Development of Query Strategies to Identify a Histologic Lymphoma Subtype in a Large Linked Database System
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 combinin...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2007-01-01
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| Series: | Cancer Informatics |
| Subjects: | |
| Online Access: | http://la-press.com/article.php?article_id=236 |
| _version_ | 1818271668194246656 |
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| author | Christopher R. Flowers Leroy Hill Ashley Hilliard Susan G. Moore Rochelle Victor Michael Graiser Michael S. Keehan |
| author_facet | Christopher R. Flowers Leroy Hill Ashley Hilliard Susan G. Moore Rochelle Victor Michael Graiser Michael S. Keehan |
| author_sort | Christopher R. Flowers |
| collection | DOAJ |
| description | 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.Abbreviations: LLDB: Large Linked Database; SEER: Surveillance Epidemiology and End Results; EMR: Electronic Medical Record; ICD-9: International Classifi cation of Diseases (9th revision); ICD-O: International Classifi cation of Diseases for Oncology; AP: Anatomical Pathology; WHO: World Health Organization. |
| first_indexed | 2024-12-12T21:29:50Z |
| format | Article |
| id | doaj.art-b9648148f7774049b5b88c6e73fa6f25 |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-12-12T21:29:50Z |
| publishDate | 2007-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-b9648148f7774049b5b88c6e73fa6f252022-12-22T00:11:21ZengSAGE PublishingCancer Informatics1176-93512007-01-013149158Development of Query Strategies to Identify a Histologic Lymphoma Subtype in a Large Linked Database SystemChristopher R. FlowersLeroy HillAshley HilliardSusan G. MooreRochelle VictorMichael GraiserMichael S. KeehanBackground: 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.Abbreviations: LLDB: Large Linked Database; SEER: Surveillance Epidemiology and End Results; EMR: Electronic Medical Record; ICD-9: International Classifi cation of Diseases (9th revision); ICD-O: International Classifi cation of Diseases for Oncology; AP: Anatomical Pathology; WHO: World Health Organization.http://la-press.com/article.php?article_id=236Large linked databasecancer outcomes researchcancer epidemiologycancer registry |
| spellingShingle | Christopher R. Flowers Leroy Hill Ashley Hilliard Susan G. Moore Rochelle Victor Michael Graiser Michael S. Keehan Development of Query Strategies to Identify a Histologic Lymphoma Subtype in a Large Linked Database System Cancer Informatics Large linked database cancer outcomes research cancer epidemiology cancer registry |
| title | Development of Query Strategies to Identify a Histologic Lymphoma Subtype in a Large Linked Database System |
| title_full | Development of Query Strategies to Identify a Histologic Lymphoma Subtype in a Large Linked Database System |
| title_fullStr | Development of Query Strategies to Identify a Histologic Lymphoma Subtype in a Large Linked Database System |
| title_full_unstemmed | Development of Query Strategies to Identify a Histologic Lymphoma Subtype in a Large Linked Database System |
| title_short | Development of Query Strategies to Identify a Histologic Lymphoma Subtype in a Large Linked Database System |
| title_sort | development of query strategies to identify a histologic lymphoma subtype in a large linked database system |
| topic | Large linked database cancer outcomes research cancer epidemiology cancer registry |
| url | http://la-press.com/article.php?article_id=236 |
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