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PLoS Med. 2006 Mar;3(3):e33.

Automated DNA sequence-based early warning system for the detection of methicillin-resistant Staphylococcus aureus outbreaks.

Author information

1
Institute for Hygiene, University Hospital Münster, Münster, Germany.

Abstract

BACKGROUND:

The detection of methicillin-resistant Staphylococcus aureus (MRSA) usually requires the implementation of often rigorous infection-control measures. Prompt identification of an MRSA epidemic is crucial for the control of an outbreak. In this study we evaluated various early warning algorithms for the detection of an MRSA cluster.

METHODS AND FINDINGS:

Between 1998 and 2003, 557 non-replicate MRSA strains were collected from staff and patients admitted to a German tertiary-care university hospital. The repeat region of the S. aureus protein A (spa) gene in each of these strains was sequenced. Using epidemiological and typing information for the period 1998-2002 as reference data, clusters in 2003 were determined by temporal-scan test statistics. Various early warning algorithms (frequency, clonal, and infection control professionals [ICP] alerts) were tested in a prospective analysis for the year 2003. In addition, a newly implemented automated clonal alert system of the Ridom StaphType software was evaluated. A total of 549 of 557 MRSA were typeable using spa sequencing. When analyzed using scan test statistics, 42 out of 175 MRSA in 2003 formed 13 significant clusters (p < 0.05). These clusters were used as the "gold standard" to evaluate the various algorithms. Clonal alerts (spa typing and epidemiological data) were 100% sensitive and 95.2% specific. Frequency (epidemiological data only) and ICP alerts were 100% and 62.1% sensitive and 47.2% and 97.3% specific, respectively. The difference in specificity between clonal and ICP alerts was not significant. Both methods exhibited a positive predictive value above 80%.

CONCLUSIONS:

Rapid MRSA outbreak detection, based on epidemiological and spa typing data, is a suitable alternative for classical approaches and can assist in the identification of potential sources of infection.

PMID:
16396609
PMCID:
PMC1325475
DOI:
10.1371/journal.pmed.0030033
[Indexed for MEDLINE]
Free PMC Article

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