Observational Medical Outcomes Partnership Common Data Model Research Articles

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.

Older Age Instead of Body Mass Index Predicts Cancer-Associated Thrombosis: Validation of Khorana Score in Cancer Patients Undergoing Chemotherapy with East Asian Ethnicity

Khorana score has been the most widely used for the prediction of cancer-associated thrombosis (CAT) and recent pivotal phase III trials including AVERT and CASSINI adopted Khorana score in the inclusion criteria. However, most studies in development and use of Khorana score were conducted in Western cancer patients. The prevalence of VTE is known to be substantially lower in Asians than in the Western population. In case of body mass index (BMI), experts suggest an adjusted cut-off points of the classification of weight status in Asian population since they are usually thinner than Westerners. In addition, risk of CAT according to the sites of cancer may need reassessment since the characteristics of each site of cancer varies among geographic regions or ethnicity. For those reasons, we thought that Khorana score needs to be separately validated in Asian population and conducted a retrospective real-world analysis.By using the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), we collected de-identified data of the newly diagnosed cancer patients who underwent chemotherapy from January 2016 to June 2019 at Seoul National University Hospital (SNUH), Seoul, South Korea. Patients were eligible if they 1) had a new diagnosis code of cancer and 2) had initiated chemotherapy within 3 months after the first recoding of the cancer diagnosis. The patients undergoing chemotherapy were identified based on their prescription of chemotherapeutic agents and the procedural code for the first-time patient education for chemotherapy. Among the selected cancer patients, values of complete blood cell counts (CBC), body weight, and height on the very day of or the day closest to the chemotherapy initiation were obtained. A patient was counted as having a VTE if he or she had both newly-recorded diagnosis code of VTE and new record of anticoagulant medication code within 1 week after the emergence of VTE diagnosis code. Cumulative incidence of CAT was estimated at the time of 3, 6, and 12 months after the date of chemotherapy initiation.A total of 10,588 patients with cancer and chemotherapy were eligible and only 1.33% (141 patients) had a CAT at 6 months after the chemotherapy initiation, suggesting lower overall incidence of CAT in Asian population. Pancreas (4.9%) was the most common primary cancer site but CAT incidence rate in patients with stomach cancer was limited to 1.6%, reflecting different characteristics of the disease between the East and the West. The CAT incidences in patients with lung cancer (1.4%) and lymphoma (1.1%) were lower than expected, in line with a recent meta-analysis (Mulder at al.; Haematologica 2019), but CAT incidences in patients with liver (3.4%) and biliary (3.0%) cancer were higher than expected considering the Khorana scoring system.Among 7,431 patients who had all data for the calculation of Khorana score, 5,549 patients (74.7%) had a BMI of < 25 kg/m 2, followed by 1,633 patients (15.4%) with a BMI of 25.0-29.9 kg/m 2. Only 39 patients (0.4%) had a BMI of 35 > kg/m 2, showing the difference of BMI distribution according to the ethnicity. Moreover, BMI was not associated with CAT development at all, whereas the 3 CBC parameters and the site of cancers were associated with CAT occurrence. In addition, patients who aged ≥ 65 years had significantly higher CAT risk compared to younger group. In a multivariate regression analysis (Table 1), age ≥ 65 years, leukocyte ≥ 11 x 10 3/μL, and sites of cancers were independently associated with the development of CAT. Hemoglobin < 10 g/dL and platelet ≥ 350 x 10 3/μL showed a tendency of association but failed to reach statistical significance.When we classified the 7,431 patients according to the Khorana scoring system, 8.0% of the patients were considered as high risk group and their incidence of CAT at 6 months was 3.36% (Table 2), showing a smaller proportion of patients assigned to high risk group and their lower absolute risk of CAT compared to Western population.In conclusion, Khorana scoring system was only partially validated in Korean cancer patients who underwent first-line chemotherapy: BMI was not but older age was a good predictor for the prediction of CAT occurrence. Weighing the risk of CAT according to the sites of cancers also needs some improvement. Further studies for better CAT risk stratification reflecting ethnic or regional differences are warranted. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Standardizing registry data to the OMOP Common Data Model: experience from three pulmonary hypertension databases

