Framing the scope of the common data model for machine-actionable Data Management Plans

Abstract Introduction: The Haematology Outcomes Network in EURope (HONEUR) is an interdisciplinary initiative aimed at improving patient outcomes by analyzing real world data across hematological centers in Europe. Its overarching goal is to create a secure network which facilitates the development of a collaborative research community and allows access to big data tools for analysis of the data. The central paradigm in the HONEUR network is a federated model whereby the data stays at the respective sites and the analysis is executed at the local data sources. To allow for a uniform data analysis, the common data model 'OMOP' (Observational Medical Outcomes Partnership) was selected and extended to accommodate specific hematology data elements. Objective: To demonstrate feasibility of the OMOP common data model for the HONEUR network. Methods: In order to validate the architecture of the HONEUR network and the applicability of the OMOP common data model, data from the EMMOS registry (NCT01241396) have been used. This registry is a prospective, non-interventional study that was designed to capture real world data regarding treatments and outcomes for multiple myeloma at different stages of the disease. Data was collected between Oct 2010 and Nov 2014 on more than 2,400 patients across 266 sites in 22 countries. Data was mapped to the OMOP common data model version 5.3. Additional new concepts to the standard OMOP were provided to preserve the semantic mapping quality and reduce the potential loss of granularity. Following the mapping process, a quality analysis was performed to assess the completeness and accuracy of the mapping to the common data model. Specific critical concepts in multiple myeloma needed to be represented in OMOP. This applies in particular for concepts like treatment lines, cytogenetic observations, disease progression, risk scales (in particular ISS and R-ISS). To accommodate these concepts, existing OMOP structures were used with the definition of new concepts and concept-relationships. Results: Several elements of mapping data from the EMMOS registry to the OMOP common data model (CDM) were evaluated via integrity checks. Core entities from the OMOP CDM were reconciled against the source data. This was applied for the following entities: person (profile of year of birth and gender), drug exposure (profile of number of drug exposures per drug, at ATC code level), conditions (profile of number of occurrences of conditions per condition code, converted to SNOMED), measurement (profile of number of measurements and value distribution per (lab) measurement, converted to LOINC) and observation (profile of number of observations per observation concept). Figure 1 shows the histogram of year of birth distribution between the EMMOS registry and the OMOP CDM. No discernible differences exist, except for subjects which have not been included in the mapping to the OMOP CDM due to lacking confirmation of a diagnosis of multiple myeloma. As additional part of the architecture validation, the occurrence of the top 20 medications in the EMMOS registry and the OMOP CDM were compared, with a 100% concordance for the drug codes, which is shown in Figure 2. In addition to the reconciliation against the different OMOP entities, a comparison was also made against 'derived' data, in particular 'time to event' analysis. Overall survival was plotted from calculated variables in the analysis level data from the EMMOS registry and derived variables in the OMOP CDM. Probability of overall survival over time was virtually identical with only one day difference in median survival and 95% confidence intervals identically overlapping over the period of measurement (Figure 3). Conclusions: The concordance of year of birth, drug code mapping and overall survival between the EMMOS registry and the OMOP common data model indicates the reliability of mapping potential in HONEUR, especially where auxiliary methods have been developed to handle outcomes and treatment data in a way that can be harmonized across platform datasets. Disclosures No relevant conflicts of interest to declare.

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Establishing the common oncology data model for the national biobank consortium of Taiwan.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e19283 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e19283-e19283

Author(s):

Yang Yi-Hsin ◽

Li-Tzong Chen ◽

Shiu-Feng Huang

Keyword(s):

