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Considerable hospital resources are dedicated to minimizing the number of methicillin-resistant Staphylococcus aureus (MRSA) infections. One tool that is commonly used to achieve this goal is surveillance for MRSA colonization. This process is costly, and false-positive test results lead to isolation of individuals who do not carry MRSA. The performance of this technique would improve if patients who are at high risk of colonization could be readily targeted.
Five MRSA colonization prediction rules of varying complexity were derived in a population of 23,314 patients who were consecutively admitted to a US hospital and tested for colonization. Rules incorporated only prospectively collected, structured electronic data found in a patient's record within 1 day of hospital admission. These rules were tested in a validation cohort of 26,650 patients who were admitted to 2 other hospitals.
The prevalence of MRSA at hospital admission was 2.2% and 4.0% in the derivation and validation cohorts, respectively. Multivariable modeling identified predictors of MRSA colonization among demographic, admission-related, pharmacologic, laboratory, physiologic, and historical variables. Five prediction rules varied in their performance, but each could be used to identify the 30% of patients who accounted for greater than 60% of all cases of MRSA colonization and approximately 70% of all MRSA-associated patient-days. Most rules could also identify the 20% of patients with a greater than 8% chance of colonization and the 40% of patients among whom colonization prevalence was 2% or less.
We report electronic prediction rules that can fully automate triage of patients for MRSA-related hospital admission testing and that offer significant improvements on previously reported rules. The efficiencies introduced may result in savings to infection control programs with little sacrifice in effectiveness.
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