用PCORnet公共数据模型实现癌症登记数据:大平原协作经验。

IF 3.3 Q2 ONCOLOGY
JCO Clinical Cancer Informatics Pub Date : 2024-12-01 Epub Date: 2024-12-17 DOI:10.1200/CCI-24-00196
Bradley D McDowell, Michael A O'Rorke, Mary C Schroeder, Elizabeth A Chrischilles, Christine M Spinka, Lemuel R Waitman, Kelechi Anuforo, Alejandro Araya, Haddyjatou Bah, Jackson Barlocker, Sravani Chandaka, Lindsay G Cowell, Carol R Geary, Snehil Gupta, Benjamin D Horne, Boyd M Knosp, Albert M Lai, Vasanthi Mandhadi, Abu Saleh Mohammad Mosa, Phillip Reeder, Giyung Ryu, Brian Shukwit, Claire Smith, Alexander J Stoddard, Mahanazuddin Syed, Shorabuddin Syed, Bradley W Taylor, Jeffrey J VanWormer
{"title":"用PCORnet公共数据模型实现癌症登记数据:大平原协作经验。","authors":"Bradley D McDowell, Michael A O'Rorke, Mary C Schroeder, Elizabeth A Chrischilles, Christine M Spinka, Lemuel R Waitman, Kelechi Anuforo, Alejandro Araya, Haddyjatou Bah, Jackson Barlocker, Sravani Chandaka, Lindsay G Cowell, Carol R Geary, Snehil Gupta, Benjamin D Horne, Boyd M Knosp, Albert M Lai, Vasanthi Mandhadi, Abu Saleh Mohammad Mosa, Phillip Reeder, Giyung Ryu, Brian Shukwit, Claire Smith, Alexander J Stoddard, Mahanazuddin Syed, Shorabuddin Syed, Bradley W Taylor, Jeffrey J VanWormer","doi":"10.1200/CCI-24-00196","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Electronic health records (EHRs) comprise a rich source of real-world data for cancer studies, but they often lack critical structured data elements such as diagnosis date and disease stage. Fortunately, such concepts are available from hospital cancer registries. We describe experiences from integrating cancer registry data with EHR and billing data in an interoperable data model across a multisite clinical research network.</p><p><strong>Methods: </strong>After sites implemented cancer registry data into a tumor table compatible with the PCORnet Common Data Model (CDM), distributed queries were performed to assess quality issues. After remediation of quality issues, another query produced descriptive frequencies of cancer types and demographic characteristics. This included linked BMI. We also report two current use cases of the new resource.</p><p><strong>Results: </strong>Eleven sites implemented the tumor table, yielding a resource with data for 572,902 tumors. Institutional and technical barriers were surmounted to accomplish this. Variations in racial and ethnic distributions across the sites were observed; the percent of tumors among Black patients ranged from <1% to 15% across sites, and the percent of tumors among Hispanic patients ranged from 1% to 46% across sites. Current use cases include a pragmatic prospective cohort study of a rare cancer and a retrospective cohort study leveraging body size and chemotherapy dosing.</p><p><strong>Conclusion: </strong>Integrating cancer registry data with the PCORnet CDM across multiple institutions creates a powerful resource for cancer studies. It provides a wider array of structured, cancer-relevant concepts, and it allows investigators to examine variability in those concepts across many treatment environments. Having the CDM tumor table in place enhances the impact of the network's effectiveness for real-world cancer research.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"8 ","pages":"e2400196"},"PeriodicalIF":3.3000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11658786/pdf/","citationCount":"0","resultStr":"{\"title\":\"Implementing Cancer Registry Data With the PCORnet Common Data Model: The Greater Plains Collaborative Experience.\",\"authors\":\"Bradley D McDowell, Michael A O'Rorke, Mary C Schroeder, Elizabeth A Chrischilles, Christine M Spinka, Lemuel R Waitman, Kelechi Anuforo, Alejandro Araya, Haddyjatou Bah, Jackson Barlocker, Sravani Chandaka, Lindsay G Cowell, Carol R Geary, Snehil Gupta, Benjamin D Horne, Boyd M Knosp, Albert M Lai, Vasanthi Mandhadi, Abu Saleh Mohammad Mosa, Phillip Reeder, Giyung Ryu, Brian Shukwit, Claire Smith, Alexander J Stoddard, Mahanazuddin Syed, Shorabuddin Syed, Bradley W Taylor, Jeffrey J VanWormer\",\"doi\":\"10.1200/CCI-24-00196\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Electronic health records (EHRs) comprise a rich source of real-world data for cancer studies, but they often lack critical structured data elements such as diagnosis date and disease stage. Fortunately, such concepts are available from hospital cancer registries. We describe experiences from integrating cancer registry data with EHR and billing data in an interoperable data model across a multisite clinical research network.</p><p><strong>Methods: </strong>After sites implemented cancer registry data into a tumor table compatible with the PCORnet Common Data Model (CDM), distributed queries were performed to assess quality issues. After remediation of quality issues, another query produced descriptive frequencies of cancer types and demographic characteristics. This included linked BMI. We also report two current use cases of the new resource.</p><p><strong>Results: </strong>Eleven sites implemented the tumor table, yielding a resource with data for 572,902 tumors. Institutional and technical barriers were surmounted to accomplish this. Variations in racial and ethnic distributions across the sites were observed; the percent of tumors among Black patients ranged from <1% to 15% across sites, and the percent of tumors among Hispanic patients ranged from 1% to 46% across sites. Current use cases include a pragmatic prospective cohort study of a rare cancer and a retrospective cohort study leveraging body size and chemotherapy dosing.</p><p><strong>Conclusion: </strong>Integrating cancer registry data with the PCORnet CDM across multiple institutions creates a powerful resource for cancer studies. It provides a wider array of structured, cancer-relevant concepts, and it allows investigators to examine variability in those concepts across many treatment environments. Having the CDM tumor table in place enhances the impact of the network's effectiveness for real-world cancer research.</p>\",\"PeriodicalId\":51626,\"journal\":{\"name\":\"JCO Clinical Cancer Informatics\",\"volume\":\"8 \",\"pages\":\"e2400196\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11658786/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JCO Clinical Cancer Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1200/CCI-24-00196\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JCO Clinical Cancer Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1200/CCI-24-00196","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/17 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

