Development of Query Strategies to Identify a Histologic Lymphoma Subtype in a Large Linked Database System

• Type of Material: Article
• Authors: Michael Graiser, Emory University; Susan J. Moore, Emory University; Rochelle Victor, Emory University; Ashley Hilliard, Emory University; Leroy Hill, Emory University; Michael S. Keehan, NuTec Health Systems; Christopher R Flowers, Emory University
• Language: English
• Date: 2007
• Publisher: Libertas Academica
• Publication Version: Final Published Version
• Copyright Statement: © 2007 The authors.
• License: Creative Commons Attribution 3.0 Unported
• Final Published Version (URL): http://www.la-press.com/development-of-query-strategies-to-identify-a-histologic-lymphoma-subt-article-a236
• Title of Journal or Parent Work: Cancer Informatics
• ISSN: 1176-9351
• Volume: 3
• Start Page: 149
• End Page: 158
• Grant/Funding Information: Sources of funding used to assist in the preparation of this manuscript include the PhRMA Foundation Health Outcomes Research Starter Grant, the Winship Cancer Institute Faculty Development Award, and the Georgia Cancer Coalition Distinguished Clinician and Scientists Award.
• Keywords: Large linked database; cancer outcomes research; cancer registry; cancer epidemiology
• Research Categories: Health Sciences, Oncology; Health Sciences, Education; Health Sciences, General

Abstract

Background: Large linked databases (LLDB) represent a novel resource for cancer outcomes research. However, accurate means of identifying a patient population of interest within these LLDBs can be challenging. Our research group developed a fully integrated platform that provides a means of combining independent legacy databases into a single cancer-focused LLDB system. We compared the sensitivity and specifi city of several SQL-based query strategies for identifying a histologic lymphoma subtype in this LLDB to determine the most accurate legacy data source for identifying a specifi c cancer patient population. Methods: Query strategies were developed to identify patients with follicular lymphoma from a LLDB of cancer registry data, electronic medical records (EMR), laboratory, administrative, pharmacy, and other clinical data. Queries were performed using common diagnostic codes (ICD-9), cancer registry histology codes (ICD-O), and text searches of EMRs. We reviewed medical records and pathology reports to confirm each diagnosis and calculated the sensitivity and specificity for each query strategy. Results: Together the queries identified 1538 potential cases of follicular lymphoma. Review of pathology and other medical reports confirmed 415 cases of follicular lymphoma, 300 pathology-verifi ed and 115 verified from other medical reports. The query using ICD-O codes was highly specific (96%). Queries using text strings varied in sensitivity (range 7–92%) and specifi city (range 86–99%). Queries using ICD-9 codes were both less sensitive (34–44%) and specific (35–87%). Conclusions: Queries of linked-cancer databases that include cancer registry data should utilize ICD-O codes or employ structured free-text searches to identify patient populations with a precise histologic diagnosis.