Analysis of Retinal Thickness in Patients With Chronic Diseases Using Standardized Optical Coherence Tomography Data: Database Study Based on the Radiology Common Data Model.

IF 3.1 3区 医学 Q2 MEDICAL INFORMATICS
ChulHyoung Park, So Hee Lee, Da Yun Lee, Seoyoon Choi, Seng Chan You, Ja Young Jeon, Sang Jun Park, Rae Woong Park
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引用次数: 0

Abstract

Background: The Observational Medical Outcome Partners-Common Data Model (OMOP-CDM) is an international standard for harmonizing electronic medical record (EMR) data. However, since it does not standardize unstructured data, such as medical imaging, using this data in multi-institutional collaborative research becomes challenging. To overcome this limitation, extensions such as the Radiology Common Data Model (R-CDM) have emerged to include and standardize these data types.

Objective: This work aims to demonstrate that by standardizing optical coherence tomography (OCT) data into an R-CDM format, multi-institutional collaborative studies analyzing changes in retinal thickness in patients with long-standing chronic diseases can be performed efficiently.

Methods: We standardized OCT images collected from two tertiary hospitals for research purposes using the R-CDM. As a proof of concept, we conducted a comparative analysis of retinal thickness between patients who have chronic diseases and those who have not. Patients diagnosed or treated for retinal and choroidal diseases, which could affect retinal thickness, were excluded from the analysis. Using the existing OMOP-CDM at each institution, we extracted cohorts of patients with chronic diseases and control groups, performing large-scale 1:2 propensity score matching (PSM). Subsequently, we linked the OMOP-CDM and R-CDM to extract the OCT image data of these cohorts and analyzed central macular thickness (CMT) and retinal nerve fiber layer (RNFL) thickness using a linear mixed model.

Results: OCT data of 261,874 images from Ajou University Medical Center (AUMC) and 475,626 images from Seoul National University Bundang Hospital (SNUBH) were standardized in the R-CDM format. The R-CDM databases established at each institution were linked with the OMOP-CDM database. Following 1:2 PSM, the type 2 diabetes mellitus (T2DM) cohort included 957 patients, and the control cohort had 1603 patients. During the follow-up period, significant reductions in CMT were observed in the T2DM cohorts at AUMC (P=.04) and SNUBH (P=.007), without significant changes in RNFL thickness (AUMC: P=.56; SNUBH: P=.39). Notably, a significant reduction in CMT during the follow-up was observed only at AUMC in the hypertension cohort, compared to the control group (P=.04); no other significant differences in retinal thickness were found in the remaining analyses.

Conclusions: The significance of our study lies in demonstrating the efficiency of multi-institutional collaborative research that simultaneously uses clinical data and medical imaging data by leveraging the OMOP-CDM for standardizing EMR data and the R-CDM for standardizing medical imaging data.

使用标准化光学相干断层扫描数据分析慢性病患者视网膜厚度:基于放射学通用数据模型的数据库研究
背景:观察性医疗结果合作伙伴共同数据模型(OMOP-CDM)是协调电子病历(EMR)数据的国际标准。然而,由于它没有标准化非结构化数据,如医学成像,在多机构合作研究中使用这些数据变得具有挑战性。为了克服这一限制,出现了诸如放射学公共数据模型(R-CDM)之类的扩展,以包含和标准化这些数据类型。目的:本工作旨在证明通过将光学相干断层扫描(OCT)数据标准化为R-CDM格式,可以有效地进行多机构合作研究,分析长期慢性疾病患者视网膜厚度的变化。方法:我们使用R-CDM对两家三级医院收集的OCT图像进行标准化。作为概念的证明,我们对患有慢性疾病的患者和没有慢性疾病的患者的视网膜厚度进行了比较分析。诊断或治疗可能影响视网膜厚度的视网膜和脉络膜疾病的患者被排除在分析之外。利用各机构现有的OMOP-CDM,我们提取了慢性疾病患者和对照组的队列,进行大规模1:2倾向评分匹配(PSM)。随后,我们将OMOP-CDM和R-CDM连接起来,提取这些队列的OCT图像数据,并使用线性混合模型分析中央黄斑厚度(CMT)和视网膜神经纤维层(RNFL)厚度。结果:亚洲大学医学中心(AUMC) 261,874张OCT数据和首尔大学盆唐医院(SNUBH) 475,626张OCT数据采用R-CDM格式进行标准化。在每个机构建立的R-CDM数据库与OMOP-CDM数据库相连。采用1:2 PSM, 2型糖尿病(T2DM)队列957例,对照组1603例。在随访期间,T2DM组在AUMC (P=.04)和SNUBH (P=.007)观察到CMT显著降低,RNFL厚度无显著变化(AUMC: P=.56;SNUBH: P =点)。值得注意的是,与对照组相比,随访期间仅在高血压组的AUMC中观察到CMT的显著降低(P= 0.04);在其余的分析中没有发现视网膜厚度的其他显著差异。结论:本研究的意义在于,利用OMOP-CDM对EMR数据进行标准化,利用R-CDM对医学影像数据进行标准化,展示了同时使用临床数据和医学影像数据的多机构协同研究的效率。
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来源期刊
JMIR Medical Informatics
JMIR Medical Informatics Medicine-Health Informatics
CiteScore
7.90
自引率
3.10%
发文量
173
审稿时长
12 weeks
期刊介绍: JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
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