Observational Medical Outcomes Partnership Common Data Model Research Articles

Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.

CHARACTERIZING THE JOHNS HOPKINS INFLAMMATORY BOWEL DISEASE COHORT USING OMOP CDM

Abstract BACKGROUND Inflammatory Bowel Disease (IBD) is an increasingly prevalent condition that results in weight loss, bloody diarrhea, and poor quality of life. Traditional population-based research on patients with IBD has involved use of relatively shallow claims data that is deficient in precise demographics. IBD research using richly detailed electronic health record (EHR) data has been difficult due to the need for laborious manual chart review to extract information, but new methods and technology allow for rapid EHR data extraction. We present here the results of the extraction of IBD patient data from our EHR into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). METHODS This study is an observational cohort study using patient health care data from our EHR extracted into the OMOP CDM. Transforming data into the standard CDM format allows for information from different databases and coding schema to be compared and aggregated. We used a de-identified dataset of all patients seen at Johns Hopkins Medicine since 2016. Our cohort of IBD, Crohn’s disease (CD), and Ulcerative Colitis (UC) patients were stratified by age, sex, race, weight, and pregnancy status. Within the cohorts, we obtained demographics, comorbidities, symptoms, labs, medications, and other data. We validated the extraction by comparison with EHR queries using Slicer/Dicer (SD). RESULTS There were 11,357 IBD patients including 6,512 CD patients and 4,224 UC patients. There were more female patients for both CD and UC (56.4% v 57.5%) and more white patients for both CD and UC (55.4% v 56.7%) compared to black (23.5% v 22.6%) and Asian patients (5.6% v 4.9%). CD and UC patients were treated with systemic steroids (49.1% v 51.5%), 5-ASA (24.9% v 47.2%) anti-TNF inhibitors (24.0% v 12%), IL12/23 inhibitors (8.4% v 2.7%), and JAK inhibitors (0.3% v 1.8%). Using SD queries, the percent differences were determined between the OMOP and EHR data, revealing a 0.54% difference in the number of CD patients and 10.36% difference in the number of UC patients (Table 1). CONCLUSION Using OMOP CDM to analyze clinical characteristics and outcomes for patients with IBD is a valuable method for obtaining de-identified patient data, reducing patient risk, and facilitating rapid creation of on-demand cohorts for multi-institutional research studies. This advantage is balanced by the lack of access to row-level data to validate cohorts. Local validation of data extraction through SD queries can allow for rapid creation of cohorts for big data research. The most significant differences between SD queries and OMOP CDM was in the calculation of prevalence of medications and race, suggesting that differing data extraction coding methods could result in queries that underestimate rates of treatment due to proprietary local coding schema.

Integrating real-world data to assess cardiac ablation device outcomes in a multicenter study using the OMOP common data model for regulatory decisions: implementation and evaluation.

The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient outcomes and covariates from electronic health record data. Several data quality assurance (QA) analyses were implemented on the OMOP CDM to validate the data extraction, transformation, and load (ETL) process. OMOP CDM-based data of relevant patient encounters were successfully established to support studies for FDA regulatory submissions. QA analyses verified that the data transformation was robust between data sources and OMOP CDM. Our efforts provided useful insights in real-world data integration using OMOP CDM for medical device evaluation coordinated across multiple healthcare systems.

Building the observational medical outcomes partnership's T-MSIS Analytic File common data model

ObjectivesThis effort used Databricks to create an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) for Transformed MSIS Analytic File (TAF) Medicaid records. Materials and methodsOur process included data volume and content assessment of TAF, translation mapping of TAF concepts to OMOP concepts and the creation of Extract Transform and Load (ETL) code. ResultsThe final CDM contained 119, 048, 562 individuals and 24, 806, 828,121 clinical observations from 2014 through 2018. DiscussionThe transformation of TAF into OMOP can support the generation of evidence with special attention to low-income patients on public insurance. Such patients are perhaps underrepresented in academic medical center patient populations. ConclusionOur effort successfully used Databricks to transform TAF records into OMOP CDM. Our CDM can be used to generate evidence for OMOP network studies.

