Development and external validation of a clinical prediction model for MRSA carriage at hospital admission in Southeast Lower Saxony, Germany.
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Authors
Raschpichler, GabrieleRaupach-Rosin, Heike
Akmatov, Manas K
Castell, Stefanie
Rübsamen, Nicole
Feier, Birgit
Szkopek, Sebastian
Bautsch, Wilfried
Mikolajczyk, Rafael
Karch, André
Issue Date
2020-10-22
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Show full item recordAbstract
In countries with low endemic Methicillin-resistant Staphylococcus aureus (MRSA) prevalence, identification of risk groups at hospital admission is considered more cost-effective than universal MRSA screening. Predictive statistical models support the selection of suitable stratification factors for effective screening programs. Currently, there are no universal guidelines in Germany for MRSA screening. Instead, a list of criteria is available from the Commission for Hospital Hygiene and Infection Prevention (KRINKO) based on which local strategies should be adopted. We developed and externally validated a model for individual prediction of MRSA carriage at hospital admission in the region of Southeast Lower Saxony based on two prospective studies with universal screening in Braunschweig (n = 2065) and Wolfsburg (n = 461). Logistic regression was used for model development. The final model (simplified to an unweighted score) included history of MRSA carriage, care dependency and cancer treatment. In the external validation dataset, the score showed a sensitivity of 78.4% (95% CI: 64.7-88.7%), and a specificity of 70.3% (95% CI: 65.0-75.2%). Of all admitted patients, 25.4% had to be screened if the score was applied. A model based on KRINKO criteria showed similar sensitivity but lower specificity, leading to a considerably higher proportion of patients to be screened (49.5%).Citation
Sci Rep. 2020 Oct 22;10(1):17998. doi: 10.1038/s41598-020-75094-6.Affiliation
HZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany.Publisher
Nature publishing group (NPG)Journal
Scientific reportsPubMed ID
33093607Type
ArticleLanguage
enEISSN
2045-2322ae974a485f413a2113503eed53cd6c53
10.1038/s41598-020-75094-6
Scopus Count
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