
authors: Marischka C E
authors: MC
authors: van der Jagt-Zwetsloot
authors: 4Department of Infection Control and Prevention,St. Elisabeth Ziekenhuis,Tilburg,The Netherlands.
authors: Jolande H
authors: JH
authors: Nelson
authors: 5Department of Intensive Care,St. Elisabeth Ziekenhuis,Tilburg,The Netherlands.
authors: Piet
authors: P
authors: Vos
authors: 6Department of Neurosurgery,Medisch Centrum Haaglanden,The Hague,The Netherlands.
authors: Mark P
authors: MP
authors: Arts
authors: 7Department of Intensive Care,Medisch Centrum Haaglanden,The Hague,The Netherlands.
authors: Paul J W
authors: PJ
authors: Dennesen
authors: 8Julius Center for Health Sciences and Primary Care,University Medical Center Utrecht,The Netherlands.
authors: Karel G M
authors: KG
authors: Moons
authors: 1Department of Medical Microbiology,University Medical Center Utrecht,The Netherlands.
authors: Marc J M
authors: MJ
authors: Bonten
mesh_terms: D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D001331:Automation; D002557:Cerebrospinal Fluid Shunts / Q000009:adverse effects*; D003428:Cross Infection / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000382:microbiology; D005260:Female; D006801:Humans; D008297:Male; D008581:Meningitis / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000382:microbiology; D008875:Middle Aged; D008954:Models, Biological; D015999:Multivariate Analysis; D011159:Population Surveillance / Q000379:methods*; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012372:ROC Curve; D018570:Risk Assessment