Conversion of CPRD AURUM data into the OMOP common data model

IntroductionEfforts to standardize clinical data using Common Data Models (CDMS) has grown in recent years. Use of CDMs allows for quicker understanding of data structure and reuse of existing tools. One CDM is the Observational Medical Outcomes Partnership (OMOP) CDM. Clinical Practice Research Datalink (CPRD) is a data collection program collecting general practitioner data in the UK. ObjectiveOur objective was to convert a static copy of CPRD AURUM data into the OMOP CDM and run existing tools on the converted data. MethodsTwo methods were used to convert each CPRD file into the OMOP CDM. The first was direct mapping used when converting CPRD files that had comparable tables in the OMOP CDM. The original names were changed to the OMOP equivalent and source values converted to standardized OMOP concepts. CPRD files: Patient (to OMOP Person), Staff (to Provider), Drug Issue (to Drug Exposure) and Practice (to Care Site) were directly mapped. The second method was indirect where for the CPRD Observation file the domain of each data row was used to assign data to proper OMOP tables or columns done by converting all source values to standard concepts. ResultsThe OMOP CDM conversion populated 12 tables and 20,240,453,339 rows, with the largest table being the Measurement table (5,202,579,174 data row). Mapping source values to OMOP standard concepts, we found 60.2% (46,413 of 77,149) of source concepts were also standard concepts. The Drug Exposure table had the fewest source values already in the standard form as only 4.7% (1433 of 30,194) of the source concepts were standard concepts. On a data retention level, only 2.00% of all data rows were excluded as they did not have a clear fit in the developed CDM and were not able to stand alone without additional information which was not present. ConclusionCPRD AURUM was successfully converted into the OMOP CDM with minimal data loss. Existing OHDSI tools were used with the converted data to show efficacy of the converted data. The existence of a standardized version of CPRD AURUM data vastly increases its reusability in future research due to increased understanding and tools available.

Association of periodontitis with menopause and hormone replacement therapy: a hospital cohort study using a common data model.

The present study was designed to compare the incidence of periodontitis according to menopausal status and to investigate the possible effect of hormone replacement therapy (HRT) on periodontitis in postmenopausal women using a common data model (CDM) at a single institution. This study involved retrospective cohort data of 950,751 patients from a 20-year database (2001 to 2020) of Ewha Womans University Mokdong Hospital converted to the Observational Medical Outcomes Partnership CDM. One-way analysis of variance models and the χ² test were used to analyze the statistical differences in patient characteristics among groups. A time-dependent Cox regression analysis was used to calculate hazard ratios and 95% confidence intervals, and P values less than 0.05 were considered to indicate statistical significance. Of the 29,729 patients, 1,307 patients were diagnosed with periodontitis and 28,422 patients were not. Periodontitis was significantly more common among postmenopausal patients regardless of HRT status than among the non-menopausal group (P<0.05). Time-dependent Cox regression analysis showed that the postmenopausal patients had a significantly higher chance of having periodontitis than non-menopausal patients (P<0.05), but after adjustment for age, body mass index, and smoking status, the difference between the non-menopausal and post-menopausal HRT-treated groups was insignificant (P=0.140). Postmenopausal women had a significantly greater risk of periodontitis than non-menopausal women. Additionally, the use of HRT in postmenopausal women could reduce the incidence of periodontitis.

Illustration of Continuous Enrollment and Beneficiary Categorization in DoD and VA Infrastructure for Clinical Intelligence.