BackgroundThe Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) can be used to transform observational health data to a common format. CDM transformation allows for analysis across disparate databases for the generation of new, real-word evidence, which is especially important in rare disease where data are limited. Pulmonary hypertension (PH) is a progressive, life-threatening disease, with rare subgroups such as pulmonary arterial hypertension (PAH), for which generating real-world evidence is challenging. Our objective is to document the process and outcomes of transforming registry data in PH to the OMOP CDM, and highlight challenges and our potential solutions.MethodsThree observational studies were transformed from the Clinical Data Interchange Standards Consortium study data tabulation model (SDTM) to OMOP CDM format. OPUS was a prospective, multi-centre registry (2014–2020) and OrPHeUS was a retrospective, multi-centre chart review (2013–2017); both enrolled patients newly treated with macitentan in the US. EXPOSURE is a prospective, multi-centre cohort study (2017–ongoing) of patients newly treated with selexipag or any PAH-specific therapy in Europe and Canada. OMOP CDM version 5.3.1 with recent OMOP CDM vocabulary was used. Imputation rules were defined and applied for missing dates to avoid exclusion of data. Custom target concepts were introduced when existing concepts did not provide sufficient granularity.ResultsOf the 6622 patients in the three registry studies, records were mapped for 6457. Custom target concepts were introduced for PAH subgroups (by combining SNOMED concepts or creating custom concepts) and World Health Organization functional class. Per the OMOP CDM convention, records about the absence of an event, or the lack of information, were not mapped. Excluding these non-event records, 4% (OPUS), 2% (OrPHeUS) and 1% (EXPOSURE) of records were not mapped.ConclusionsSDTM data from three registries were transformed to the OMOP CDM with limited exclusion of data and deviation from the SDTM database content. Future researchers can apply our strategy and methods in different disease areas, with tailoring as necessary. Mapping registry data to the OMOP CDM facilitates more efficient collaborations between researchers and establishment of federated data networks, which is an unmet need in rare diseases.

Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study.

BackgroundElectronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data and tools is generally affected by the lack of system interoperability. To overcome these issues, Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. Anesthesia data have not previously been mapped into the OMOP CDM.ObjectiveThe primary objective was to transform anesthesia data into the OMOP CDM. The secondary objective was to provide vocabularies, queries, and dashboards that might promote the exploitation and sharing of anesthesia data through the CDM.MethodsUsing our local anesthesia data warehouse, a group of 5 experts from 5 different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the OHDSI vocabularies. We performed structural mapping between the design of our local anesthesia data warehouse and the OMOP CDM tables and fields. To validate the implementation of anesthesia data into the OMOP CDM, we developed a set of queries and dashboards.ResultsWe identified 522 concepts related to anesthesia care. They were classified as demographics, units, measurements, operating room steps, drugs, periods of interest, and features. After semantic mapping, 353 (67.7%) of these anesthesia concepts were mapped to OHDSI concepts. Further, 169 (32.3%) concepts related to periods and features were added to the OHDSI vocabularies. Then, 8 OMOP CDM tables were implemented with anesthesia data and 2 new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 5,72,609 operations and provided the code for a set of 8 queries and 4 dashboards related to anesthesia care.ConclusionsGeneric data concerning demographics, drugs, units, measurements, and operating room steps were already available in OHDSI vocabularies. However, most of the intraoperative concepts (the duration of specific steps, an episode of hypotension, etc) were not present in OHDSI vocabularies. The OMOP mapping provided here enables anesthesia data reuse.

Towards the Representation of Genomic Data in HL7 FHIR and OMOP CDM.

High throughput sequencing technologies have facilitated an outburst in biological knowledge over the past decades and thus enables improvements in personalized medicine. In order to support (international) medical research with the combination of genomic and clinical patient data, a standardization and harmonization of these data sources is highly desirable. To support this increasing importance of genomic data, we have created semantic mapping from raw genomic data to both FHIR (Fast Healthcare Interoperability Resources) and OMOP (Observational Medical Outcomes Partnership) CDM (Common Data Model) and analyzed the data coverage of both models. For this, we calculated the mapping score for different data categories and the relative data coverage in both FHIR and OMOP CDM. Our results show, that the patients genomic data can be mapped to OMOP CDM directly from VCF (Variant Call Format) file with a coverage of slightly over 50%. However, using FHIR as intermediate representation does not lead to further information loss as the already stored data in FHIR can be further transformed into OMOP CDM format with almost 100% success. Our findings are in favor of extending OMOP CDM with patient genomic data using ETL to enable the researchers to apply different analysis methods including machine learning algorithms on genomic data.

Transformation of Electronic Health Records and Questionnaire Data to OMOP CDM: A Feasibility Study Using SG_T2DM Dataset.

