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


RESULTS
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.
Mellmann
Alexander
A
Institute for Hygiene, University Hospital Münster, Münster, Germany.
Friedrich
Alexander W
AW
Rosenkötter
Nicole
N
Rothgänger
Jörg
J
Karch
Helge
H
Reintjes
Ralf
R
Harmsen
Dag
D
D000465:Algorithms; D001331:Automation; D015373:Bacterial Typing Techniques / Q000379:methods*; D002353:Carrier State / Q000175:diagnosis / Q000453:epidemiology / Q000382:microbiology*; D003428:Cross Infection / Q000175:diagnosis / Q000453:epidemiology / Q000382:microbiology* / Q000517:prevention & control; D004269:DNA, Bacterial / Q000032:analysis*; D004196:Disease Outbreaks; D018562:Disease Transmission, Infectious / Q000517:prevention & control; D005858:Germany; D006801:Humans; D017053:Infection Control / Q000379:methods*; D007297:Inpatients; D016106:Methicillin Resistance / Q000235:genetics; D010564:Personnel, Hospital; D011159:Population Surveillance / Q000379:methods; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D013203:Staphylococcal Infections / Q000175:diagnosis / Q000453:epidemiology / Q000382:microbiology* / Q000517:prevention & control; D013205:Staphylococcal Protein A / Q000235:genetics; D013211:Staphylococcus aureus / Q000187:drug effects* / Q000235:genetics / Q000302:isolation & purification
D004269:DNA, Bacterial; D013205:Staphylococcal Protein A