The DoD and VA Infrastructure for Clinical Intelligence (DaVINCI) data-sharing initiative has bridged the gap between DoD and VA data. DaVINCI utilizes the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to map DoD and VA-specific health care codes to a standardized terminology. Although OMOP CDM provides a standardized longitudinal view of health care concepts, it fails in capturing multiple and changing relationships beneficiaries have with DoD and VA as it has a static (vs. yearly) person characteristic table. Furthermore, DoD and VA utilize different policies and terminology to identify their respective beneficiaries, which makes it difficult to track patients longitudinally. The primary purpose of this report is to provide a methodology for categorizing beneficiaries and creating continuous longitudinal patient records to maximize the use of the joint DoD and VA data in DaVINCI. For calendar year 2000-2020, we combined DoD and VA OMOP CDM and source databases to uniquely categorize beneficiaries into the following hierarchical groups: Active Duty, Guard, and Reserve Service Members (ADSMs); Separatees; Retirees; Veterans; and Deceased. Once the cohorts were identified, we examined calendar year 2020 health care utilization data using the OMOP VISIT_OCCURRENCE, DRUG_EXPOSURE, MEASUREMENT, and PROCEDURE tables. We also used the Defense Enrollment and Eligibility Reporting System source table to derive enrollment periods for DoD beneficiaries. As VA does not have enrollment plans, we utilized the VA's priority groups (1-5) in the SPATIENT source table to crosswalk the DoD's enrollment concept to the VA. We then assessed lengths of continuous enrollments in DoD and VA and the impact of appending the longitudinal records together. The majority of the ADSMs utilized the DoD system, but about 60,557 (3%) were seen in the VA for varied types of care. The market share of care provided to ADSMs by the VA varied by specialty and location. For Retirees, the split between DoD (1,625,874 [75%]) and VA (895,992 [41%]) health care utilization was more significant. The value added for utilizing DaVINCI in longitudinal studies was the highest for researchers normally limited to DoD data only. For beneficiaries who had 5 years of continuous enrollment, DaVINCI increased the potential study population by over 202% compared to using DoD data alone and by over 14% compared to VA data alone. Among beneficiaries with 20 years of continuous enrollment, DaVINCI increased the potential study population by over 133% compared to DoD data and by nearly 39% compared to VA data. DaVINCI has successfully combined DoD and VA data and utilized OMOP CDM to standardize health care concepts. However, to fully maximize the potential of DaVINCI's DoD and VA OMOP databases, researchers must uniquely categorize the DaVINCI cohort and build longitudinal patient records across DoD and VA. Because of the low other health insurance rates among DoD enrollees and their choice to enroll to a DoD Primary Care Manager, we believe this population to be the least censored in the DoD. Applying a similar concept through VA's priority groups (1-5) would enable researchers to follow ADSMs as they transition from the military.

The sociodemographic characteristics and clinical features of the late-life depression patients: results from the Beijing Anding Hospital mental health big data platform

BackgroundThe sociodemographic characteristics and clinical features of the Late-life depression (LLD) patients in psychiatric hospitals have not been thoroughly studied in China. This study aimed to explore the psychiatric outpatient attendance of LLD patients at a psychiatric hospital in China, with a subgroup analysis, such as with or without anxiety, gender differences.MethodsThis retrospective study examined outpatients with LLD from January 2013 to August 2019 using data in the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in Beijing Anding Hospital. Age, sex, number of visits, use of drugs and comorbid conditions were extracted from medical records.ResultsIn a sample of 47,334 unipolar depression patients, 31,854 (67.30%) were women, and 15,480 (32.70%) were men. The main comorbidities of LDD are generalized anxiety disorder (GAD) (83.62%) and insomnia (74.52%).Among patients with unipolar depression, of which benzodiazepines accounted for the largest proportion (77.77%), Selective serotonin reuptake inhibitors (SSRIs) accounted for 59.00%, a noradrenergic and specific serotonergic antidepressant (NaSSAs) accounted for 36.20%. The average cost of each visit was approximately 646.27 yuan, and the cost of each visit was primarily attributed to Western medicine (22.97%) and Chinese herbal medicine (19.38%). For the cost of outpatient visits, depression comorbid anxiety group had a higher average cost than the non-anxiety group (p < 0.05). There are gender differences in outpatient costs, men spend more than women, for western medicine, men spend more than women, for Chinese herbal medicine, women spend more than men (all p < 0.05). The utilization rate of SSRIs and benzodiazepines in female patients is significantly higher than that in male patients (p < 0.05).ConclusionLLD patients are more commonly women than men and more commonly used SSRIs and NaSSAs. Elderly patients with depression often have comorbid generalized anxiety. LLD patients spend most of their visits on medicines, and while the examination costs are lower.

Coronary Artery Computed Tomography Angiography for Preventing Cardio-Cerebrovascular Disease: Observational Cohort Study Using the Observational Health Data Sciences and Informatics' Common Data Model.