National Health ◽

Real World ◽

Data Model ◽

Data Science ◽

Common Data Model ◽

Data Interoperability ◽

Cancer Data ◽

Common Definition ◽

The Common ◽

Data Elements

e19283 Background: Taiwan has 32 biobanks under Government’ governance. The Ministry of Health and Welfare have established a National Biobank Consortium of Taiwan to unify the specimen quality and the medical record database. The total recruited participants exceed 350,000. The National Health Research Institutes in Taiwan hold the responsibility of establish a common data model for aggregating data elements from electronic health records (EHRs) of institutes through direct feeds. The goals are to assemble a set of common oncology data elements and to facilitate cancer data interoperability for patient care and research across institutes of Biobank Consortium. Methods: We first conduct a thorough review of available EHR data elements for patient characteristics, diagnosis/staging, treatments, laboratory results, vital signs and outcomes. The data dictionary was organized based on HL7 FHIR and also included data elements from Taiwan Cancer Registry (TCR) and National Health Insurance (NHI) Program, which the common definition has already been established and implemented for years. Data elements suggested by ASCO CancerLinQ and minimal Common Oncology Data Elements (mCODE) are also referenced during planning. The final common model was then reviewed by a panel of experts consisting oncologists as well as data science specialists. Results: There are finally 9 data tables with 281 data elements, in which 248 of them are from the routinely uploaded data elements to government agencies (TCR & NHI) and 33 elements are collected with partial common definition among institutes. There are 164 data elements which are to be collected one observation per case, while 117 elements will be accumulated periodically. Conclusions: A comprehensive understanding of genetics, phenotypes, disease variation as well as treatment responses is crucial to fulfill the needs of real-world studies, which potentially would lead to personalized treatment and drug development. At the first stage of this project, we aim to accumulate available EHR structured data elements and to maintain sufficient cancer data quality. Consequently, the database can provide real-world evidence to promote evidence-based & data-driven cancer care.

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Measuring Ethnicity and Race Using the Common Data Model: Implications for Precision in Health Disparities Research

Health Services Research ◽

10.1111/1475-6773.13389 ◽

2020 ◽

Vol 55 (S1) ◽

pp. 45-46

Author(s):

S. Javier ◽

L. Zimmerman ◽

R. Kimerling

Keyword(s):

Health Disparities ◽

Data Model ◽

Common Data Model ◽

The Common ◽

Ethnicity And Race

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How closely is COVID-19 related to HCoV, SARS, and MERS? : Clinical comparison of coronavirus infections and identification of risk factors influencing the COVID-19 severity using common data model (CDM)

10.1101/2020.11.23.20237487 ◽

2020 ◽

Author(s):

Yeon Hee Kim ◽

YeHee Ko ◽

Soo Young Kim ◽

Kwangsoo Kim

Keyword(s):

Risk Factors ◽

Data Model ◽

Gastrointestinal Disease ◽

Statistical Significance ◽

World Health ◽

Categorical Variables ◽

Common Data Model ◽

Continuous Variables ◽

The Common ◽

Post Hoc

AbstractSouth Korea was one of the epicenters for both the 2015 Middle East Respiratory Syndrome (MERS) and 2019 COVID-19 outbreaks. However, there has been a lack of published literature, especially using the EMR records, that provides a comparative summary of the prognostic factors present in the coronavirus-derived diseases. Therefore, in this study, we aimed to compare and evaluate the distinct clinical traits between the infected patients of different coronaviruses, including the lesser pathogenic HCoV strains, SARS-CoV, MERS-CoV, and SARS-CoV-2. We aimed to observe the extent of resemblance within the clinical features between the different coronavirus disease groups and to identify unique factors by disease severity that may influence the prognosis of COVID-19 patients. Here, we utilize the common data model (CDM), which is the database that houses EMR records transformed into the common format to be used by multiple institutions. For the comparative analyses between the different disease groups as well as the mild and non-mild COVID-19 patients, we used independent t-test, Scheffe post-hoc test, and Games-howell post-hoc test for continuous variables, and chi-square test and Fisher’s exact test for categorical variables. From the analyses, we selected variables that showed p-values less than 0.05 to predict COVID-19 severity by a nominal logistic regression with adjustments to age and gender. The results showed diabetes, cardiovascular and cerebrovascular diseases, cancer, pulmonary disease, gastrointestinal disease, and renal disease in all patient groups. The proportions of cancer patients were the highest compared to other comorbidities in every comparative analysis, with no statistical significance. Additionally, we observed high degree of clinical similarity between COVID-19 and SARS patients within more than 50% of the selected clinical variables in the analyses, with no statistical significance between the two groups. Our research effectively utilized the integrated CDM to reflect real-world health challenges in the context of coronavirus. We expect the results from our study to provide clinical insights that can serve as predictor of risk factors for the future coronavirus-derived outbreak as well as the prospective guidelines for the clinical treatments.