Background Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats. Objective The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept. Methods We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard. Results The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks. Conclusion Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.

A two-stage workflow to extract and harmonize drug mentions from clinical notes into observational databases

BackgroundThe content of the clinical notes that have been continuously collected along patients’ health history has the potential to provide relevant information about treatments and diseases, and to increase the value of structured data available in Electronic Health Records (EHR) databases. EHR databases are currently being used in observational studies which lead to important findings in medical and biomedical sciences. However, the information present in clinical notes is not being used in those studies, since the computational analysis of this unstructured data is much complex in comparison to structured data. MethodsWe propose a two-stage workflow for solving an existing gap in Extraction, Transformation and Loading (ETL) procedures regarding observational databases. The first stage of the workflow extracts prescriptions present in patient’s clinical notes, while the second stage harmonises the extracted information into their standard definition and stores the resulting information in a common database schema used in observational studies. ResultsWe validated this methodology using two distinct data sets, in which the goal was to extract and store drug related information in a new Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) database. We analysed the performance of the used annotator as well as its limitations. Finally, we described some practical examples of how users can explore these datasets once migrated to OMOP CDM databases. ConclusionWith this methodology, we were able to show a strategy for using the information extracted from the clinical notes in business intelligence tools, or for other applications such as data exploration through the use of SQL queries. Besides, the extracted information complements the data present in OMOP CDM databases which was not directly available in the EHR database.

Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research

ObjectiveThe rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production and incremental updates of data feeds to the networks from raw EHR data. However, the demands of COVID-19 research for timely data are far higher, and the requirements for updating faster than previous collaborative research using national data networks have increased. New approaches need to be developed to address these demands.MethodsIn this article, we describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical data model and the automated transformation of clinical data to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs for data sharing and research collaboration on COVID-19.ResultsFHIR data resources could be transformed to operational PCORnet and OMOP CDMs with minimal production delays through a combination of real-time and postprocessing steps, leveraging the FHIR data subscription feature.ConclusionsThe approach leverages evolving standards for the availability of EHR data developed to facilitate data exchange under the 21st Century Cures Act and could greatly enhance the availability of standardized datasets for research.

Evaluation of the Privacy Risks of Personal Health Identifiers and Quasi-Identifiers in a Distributed Research Network: Development and Validation Study.

BackgroundPrivacy should be protected in medical data that include patient information. A distributed research network (DRN) is one of the challenges in privacy protection and in the encouragement of multi-institutional clinical research. A DRN standardizes multi-institutional data into a common structure and terminology called a common data model (CDM), and it only shares analysis results. It is necessary to measure how a DRN protects patient information privacy even without sharing data in practice.ObjectiveThis study aimed to quantify the privacy risk of a DRN by comparing different deidentification levels focusing on personal health identifiers (PHIs) and quasi-identifiers (QIs).MethodsWe detected PHIs and QIs in an Observational Medical Outcomes Partnership (OMOP) CDM as threatening privacy, based on 18 Health Insurance Portability and Accountability Act of 1996 (HIPPA) identifiers and previous studies. To compare the privacy risk according to the different privacy policies, we generated limited and safe harbor data sets based on 16 PHIs and 12 QIs as threatening privacy from the Synthetic Public Use File 5 Percent (SynPUF5PCT) data set, which is a public data set of the OMOP CDM. With minimum cell size and equivalence class methods, we measured the privacy risk reduction with a trust differential gap obtained by comparing the two data sets. We also measured the gap in randomly sampled records from the two data sets to adjust the number of PHI or QI records.ResultsThe gaps averaged 31.448% and 73.798% for PHIs and QIs, respectively, with a minimum cell size of one, which represents a unique record in a data set. Among PHIs, the national provider identifier had the highest gap of 71.236% (71.244% and 0.007% in the limited and safe harbor data sets, respectively). The maximum size of the equivalence class, which has the largest size of an indistinguishable set of records, averaged 771. In 1000 random samples of PHIs, Device_exposure_start_date had the highest gap of 33.730% (87.705% and 53.975% in the data sets). Among QIs, Death had the highest gap of 99.212% (99.997% and 0.784% in the data sets). In 1000, 10,000, and 100,000 random samples of QIs, Device_treatment had the highest gaps of 12.980% (99.980% and 87.000% in the data sets), 60.118% (99.831% and 39.713%), and 93.597% (98.805% and 5.207%), respectively, and in 1 million random samples, Death had the highest gap of 99.063% (99.998% and 0.934% in the data sets).ConclusionsIn this study, we verified and quantified the privacy risk of PHIs and QIs in the DRN. Although this study used limited PHIs and QIs for verification, the privacy limitations found in this study could be used as a quality measurement index for deidentification of multi-institutional collaboration research, thereby increasing DRN safety.

