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Ari Robicsek, MD, from the University of Chicago Pritzker School of Medicine, and colleagues, acknowledge that a significant number of hospital resources are dedicated to minimizing the number of methicillin-resistant Staphylococcus aureus (MRSA) infections, including engaging in surveillance for MRSA colonization. Robicsek, et al. say this process is costly, and false-positive test results lead to isolation of individuals who do not carry MRSA. They add that the performance of this technique would improve if patients who are at high risk of colonization could be readily targeted.
The researchers derived five MRSA colonization prediction rules of varying complexity in a population of 23,314 patients who were consecutively admitted to a U.S. hospital and tested for colonization. Rules incorporated only prospectively collected, structured electronic data found in a patients record within one day of hospital admission. These rules were tested in a validation cohort of 26,650 patients who were admitted to two other hospitals.
Robicsek, et al. report that the prevalence of MRSA at hospital admission was 2.2 percent and 4.0 percent 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 percent of patients who accounted for greater than 60 percent of all cases of MRSA colonization and approximately 70 percent of all MRSAassociated patient-days. Most rules could also identify the 20 percent of patients with a greater than 8 percent chance of colonization and the 40 percent of patients among whom colonization prevalence was 2 percent or less.
The researchers therefore conclude that electronic prediction rules can fully automate triage of patients for MRSA-related hospital admission testing and offer significant improvements on previously reported rules. They say that efficiencies introduced may result in savings to infection control programs with little sacrifice in effectiveness. Their research was published in Infection Control and Hospital Epidemiology.
Reference: Robicsek A, Beaumont JL, Wright MO, Thomson RB, Kaul KL and Peterson LR. Electronic Prediction Rules for Methicillin-Resistant Staphylococcus aureus Colonization. Infect Control Hosp Epidem. Vol. 32, No. 1. January 2011.