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Related Experiment Video

Updated: May 27, 2026

Modeling Cataract Surgery in Mice

Modeling Cataract Surgery in Mice

Published on: December 1, 2023

Cataract research using electronic health records.

Carol J Waudby¹, Richard L Berg, James G Linneman

¹Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA. waudby.carol@mcrf.mfldclin.edu.

BMC Ophthalmology

|November 15, 2011

Summary

Electronic health records (EHRs) efficiently identify cataracts for genetic research. This study confirmed known risk factors like age, gender, diabetes, and steroid use in a large biobank population.

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Published on: July 24, 2020

Area of Science:

Genomics and bioinformatics
Ophthalmology research
Epidemiology

Background:

The electronic MEdical Records and Genomics (eMERGE) network utilizes electronic health records (EHRs) and DNA biorepositories for genetic research.
Marshfield Clinic, an eMERGE site, focused on age-related cataract and HDL-cholesterol.
This study describes using EHRs to evaluate cataract epidemiology in a large biobank.

Purpose of the Study:

To describe the electronic evaluation of cataract epidemiology using EHR data.
To assess known epidemiologic risk factors for cataracts in cases identified via electronic algorithms.

Main Methods:

Electronic algorithms were developed to identify individuals with cataracts within the Personalized Medicine Research Project database.
Analysis included cataract prevalence, age at diagnosis, and established risk factors.

Main Results:

Electronic algorithms successfully identified cataract diagnoses and surgeries, but not cataract type.
Age-specific prevalence of cataract (22%) and cataract surgery (11%) were higher than reported by the Eye Diseases Prevalence Research Group.
Confirmed risk factors included age, gender, diabetes, and steroid use.

Conclusions:

Electronic health records provide a viable and efficient method for identifying cataracts in research populations.
Retrospective EHR data may present limitations due to historical data availability and healthcare utilization variations.