Pharmacovigilance and Clinical Environment: Utilizing OMOP-CDM and OHDSI Software Stack to Integrate EHR Data.

Information Technology (IT) and specialized systems could have a prominent role towards the support of drug safety processes, both in the clinical context but also beyond that. PVClinical project aims to build an IT platform, enabling the investigation of potential Adverse Drug Reactions (ADRs). In this paper, we outline the utilization of Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) and the openly available Observational Health Data Sciences and Informatics (OHDSI) software stack as part of PVClinical platform. OMOP-CDM offers the capacity to integrate data from Electronic Health Records (EHRs) (e.g., encounters, patients, providers, diagnoses, drugs, measurements and procedures) via an accepted data model. Furthermore, the OHDSI software stack provides valuable analytics tools which could be used to address important questions regarding drug safety quickly and efficiently, enabling the investigation of potential ADRs in the clinical environment.

The Risk of Osteoporosis and Osteoporotic Fracture Following the Use of Irritable Bowel Syndrome Medical Treatment: An Analysis Using the OMOP CDM Database.

Patients with irritable bowel syndrome (IBS) are at increased risk of osteoporosis and osteoporotic fracture. This study investigated whether IBS medication attenuated the rate of osteoporosis and osteoporotic fracture risk. We conducted a retrospective large-scale multicenter study across eight hospital databases encoded in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The primary outcome was the incidence of osteoporosis, whereas secondary outcomes were osteoporotic fractures. After 1:4 matching, 24,723 IBS patients, 78,318 non-IBS patients, 427,640 non-IBS patients with IBS medication, and 827,954 non-IBS patients without IBS medication were selected. The risk of osteoporosis was significantly increased in the IBS group compared to the non-IBS group (hazard ratio (HR) 1.33; confidence interval (CI) 1.17~1.51). Even in patients who were not diagnosed with IBS, the risk of osteoporosis was significantly increased in those with IBS medication compared to those without (HR 1.77, CI 1.62~1.93). The risk of osteoporotic fracture was significantly increased in the IBS medication group (HR 1.69, CI 1.55~1.84). Patients exposed to IBS treatment even without IBS diagnosis were at increased risk of osteoporosis and osteoporotic fracture. Early diagnosis and treatment of osteoporosis should be considered in patients who have received medication for IBS symptoms.

A knowledge base of clinical trial eligibility criteria

ObjectiveWe present the Clinical Trial Knowledge Base, a regularly updated knowledge base of discrete clinical trial eligibility criteria equipped with a web-based user interface for querying and aggregate analysis of common eligibility criteria. Materials and methodsWe used a natural language processing (NLP) tool named Criteria2Query (Yuan et al., 2019) to transform free text clinical trial eligibility criteria from ClinicalTrials.gov into discrete criteria concepts and attributes encoded using the widely adopted Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and stored in a relational SQL database. A web application accessible via RESTful APIs was implemented to enable queries and visual aggregate analyses. We demonstrate CTKB’s potential role in EHR phenotype knowledge engineering using ten validated phenotyping algorithms. ResultsAt the time of writing, CTKB contained 87,504 distinctive OMOP CDM standard concepts, including Condition (47.82%), Drug (23.01%), Procedure (13.73%), Measurement (24.70%) and Observation (5.28%), with 34.78% for inclusion criteria and 65.22% for exclusion criteria, extracted from 352,110 clinical trials. The average hit rate of criteria concepts in eMERGE phenotype algorithms is 77.56%. ConclusionCTKB is a novel comprehensive knowledge base of discrete eligibility criteria concepts with the potential to enable knowledge engineering for clinical trial cohort definition, clinical trial population representativeness assessment, electronical phenotyping, and data gap analyses for using electronic health records to support clinical trial recruitment.

Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model.

