How to customize Common Data Models for rare diseases: an OMOP-based implementation and lessons learned

doi:10.21203/rs.3.rs-3719430/v1

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Research Article

How to customize Common Data Models for rare diseases: an OMOP-based implementation and lessons learned

https://doi.org/10.21203/rs.3.rs-3719430/v1

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Journal Publication

published 14 Aug, 2024

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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 genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs.

Discussion

This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM.

Conclusion

The customized data structure related our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

Common Data Model

Rare Disease

Interoperability

Data Standardization

OMOP

OHDSI

Genotypes and Phenotypes

multi-center studies

Artificial intelligence.

Rare diseases (RD) can be seen as an intricate puzzle in medical research, compelling us to decipher their complex phenotypes and genetic origins to unlock the potential for personalized therapeutic interventions. In the US, a disease is considered rare, if it affects fewer than 200,000 people (1), while in Europe, it is 5 per 10,000 people (2). About 7,000 rare diseases are known to affect 3.5–5.9% of the world’s population (2, 3). About 80% of RD have a genetic basis (2–6), resulting from mutations in an individual's genome that can be inherited through parental chromosomes (6).

Timely and accurate identification of RD is oftentimes dependent on multiple factors, such as the amount and variety of individual patient information and comparable data, as well as knowledge about the specific RD itself (7). The large variety and infrequent, highly individual nature of RD patients that physicians encounter create a major challenge in diagnosis and treatment (8). Moreover, assembling a sufficiently large study cohort to investigate and characterize specific RD bears an additional challenge (9). Here, one key aspect is the availability of disease-specific data, including genotype and phenotype information that can be used for the in-depth characterization and accurate identification of RD. Since diagnosis of RD, especially those with a genetic background, based solely on non-genetic clinical features is often misleading, or inaccurate (10), a genetic underpinning of the diagnosis is inevitable to allow identification of a more precise molecular cause that may explain the clinical phenotype (5). However, due to genotype and phenotype variability, lack of knowledge about individual gene variations and their interplay, and an individual patient journey, the investigation of RD requires a more customized approach. This is also reflected by objectives defined by the Horizon 2020 Advisory Group as “a medical model using the characterization of individuals’ phenotypes and genotypes (e.g., molecular profiling, medical imaging, lifestyle data) for tailoring the right therapeutic strategy for the right person at the right time, and/or to determine the predisposition to disease and/or to deliver timely and targeted prevention.” (11).

To allow for such an improved and extended investigation of RD, multi-center studies are an inevitable asset to cover a reasonably large patient cohort. However, these also increase the computational complexity. Here, novel computational concepts that satisfy FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles (12, 13) are necessary to obtain more transparent and sustainable diagnostic processes at a large scale (13, 14). In addition, data from various study sites need to be in the same format, which can be attributed to the different layers of interoperability (15, 16). Consequently, the syntactic and semantic interoperability of the source data of the study sites has to be ensured. Syntactic interoperability refers to the harmonization and definition of data formats, as well as information models for the specification of information units and their interface whereas semantic interoperability focuses on the enablement of shared understanding of message content between systems and/or users (17).

During the last decade, efforts have been made to reach syntactic and semantic interoperability in the medical domain. Communications standards, such as Fast Healthcare Interoperability Resources (FHIR) (18), are often used to ease communication between different computational infrastructures. A recent example is the FHIR-based German Medical Informatics Initiative Core Dataset (MII-CDS), which is specifically designed for German university hospital patient data, with the aim of enhancing its usability in joint research endeavors (19). In addition, Common Data Elements (CDEs) for RD registration have been introduced (20) e.g., via the European Platform for Rare Disease Registration (ERDRI) (21), the French National Plan for Rare Diseases, (3, 22), and more recently by Abaza et al., (23), who describe the feasibility and applicability of domain-specific CDEs for RDs registries.

In the RD field however, data standardization still poses a major limitation (14), data harmonization concepts like Common Data Models (CDM), are being more widely employed to address the issue of data harmonization. A CDM can harmonize data from disparate sources, by utilizing communication (e.g., FHIR) and semantic (e.g., ICD-10, SNOMED) standards, enabling operations and analyses solely based on standard methods (24). One very promising approach is the Observational Medical Outcomes Partnership (OMOP) CDM from the Observational Health Data Sciences and Informatics (OHDSI) community, which comes with FAIR compliance, an international community, and ready-to-use tools for data integration and analysis (25–27). Compared to other CDMs like Informatics for Integrating Biology & the Bedside (i2b2), OMOP CDM offers broader terminology coverage, enabling data harmonization from different sources with minimal loss of data (25, 28, 29).

The adaptation of OMOP terminology for RD has already been shown to be essential for improved patient care (30). Importantly, OMOP CDM also supports genomic data representations (25, 31, 32), and works are underway to integrate Human Phenotype Ontologies (HPO) (33) and Orpha Codes (34), further enriching the terminology coverage of OMOP CDM and thus creating an increased interest in the domain of RD (30, 35, 36). In summary, all necessary tools and approaches are on the way for dedicated use and, if conducted correctly, a large-scale investigation of RD can be designed and conducted via OMOP CDM.