BackgroundCardio-cerebrovascular diseases (CVDs) result in 17.5 million deaths annually worldwide, accounting for 46.2% of noncommunicable causes of death, and are the leading cause of death, followed by cancer, respiratory disease, and diabetes mellitus. Coronary artery computed tomography angiography (CCTA), which detects calcification in the coronary arteries, can be used to detect asymptomatic but serious vascular disease. It allows for noninvasive and quick testing despite involving radiation exposure.ObjectiveThe objective of our study was to investigate the effectiveness of CCTA screening on CVD outcomes by using the Observational Health Data Sciences and Informatics’ Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) data and the population-level estimation method.MethodsUsing electronic health record–based OMOP-CDM data, including health questionnaire responses, adults (aged 30-74 years) without a history of CVD were selected, and 5-year CVD outcomes were compared between patients undergoing CCTA (target group) and a comparison group via 1:1 propensity score matching. Participants were stratified into low-risk and high-risk groups based on the American College of Cardiology/American Heart Association atherosclerotic cardiovascular disease (ASCVD) risk score and Framingham risk score (FRS) for subgroup analyses.ResultsThe 2-year and 5-year risk scores were compared as secondary outcomes between the two groups. In total, 8787 participants were included in both the target group and comparison group. No significant differences (calibration P=.37) were found between the hazard ratios of the groups at 5 years. The subgroup analysis also revealed no significant differences between the ASCVD risk scores and FRSs of the groups at 5 years (ASCVD risk score: P=.97; FRS: P=.85). However, the CCTA group showed a significantly lower increase in risk scores at 2 years (ASCVD risk score: P=.03; FRS: P=.02).ConclusionsAlthough we could not confirm a significant difference in the preventive effects of CCTA screening for CVDs over a long period of 5 years, it may have a beneficial effect on risk score management over 2 years.

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review.

The current generation of sequencing technologies has led to significant advances in identifying novel disease-associated mutations and generated large amounts of data in a high-throughput manner. Such data in conjunction with clinical routine data are proven to be highly useful in deriving population-level and patient-level predictions, especially in the field of cancer precision medicine. However, data harmonization across multiple national and international clinical sites is an essential step for the assessment of events and outcomes associated with patients, which is currently not adequately addressed. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an internationally established research data repository introduced by the Observational Health Data Science and Informatics (OHDSI) community to overcome this issue. To address the needs of cancer research, the genomic vocabulary extension was introduced in 2020 to support the standardization of subsequent data analysis. In this review, we evaluate the current potential of the OMOP CDM to be applicable in cancer prediction and how comprehensively the genomic vocabulary extension of the OMOP can serve current needs of AI-based predictions. For this, we systematically screened the literature for articles that use the OMOP CDM in predictive analyses in cancer and investigated the underlying predictive models/tools. Interestingly, we found 248 articles, of which most use the OMOP for harmonizing their data, but only 5 make use of predictive algorithms on OMOP-based data and fulfill our criteria. The studies present multicentric investigations, in which the OMOP played an essential role in discovering and optimizing machine learning (ML)-based models. Ultimately, the use of the OMOP CDM leads to standardized data-driven studies for multiple clinical sites and enables a more solid basis utilizing, e.g., ML models that can be reused and combined in early prediction, diagnosis, and improvement of personalized cancer care and biomarker discovery.

FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model

BackgroundKnowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by the research and healthcare AI communities. From the standardization perspective, community-based standards such as the Fast Healthcare Interoperability Resources (FHIR) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) are increasingly used to represent and standardize EHR data for clinical data analytics, however, the potential of such a standard on building CKG has not been well investigated. ObjectiveTo develop and evaluate methods and tools that expose the OMOP CDM-based clinical data repositories into virtual clinical KGs that are compliant with FHIR Resource Description Framework (RDF) specification. MethodsWe developed a system called FHIR-Ontop-OMOP to generate virtual clinical KGs from the OMOP relational databases. We leveraged an OMOP CDM-based Medical Information Mart for Intensive Care (MIMIC-III) data repository to evaluate the FHIR-Ontop-OMOP system in terms of the faithfulness of data transformation and the conformance of the generated CKGs to the FHIR RDF specification. ResultsA beta version of the system has been released. A total of more than 100 data element mappings from 11 OMOP CDM clinical data, health system and vocabulary tables were implemented in the system, covering 11 FHIR resources. The generated virtual CKG from MIMIC-III contains 46,520 instances of FHIR Patient, 716,595 instances of Condition, 1,063,525 instances of Procedure, 24,934,751 instances of MedicationStatement, 365,181,104 instances of Observations, and 4,779,672 instances of CodeableConcept. Patient counts identified by five pairs of SQL (over the MIMIC database) and SPARQL (over the virtual CKG) queries were identical, ensuring the faithfulness of the data transformation. Generated CKG in RDF triples for 100 patients were fully conformant with the FHIR RDF specification. ConclusionThe FHIR-Ontop-OMOP system can expose OMOP database as a FHIR-compliant RDF graph. It provides a meaningful use case demonstrating the potentials that can be enabled by the interoperability between FHIR and OMOP CDM. Generated clinical KGs in FHIR RDF provide a semantic foundation to enable explainable AI applications in healthcare.

