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使用模块化软件在快速医疗保健互操作性资源上进行联合分析以弥合肿瘤学中的数据孤岛:多站点实施研究

Bridging Data Silos in Oncology with Modular Software for Federated Analysis on Fast Healthcare Interoperability Resources: Multisite Implementation Study.

作者信息

Ziegler Jasmin, Erpenbeck Marcel Pascal, Fuchs Timo, Saibold Anna, Volkmer Paul-Christian, Schmidt Guenter, Eicher Johanna, Pallaoro Peter, De Souza Falguera Renata, Aubele Fabio, Hagedorn Marlien, Vansovich Ekaterina, Raffler Johannes, Ringshandl Stephan, Kerscher Alexander, Maurer Julia Karolin, Kühnel Brigitte, Schenkirsch Gerhard, Kampf Marvin, Kapsner Lorenz A, Ghanbarian Hadieh, Spengler Helmut, Soto-Rey Iñaki, Albashiti Fady, Hellwig Dirk, Ertl Maximilian, Fette Georg, Kraska Detlef, Boeker Martin, Prokosch Hans-Ulrich, Gulden Christian

机构信息

Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany.

Bavarian Cancer Research Center (BZKF), Erlangen, Germany.

出版信息

J Med Internet Res. 2025 Apr 15;27:e65681. doi: 10.2196/65681.

DOI:10.2196/65681
PMID:40233352
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12041822/
Abstract

BACKGROUND

Real-world data (RWD) from sources like administrative claims, electronic health records, and cancer registries offer insights into patient populations beyond the tightly regulated environment of randomized controlled trials. To leverage this and to advance cancer research, 6 university hospitals in Bavaria have established a joint research IT infrastructure.

OBJECTIVE

This study aimed to outline the design, implementation, and deployment of a modular data transformation pipeline that transforms oncological RWD into a Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) format and then into a tabular format in preparation for a federated analysis (FA) across the 6 Bavarian Cancer Research Center university hospitals.

METHODS

To harness RWD effectively, we designed a pipeline to convert the oncological basic dataset (oBDS) into HL7 FHIR format and prepare it for FA. The pipeline handles diverse IT infrastructures and systems while maintaining privacy by keeping data decentralized for analysis. To assess the functionality and validity of our implementation, we defined a cohort to address two specific medical research questions. We evaluated our findings by comparing the results of the FA with reports from the Bavarian Cancer Registry and the original data from local tumor documentation systems.

RESULTS

We conducted an FA of 17,885 cancer cases from 2021/2022. Breast cancer was the most common diagnosis at 3 sites, prostate cancer ranked in the top 2 at 4 sites, and malignant melanoma was notably prevalent. Gender-specific trends showed larynx and esophagus cancers were more common in males, while breast and thyroid cancers were more frequent in females. Discrepancies between the Bavarian Cancer Registry and our data, such as higher rates of malignant melanoma (3400/63,771, 5.3% vs 1921/17,885, 10.7%) and lower representation of colorectal cancers (8100/63,771, 12.7% vs 1187/17,885, 6.6%) likely result from differences in the time periods analyzed (2019 vs 2021/2022) and the scope of data sources used. The Bavarian Cancer Registry reports approximately 3 times more cancer cases than the 6 university hospitals alone.

CONCLUSIONS

The modular pipeline successfully transformed oncological RWD across 6 hospitals, and the federated approach preserved privacy while enabling comprehensive analysis. Future work will add support for recent oBDS versions, automate data quality checks, and integrate additional clinical data. Our findings highlight the potential of federated health data networks and lay the groundwork for future research that can leverage high-quality RWD, aiming to contribute valuable knowledge to the field of cancer research.

摘要

背景

来自行政索赔、电子健康记录和癌症登记处等来源的真实世界数据(RWD),能让我们深入了解随机对照试验严格监管环境之外的患者群体。为了利用这些数据并推动癌症研究,巴伐利亚州的6所大学医院建立了一个联合研究信息技术基础设施。

目的

本研究旨在概述一个模块化数据转换管道的设计、实施和部署,该管道将肿瘤学真实世界数据转换为健康级别7(HL7)快速医疗互操作性资源(FHIR)格式,然后转换为表格格式,为巴伐利亚州6所癌症研究中心大学医院之间的联合分析(FA)做准备。

方法

为了有效利用真实世界数据,我们设计了一个管道,将肿瘤学基本数据集(oBDS)转换为HL7 FHIR格式,并为联合分析做准备。该管道可处理各种信息技术基础设施和系统,同时通过分散数据进行分析来维护隐私。为了评估我们实施的功能和有效性,我们定义了一个队列来解决两个特定的医学研究问题。我们通过将联合分析的结果与巴伐利亚癌症登记处的报告以及当地肿瘤记录系统的原始数据进行比较,来评估我们的发现。

结果

我们对2021/2022年的17885例癌症病例进行了联合分析。乳腺癌是3个地点最常见的诊断,前列腺癌在4个地点排名前2,恶性黑色素瘤明显普遍。按性别划分的趋势显示,喉癌和食管癌在男性中更常见,而乳腺癌和甲状腺癌在女性中更常见。巴伐利亚癌症登记处与我们的数据之间存在差异,例如恶性黑色素瘤的发病率较高(3400/63771,5.3% 对1921/17885,10.7%),结直肠癌的占比较低(8100/63771,12.7% 对1187/17885,6.6%),这可能是由于分析时间段(2019年对2021/2022年)和所用数据源范围的不同造成的。巴伐利亚癌症登记处报告的癌症病例数大约是这6所大学医院单独报告病例数的3倍。

结论

模块化管道成功地转换了6所医院的肿瘤学真实世界数据,联合方法在实现全面分析的同时保护了隐私。未来的工作将增加对最新oBDS版本的支持,自动进行数据质量检查,并整合更多临床数据。我们的发现凸显了联合健康数据网络的潜力,为未来利用高质量真实世界数据的研究奠定了基础,旨在为癌症研究领域贡献有价值的知识。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9d0/12041822/41c75fc521fd/jmir_v27i1e65681_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9d0/12041822/b44f5218d342/jmir_v27i1e65681_fig1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9d0/12041822/0994c99ea48b/jmir_v27i1e65681_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9d0/12041822/41c75fc521fd/jmir_v27i1e65681_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9d0/12041822/b44f5218d342/jmir_v27i1e65681_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9d0/12041822/9285cd032169/jmir_v27i1e65681_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9d0/12041822/5b83c583370e/jmir_v27i1e65681_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9d0/12041822/960e082858ee/jmir_v27i1e65681_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9d0/12041822/0994c99ea48b/jmir_v27i1e65681_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9d0/12041822/41c75fc521fd/jmir_v27i1e65681_fig6.jpg

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Bridging Real-World Data Gaps: Connecting Dots Across 10 Asian Countries.弥合现实世界的数据差距:连接10个亚洲国家的数据点。
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When Will Real-World Data Fulfill Its Promise to Provide Timely Insights in Oncology?
真实世界数据何时才能兑现其在肿瘤学领域提供及时洞察的承诺?
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[The collaborative project "Personalized medicine for oncology" (PM4Onco) as part of the Medical Informatics Initiative (MII)].作为医学信息学计划(MII)一部分的“肿瘤个性化医疗”(PM4Onco)合作项目。
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