Our study is taking up these developments and demonstrates how customized CDMs and underlying data structures can be designed to facilitate and accelerate the understanding of RDs. In particular, we provide insights into current computational methods used for modulating standardized concepts, such as laboratory findings and medication, but also more specific data, such as genotypes to investigate different clinical phenotypes. We conducted this study as a practical continuation and application of the derived results made by our recently published scoping review on development methods of CDMs in Healthcare (37) in a larger project entitled SATURN (“Smart physician portal for patients with unclear disease”) (38) dealing with RDs in the fields of endocrinology, gastroenterology, and pneumonology. In addition, our evaluation use case domain of hematology (acute myeloid leukemia) data was transformed to the CDM for external validation of the model.

Contribution of this study

Our primary objective is to outline a customization process aimed at identifying and systematically modeling pertinent data from patients with rare diseases. The outcome customized CDMs, will be serving as a foundational source for transferring observational data into the OMOP CDM. This intermediate step is instrumental in establishing a shared knowledge base between medical experts and data scientists, ensuring simultaneous enhancement of semantic and syntactic interoperability.

Clinical use cases and their underlying data

Based on our available clinical resources and datasets, we have exemplarily chosen four RD domains, to begin with, namely endocrinology, gastroenterology, pneumonology for general CDM customization and development, and hematology as a proof-of-concept domain in particular for genotype information. The study characteristics including the inclusion criteria, and International Statistical Classification of Diseases and Related Health Problems, 10th revision, German Modification (ICD-10-GM) codes are shown in Table 1, respectively. As a study of this nature relies on the availability of data, which is typically scarce when it concerns RDs, our selected list of diseases encompasses a mixture of both rare and more common conditions. Additionally, the SATURN project does not only focus on RDs, but also unclear diseases. We have therefore decided to focus on a list of phenotypes that can be caused by rare and non-rare diseases. In endocrinology, this resulted in the symptom complex hyper- and hypothyroidism with various diseases, some of which are rare (e.g. TSH-oma, thyroid receptor resistance), while others are common (autoimmune thyroiditis).

Table 1

Study characteristics including study population, inclusion criteria, and number of patients in each one of our cohorts.
Study population	Inclusion criteria	Number of patients period 2015–2022 from two German university hospitals
Full or partial inpatient cases of adult patients	Patients are older than 18 years at the time of (visit) admission.	-
Cases of patients in the period from 2015–2022	The admission and discharge dates of the patients are in the period from 01/01/2015 to 12/31/2022.	-
Cases of patients of the selected endocrinological diseases.	Patients diagnosed with one of the following conditions: - Clinical hyperthyroidism (E05.-), incl.: o Latent hyperthyroidism o Manifest hyperthyroidism o Central hyperthyroidism o Graves’ disease o Iatrogenic (therapy-induced) hyperthyroidism o Autonomy of the thyroid gland o Amiodarone-induced hyperthyroidism o Pituitary thyroid receptor resistance o TSH-oma o Thyroiditis de Quervain (E06.1) - Hypothyroidism incl.: o Aplasia of the thyroid gland (E03.1) o Iatrogenic (therapy-related) drug-induced (E03.2) o Iatrogenic hypothyroidism/condition after surgery (E89.0) o Autoimmune thyroiditis (E06.3) o Secondary & tertiary hypothyroidism (E23.0) o Sarcoidosis (D86.-)	41,559
Cases of patients of the selected gastroenterological diseases.	Patients diagnosed with one of the following conditions: - Acute viral hepatitis A (B15.-) - Acute viral hepatitis B (B16.-) - Other acute viral hepatitis (B17.-) - Chronic viral hepatitis (B18.-) - Wilson’s disease (E83.0) - Hemochromatosis (E83.1) - Alpha-1 antitrypsin deficiency in adults (E88.0)	1,324
Cases of patients of the selected pneumonological diseases.	Patients diagnosed with one of the following conditions: - Bronchial carcinoma (C34.-) - Tuberculosis (A15.-) - Sarcoidosis (D86.-) - Influenza (J10.-)	17,141
Cases of patients of the selected hematological diseases.	Patients diagnosed with acute myeloid leukemia (C92.0)	1,673
Sum		61,697

Customized RD-CDM

Given the wide range of existing information models, legislative regulations for data exchange and terminology standards in the medical domain, it is crucial to know and leverage their features and combine them effectively, especially when dealing with RD. Therefore, we utilized semantic standards, such as ICD-10-GM, which is the German modified version of the World Health Organization’s ICD-10, to define the inclusion criteria in the study.

Moreover, in order to ensure the reproducibility of the anticipated CDM workflow of a novel study, it is essential to choose a widely adopted and commonly used data standard, which ensures semantic and syntactic interoperability. Therefore, we have utilized the FHIR format in its German University Hospital-centric MII-CDS as a central point for our data transfer (19). The MII-CDS basic modules include “Person”, “Case”, “Diagnosis”, “Procedure”, “Laboratory findings”, and “Medications” (19). The MII-CDS profiles are comparable to FHIR because they are built using FHIR profiles.

