ArticleBMC ophthalmology2011
Cataract research using electronic health records.
Article in BMC ophthalmology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Statin Use and the Risk of Cataracts: A Systematic Review and Meta-Analysis.Journal of the American Heart Association · 2017Pooled it
- Clinical Natural Language Processing in languages other than English: opportunities and challenges.Journal of biomedical semantics · 2018Review
- Validation of the Total Visual Acuity Extraction Algorithm (TOVA) for Automated Extraction of Visual Acuity Data From Free Text, Unstructured Clinical Records.Translational vision science & technology · 2017Article
- Statin Use and Incident Cataract Surgery: A Case-Control Study.Ophthalmic epidemiology · 2016Article
- ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow.International journal of biomedical data mining · 2015Article
- Desiderata for computable representations of electronic health records-driven phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Evaluating the state of the art in disorder recognition and normalization of the clinical narrative.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Design patterns for the development of electronic health record-driven phenotype extraction algorithms.Journal of biomedical informatics · 2014Article
- Changing incidence of lens extraction over 20 years: the Beaver Dam eye study.Ophthalmology · 2014Article
- Walking and running are associated with similar reductions in cataract risk.Medicine and science in sports and exercise · 2013Article
- Electronic health records: a new tool to combat chronic kidney disease?Clinical nephrology · 2013Article
- Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using Biofilter, and gene-environment interactions using the PhenX Toolkit.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2013Article
- The oral-systemic personalized medicine model at Marshfield Clinic.Oral diseases · 2013Review
- Predicting changes in hypertension control using electronic health records from a chronic disease management program.Journal of the American Medical Informatics Association : JAMIAArticle
- Importance of multi-modal approaches to effectively identify cataract cases from electronic health records.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundThe eMERGE (electronic MEdical Records and Genomics) network, funded by the National Human Genome Research Institute, is a national consortium formed to develop, disseminate, and apply approaches to research that combine DNA biorepositories with electronic health record (EHR) systems for large-scale, high-throughput genetic research. Marshfield Clinic is one of five sites in the eMERGE network and primarily studied: 1) age-related cataract and 2) HDL-cholesterol levels. The purpose of this paper is to describe the approach to electronic evaluation of the epidemiology of cataract using the EHR for a large biobank and to assess previously identified epidemiologic risk factors in cases identified by electronic algorithms.
methodsElectronic algorithms were used to select individuals with cataracts in the Personalized Medicine Research Project database. These were analyzed for cataract prevalence, age at cataract, and previously identified risk factors.
resultsCataract diagnoses and surgeries, though not type of cataract, were successfully identified using electronic algorithms. Age specific prevalence of both cataract (22% compared to 17.2%) and cataract surgery (11% compared to 5.1%) were higher when compared to the Eye Diseases Prevalence Research Group. The risk factors of age, gender, diabetes, and steroid use were confirmed.
conclusionsUsing electronic health records can be a viable and efficient tool to identify cataracts for research. However, using retrospective data from this source can be confounded by historical limits on data availability, differences in the utilization of healthcare, and changes in exposures over time.
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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.