Towards quality improvement of vaccine concept mappings in the OMOP vocabulary with a semi-automated method

The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a unified model to integrate disparate real-world data (RWD) sources. An integral part of the OMOP CDM is the Standardized Vocabularies (henceforth referred to as the OMOP vocabulary), which enables organization and standardization of medical concepts across various clinical domains of the OMOP CDM. For concepts with the same meaning from different source vocabularies, one is designated as the standard concept, while the others are specified as non-standard or source concepts and mapped to the standard one. However, due to the heterogeneity of source vocabularies, there may exist mapping issues such as erroneous mappings and missing mappings in the OMOP vocabulary, which could affect the results of downstream analyses with RWD. In this paper, we focus on quality assurance of vaccine concept mappings in the OMOP vocabulary, which is necessary to accurately harness the power of RWD on vaccines. We introduce a semi-automated lexical approach to audit vaccine mappings in the OMOP vocabulary. We generated two types of vaccine-pairs: mapped and unmapped, where mapped vaccine-pairs are pairs of vaccine concepts with a “Maps to” relationship, while unmapped vaccine-pairs are those without a “Maps to” relationship. We represented each vaccine concept name as a set of words, and derived term-difference pairs (i.e., name differences) for mapped and unmapped vaccine-pairs. If the same term-difference pair can be obtained by both mapped and unmapped vaccine-pairs, then this is considered as a potential mapping inconsistency. Applying this approach to the vaccine mappings in OMOP, a total of 2087 potentially mapping inconsistencies were obtained. A randomly selected 200 samples were evaluated by domain experts to identify, validate, and categorize the inconsistencies. Experts identified 95 cases revealing valid mapping issues. The remaining 105 cases were found to be invalid due to the external and/or contextual information used in the mappings that were not reflected in the concept names of vaccines. This indicates that our semi-automated approach shows promise in identifying mapping inconsistencies among vaccine concepts in the OMOP vocabulary.

Methodological Issues in Using a Common Data Model of COVID-19 Vaccine Uptake and Important Adverse Events of Interest: Feasibility Study of Data and Connectivity COVID-19 Vaccines Pharmacovigilance in the United Kingdom.