Finally, to facilitate an international usage of the data in large scale, multi-center studies, we have utilized OMOP, which in addition to standardized semantics and syntax on an international level, also motivates the usage of standard vocabularies (e.g., SNOMED CT).

CDM development: Our recently published scoping review (37), describes an analysis of 1309 articles spanning the last 20 years (2000–2022) on CDM development for the health domain and an extensive metadata extraction of 59 articles. The extraction step was specifically focused on stakeholder involvement, the methods employed for their engagement, and the detailed description of the development process of the respective CDMs. As a result, we have delineated the process of customizing a CDM for RD into three distinct phases: conception, collection of users' needs, and implementation, as illustrated in Fig. 1. We followed these three phases in our current study to obtain a list of elements for our focused RD domains and structured all elements into a customizable CDM.

Customization need: Building on the insights from our scoping review (37), we recognized the importance of involving stakeholders early on in our study. Thus, we conducted consensus meetings and teleconferences with an interdisciplinary team of researchers, medical experts, and Data Scientists to develop and evaluate the customized CDM. Throughout the process, we ensured that at least one clinician for each domain was involved in the upcoming stages i) – iv).

Conception: To accommodate the unique goals of individual projects, creating a project-specific CDM is crucial for supporting diverse clinical investigations. Importantly, the development of such a customized CDM opens the possibility for its utilization across multiple projects, emphasizing efficiency and fostering a shared knowledge base through collaborative efforts of medical experts and data scientists in identifying and systematically modeling relevant data. Thus, a list of diseases with similar phenotypes and clinical procedures, which could cause confusion while diagnosing, was compiled by medical experts. We aimed to develop a customized CDM to serve as source data for the OMOP CDM. Ensuring accurate transfer, the adapted CDMs closely align with the data and information model of the OMOP CDM.

Users’ needs collection: Based on medical literature, clinical guidelines, and medical domain expertise, we created a list of required clinical phenotypic and genotypic elements, laboratory findings, medications, and procedures for diagnosis and therapy of each disease. The expert knowledge was formalized by the medical data scientist in the form of pre-defined list of elements modules using an entity-relationship model. An entity-relationship model defines an interrelated set of elements and the relationship that can exist among them (39).

Implementation: This stage involves formalizing the data model, aligning it with OMOP, and integrating data through ETL. The predefined modules from individual use cases are transformed into MII-CDS modules. We assessed the feasibility of mapping formalized information into both FHIR and OMOP CDM. The MII-CDS serves as the foundation for the FHIR-to-OMOP (40) ETL process, eliminating the need for manual mapping, as the existing ETL process seamlessly transfers data from FHIR to OMOP, ensuring semantic and syntactic mapping. However, manual mapping was required for genotype and phenotype elements, as they are not covered in the FHIR-to-OMOP ETL. An additional ETL process (Genotype/Phenotype-to-OMOP) was developed for their manual mapping to unique concept IDs in OMOP. This ETL was tested using our proof-of-concept domain hematology data to our OMOP based RD-CDM.

Evaluation: The modules were then assessed by the medical experts in an iterative process equally involving medical experts and data scientists. The experts’ comments and input were integrated into the modules by the data scientist until a final consensus model was reached and agreed upon by all stakeholders. Once the modules were finalized, a second independent medical expert reviewed and evaluated the completeness and correctness of the included elements.

CDM data integration for development domains and the validation domain

To ensure syntactic and semantic interoperability, we decided to transfer the developed CDM to OMOP to allow for streamlined analysis of the data based on already available tools suitable for Machine Learning (e.g., PLP, HADES) (41, 42) and conducting larger multiple site studies. Our utilized FHIR-based MII CDS basic modules have already been mapped to OMOP CDM in recently published works (FHIR-to-OMOP) (40). However, the phenotypic and genotypic entities are not part of the MII CDS basic modules. Therefore, we directly mapped those elements to OMOP CDM concept ids. Thus, we developed the Genotype/Phenotype-to-OMOP ETL (43) processes using Pentaho Data Integration (44) to transfer elements not included in MII CDS basic modules to OMOP CDM. MII CDS basic modules to OMOP ETL should be executed before the Genotype/Phenotype-to-OMOP ETL processes to guarantee referential integrity between the patient ID and visit IDs. As HPO is not currently offered as an OMOP-compliant vocabulary, the required HPO terms are loaded into the SOURCE_TO_CONCEPT_MAP as individual source codes by the ETL process. The three ETL processes are used together to transfer the information for endocrinology, gastroenterology and pneumonology to the customized CDM from two university hospitals (Dresden and Frankfurt) and hematology dataset from the university hospital Dresden to OMOP CDM. The process is also highlighted in Fig. 2.

Derived blue-print and lessons learned

Based on the experience gained from the development and implementation steps along with various stakeholders and interdisciplinary experts involved in this study, we have derived a list of important steps to ultimately enable customization of our CDM beyond the shown RD. In addition, we also added important lessons learned that we experienced during the entire process.

