Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Kim GL

PROVIDER: S-EPMC8126251 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Kim Gyu Lee GL Yi Yu Hyeon YH Hwang Hye Rim HR Kim Jinmi J Park Youngmin Y Kim Yun Jin YJ Lee Jeong Gyu JG Tak Young Jin YJ Lee Seung Hun SH Lee Sang Yeoup SY Cho Youn Hye YH Park Eun Ju EJ Lee Youngin Y

Journal of clinical medicine 20210510 9

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 osteopo ...[more]

PMID: 34068814

Similar Datasets

Project description:ObjectiveThe large-scale collection of observational data and digital technologies could help curb the COVID-19 pandemic. However, the coexistence of multiple Common Data Models (CDMs) and the lack of data extract, transform, and load (ETL) tool between different CDMs causes potential interoperability issue between different data systems. The objective of this study is to design, develop, and evaluate an ETL tool that transforms the PCORnet CDM format data into the OMOP CDM.MethodsWe developed an open-source ETL tool to facilitate the data conversion from the PCORnet CDM and the OMOP CDM. The ETL tool was evaluated using a dataset with 1000 patients randomly selected from the PCORnet CDM at Mayo Clinic. Information loss, data mapping accuracy, and gap analysis approaches were conducted to assess the performance of the ETL tool. We designed an experiment to conduct a real-world COVID-19 surveillance task to assess the feasibility of the ETL tool. We also assessed the capacity of the ETL tool for the COVID-19 data surveillance using data collection criteria of the MN EHR Consortium COVID-19 project.ResultsAfter the ETL process, all the records of 1000 patients from 18 PCORnet CDM tables were successfully transformed into 12 OMOP CDM tables. The information loss for all the concept mapping was less than 0.61%. The string mapping process for the unit concepts lost 2.84% records. Almost all the fields in the manual mapping process achieved 0% information loss, except the specialty concept mapping. Moreover, the mapping accuracy for all the fields were 100%. The COVID-19 surveillance task collected almost the same set of cases (99.3% overlaps) from the original PCORnet CDM and target OMOP CDM separately. Finally, all the data elements for MN EHR Consortium COVID-19 project could be captured from both the PCORnet CDM and the OMOP CDM.ConclusionWe demonstrated that our ETL tool could satisfy the data conversion requirements between the PCORnet CDM and the OMOP CDM. The outcome of the work would facilitate the data retrieval, communication, sharing, and analysis between different institutions for not only COVID-19 related project, but also other real-world evidence-based observational studies.

Project description: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.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets