Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies.

Kathryn L Jackson,David A Carrell,Frank D Mentch,Abel N Kho,Jennifer A Pacheco,Laura J Rasmussen-Torvik,James G Linneman,Daniel J Viox,Abigail S Baldridge,Joshua C Denny,Michael Mbagwu,Kenneth M Borthwick,Peggy L Peissig,Suzette J Bielinski,Sanjay K Shukla,Lyam M Vazquez,Jacqueline C Kirby

doi:10.1186/s12879-016-2020-2

Abstract

BackgroundCommunity associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is one of the most common causes of skin and soft tissue infections in the United States, and a variety of genetic host factors are suspected to be risk factors for recurrent infection. Based on the CDC definition, we have developed and validated an electronic health record (EHR) based CA-MRSA phenotype algorithm utilizing both structured and unstructured data.MethodsThe algorithm was validated at three eMERGE consortium sites, and positive predictive value, negative predictive value and sensitivity, were calculated. The algorithm was then run and data collected across seven total sites. The resulting data was used in GWAS analysis.ResultsAcross seven sites, the CA-MRSA phenotype algorithm identified a total of 349 cases and 7761 controls among the genotyped European and African American biobank populations. PPV ranged from 68 to 100% for cases and 96 to 100% for controls; sensitivity ranged from 94 to 100% for cases and 75 to 100% for controls. Frequency of cases in the populations varied widely by site. There were no plausible GWAS-significant (p < 5 E −8) findings.ConclusionsDifferences in EHR data representation and screening patterns across sites may have affected identification of cases and controls and accounted for varying frequencies across sites. Future work identifying these patterns is necessary.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-016-2020-2) contains supplementary material, which is available to authorized users.

Highlights

Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is one of the most common causes of skin and soft tissue infections in the United States, and a variety of genetic host factors are suspected to be risk factors for recurrent infection
Algorithm development The CA-MRSA phenotype algorithm was based on the Centers for Disease Control and Prevention (CDC) definition and prior work in this space [3] and developed at Northwestern University (NU)
It should be noted that the CDC definition considers MRSA to be hospital acquired (HA) if the infection occurs >48 h after admission, whereas we use ≥72 h in our definition to minimize the possibility of incorrectly categorizing CA as HA

Summary

Introduction

Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is one of the most common causes of skin and soft tissue infections in the United States, and a variety of genetic host factors are suspected to be risk factors for recurrent infection. Methicillin-resistant Staphylococcus aureus is one of the most common causes of skin and soft tissue infections (SSTIs) in the United States [1]. Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) has replaced traditional healthcare associated strains in many communities where it previously did not exist [2, 3]. A variety of genetic factors are suspected as a risk factor for recurrent CA-MRSA infection [4, 6, 7], with an increased prevalence in younger, healthier populations with no other identifiable risk factors [8].

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Results

Conclusion