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JCO Clin Cancer Inform. 2024 Dec;8:e2400196. doi: 10.1200/CCI-24-00196. Epub 2024 Dec 17.
2
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本文引用的文献

1
Making progress against rare cancers: A case study on neuroendocrine tumors.攻克罕见癌症:神经内分泌肿瘤案例研究。
Cancer. 2024 May 1;130(9):1568-1574. doi: 10.1002/cncr.35184. Epub 2024 Jan 20.
2
Supporting Structured Data Capture for Patients With Cancer: An Initiative of the University of Wisconsin Carbone Cancer Center Survivorship Program to Improve Capture of Malignant Diagnosis and Cancer Staging Data.支持癌症患者的结构化数据采集:威斯康星大学 Carbone 癌症中心生存项目的一项举措,旨在改进恶性诊断和癌症分期数据的采集。
JCO Clin Cancer Inform. 2022 Jun;6:e2200020. doi: 10.1200/CCI.22.00020.
3
Cancer Registry Data Linkage of Electronic Health Record Data From ASCO's CancerLinQ: Evaluation of Advantages, Limitations, and Lessons Learned.癌症登记数据与 ASCO 的 CancerLinQ 电子健康记录数据的链接:优势、局限性和经验教训的评估。
JCO Clin Cancer Inform. 2022 Mar;6:e2100149. doi: 10.1200/CCI.21.00149.
4
PCORnet® 2020: current state, accomplishments, and future directions.PCORnet® 2020:现状、成就和未来方向。
J Clin Epidemiol. 2021 Jan;129:60-67. doi: 10.1016/j.jclinepi.2020.09.036. Epub 2020 Sep 28.
5
Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®).评估国家以患者为中心的临床研究网络(PCORnet®)中的基础数据质量。
EGEMS (Wash DC). 2018 Apr 13;6(1):3. doi: 10.5334/egems.199.
6
The Greater Plains Collaborative: a PCORnet Clinical Research Data Network.大平原协作组织:PCORnet 临床研究数据网络。
J Am Med Inform Assoc. 2014 Jul-Aug;21(4):637-41. doi: 10.1136/amiajnl-2014-002756. Epub 2014 Apr 28.
7
Building a virtual cancer research organization.建立一个虚拟癌症研究组织。
J Natl Cancer Inst Monogr. 2005(35):12-25. doi: 10.1093/jncimonographs/lgi033.

使用PCORnet通用数据模型实施癌症登记数据:大平原协作经验。

Implementing Cancer Registry Data With the PCORnet Common Data Model: The Greater Plains Collaborative Experience.

作者信息

McDowell Bradley D, O'Rorke Michael A, Schroeder Mary C, Chrischilles Elizabeth A, Spinka Christine M, Waitman Lemuel R, Anuforo Kelechi, Araya Alejandro, Bah Haddyjatou, Barlocker Jackson, Chandaka Sravani, Cowell Lindsay G, Geary Carol R, Gupta Snehil, Horne Benjamin D, Knosp Boyd M, Lai Albert M, Mandhadi Vasanthi, Mohammad Mosa Abu Saleh, Reeder Phillip, Ryu Giyung, Shukwit Brian, Smith Claire, Stoddard Alexander J, Syed Mahanazuddin, Syed Shorabuddin, Taylor Bradley W, VanWormer Jeffrey J

机构信息

Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA.

Department of Epidemiology, University of Iowa, Iowa City, IA.

出版信息

JCO Clin Cancer Inform. 2024 Dec;8:e2400196. doi: 10.1200/CCI-24-00196. Epub 2024 Dec 17.

DOI:10.1200/CCI-24-00196
PMID:39689273
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11658786/
Abstract

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.

摘要

目的

电子健康记录(EHRs)是癌症研究中丰富的真实世界数据来源,但它们往往缺乏关键的结构化数据元素,如诊断日期和疾病分期。幸运的是,这些概念可从医院癌症登记处获得。我们描述了在一个多中心临床研究网络中,将癌症登记数据与EHR和计费数据整合到一个可互操作的数据模型中的经验。

方法

在各站点将癌症登记数据实施到与PCORnet通用数据模型(CDM)兼容的肿瘤表中后,进行分布式查询以评估质量问题。在解决质量问题后,另一个查询生成了癌症类型和人口统计学特征的描述性频率。这包括关联的体重指数(BMI)。我们还报告了新资源的两个当前用例。

结果

11个站点实施了肿瘤表,生成了一个包含572,902个肿瘤数据的资源。为实现这一目标克服了机构和技术障碍。观察到各站点之间种族和民族分布存在差异;黑人患者中的肿瘤百分比在各站点之间从<1%到15%不等,西班牙裔患者中的肿瘤百分比在各站点之间从1%到46%不等。当前的用例包括一项针对罕见癌症的务实前瞻性队列研究和一项利用体型和化疗剂量的回顾性队列研究。

结论

跨多个机构将癌症登记数据与PCORnet CDM整合,为癌症研究创造了一个强大的资源。它提供了更广泛的结构化、与癌症相关的概念,并且允许研究人员在许多治疗环境中检查这些概念的变异性。拥有CDM肿瘤表增强了该网络在真实世界癌症研究中的有效性影响。