Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies
Abstract Background 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. Based on the CDC de...
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doaj-4d221b043ca74636bd5c1786c9603d922020-11-25T01:38:37ZengBMCBMC Infectious Diseases1471-23342016-11-011611710.1186/s12879-016-2020-2Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studiesKathryn L. Jackson0Michael Mbagwu1Jennifer A. Pacheco2Abigail S. Baldridge3Daniel J. Viox4James G. Linneman5Sanjay K. Shukla6Peggy L. Peissig7Kenneth M. Borthwick8David A. Carrell9Suzette J. Bielinski10Jacqueline C. Kirby11Joshua C. Denny12Frank D. Mentch13Lyam M. Vazquez14Laura J. Rasmussen-Torvik15Abel N. Kho16Feinberg School of Medicine, Northwestern UniversityFeinberg School of Medicine, Northwestern UniversityFeinberg School of Medicine, Northwestern UniversityFeinberg School of Medicine, Northwestern UniversityFeinberg School of Medicine, Northwestern UniversityBiomedical Informatics Research Center, Marshfield Clinic Research FoundationMarshfield Clinic Research FoundationBiomedical Informatics Research Center, Marshfield Clinic Research FoundationGeisinger Health SystemGroup Health Research Institute, Group Health CooperativeMayo ClinicDepartment of Biomedical Informatics, Vanderbilt UniversityDepartment of Biomedical Informatics, Vanderbilt UniversityThe Center for Applied Genomics, Children’s Hospital of PhiladelphiaThe Center for Applied Genomics, Children’s Hospital of PhiladelphiaFeinberg School of Medicine, Northwestern UniversityFeinberg School of Medicine, Northwestern UniversityAbstract Background 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. 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. Methods The 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. Results Across 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. Conclusions Differences 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.http://link.springer.com/article/10.1186/s12879-016-2020-2ca_MRSAPhenotypingElectronic Health Recordca-MRSA PhenotypeGWAS |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kathryn L. Jackson Michael Mbagwu Jennifer A. Pacheco Abigail S. Baldridge Daniel J. Viox James G. Linneman Sanjay K. Shukla Peggy L. Peissig Kenneth M. Borthwick David A. Carrell Suzette J. Bielinski Jacqueline C. Kirby Joshua C. Denny Frank D. Mentch Lyam M. Vazquez Laura J. Rasmussen-Torvik Abel N. Kho |
spellingShingle |
Kathryn L. Jackson Michael Mbagwu Jennifer A. Pacheco Abigail S. Baldridge Daniel J. Viox James G. Linneman Sanjay K. Shukla Peggy L. Peissig Kenneth M. Borthwick David A. Carrell Suzette J. Bielinski Jacqueline C. Kirby Joshua C. Denny Frank D. Mentch Lyam M. Vazquez Laura J. Rasmussen-Torvik Abel N. Kho Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies BMC Infectious Diseases ca_MRSA Phenotyping Electronic Health Record ca-MRSA Phenotype GWAS |
author_facet |
Kathryn L. Jackson Michael Mbagwu Jennifer A. Pacheco Abigail S. Baldridge Daniel J. Viox James G. Linneman Sanjay K. Shukla Peggy L. Peissig Kenneth M. Borthwick David A. Carrell Suzette J. Bielinski Jacqueline C. Kirby Joshua C. Denny Frank D. Mentch Lyam M. Vazquez Laura J. Rasmussen-Torvik Abel N. Kho |
author_sort |
Kathryn L. Jackson |
title |
Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies |
title_short |
Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies |
title_full |
Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies |
title_fullStr |
Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies |
title_full_unstemmed |
Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies |
title_sort |
performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant staphylococcus aureus cases and controls for genetic association studies |
publisher |
BMC |
series |
BMC Infectious Diseases |
issn |
1471-2334 |
publishDate |
2016-11-01 |
description |
Abstract Background 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. 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. Methods The 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. Results Across 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. Conclusions Differences 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. |
topic |
ca_MRSA Phenotyping Electronic Health Record ca-MRSA Phenotype GWAS |
url |
http://link.springer.com/article/10.1186/s12879-016-2020-2 |
work_keys_str_mv |
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