| Title: |
How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned |
| Authors: |
Ahmadi, Najia; Zoch, Michele; Guengoeze, Oya; Facchinello, Carlo; Mondorf, Antonia; Stratmann, Katharina; Musleh, Khader; Erasmus, Hans-Peter; Tchertov, Jana; Gebler, Richard; Schaaf, Jannik; Frischen, Lena S.; Nasirian, Azadeh; Dai, Jiabin; Henke, Elisa; Tremblay, Douglas; Srisuwananukorn, Andrew; Bornhäuser, Martin; Röllig, Christoph; Eckardt, Jan-Niklas; Middeke, Jan Moritz; Wolfien, Markus; Sedlmayr, Martin |
| Source: |
http://lobid.org/resources/99370675069506441#!, 19(1):298. |
| Publication Year: |
2024 |
| Collection: |
Publisso (ZB MED-Publikationsportal Lebenswissenschaften) |
| Subject Terms: |
Common data model; Genotypes and phenotypes; Data standardization; Multi-center studies; Rare disease; Research; Humans [MeSH]; Interoperability; Artificial intelligence; OHDSI; OMOP; Rare Diseases [MeSH] |
| Description: |
Abstract Background Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. Methods In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. Results We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://repository.publisso.de/resource/frl:6494892; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11325822/ |
| DOI: |
10.1186/s13023-024-03312-9 |
| Availability: |
https://repository.publisso.de/resource/frl:6494892; https://doi.org/10.1186/s13023-024-03312-9; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11325822/ |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.86C9B1A |
| Database: |
BASE |