建立标准:在OMOP公共数据模型中协调最小癌症数据集的编码原则

A. Ajmal , O. Bouissou , J. Brash , S. Cheeseman , P.G. Banduge , A.L. Gomes , L. Revie , E. Ross , S. Theophanous , J. Thonnard , A. Van Maanen , A. Vengadeswaran , A. Wolf , X.M. Fernandez
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引用次数: 0

摘要

由于数据收集、存储和可用性的异质性,跨网络分析临床信息带来了挑战。观察性医疗结果伙伴关系(OMOP)公共数据模型(CDM)为临床数据提供了一个标准化框架,允许进行网络级比较和数据组合,以增强分析能力并增加研究的稳健性。为了在欧洲数字肿瘤网络(DigiONE)中使用OMOP获取特定的肿瘤信息,我们开发了癌症的最小基本描述(MEDOC)框架。材料和方法smedoc经过多次迭代开发,然后在DigiONE试点研究中使用。这是一个社区驱动的过程,通过讨论来区分医院数据的差异,并通过开展深入的会议来解决使源数据与MEDOC结构一致的具体问题,使之成为可能。迄今为止,MEDOC的初始版本已在两项DigiONE观察性癌症研究中使用,另外两项研究正在进行中,并开发了包括实施指南在内的培训资源。我们在MEDOC与OMOP比对的发展过程中吸取的经验教训包括,由于可用数据的粒度,在确定诊断日期、确认转移位置和肿瘤分类代码方面存在挑战,以及个别中心特有的挑战。结论MEDOC的效用已经在研究应用中得到了证明,并将随着中心的学习和肿瘤领域的发展而不断发展。鉴于欧洲协调卫生保健数据系统的举措,按照OMOP清洁发展机制实施MEDOC是及时的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Establishing standards: harmonising coding principles for a minimal cancer dataset in the OMOP Common Data Model

Background

Analysing clinical information across a network poses challenges due to heterogeneity of data collection, storage and availability. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a standardised framework for clinical data, allowing network-level comparisons and the combination of data to enhance analytical power and increase research robustness. To capture specific oncology information across the Digital Oncology Network for Europe (DigiONE) using OMOP, we developed the Minimal Essential Description of Cancer (MEDOC) framework.

Materials and methods

MEDOC was developed through several iterations and was then utilised in DigiONE pilot studies. This was a community-driven process, made possible by discussions to distinguish differences in hospital data and by conducting deep-dive sessions to solve specific issues in aligning source data with the MEDOC structure.

Results

The initial version of MEDOC has been utilised in two DigiONE observational cancer studies to date with a further two studies in progress, and training resources including the implementation guide have been developed. Lessons learned in the development of our MEDOC to OMOP alignment include challenges in establishing diagnosis date, confirming metastasis location and tumour classification code due to granularity of available data, among other challenges specific to individual centres.

Conclusion

The utility of MEDOC has been evidenced in research applications and will be continually developed in line with both learnings from centres and developments in the field of oncology. The implementation of MEDOC in line with the OMOP CDM is timely, given European initiatives to harmonise health care data systems.
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