The identification of patient cohorts for recruiting patients into clinical trials requires an evaluation of study-specific inclusion and exclusion criteria. These criteria are specified depending on corresponding clinical facts. Some of these facts may not be present in the clinical source systems and need to be calculated either in advance or at cohort query runtime (so-called feasibility query). We use the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) as the repository for our clinical data. However, Atlas, the graphical user interface of OMOP, does not offer the functionality to perform calculations on facts data. Therefore, we were in search for a different approach. The objective of this study is to investigate whether the Arden Syntax can be used for feasibility queries on the OMOP CDM to enable on-the-fly calculations at query runtime, to eliminate the need to precalculate data elements that are involved with researchers' criteria specification. We implemented a service that reads the facts from the OMOP repository and provides it in a form which an Arden Syntax Medical Logic Module (MLM) can process. Then, we implemented an MLM that applies the eligibility criteria to every patient data set and outputs the list of eligible cases (i.e., performs the feasibility query). The study resulted in an MLM-based feasibility query that identifies cases of overventilation as an example of how an on-the-fly calculation can be realized. The algorithm is split into two MLMs to provide the reusability of the approach. We found that MLMs are a suitable technology for feasibility queries on the OMOP CDM. Our method of performing on-the-fly calculations can be employed with any OMOP instance and without touching existing infrastructure like the Extract, Transform and Load pipeline. Therefore, we think that it is a well-suited method to perform on-the-fly calculations on OMOP.

Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models.

BackgroundDe-identifying personal information is critical when using personal health data for secondary research. The Observational Medical Outcomes Partnership Common Data Model (CDM), defined by the nonprofit organization Observational Health Data Sciences and Informatics, has been gaining attention for its use in the analysis of patient-level clinical data obtained from various medical institutions. When analyzing such data in a public environment such as a cloud-computing system, an appropriate de-identification strategy is required to protect patient privacy.ObjectiveThis study proposes and evaluates a de-identification strategy that is comprised of several rules along with privacy models such as k-anonymity, l-diversity, and t-closeness. The proposed strategy was evaluated using the actual CDM database.MethodsThe CDM database used in this study was constructed by the Anam Hospital of Korea University. Analysis and evaluation were performed using the ARX anonymizing framework in combination with the k-anonymity, l-diversity, and t-closeness privacy models.ResultsThe CDM database, which was constructed according to the rules established by Observational Health Data Sciences and Informatics, exhibited a low risk of re-identification: The highest re-identifiable record rate (11.3%) in the dataset was exhibited by the DRUG_EXPOSURE table, with a re-identification success rate of 0.03%. However, because all tables include at least one “highest risk” value of 100%, suitable anonymizing techniques are required; moreover, the CDM database preserves the “source values” (raw data), a combination of which could increase the risk of re-identification. Therefore, this study proposes an enhanced strategy to de-identify the source values to significantly reduce not only the highest risk in the k-anonymity, l-diversity, and t-closeness privacy models but also the overall possibility of re-identification.ConclusionsOur proposed de-identification strategy effectively enhanced the privacy of the CDM database, thereby encouraging clinical research involving multiple centers.

Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models.

ObjectivesThe time-dependent study of comorbidities provides insight into disease progression and trajectory. We hypothesize that understanding longitudinal disease characteristics can lead to more timely intervention and improve clinical outcomes. As a first step, we developed an efficient and easy-to-install toolkit, the Time-based Elixhauser Comorbidity Index (TECI), which pre-calculates time-based Elixhauser comorbidities and can be extended to common data models (CDMs).MethodsA Structured Query Language (SQL)-based toolkit, TECI, was built to pre-calculate time-specific Elixhauser comorbidity indices using data from a clinical data repository (CDR). Then it was extended to the Informatics for Integrating Biology and the Bedside (I2B2) and Observational Medical Outcomes Partnership (OMOP) CDMs.ResultsAt the University of Arkansas for Medical Sciences (UAMS), the TECI toolkit was successfully installed to compute the indices from CDR data, and the scores were integrated into the I2B2 and OMOP CDMs. Comorbidity scores calculated by TECI were validated against: scores available in the 2015 quarter 1–3 Nationwide Readmissions Database (NRD) and scores calculated using the comorbidities using a previously validated algorithm on the 2015 quarter 4 NRD. Furthermore, TECI identified 18,846 UAMS patients that had changes in comorbidity scores over time (year 2013 to 2019). Comorbidities for a random sample of patients were independently reviewed, and in all cases, the results were found to be 100% accurate.ConclusionsTECI facilitates the study of comorbidities within a time-dependent context, allowing better understanding of disease associations and trajectories, which has the potential to improve clinical outcomes.