Population risk factor estimates for abdominal aortic aneurysm from electronic medical records: a case control study

Using abdominal aortic aneurysm (AAA) as a model, this case-control study used electronic medical record (EMR) data to assess known risk factors and identify new associations. The study population consisted of cases with AAA (n =888) and controls (n =10,523) from the Geisinger Health System EMR in C...

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Bibliographic Details
Published in:BMC cardiovascular disorders 2014-12, Vol.14 (1), p.174-174, Article 174
Main Authors: Smelser, Diane T, Tromp, Gerard, Elmore, James R, Kuivaniemi, Helena, Franklin, David P, Kirchner, H Lester, Carey, David J
Format: Article
Language:English
Subjects:
Abdominal aneurysm
Age Factors
Aortic Aneurysm, Abdominal - etiology
Care and treatment
Case-Control Studies
Coronary Stenosis - complications
Electronic Health Records
Female
Hospitals
Humans
Hypertension - complications
Lung Diseases - complications
Male
Medical research
Mortality
Neoplasms - complications
Peripheral Arterial Disease - complications
Primary care
Respiratory tract diseases
Retrospective Studies
Risk Assessment - methods
Risk Factors
Sex Factors
Smoking - adverse effects
Studies
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Summary:Using abdominal aortic aneurysm (AAA) as a model, this case-control study used electronic medical record (EMR) data to assess known risk factors and identify new associations. The study population consisted of cases with AAA (n =888) and controls (n =10,523) from the Geisinger Health System EMR in Central and Northeastern Pennsylvania. We extracted all clinical and diagnostic data for these patients from January 2004 to December 2009 from the EMR. From this sample set, bootstrap replication procedures were used to randomly generate 2,500 iterations of data sets, each with 500 cases and 2000 controls. Estimates of risk factor effect sizes were obtained by stepwise logistic regression followed by bootstrap aggregation. Variables were ranked using the number of inclusions in iterations and P values. The benign neoplasm diagnosis was negatively associated with AAA, a novel finding. Similarly, type 2 diabetes, diastolic blood pressure, weight and myelogenous neoplasms were negatively associated with AAA. Peripheral artery disease, smoking, age, coronary stenosis, systolic blood pressure, age, height, male sex, pulmonary disease and hypertension were associated with an increased risk for AAA. This study utilized EMR data, retrospectively, for risk factor assessment of a complex disease. Known risk factors for AAA were replicated in magnitude and direction. A novel negative association of benign neoplasms was identified. EMRs allow researchers to rapidly and inexpensively use clinical data to expand cohort size and derive better risk estimates for AAA as well as other complex diseases.
ISSN:1471-2261
1471-2261
DOI:10.1186/1471-2261-14-174