Use case-specific data models

We have included nine groups of diseases from the endocrinology domain, two groups from gastroenterology, four from pneumonology, and one from the hematology domain. The list of diseases and their respective ICD-10-GM and Orpha codes (45) are shown in Table 2. In total 61,697 patients were included in this study.

Table 2

List of diseases in three development domains (endocrinology, pneumonology, and gastroenterology) of rare diseases and the validation domain (hematology).
Domain	Disease Groups	Diseases	ICD-10-GM	ORPHAcode
Endocrinology	Hyperthyroidism	Clinical hyperthyroidism	E05.9	ORPHA:181399
		Latent hyperthyroidism	E05.9
		Manifest hyperthyroidism	E05.9
		Graves' disease	E05.0	ORPHA:525731
		Iatrogenic (therapy induced)	E05.8	ORPHA:576379
		Unifocal iatrogenic (therapy-induced)	E05.8
		Autonomy of the thyroid gland: multifocal autonomy of the thyroid gland	E05.8	ORPHA:3143
		Autonomy of the thyroid gland: disseminated autonomy of the thyroid gland	E05.8
		Thyroiditis de Quervain	E06.1
		Amiodarone-induced hyperthyroidism type I	E05.8
		Amiodarone-induced hyperthyroidism type II	E05.8
		Pituitary thyroid receptor resistance.	E05.8
		TSH-oma	D35.2	ORPHA:424
	Hypothyroidism	Clinical Hypothyroidism	E03.-
		Aplasia of the thyroid gland	E03.1
		Iatrogenic (therapy-related) drug-induced	E03.2
		Iatrogenic hypothyroidism/condition after surgery	E89.0
		Autoimmune thyroiditis	E06.3
		Secondary & tertiary hypothyroidism	E23.0
		Sarcoidosis	D86.-
Gastroenterology	Viral Hepatitis	Hepatitis A	B15.0
		Acute hepatitis B	B16.0
		Acute hepatitis C	B17.1
		Acute Hepatitis E	B17.2
	Hereditary liver diseases	Hemochromatosis	E83.1
		Wilson's disease	E83.0
		Alpha-1 antitrypsin deficiency in adults	E88.0
Pneumonology	Solid malignancy	Solid malignancy (Bronchial carcinoma)	C34.-
	Tuberculosis	Tuberculosis	A15.-
	Sarcoidosis	Sarcoidosis	D86.-
	Viral infections	Influenza	J10.0
Hematology	Cancer	Acute myeloid leukemia (AML)	C92.0	ORPHA:319465

Entity relationship model to aggregate a list of relevant elements

In Fig. 3 (A), an entity-relationship model is presented, which illustrates an aggregated list of elements from the diseases that were used to create the customizable RD-CDM structure. A list of attributes and their formalization on the CDM modules are demonstrated in Fig. 3 (B). The number on each bar is indicating the number of elements for each category in our domain specific data models. The full list of these elements can be viewed in our CDM model as Additional file 1 and on our GitHub page (43).

Joint symptoms within domains

Table 3 shows the overlapping symptoms and their HPO codes within the diseases in each domain. Our hematology domain contains only one disease (AML) and only one symptom (fever) is included among the data elements, hence it is not part of this table.

