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Open Access Research article

Prevalence dependent calibration of a predictive model for nasal carriage of methicillin-resistant Staphylococcus aureus

Johannes Elias1*, Peter U Heuschmann2, Corinna Schmitt111, Frithjof Eckhardt3, Hartmut Boehm4, Sebastian Maier5, Annette Kolb-Mäurer6, Hubertus Riedmiller7, Wolfgang Müllges8, Christoph Weisser9, Christian Wunder10, Matthias Frosch1 and Ulrich Vogel1

Author Affiliations

1 Institute for Hygiene and Microbiology, University of Würzburg, Josef Schneider-Strasse 2, Würzburg, 97080, Germany

2 Institute for Clinical Epidemiology and Biometrics, University of Würzburg; Center for Clinical Studies, University Hospital Würzburg, Josef Schneider-Strasse 2, Würzburg, 97080, Germany

3 Service Center for Medical Informatics, University Hospital Würzburg, Josef Schneider-Strasse 2, Würzburg, 97080, Germany

4 Department of Maxillofacial Surgery, University Hospital Würzburg, Pleicherwall 2, Würzburg, 97070, Germany

5 Department of Medicine I, University Hospital Würzburg, Oberduerrbacher Strasse 6, Würzburg, 97080, Germany

6 Department of Dermatology, University Hospital Würzburg, Josef Schneider-Strasse 2, Würzburg, 97080, Germany

7 Department of Urology and Paediatric Urology, University Hospital Würzburg, Oberduerrbacher Strasse 6, Würzburg, 97080, Germany

8 Department of Neurology, University Hospital Würzburg, Josef Schneider-Strasse 11, Würzburg, 97080, Germany

9 Department of Sugery II, University Hospital Würzburg, Oberduerrbacher Strasse 6, Würzburg, 97080, Germany

10 Department of Anesthesiology, University Hospital Würzburg, Oberduerrbacher Strasse 6, Würzburg, 97080, Germany

11 Present address: Hannover Medical School, Institute of Virology, Carl Neuberg-Strasse 1, Hannover, 30625, Germany

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BMC Infectious Diseases 2013, 13:111  doi:10.1186/1471-2334-13-111

Published: 28 February 2013

Abstract

Background

Published models predicting nasal colonization with Methicillin-resistant Staphylococcus aureus among hospital admissions predominantly focus on separation of carriers from non-carriers and are frequently evaluated using measures of discrimination. In contrast, accurate estimation of carriage probability, which may inform decisions regarding treatment and infection control, is rarely assessed. Furthermore, no published models adjust for MRSA prevalence.

Methods

Using logistic regression, a scoring system (values from 0 to 200) predicting nasal carriage of MRSA was created using a derivation cohort of 3091 individuals admitted to a European tertiary referral center between July 2007 and March 2008. The expected positive predictive value of a rapid diagnostic test (GeneOhm, Becton & Dickinson Co.) was modeled using non-linear regression according to score. Models were validated on a second cohort from the same hospital consisting of 2043 patients admitted between August 2008 and January 2012. Our suggested correction score for prevalence was proportional to the log-transformed odds ratio between cohorts. Calibration before and after correction, i.e. accurate classification into arbitrary strata, was assessed with the Hosmer-Lemeshow-Test.

Results

Treating culture as reference, the rapid diagnostic test had positive predictive values of 64.8% and 54.0% in derivation and internal validation corhorts with prevalences of 2.3% and 1.7%, respectively. In addition to low prevalence, low positive predictive values were due to high proportion (> 66%) of mecA-negative Staphylococcus aureus among false positive results. Age, nursing home residence, admission through the medical emergency department, and ICD-10-GM admission diagnoses starting with “A” or “J” were associated with MRSA carriage and were thus included in the scoring system, which showed good calibration in predicting probability of carriage and the rapid diagnostic test’s expected positive predictive value. Calibration for both probability of carriage and expected positive predictive value in the internal validation cohort was improved by applying the correction score.

Conclusions

Given a set of patient parameters, the presented models accurately predict a) probability of nasal carriage of MRSA and b) a rapid diagnostic test’s expected positive predictive value. While the former can inform decisions regarding empiric antibiotic treatment and infection control, the latter can influence choice of screening method.

Keywords:
Methicillin-resistant staphylococcus aureus; Infection control; Clinical prediction rule; Predictive value of tests; False positive reactions; Calibration