BackgroundThe Data and Connectivity COVID-19 Vaccines Pharmacovigilance (DaC-VaP) UK-wide collaboration was created to monitor vaccine uptake and effectiveness and provide pharmacovigilance using routine clinical and administrative data. To monitor these, pooled analyses may be needed. However, variation in terminologies present a barrier as England uses the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), while the rest of the United Kingdom uses the Read v2 terminology in primary care. The availability of data sources is not uniform across the United Kingdom.ObjectiveThis study aims to use the concept mappings in the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to identify common concepts recorded and to report these in a repeated cross-sectional study. We planned to do this for vaccine coverage and 2 adverse events of interest (AEIs), cerebral venous sinus thrombosis (CVST) and anaphylaxis. We identified concept mappings to SNOMED CT, Read v2, the World Health Organization’s International Classification of Disease Tenth Revision (ICD-10) terminology, and the UK Dictionary of Medicines and Devices (dm+d).MethodsExposures and outcomes of interest to DaC-VaP for pharmacovigilance studies were selected. Mappings of these variables to different terminologies used across the United Kingdom’s devolved nations’ health services were identified from the Observational Health Data Sciences and Informatics (OHDSI) Automated Terminology Harmonization, Extraction, and Normalization for Analytics (ATHENA) online browser. Lead analysts from each nation then confirmed or added to the mappings identified. These mappings were then used to report AEIs in a common format. We reported rates for windows of 0-2 and 3-28 days postvaccine every 28 days.ResultsWe listed the mappings between Read v2, SNOMED CT, ICD-10, and dm+d. For vaccine exposure, we found clear mapping from OMOP to our clinical terminologies, though dm+d had codes not listed by OMOP at the time of searching. We found a list of CVST and anaphylaxis codes. For CVST, we had to use a broader cerebral venous thrombosis conceptual approach to include Read v2. We identified 56 SNOMED CT codes, of which we selected 47 (84%), and 15 Read v2 codes. For anaphylaxis, our refined search identified 60 SNOMED CT codes and 9 Read v2 codes, of which we selected 10 (17%) and 4 (44%), respectively, to include in our repeated cross-sectional studies.ConclusionsThis approach enables the use of mappings to different terminologies within the OMOP CDM without the need to catalogue an entire database. However, Read v2 has less granular concepts than some terminologies, such as SNOMED CT. Additionally, the OMOP CDM cannot compensate for limitations in the clinical coding system. Neither Read v2 nor ICD-10 is sufficiently granular to enable CVST to be specifically flagged. Hence, any pooled analysis will have to be at the less specific level of cerebrovascular venous thrombosis. Overall, the mappings within this CDM are useful, and our method could be used for rapid collaborations where there are only a limited number of concepts to pool.

Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project): study design and rationale

BackgroundSince the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates.MethodsThe project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM.Expected resultsThis study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini (“data node”), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda.DiscussionThe project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning.

Translating the Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) Electronic Health Records to an OWL Ontology.

The heterogeneity of electronic health records model is a major problem: it is necessary to gather data from various models for clinical research, but also for clinical decision support. The Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) has emerged as a standard model for structuring health records populated from various other sources. This model is proposed as a relational database schema. However, in the field of decision support, formal ontologies are commonly used. In this paper, we propose a translation of OMOP-CDM into an ontology, and we explore the utility of the semantic web for structuring EHR in a clinical decision support perspective, and the use of the SPARQL language for querying health records. The resulting ontology is available online.

Development and validation of a management system and dataset quality assessment tool for the Radiology Common Data Model (R_CDM): A case study in liver disease

BackgroundThe Observational Medical Outcomes Partnership—Common Data Model (OMOP-CDM), a distributed research network, has low clinical data coverage. Radiological data are valuable, but imaging metadata are often incomplete, and a standardized recording format in the OMOP-CDM is lacking. We developed a web-based management system and data quality assessment (RQA) tool for a radiology_CDM (R_CDM) and evaluated the feasibility of clinically applying this dataset. MethodsWe designed an R_CDM with Radiology_Occurrence and Radiology_Image tables. This was seamlessly linked to the OMOP-CDM clinical data. We adopted the standardized terminology using the RadLex playbook and mapped 5,753 radiology protocol terms to the OMOP vocabulary. An extract, transform, and load (ETL) process was developed to extract detailed information that was difficult to extract from metadata and to compensate for missing values. Image-based quantification was performed to measure liver surface nodularity (LSN), using customized Wonkwang abdomen and liver total solution (WALTS) software. ResultsOn a PACS, 368,333,676 DICOM files (1,001,797 cases) were converted to R_CDM chronic liver disease (CLD) data (316,596 MR images, 228 cases; 926,753 CT images, 782 cases) and uploaded to a web-based management system. Acquisition date and resolution were extracted accurately, but other information, such as “contrast administration status” and “photography direction”, could not be extracted from the metadata. Using WALTS, 9,609 pre-contrast axial-plane abdominal MR images (197 CLD cases) were assigned LSN scores by METAVIR fibrosis grades, which differed significantly by ANOVA (p < 0.001). The mean RQA score (83.5) indicated good quality. ConclusionThis study developed a web-based system for management of the R_CDM dataset, RQA tool, and constructed a CLD R_CDM dataset, with good quality for clinical application. Our management system and R_CDM CLD dataset would be useful for multicentric and image-based quantification researches.