Table 3

*overlapping symptoms within the domain. In the* Diseases *column, all diseases that include the corresponding symptoms are listed.*
Group	Symptoms	HPO Code	Diseases
Gastroenterology	Icterus or Jaundice	HP:0000952	Viral hepatitis (A, B, C, D, E), alpha-1 antitrypsin deficiency, De Quervain's thyroiditis, amiodarone-induced hyperthyroiditis
	Skin rash	HP:0000988	Viral hepatitis (A, B, D and E)
	Ascites	HP:0001541	Viral hepatitis B, C, hemochromatosis, Wilson’s disease, and alpha-1 antitrypsin
	Gastrointestinal bleeding	HP:0002239
	Fever	HP:0001945	Viral hepatitis, A, B, C, D and E
	Vomiting	HP:0002013	Viral hepatitis A, C, and E
	Diarrhea	HP:0002014
	Nausea	HP:0002018
	Hepatic encephalopathy	HP:0002480	Viral hepatitis B, C, hemochromatosis, Wilson’s disease, and alpha-1 antitrypsin deficiency in adults
	Arthralgia	HP:0002829	Viral hepatitis B, C, D, and hemochromatosis
	Myalgia	HP:0003326	Viral hepatitis B, C, D,
	Poor appetite	HP:0004396	Viral hepatitis B, D
	Fatigue	HP:0012378	Viral hepatitis B, D, Wilson’s disease, and alpha-1 antitrypsin deficiency
	General (health) Condition Reduction	HP:0033666	Viral hepatitis (A, B, C, D and E)
	Upper abdominal pain	HP:0410019	Viral hepatitis (A, B, C, D and E)
Pneumonology	Dyspnea	HP:0002094	Solid malignancy, sarcoidosis general, sarcoidosis acute form
	Chest pain	HP:0100749
	Weight loss	HP:0001824	Solid malignancy, tuberculosis, and sarcoidosis both form acute and chronic
	Fever	HP:0001945	Solid malignancy, tuberculosis, sarcoidosis both forms acute and chronic, influenza
	Fatigue	HP:0012378
	Cough	HP:0012735	Solid malignancy, tuberculosis, sarcoidosis general, sarcoidosis acute form, viral infection e.g., influenza, sarcoidosis chronic form
	Leukocytosis	HP:0001974	Tuberculosis, sarcoidosis general, viral infection e.g. influenza
	Headache	HP:0002315	Solid malignancy, influenza
	Poor appetite	HP:0004396	Tuberculosis, solid malignancy, sarcoidosis acute form
	Abnormal blood sodium concentration	HP:0010931	solid malignancy
	Night sweats	HP:0030166	Solid malignancy, tuberculosis
Endocrinology: Hyperthyroidism	Palpitations	HP:0001962	Clinical hyperthyroidism, latent hyperthyroidism, manifest hyperthyroidism, Graves' disease, iatrogenic (therapy-related), unifocal autonomy of the thyroid gland, multifocal autonomy, disseminated autonomy, De Quervain's thyroiditis, amiodarone-induced hypothyroidism type 1 and 2, Pituitary thyroid receptor resistance, TSH-oma
	Anxiety	HP:0000739
	Hyperhidrosis	HP:0000975
	Hand tremor	HP:0002378
	Restlessness	HP:0000711
	Irritability	HP:0000737
	Diarrhea	HP:0002014
	Weight loss	HP:0001824
	Abnormal eating behavior	HP:0100738
	Heat intolerance	HP:0002046
	Sleep disturbance	HP:0002360
Endocrinology: Hypothyroidism	Abnormality of the menstrual cycle	HP:0000140	Hypothyroidism in general, secondary hypothyroidism: originating from the anterior pituitary lobe, tertiary hypothyroidism: originating from the hypothalamus
	Depression	HP:0000716
	Gynecomastia	HP:0000771
	Dry skin	HP:0000958
	Hyporeflexia	HP:0001265
	Bradycardia	HP:0001662
	Pericardial effusion	HP:0001698
	Constipation	HP:0002019
	Hypotension	HP:0002615
	Increased body weight	HP:0004324
	Hyperlipoproteinemia	HP:0010980
	Fatigue	HP:0012378
	Diminished ability to concentrate	HP:0031987
	Coldness	HP:0033850
	Pretibial myxedema	HP:0200028	Hypothyroidism in general, secondary hypothyroidism: originating from the anterior pituitary lobe

There are also symptoms that are overlapping between two or more domains. Fever is the only symptom that is part of the elements in all four of our domains including hematology. Additionally, diarrhea, fatigue, and myalgia are common symptoms overlapping between the endocrinology, gastroenterology, and pneumonology domains. Additionally, the overlapping symptoms between domains in pairs are also shown in Fig. 4.

Mappings and ETL processes to streamline RD-CDM applicability

Since we are using FHIR MII-CDS as our intermediate step, we mapped our data entities to the modules of MII-CDS. Apart from the phenotypes and genotypes, we could map the rest of our entities into modules of MII-CDS. Since an ETL and FHIR-to-OMOP mapping are already available, we did not need to do a manual mapping for those entities.

For phenotypic and genotypic entities, we performed a manual mapping of our model entities to OMOP concept IDs using the terminology search engine, Athena (46). As shown in Table 4 in OMOP the demographic information can be mapped using Gender Concepts. The diagnosis can be mapped using ICD-10-GM and SNOMED concepts. For procedures, Operation and procedure cod (OPS) (47) codes and SNOMED concepts are available. The laboratory findings are mapped using Logical Observation Identifiers Names and Codes (LOINC) (48) concepts and the unit codes are documented using Unified Code for Units of Measure (UCUM). The medication can be integrated using RxNorm (49) and Anatomical Therapeutic Chemical (ATC) (50) concepts. Most of the genotype elements can be mapped using OMOP Genomic terminology released from OHDSI (31).

Table 4

Mappings between the RD-CDM, FHIR modules, and OMOP tables as well as the terminology used for semantic mapping.
RD-CDM	FHIR (MII-CDS)	OMOP tables	Vocabulary
Person	Person	Person	SNOMED; Gender
Diagnosis	Diagnosis	condition_occurrence, procedure_occurrence, measurement, or observation	SNOMED; ICD-10-GM; OrphaCode
Procedure	Procedure	drug_exposure, procedure_occurrence, measurement or observation	SNOMED; OPS
Laboratory Findings	Laboratory test results	Observation, measurement or procedure_occurrence	LOINC; UCUM
Medication	Medication	drug_exposure or observation	RxNorm; ATC
Genotype	-	Observation; measurement	OMOP Genomic
Phenotypes	-	Observation; measurement	HPO; SNOMED; LOINC
Hematology as a validation use case

The Genotype-to-OMOP ETL process was tested to write the hematology dataset, including 1,674 patients and 124 clinical and gene mutation data features, and is available for download and modification from GitHub (43). we utilized mostly, OMOP Genomics terminology to map our gene mutation elements to OMOP concept ids. additionally, SNOMED, LOINC, the Diagnosis-related group (DRG) (51), and UCUM were used for this dataset. We were able to map and integrate our data entities with 100% success from a CSV file to our previously developed OMOP instance. However, in some cases, we had to map multiple gene mutation information to one concept id in OMOP. An example is the “35948202 - CEBPA (CCAAT enhancer binding protein alpha) gene variant measurement, that we used to transform, “CEBPA.bZIP”, “CEBPA.bZIP.inframe”, “CEBPA.TAD” and “CEBPASTAT” information of the patients to OMOP. The full mapping table is also available on GitHub (43).

