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 (2008-2012). 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.

Fig. 1.

Summary of study population case inclusion and exclusion criteria. Women aged 18 and older were included in the analysis. A history of pelvic imaging was queried by investigation of Current Procedural Terminology (CPT) codes corresponding to ultrasound, computed tomography (CT), or magnetic resonance imaging (MRI) of the pelvis. Those without evidence of pelvic imaging studies were excluded. In records documenting at least one imaging event, those that also carried a fibroid diagnosis were classified as cases. A fibroid diagnosis was defined by International Classification of Diseases (ICD) codes indicating the presence of fibroids or ICD and CPT codes indicating a history of fibroid treatment procedures. Those that did not carry a fibroid diagnosis were included as controls only if a second imaging event on a separate day was also documented. Finally, we determined that since it is unreasonable to assess the fibroid status of women without uteri, we excluded those controls that had a history of hysterectomy. This was accomplished through both the exclusion charts containing certain ICD codes and also with a free text search of records.