Electronic Prediction Rules for Methicillin-Resistant Staphylococcus aureus (MRSA) Colonization

Background:   Considerable resources have been dedicated to reducing the rate of MRSA infections.  One frequently-employed infection control tool is surveillance testing among patients not known to be MRSA colonized.  This process is costly, and false-positive tests lead to isolation of non-carriers.  This technique would improve if patients at high risk of colonization could be readily identified for testing using an automated system.

Objective:   To derive and validate automatable prediction rules for identifying patients at high risk for MRSA colonization at the time of hospital admission, and to compare them to previously published models.

Methods:   MRSA-colonization prediction rules were designed using only prospectively collected electronic data resident in a patient's electronic medical record (EMR) within a day of admission.  The strategies for developing the rules were as follows: 1) ‘Comprehensive' rule – any variables resident in the EMR; 2) ‘Parsimonious' rule – only variables remaining after those with P > 0.01 were removed in stepwise fashion from the ‘Comprehensive' model; 3) ‘Clinician Entry' rule – only variables readily available to an admitting physician after meeting a patient for the first time; 4) ‘Simple EMR' rule – only variables available to a rudimentary EMR based on registration, laboratory, and admission-discharge-transfer data; 5) ‘Centers for Disease Control and Prevention (CDC) Risk Factor' rule – only simple-to-obtain variables that have been strongly identified with healthcare-associated MRSA colonization in the past.  Rules were derived using multivariable modeling in a population of 23,314 patients consecutively admitted and tested for MRSA colonization at a US hospital, who did not have known MRSA colonization.  Rules were validated in a cohort of 26,650 patients admitted to two other hospitals.

Results:   MRSA admission prevalence was 2.2% and 4.0% in the derivation and validation cohorts respectively.  Multivariable modeling identified independent predictors of MRSA colonization among demographic, admission-related, pharmacological, laboratory, physiological and historical variables.  The five prediction rules varied in their performance, but all could be used to identify the 30% of patients who accounted for more than 60% of all MRSA colonization, and about 70% of all MRSA patient-days (see Figure for ‘Comprehensive' rule performance).  All rules could also identify the 20% of patients with a >8% chance of colonization, and the 40% of patients in whom colonization prevalence was 2% or less.  The new rules outperformed 2 published prediction rules (Figure).

Conclusions:   We report prediction rules that can be used to automate the triage of patients for MRSA admission testing.  The efficiencies introduced by these rules could result in substantial cost savings to infection control programs with potentially little sacrifice in program effectiveness.