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Implementing FAIR principles for dissemination of data from the French OZCAR Critical Observatory network: the Theia/OZCAR information system

10.5194/egusphere-egu2020-3708 ◽

2020 ◽

Author(s):

Isabelle Braud ◽

Véronique Chaffard ◽

Charly Coussot ◽

Sylvie Galle ◽

Rémi Cailletaud

Keyword(s):

Information System ◽

Data Management ◽

Data Model ◽

Distribution Systems ◽

Critical Zone ◽

Research Infrastructure ◽

Common Data Model ◽

In Situ Observation ◽

Data Discovery ◽

Fair Principles

<p>OZCAR-RI, the French Critical Zone Research Infrastructure gathers 20 observatories sampling various compartments of the Critical Zone, and having historically developed their own data management and distribution systems. However, these efforts have generally been conducted independently. This has led to a very heterogeneous situation, with different levels of development and maturity of the systems and a general lack of visibility of data from the entire OZCAR-RI community. To overcome this difficulty, a common Information System (Theia/OZCAR IS) was built to make these in situ observation FAIR (Findable, Accessible, Interoperable, Reusable). The IS will allow the data to be visible in the European eLTER-RI (European Long Term Ecosystem Research) Research Infrastructure to which OZCAR-RI contributes.</p><p>The IS architecture was designed after consultation of the users, data producers and IT teams involved in data management. A common data model including all the requested information and based on several metadata standards was defined to set up information fluxes between observatories IS and the Theia/OZCAR IS. Controlled vocabularies were defined to develop a data discovery web portal offering a faceted search with various criteria, including variables names and categories that were harmonized in a thesaurus published on the web. The communication will describe the IS architecture, the pivot data model and open source solutions used to implement the data portal that allows data discovery. The communication will also present future steps to implement data downloading and interoperability services that will allow a full implementation of these FAIR principles.</p>

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Application for Epidemiological Geographic Information System: An Open-Source Spatial Analysis Tool based on the Common Data Model (Preprint)

JMIR Public Health and Surveillance ◽

10.2196/15804 ◽

2019 ◽

Author(s):

Jaehyeong Cho ◽

Seng Chan You ◽

Seongwon Lee ◽

DongSu Park ◽

Bumhee Park ◽

...

Keyword(s):

Information System ◽

Geographic Information System ◽

Spatial Analysis ◽

Open Source ◽

Data Model ◽

Geographic Information ◽

Common Data Model ◽

Analysis Tool ◽

The Common

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Trajectories in glycated hemoglobin and body mass index in children and adolescents with diabetes using the common data model

Scientific Reports ◽

10.1038/s41598-021-94194-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yun Jeong Lee ◽

Sooyoung Yoo ◽

Soyoung Yi ◽

Seok Kim ◽

Chunggak Lee ◽

...

Keyword(s):

Body Mass Index ◽

Children And Adolescents ◽

Body Mass ◽

Data Model ◽

Glycated Hemoglobin ◽

Common Data Model ◽

Korean Children ◽

The Common ◽

Hba1c Target ◽

Hba1c Levels

AbstractWe evaluated trajectories of glycated hemoglobin (HbA1c) levels and body mass index z-scores (BMIz) for 5 years after diagnosis among Korean children and adolescents with type 1 diabetes (T1D) or type 2 diabetes (T2D) using the common data model. From the de-identified database of three hospitals, 889 patients < 15 years of age diagnosed with T1D or T2D (393 boys, 664 T1D patients) were enrolled. Diagnosis was defined as first exposure to antidiabetic drug at each center. Compared with T2D patients, T1D patients had lower BMIz at diagnosis (− 0.4 ± 1.2 vs. 1.5 ± 1.4, p < 0.001) and 3 months (− 0.1 ± 1.0 vs. 1.5 ± 1.5, p < 0.001), and higher HbA1c levels at diagnosis (10.0 ± 2.6% vs. 9.5 ± 2.7%, p < 0.01). After 3 months, HbA1c levels reached a nadir of 7.6% and 6.5% in T1D and T2D patients, respectively, followed by progressive increases; only 10.4% of T1D and 29.7% of T2D patients achieved the recommended HbA1c target (< 7.0%) at 60 months. T1D patients showed consistent increases in BMIz; T2D patients showed no significant change in BMIz during follow-up. Peri-pubertal girls with T1D had higher HbA1c and BMIz values. Achieving optimal glycemic control and preventing obesity should be emphasized in pediatric diabetes care.

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