Dataset Information

Electronic medical records and genomics (eMERGE) network exploration in cataract: several new potential susceptibility loci.

ABSTRACT: Cataract is the leading cause of blindness in the world, and in the United States accounts for approximately 60% of Medicare costs related to vision. The purpose of this study was to identify genetic markers for age-related cataract through a genome-wide association study (GWAS).In the electronic medical records and genomics (eMERGE) network, we ran an electronic phenotyping algorithm on individuals in each of five sites with electronic medical records linked to DNA biobanks. We performed a GWAS using 530,101 SNPs from the Illumina 660W-Quad in a total of 7,397 individuals (5,503 cases and 1,894 controls). We also performed an age-at-diagnosis case-only analysis.We identified several statistically significant associations with age-related cataract (45 SNPs) as well as age at diagnosis (44 SNPs). The 45 SNPs associated with cataract at p<1×10(-5) are in several interesting genes, including ALDOB, MAP3K1, and MEF2C. All have potential biologic relationships with cataracts.This is the first genome-wide association study of age-related cataract, and several regions of interest have been identified. The eMERGE network has pioneered the exploration of genomic associations in biobanks linked to electronic health records, and this study is another example of the utility of such resources. Explorations of age-related cataract including validation and replication of the association results identified herein are needed in future studies.

SUBMITTER: Ritchie MD

PROVIDER: S-EPMC4168835 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Electronic medical records and genomics (eMERGE) network exploration in cataract: several new potential susceptibility loci.

Ritchie Marylyn D MD Verma Shefali S SS Hall Molly A MA Goodloe Robert J RJ Berg Richard L RL Carrell Dave S DS Carlson Christopher S CS Chen Lin L Crosslin David R DR Denny Joshua C JC Jarvik Gail G Li Rongling R Linneman James G JG Pathak Jyoti J Peissig Peggy P Rasmussen Luke V LV Ramirez Andrea H AH Wang Xiaoming X Wilke Russell A RA Wolf Wendy A WA Torstenson Eric S ES Turner Stephen D SD McCarty Catherine A CA

Molecular vision 20140919

<h4>Purpose</h4>Cataract is the leading cause of blindness in the world, and in the United States accounts for approximately 60% of Medicare costs related to vision. The purpose of this study was to identify genetic markers for age-related cataract through a genome-wide association study (GWAS).<h4>Methods</h4>In the electronic medical records and genomics (eMERGE) network, we ran an electronic phenotyping algorithm on individuals in each of five sites with electronic medical records linked to D ...[more]

PMID: 25352737

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Project description:Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t?=?5.14; p?<?0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

Dataset Information

Electronic medical records and genomics (eMERGE) network exploration in cataract: several new potential susceptibility loci.

Publications

Electronic medical records and genomics (eMERGE) network exploration in cataract: several new potential susceptibility loci.

Similar Datasets

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