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J Hosp Infect. 2010 Mar;74(3):258-65. doi: 10.1016/j.jhin.2009.08.015. Epub 2009 Nov 14.

Building a benchmark through active surveillance of intensive care unit-acquired infections: the Italian network SPIN-UTI.

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Department of Biomedical Sciences, University of Catania, Via S. Sofia n. 87e95123 Catania, Italy.


The Italian Nosocomial Infections Surveillance in Intensive Care Units (ICUs) (SPIN-UTI) project of the Italian Study Group of Hospital Hygiene (GISIO - SItI) was undertaken to ensure standardisation of definitions, data collection and reporting procedures using the Hospital in Europe Link for Infection Control through Surveillance (HELICS)-ICU benchmark. Before starting surveillance, participant ICUs met in order to involve the key stakeholders in the project through participation in planning. Four electronic data forms for web-based data collection were designed. The six-month patient-based prospective survey was undertaken from November 2006 to May 2007, preceded by a one-month surveillance pilot study to assess the overall feasibility of the programme and to determine the time needed and resources for participant hospitals. The SPIN-UTI project included 49 ICUs, 3053 patients with length of stay >2 days and 35 498 patient-days. The cumulative incidence of infections was 19.8 per 100 patients and the incidence density was 17.1 per 1000 patient-days. The most frequently encountered infection type was pneumonia, Pseudomonas aeruginosa being the most frequent infection-associated micro-organism, followed by Staphylococcus aureus and Acinetobacter baumannii. Site-specific infection rates for pneumonia, bloodstream infections, central venous catheter-related bloodstream infections and urinary tract infections, stratified according to patient risk factors, were below the 75th centile reported by the HELICS network benchmark. The SPIN-UTI project showed that introduction of ongoing surveillance should be possible in many Italian hospitals. The study provided the opportunity to participate in the HELICS project using benchmark data for comparison and for better understanding of factors influencing risks.

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