West Virginia University (WVU) is expanding its research capabilities with the migration of TriNetX, a web-based cohort creation database and retrospective research tool, to a cloud-based appliance. This move will significantly improve the platform's performance, providing researchers with swift and secure access to de-identified clinical research data. The migration will take place on Thursday, March 21, and no downtime is expected.
The West Virginia Clinical and Translational Science institute (WVCTSI) at WVU facilitates the usage of TriNetX for biomedical research teams across the institution.
TriNetX enables researchers to explore patient data, spanning diagnoses, procedures, medications, laboratory results, and demographics, in a fully secure and de-identified setting. The transition is expected to streamline the assessment of clinical trial feasibility and support prospective studies.
WVU's participation in the TriNetX Research Network has expanded the possibilities for collaborative research since it became a member in 2017. With the ability to run queries against the 85+ organizations within the Research network, solely against the WVU Medicine network, or against another of the several collaborative networks; researchers can access de-identified datasets, enhancing the depth and breadth of their investigations.
To obtain a TriNetX account, WVU researchers are encouraged to complete the Data Use and Confidentiality User Agreement in iLab. Additionally, those interested in a demonstration or training can submit a Consultation Request via iLab. The current practice of uploading WVU Medicine clinical data into TriNetX on a quarterly basis is expected to evolve, with more frequent updates becoming available as the underlying data structure undergoes enhancements.
Data improvements are expected to accompany this migration, including the integration of the North American Association of Central Cancer Registries (NAACCR) oncology data. This addition brings oncology and genomic data to the platform, providing a comprehensive view for researchers. The Natural Language Processing (NLP) pipeline has also been bolstered with biomarker extraction, introducing genomic insights into the mix. Looking ahead, WVU anticipates further enhancements to TriNetX, with additional biomarkers incorporated via NLP and a broader set of genomic facts derived from genomic test results.
Existing queries on the platform will remain unchanged and can be re-run beginning on March 22 for updated counts and cohort information, ensuring a seamless transition for researchers.
For more information, researchers can request assistance from WVCTSI’s Biostatistics, Epidemiology, and Research Design (BERD) team here.