Extraction of echocardiographic data from the electronic medical record is a rapid and efficient method for study of cardiac structure and function

Journal of Clinical Bioinformatics

Dana C Crawford¹  Eric Farber-Eger²  Quinn S Wells³ 

[1] Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA;Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA;Vanderbilt University Medical Center, 2525 West End Avenue, Suite 300, Nashville TN 37203, USA

关键词: Natural language processing;    Echocardiography;    Electronic health records;   

Others  :  1133396
DOI  :  10.1186/2043-9113-4-12

 received in 2014-06-23, accepted in 2014-09-11,  发布年份 2014

【 摘 要 】

Background

Measures of cardiac structure and function are important human phenotypes that are associated with a range of clinical outcomes. Studying these traits in large populations can be time consuming and costly. Utilizing data from large electronic medical records (EMRs) is one possible solution to this problem. We describe the extraction and filtering of quantitative transthoracic echocardiographic data from the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study, a large, racially diverse, EMR-based cohort (n = 15,863).

Results

There were 6,076 echocardiography reports for 2,834 unique adult subjects. Missing data were uncommon with over 90% of data points present. Data irregularities are primarily related to inconsistent use of measurement units and transcriptional errors. The reported filtering method requires manual review of very few data points (<1%), and filtered echocardiographic parameters are similar to published data from epidemiologic populations of similar ethnicity. Moreover, the cohort is comparable in size, and in some cases larger than community-based cohorts of similar race/ethnicity.

Conclusions

These results demonstrate that echocardiographic data can be efficiently extracted from EMRs, and suggest that EMR-based cohorts have the potential to make major contributions toward the study of epidemiologic and genotype-phenotype associations for cardiac structure and function in diverse populations.

【 授权许可】

   
2014 Wells et al.; licensee BioMed Central Ltd.

【 图 表 】

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