Customize your RD-CDM based study in six brief steps

Here, we provide a brief description to demonstrate how the RD-CDM can be reused on other data and medical domains for RD.

1. Definition of the use case(s).

● Definition of modules that are needed or are of interest to investigate the research hypothesis at hand.

● Identification of all relevant elements within the modules that contribute to answering the research question.

2. Definition of the stakeholders’ circle based on the use case(s).

● After the definition of the required modules and elements, they should be communicated and iterated with other medical experts to check for their availability at each and every participating study site.

● It is essential to take into account the perspective of the data providers. In the described study, it becomes crucial for each participating site to reach out to the respective data integration center. This step is necessary to verify the feasibility of automatically retrieving the requested data, potentially in standard formats such as FHIR or with associated terminologies like SNOMED.

● Given the sensitive nature of medical data, it is imperative to adhere to ethical standards, prioritize data security, and safeguard privacy. In light of these considerations, the responsible organizational entity must initiate contact with legal authorities to ensure a comprehensive and legally sound approach to managing and addressing the data's sensitivity.

3. A list of diagnostic entities may be created together with the stakeholders for the targeted use case(s).

● Once an iteration between all stakeholders and the initial set of modules and elements has been done, one may continue with the full or restricted list of data elements for the study.

4. The use of use case-specific entities may be mapped to the modules of RD-CDM.

● Individual data elements from the specific data source should be directed towards the unified CDM structure (common target data structure, jointly used by all involved study sites).

5. ETL processes, such as our FHIR-to-OMOP ETL process, should be used for transferring data to OMOP in the CDM dedicated “Person”, “Diagnosis”, “Laboratory findings”, “Procedure”, and “Medications” modules to unify the data elements among all participating study sites.

6. The “Genotype” and “Phenotype” modules should use direct ETL processes from CSV to OMOP CDM to transfer the data into OMOP.

● An example of such an individual ETL process can be obtained from our GitHub instance (Genotype/Phenotype-to-OMOP) (43).

Despite efforts to standardize infrastructure and integrate large-scale data in clinical research, challenges persist in personalized medicine (52, 53), especially in the documentation of genotype, phenotype, and clinical data for rare diseases (14, 52). Comprehensive documentation can aid patient recruitment, standard care monitoring, natural history assessment, genotype-phenotype correlation analyses, and disease burden evaluation, ultimately enhancing our understanding of rare diseases (30). However, the planning, design, maintenance, and sustainability of such large-scale medical studies need to be improved and streamlined (14).

In this study, we illuminated the impactful role of customized CDMs, particularly in the context of forming a collaborative foundation between medical experts and data scientists within domain-specific projects. By modeling and utilizing these tailored CDMs, we achieved two critical objectives: (a) establishing a shared knowledge base that enhances communication and understanding between medical experts and data scientists, and (b) streamlining the harmonization of source data. This harmonization, in turn, facilitates a seamless transfer to the internationally recognized research database, OMOP CDM, underscoring the versatility and effectiveness of our approach in advancing multi-center data-driven studies.

In particular, we developed a customized RD-CDM based on the OMOP information model and utilizing the FHIR communication standard. The combination of both enabled us to efficiently utilize the already existing ETL processes (40) for the mappings to OMOP. The final CDM consists of several modules, including “Person”, “Diagnosis”, “Procedure”, “Laboratory findings”, and “Medications”. These modules are also part of the FHIR MII CDS. Moreover, two additional modules, “Genotype” and “Phenotypes”, were added to the customizable CDM structure to better capture the unique characteristics of rare diseases.

Use case-specific application examples

Some diseases from disease groups, such as hyperthyroidism and its distinct etiologies can present with very close similarities and thus be challenging to differentiate solely based on clinical data. Our customizable RD-CDM can improve this, which will be further evaluated during the SATURN project, which seeks to assist the diagnostic process. Additionally, the RD-CDM-based data will be used in analytical studies to answer clinical questions. Here, the inclusion of the Genotype and Phenotypes modules into the CDM structure allows for the capture of more detailed and specific information related to rare diseases that may not be available in other standard CDMs.

The CDM has additional advantages applicable to all rare disease groups. These encompass the formulation of specific clinical questions for international and multi-center studies, including the prospective integration of genotype data, particularly concerning the potential incorporation of Human Genome Variation Society (HGVS) nomenclature (54) with a focus on exploring genotype-phenotype correlations.

