Enhancing Uterine Fibroid Research Through Utilization of Biorepositories Linked to Electronic Medical Record Data

Abstract

Background: Uterine leiomyomata (fibroids) affect up to 77% of women by menopause and account for $9.4 billion in yearly healthcare costs. Most studies rely on self-reported diagnosis, which may result in misclassification of controls since as many as 50% of cases are asymptomatic and thus undiagnosed. Our objective was to evaluate the performance and accuracy of a fibroid phenotyping algorithm constructed from electronic medical record (EMR) data, limiting to subjects with pelvic imaging. Methods: Our study population includes women from a clinical population at Vanderbilt University Medical Center (2008)(2009)(2010)(2011)(2012). Analyses were restricted to women 18 years and older with at least one fibroid diagnosis confirmed by imaging for cases or at least two separate pelvic imaging procedures without a diagnosis for controls. We randomly reviewed 218 records to evaluate the accuracy of our algorithm and assess the indications for pelvic imaging. Participant characteristics and indications for imaging were compared between cases and controls in unadjusted and adjusted logistic regression analyses. Results: Our algorithm had a positive predictive value of 96% and negative predictive value of 98%. Increasing age (odds ratio = 1.05, 95% confidence interval 1.03-1.08) and Black race (odds ratio = 2.15, 95% confidence interval 1.18-3.94) were identified as risk factors for fibroids. The most common indications for imaging in both cases and controls were pain, bleeding, and reproductive factors, and the most common imaging modality was a pelvic ultrasound. Conclusions: These data suggest that using biorepositories linked to EMR data is a feasible way to identify populations of imaged women that facilitate investigations of fibroid risk factors.

Admixture mapping of uterine fibroid size and number in African American women

“…A validated phenotyping algorithm with a positive predictive value of 96% was used to identify fibroid cases (30). We included AA women who were at least 18 years old, who had at least one documented fibroid and one pelvic imaging or surgery to treat fibroids, as indicated by the international classification of diseases, ninth revision, (ICD-9) or current procedural terminology (CPT) codes.…”

Overcoming the Challenges of Studying Uterine Fibroids

“…The study by Feingold-Link et al 5 published in the December issue offers a new approach to solving this problem. They present an algorithm to identify cases with fibroids and controls without fibroids using diagnostic codes, clinical notes, and imaging findings.…”

“…5 In genetic studies of fibroid development, it may be necessary to have complete case ascertainment, which would occur at menopause, when development of fibroids ceases. FeingoldLink et al demonstrated high reliability of the algorithm in women over age 55.…”

“…Although Feingold-Link et al excluded controls that lacked a uterus for the testing of reliability, their use of natural language processing to review notes for a history of fibroids has potential to alleviate this problem in the future. 5 The challenges of studying fibroids are many, but the value in prevention and reduction in risk is high. Researchers continue to make great strides in refining our ability to study these tumors effectively.…”

“…Researchers continue to make great strides in refining our ability to study these tumors effectively. This welcome JOURNAL OF WOMEN'S HEALTH Volume 24, Number 2, 2015 ª Mary Ann Liebert, Inc. DOI: 10.1089/jwh.2015.1520 addition by Feingold-Link et al 5 to the methodology of utilizing population-based cohorts continues on this path.…”