目的:电子健康记录(EHRs)为癌症研究提供了丰富的真实数据来源,但它们往往缺乏关键的结构化数据元素,如诊断日期和疾病阶段。幸运的是,这些概念可以从医院癌症登记处获得。我们描述了在跨多站点临床研究网络的可互操作数据模型中整合癌症注册数据与电子病历和计费数据的经验。方法:在站点将癌症注册数据导入与PCORnet公共数据模型(CDM)兼容的肿瘤表后,执行分布式查询以评估质量问题。在修复了质量问题后,另一个查询产生了癌症类型和人口统计学特征的描述性频率。这包括关联BMI。我们还报告了新资源的两个当前用例。结果:11个站点实现了肿瘤表,产生了572,902个肿瘤的数据资源。为了实现这一目标,克服了体制和技术障碍。观察到各地点种族和民族分布的差异;结论:将癌症登记数据与跨多个机构的PCORnet CDM相结合,为癌症研究创造了强大的资源。它提供了更广泛的结构化的、与癌症相关的概念,它允许研究人员在许多治疗环境中检查这些概念的可变性。CDM肿瘤表的建立增强了网络对真实世界癌症研究的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementing Cancer Registry Data With the PCORnet Common Data Model: The Greater Plains Collaborative Experience.

Purpose: Electronic health records (EHRs) comprise a rich source of real-world data for cancer studies, but they often lack critical structured data elements such as diagnosis date and disease stage. Fortunately, such concepts are available from hospital cancer registries. We describe experiences from integrating cancer registry data with EHR and billing data in an interoperable data model across a multisite clinical research network.

Methods: After sites implemented cancer registry data into a tumor table compatible with the PCORnet Common Data Model (CDM), distributed queries were performed to assess quality issues. After remediation of quality issues, another query produced descriptive frequencies of cancer types and demographic characteristics. This included linked BMI. We also report two current use cases of the new resource.

Results: Eleven sites implemented the tumor table, yielding a resource with data for 572,902 tumors. Institutional and technical barriers were surmounted to accomplish this. Variations in racial and ethnic distributions across the sites were observed; the percent of tumors among Black patients ranged from <1% to 15% across sites, and the percent of tumors among Hispanic patients ranged from 1% to 46% across sites. Current use cases include a pragmatic prospective cohort study of a rare cancer and a retrospective cohort study leveraging body size and chemotherapy dosing.

Conclusion: Integrating cancer registry data with the PCORnet CDM across multiple institutions creates a powerful resource for cancer studies. It provides a wider array of structured, cancer-relevant concepts, and it allows investigators to examine variability in those concepts across many treatment environments. Having the CDM tumor table in place enhances the impact of the network's effectiveness for real-world cancer research.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.20
自引率
4.80%
发文量
190
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信