Moreover, the CDM incorporates information about certain procedures by using OPS. This enhances research by facilitating the establishment of connections between imaging results with genotype information. Use case-specific applications would include the matching of genotype with liver imaging data in hemochromatosis or with sonography and scintigraphy data in diseases that affect the thyroid gland.

Furthermore, the CDM could be helpful for the clustering of subgroups for certain rare diseases based on phenotype or genotype. Within the field of endocrinology, it allows for differentiation based on laboratory parameters, enabling the distinction between latent and manifest forms of hypothyroidism or autoimmune forms, depending on the presence of specific antibodies. Tuberculosis can affect various organs, leading to different phenotypic expressions. Using a suitable CDM, patients with diverse manifestations could be categorized based on their phenotypic profiles. In the context of hemochromatosis, it allows subgroup formation based on genetic factors. When genetic information is unknown or inconspicuous, the CDM can facilitate the creation of subgroups based on symptoms only.

These benefits also encompass the collection of patients with similar Variants of Uncertain Significance (VUS) and the comparative analysis of symptoms and phenotypes, facilitating their categorization into subgroups for research purposes or potential reclassification. Additionally, the potential exists for investigating the co-occurrence of specific rare diseases and assessing whether certain mutations may render individuals more vulnerable to other conditions, such as heightened infection risk for hepatitis.

In cases of incomplete penetrance of certain mutations, f. e. in hemochromatosis, the CDM is valuable for aggregating asymptomatic or minimally symptomatic patients based on their genotype for long-term risk assessment. It also supports the categorization of asymptomatic family members who have been subsequently examined as a distinct group.

Moreover, the flexibility of tailoring therapy studies according to the specific genotype for gene therapy is a valuable prospect. A coherent mapping of clinical information and underlying disease biology as genotype-phenotype maps may not only aid in identifying disease categories with different clinical presentations but also tailor personalized treatment approaches to patient biology. Especially in high-stakes environments such as AML treatment where fast and accurate diagnosis, as well as rapid treatment initiation according to molecular subtypes, is crucial (55), a better understanding of genotype-phenotype associations in multi-center data-driven studies holds the promise of improved treatment outcomes with targeted therapies while avoiding resistance and relapse.

The benefits of RD-CDM are therefore evident for all use cases. Overall, the CDM significantly enhances our ability to comprehensively study and understand the complexities of RDs regardless of the focus domain.

General benefits of using CDMs

Integration of heterogeneous data is a ubiquitous topic in modern medicine. This arising large variety of data has the potential to be used for deriving insights about the different aspects of care and lead to improvements in health care. Yet, challenges, such as identifying and accessing relevant data, the association between different data sources, and ensuring the data quality given the structural variations amongst data sources are posing a barrier (56, 57). That is why data is still sparse, especially more patient-specific data such as genotypes and phenotypes which are especially important for RDs. Therefore, the development of a comprehensive CDM tailored to the unique domains of rare diseases is of importance. Our RD-CDM, built on the foundation of the Observational Medical Outcomes Partnership (OMOP), serves as a framework for standardizing additional data components across multiple domains. It is suitable for usage in analytic processes involving machine learning and statistical models. In addition, because OMOP is well established as a research data model, our CDM facilitates collaboration with different research groups at different sites on an international level, effectively addressing the challenge of data scarcity, which is particularly critical in the field of rare diseases.

Limitations of our model

The RD-CDM model is a prototype model developed using the data elements of four domains of RDs; endocrinology, gastroenterology, and pneumonology and hematology. Therefore, the RD-CDM tables are limited to the focus domains. Although the RD-CDM modules cover most of the medical data, to be able to use it for other domains, a customization step might be necessary. Additionally, the genomic terminologies used in the RD-CDM are limited to the mutations and clinical elements that are part of data entities in our included cohorts. Although the genomic elements were mapped with a 100% success rate, we often faced two or more to one mappings (31). Moreover, we used HPO terminologies for mapping the symptoms to OMOP, but the HPO terminology is still not integrated into the Athena terminology management tool for OMOP. Our implemented ETL only provides a quick and temporary solution. Further work is necessary to integrate HPO into OMOP terminology and introduce specific concept IDs for them.

Lessons learned for the six individual customization steps

Bridging the gap between clinical experts and technical implementation is important for the design of such a model, which is why we consider the inclusion of experts from both domains and interdisciplinary collaboration as essential. Regular communication with the stakeholders helps to keep everyone aligned and informed about the progress and possible feedback. An iterative design process is essential to incorporate evolving requirements and insights.

A clear definition of the use case(s) must be provided.

This is of particular significance for interdisciplinary use cases, in which multiple domains are included (e.g., clinical, computational, organizational).

An interdisciplinary team of stakeholders should be defined based on the use case(s) as early as possible.

A list of diagnostic entities should be created together with the stakeholders for the targeted use case(s). A large group of medical experts is necessary for the definition and evaluation of data elements to ensure that the list of included elements in the final model is comprehensive. A consensus method for final models should be defined beforehand to objectively quantify the time period.

