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Updated: Jun 28, 2026
Biosensor for Detection of Antibiotic Resistant Staphylococcus Bacteria
Published on: May 8, 2013
A predictive model for identifying surgical patients at risk of methicillin-resistant Staphylococcus aureus carriage
Stephan Harbarth1, Hugo Sax, Ilker Uckay
1Infection Control Program, University of Geneva Hospitals and Medical Schools, Geneva, Switzerland. stephan.harbarth@hcuge.ch
Background:
Legislative mandates and current guidelines for control of nosocomial transmission of methicillin-resistant Staphylococcus aureus (MRSA) recommend screening of patients at risk of MRSA carriage on hospital admission. Indiscriminate application of these guidelines can result in a large number of unnecessary screening tests.
Study Design:
This study was conducted to develop and validate a prediction model to define surgical patients at risk of previously unknown MRSA carriage on admission. We used data from two prospective studies to derivate and validate predictors of previously unknown MRSA carriage on admission, using logistic regression analysis.
Results:
A total of 13,262 patients (derivation cohort, 3,069; validation cohort, 10,193) were admitted to the surgery department and screened for MRSA. Prevalence of MRSA carriage at time of admission increased from 3.2% in 2003 to 5.1% in the period 2004 to 2006, with a majority of newly identified MRSA carriers (64%). Three independent factors were correlated with previously unknown MRSA carriage: recent antibiotic treatment (adjusted odds ratio [OR]: 4.5; p < 0.001), history of hospitalization (adjusted OR: 2.7; p = 0.03), and age older than 75 years (adjusted OR: 1.9; p = 0.048). A score (range 0 to 9 points) calculated from these variables was developed. Probability of previously unknown MRSA carriage was 5% (8 of 152) in patients with a low score (< 2 points), 11% (19 of 166) in those with an intermediate score (2 to 6 points), and 34% (30 of 87) in those with a high score (> or = 7 points). Limiting screening to patients with all 3 risk factors (21% and 26% of patients in the derivation and validation cohort, respectively) would have correctly identified 53% and 37% of MRSA carriers in both cohorts.
Conclusions:
A predictive model using three easily retrievable determinants might help to better target surgical patients at risk of MRSA carriage on admission.
Insights
A new predictive model identifies surgical patients at high risk for methicillin-resistant Staphylococcus aureus (MRSA) carriage. This tool helps target screening, reducing unnecessary tests for MRSA.
Area of Science:
- Infectious Diseases
- Epidemiology
- Clinical Medicine
Background:
- Current guidelines recommend screening all at-risk patients for methicillin-resistant Staphylococcus aureus (MRSA) carriage upon hospital admission.
- Indiscriminate screening leads to a high volume of unnecessary tests, increasing healthcare costs.
Purpose of the Study:
- To develop and validate a prediction model for identifying surgical patients with previously unknown MRSA carriage on admission.
- To optimize MRSA screening protocols by targeting high-risk individuals.
Main Methods:
- Logistic regression analysis was used to derive and validate predictors of MRSA carriage.
- Data from two prospective studies involving 13,262 surgical patients were analyzed.
- A scoring system was developed based on independent risk factors.
Main Results:
- Prevalence of MRSA carriage increased from 3.2% to 5.1% between 2003 and 2006.
- Three independent predictors of MRSA carriage were identified: recent antibiotic treatment, history of hospitalization, and age over 75.
- A scoring system demonstrated varying probabilities of MRSA carriage based on risk level.
Conclusions:
- A predictive model utilizing three easily assessable determinants can effectively identify surgical patients at risk for MRSA carriage.
- Targeted screening based on this model can improve resource allocation and reduce unnecessary testing.
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