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.Infect Control Hosp Epidemiol 2015;36(1): [65][66][67][68][69][70][71][72][73][74][75] introduction Electronically collected routine care data are increasingly employed to meet growing demands for reliable and timely information on healthcare-associated infection (HAI) rates. [1][2][3] For several decades, surveillance of and feedback regarding HAI rates, for example within national networks, have been fundamental components of infection prevention programs. [4][5][6][7] Traditionally, surveillance is performed by infection preventionists who manually review patient charts for the occurrence of targeted HAIs. This approach, however, is known to be labor intensive, effort dependent, and vulnerable to subjective interpretation. [8][9][10][11] Expansion of surveillance volume requirements and public reporting of HAI rates has stimulated the use of (semi)automated systems that combine various sources of data captured in electronic health records (EHRs) to support or replace manual surveillance. 12,13 Most of these automated surveillance systems aim to classify patients by their likelihood of having developed the targeted HAI and thereby restrict manual chart review to...

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