The use of use case-specific entities should be mapped to the modules of customizable RD-CDM.

For the Person, Diagnosis, Laboratory Findings, Procedure, and Medication, the FHIR-to-OMOP ETL process should be used to transfer the data into OMOP. Testing of the ETL processes using smaller synthetic data that has the same attributes as the real-world data is recommended to become accustomed to the logs and outcomes of the ETL process, especially if the real-world data is not directly available. For Genotypes, the direct ETL processes from CSV to OMOP CDM should be used to transfer the data into OMOP. The standard genomic vocabulary in OMOP has been used to map the mutations to OMOP. By writing the study-specific HPO concepts to the SOURCE_TO_CONCEPT_MAP table, a temporary solution has been provided for the integration of phenotype information into OMOP.

A robust ETL process is essential for the accurate transfer of data into the OMOP framework. This requires careful planning, thorough testing, and validation to process multiple data sources and maintain data integrity. Familiarity with the data and ETL tools is also key to effective implementation and problem solving.

Outlook

This RD-CDM is the basis for the development of a decision support system, namely the SATURN platform, to be used at the point of care by the family practitioners. The platform will be equipped with different rule-based, case-based reasoning, and machine learning algorithms that aim to combine the available medical knowledge and clinical guidelines in the field with retrospective patient outcomes to support the diagnosis process of upcoming patients. General practitioners are often challenged with patients with symptoms that they do not have experience with. Therefore, this platform could support them in reaching a diagnosis in a shorter time.

A forward-looking usage for the platform could be integrating patient engagement features, such as a mobile app for tracking symptoms and facilitating communication with healthcare providers. This enhancement has the potential to empower patients significantly and enhance the management of their conditions. These additions could substantially boost the platform's effectiveness and reinforce its patient-centric orientation.

Impact to RD

The complexity attached to RDs is due to their heterogeneity and geographical dispersion limiting available knowledge (2). Patients are scattered in different countries and continents, and comprehensive data assembly is complicated not only by organizational, logistical, and communicative reasons but also by a lack of data collection standards and common frameworks. We provide a framework to easily integrate genetic information in large scale, multi-center studies, which in turn could reduce the amount of time spent on the characterization of phenotypes.

The customized RD-CDM based on OMOP can facilitate collaborations and investigations on an international level and in the long run improve patients’ quality of life through a faster diagnostic process.

We refined our initially developed process for customized RD-CDM based on a prominent common data model in healthcare, OMOP CDM, using the data exchange standard FHIR (18). The OMOP-based customized RD-CDM can enhance the harmonization of patient data in a standardized format that ensures international syntactic and semantic interoperability. That, in turn, enables - in addition to use the RD-CDM for decision support systems - retrospective studies including patient-level predictions possible using the tools offered by the OHDSI community and any other AI-based methods. Additionally, it allows attendance in international studies to deepen the findings by performing longitudinal studies on multi-center datasets.

RDs Rare Diseases

CDM Common Data Model

RD-CDM Rare Diseases Common Data Model

OMOP Observational Medical Outcomes Partnership

OHDSI Observational Health Data Sciences and Informatics

ETL Extract, Transform, Load

Ethics approval and consent to participate

The SATURN Project received approval from the Ethics Committee of the Department of Medicine at Goethe University, located at University Hospital, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, under reference number 2022-1088, and from the Ethics Committee of Technische Universität Dresden, situated at Fetscherstraße 74, 01307 Dresden, under reference number SR-EK-56012023.

Consent for publication

Not applicable

Availability of data and materials

The ETL processes used to transfer data in this study including their mappings are available on our GitHub page: https://github.com/NajiaAhmadi/ETL-Genotype-Phenotype-to-OMOP.

Competing interests

The authors declare that they have no competing interests

Funding

This work is accomplished as part of the SATURN project funded by the Federal Ministry of Health (BMG), FKZ: ZMI1-2520DAT02C. The funding source had no involvement in conducting the research and preparing the article.

The Article Processing Charge (APC) was funded by the joint publication funds of the TU Dresden, including Carl Gustav Carus Faculty of Medicine, the SLUB Dresden, and the Open Access Publication Funding of the DFG.

Authors’ Contributions

NA and MZ contributed to conceptualization, design, and methodology.

OG, CF, AM, KS Initial medical use case definition.

KS, KM, HPE, JT, DT, AS, JNE, and JMM medical use case validation.

NA, EH, AN, JD, and RG Technical implementation and data integration.

JS, MB, CR, and MS, Project acquisition and resources.

MW, Supervision.

NA and MW contributed to the writing-original draft preparation.

NA, MW, MZ, OG, CF, AM, KS, KM, HPE, JT, RG, LF, DT, AS, JNE, and MS contributed to writing reviews and editing.

NA and MW contributed to the visualization.

All authors take responsibility for the scientific integrity of the work.

All authors have read the final version of the manuscript and agreed to the decision to publish.

Acknowledgments

Not applicable

Authors' information (optional)

Not applicable

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How to customize Common Data Models for rare diseases: an OMOP-based implementation and lessons learned

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