Gender differences in outpatients with dementia from a large psychiatric hospital in China

BackgroundThe sociodemographic characteristics and clinical features of dementia patients in psychiatric hospitals have not been thoroughly studied in China. This study aimed to explore the psychiatric outpatient attendance of dementia patients at a psychiatric hospital in China, with particular emphasis on gender differences.MethodsThis retrospective study examined outpatients with dementia from January 2013 to August 2019 using data in the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in Beijing Anding Hospital. Age, sex, number of visits, use of drugs and comorbid conditions were extracted from medical records.ResultsNine thousand four patients were recruited from a specific outpatient clinic of a hospital in Beijing, and the mean number of visits was 6.92. There were 3,433 (38.13%) male patients and 5,571 (61.87%) female patients. The most common comorbidities were generalized anxiety disorder, nonorganic insomnia, delusional disorder and depressive disorder. The proportion of patients using antidementia was the highest, with the rate of 68.3%, followed by benzodiazepines (48.83%), antipsychotics (45.43%), antidepressants (22.24%) and nonbenzodiazepines (19.96%). Patients with dementia showed a significant gender difference in average age (t = 6.36, P < 0.0001). Compared to male patients, female patients had a higher number of visits (7.40 ± 12.90 vs 6.15 ± 10.50, t = 4.81, P < 0.0001). There were significant differences in comorbidity composition between male and female patients (t = 23.09, P < 0.0001).ConclusionsOur present findings suggested significant gender differences in the proportion of age, number of visits and comorbidity composition in outpatients with dementia.

The Effect of Statins on Mortality of Patients With Chronic Kidney Disease Based on Data of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) and Korea National Health Insurance Claims Database.

The role of statins in chronic kidney disease (CKD) has been extensively evaluated, but it remains controversial in specific population such as dialysis-dependent CKD. This study examined the effect of statins on mortality in CKD patients using two large databases. In data from the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) from two hospitals, CKD was defined as an estimated glomerular filtration rate < 60 mL/min/m2; we compared survival between patients with or without statin treatment. As a sensitivity analysis, the results were validated with the Korea National Health Insurance (KNHI) claims database. In the analysis of CDM datasets, statin users showed significantly lower risks of all-cause and cardiovascular mortality in both hospitals, compared to non-users. Similar results were observed in CKD patients from the KNHI claims database. Lower mortality in the statin group was consistently evident in all subgroup analyses, including patients on dialysis and low-risk young patients. In conclusion, we found that statins were associated with lower mortality in CKD patients, regardless of dialysis status or other risk factors.

Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study.

BackgroundIn the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world.ObjectiveThe objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts.MethodsWe transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon.ResultsWith an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a maximum duration of 1 minute.ConclusionsThe resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.

Real-world incidence of endopthalmitis after intravitreal anti-VEGF injections in Korea: findings from the Common Data Model in ophthalmology.

OBJECTIVESThe aim of this study was to evaluate the real-world incidence of endophthalmitis after intravitreal anti-vascular endothelial growth factor (VEGF) injections using data from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).METHODSPatients with endophthalmitis that developed within 6 weeks after intravitreal anti-VEGF injections were identified in 3 large OMOP CDM databases.RESULTSWe identified 23,490 patients who received 128,123 intravitreal anti-VEGF injections. The incidence rates of endophthalmitis were 15.75 per 10,000 patients and 2.97 per 10,000 injections. The incidence rates of endophthalmitis for bevacizumab, ranibizumab, and aflibercept (per 10,000 injections) were 3.64, 1.39, and 0.76, respectively. The annual incidence has remained below 5.00 per 10,000 injections since 2011 despite the increasing number of intravitreal anti-VEGF injections. Bevacizumab presented a higher incidence rate for endophthalmitis than ranibizumab and aflibercept (incidence rate ratio, 3.17; p=0.021).CONCLUSIONSThe incidence of endophthalmitis after intravitreal anti-VEGF injections has stabilized since 2011 despite the explosive increase in anti-VEGF injections. The off-label use of bevacizumab accounted for its disproportionately high incidence of endophthalmitis. The OMOP CDM, which includes off-label uses, laboratory data, and a scalable standardized database, could provide a novel strategy to reveal real-world evidence, especially in ophthalmology.