ArticleBMC infectious diseases2016
Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies.
Article in BMC infectious diseases, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03394859 (Electronic Medical Records and Genomics), which is not on this map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Electronic Medical Records and Genomics (eMERGE) Phase III
Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- An Online Tool for Correcting Performance Measures of Electronic Phenotyping Algorithms for Verification Bias.ACI open · 2024Article
- Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.Scientific reports · 2023Article
- A strategy for validation of variables derived from large-scale electronic health record data.Journal of biomedical informatics · 2021Article
- The effect of number of healthcare visits on study sample selection in electronic health record data.International journal of population data science · 2020Article
- Prevalence of and risk factors for methicillin-resistant Staphylococcus aureus nasal carriage in the West of Iran: a population-based cross-sectional study.BMC infectious diseases · 2019Article
- Considerations for Improving the Portability of Electronic Health Record-Based Phenotype Algorithms.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- A case study evaluating the portability of an executable computable phenotype algorithm across multiple institutions and electronic health record environments.Journal of the American Medical Informatics Association : JAMIA · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 7 institutions in 1 country.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.