Abstract B16: Resources for research: Identification and validation of ovarian cancer cases from the Synthetic Derivative

Abstract

Abstract As a rare and highly lethal cancer, research on ovarian cancer is prone to sample size limitations. To build a research resource, we set out to identify women with ovarian cancer from a large database of de-identified electronic medical records (EMR) of subjects who received medical care from the Vanderbilt University Medical Center. Known as the Synthetic Derivative (SD), this database was initiated in 2006, and now includes more than 20 years of EMR for approximately 1.7 million subjects. We developed a computational algorithm to identify subjects whose EMR included a minimum of three occurrences of an International Classification of Disease (ICD) code of 183 (Malignant neoplasm of ovary and other uterine adnexa) or with local Tumor Registry data that included a primary cancer site of ovary (C569) or fallopian tube (C570). A total of 1,755 women were identified; review of EMR and data abstraction is currently underway. Automated data downloads were conducted for Tumor Registry data, demographics and vital status, CA125 levels and dates, and surgical types and dates. Of the 1,755 women, the majority were Caucasian (83.7%) and deceased (61.7%). Tumor Registry data was available for 1,500 women; epithelial ovarian or fallopian tube cancers were confirmed for 1,141 women. In agreement with national numbers, the 5 year survival rate of these women was only 42.7%. Stored DNA is currently available for 424 women; genotyping data from a variety of platforms is available for 132 women. After additional EMR review and data abstraction, we anticipate being able to verify approximately 1,415 cases of epithelial ovarian cancer. This approach can be utilized at other institutions in the Electronic Medical Records and Genomics (eMERGE) Network; our goal is to build a large resource of validated ovarian cancer cases to support both etiologic and prognostic investigations of this insidious disease. Citation Format: Alicia Beeghly-Fadiel, Whitney Lovett, Ryan J. Delahanty, Dineo Khabele, Wei Zhang. Resources for research: Identification and validation of ovarian cancer cases from the Synthetic Derivative. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr B16.