title: Automated detection of nosocomial infections: evaluation of different strategies in an intensive care unit 2000-2006.
abstract: The aim of this study was to evaluate seven different strategies for the automated detection of nosocomial infections (NIs) in an intensive care unit (ICU) by using different hospital information systems: microbiology database, antibiotic prescriptions, medico-administrative database, and textual hospital discharge summaries. The study involved 1,499 patients admitted to an ICU of the University Hospital of Lyon (France) between 2000 and 2006. The data were extracted from the microbiology laboratory information system, the clinical information system on the ward and the medico-administrative database. Different algorithms and strategies were developed, using these data sources individually or in combination. The performances of each strategy were assessed by comparing the results with the ward data collected as a national standardised surveillance protocol, adapted from the National Nosocomial Infections Surveillance system as the gold standard. From 1,499 patients, 282 NIs were reported. The strategy with the best sensitivity for detecting these infections using an automated method was the combination of antibiotic prescription or microbiology, with a sensitivity of 99.3% [95% confidence interval (CI): 98.2-100] and a specificity of 56.8% (95% CI: 54.0-59.6). Automated methods of NI detection represent an alternative to traditional monitoring methods. Further study involving more ICUs should be performed before national recommendations can be established.
authors: Université de Lyon, Université Lyon I - CNRS-UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, Villeurbanne, France.
authors: S
authors: S
authors: Bouzbid
authors: Q
authors: Q
authors: Gicquel
authors: S
authors: S
authors: Gerbier
authors: M
authors: M
authors: Chomarat
authors: E
authors: E
authors: Pradat
authors: J
authors: J
authors: Fabry
authors: A
authors: A
authors: Lepape
authors: M-H
authors: MH
authors: Metzger
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001331:Automation / Q000379:methods*; D003428:Cross Infection / Q000175:diagnosis*; D005260:Female; D005602:France; D006751:Hospital Information Systems / Q000706:statistics & numerical data*; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D012680:Sensitivity and Specificity