Programmatic Theme: Clinical Research Informatics

Abstract: With widespread adoption of electronic health records (EHRs), Real World Data and Real World Evidence (RWE) have been increasingly used by FDA for evaluating drug safety and effectiveness. However, integration of heterogeneous drug safety data sources and systems remains an impediment for effective pharmacovigilance studies. In an ongoing project, we have developed a next generation pharmacovigilance signal detection framework known as ADEpedia-on-OHDSI using the OHDSI common data model (CDM). The objective of the study is to demonstrate the feasibility of the framework for integrating both spontaneous reporting data and EHR data for improved signal detection with a case study of immune-related adverse events. We first loaded the OHDSI CDM with both recent and legacy FAERS data (from the time period between Jan. 2004 and Dec. 2018). We also integrated the clinical data from the Mayo Clinic EHR system for six oncological immunotherapy drugs. We implemented a signal detection algorithm and compared the timelines of positive signals detected from both FAERS and EHR data. We found that the signals detected from EHRs are 4 months earlier than signals detected from FAERS database (depending on the signal detection methods used) for the ipilimumab-induced hypopituitarism. Our CDM-based approach would be useful to provide a scalable solution to integrate both drug safety data and EHR data to generate RWE for improved signal detection.

Learning Objective: Using OMOP CDM to integrate FAERS and EHR data to detect drug adverse event signals.

Authors:

Yue Yu (Presenter)
Mayo Clinic

Kathryn Ruddy, Mayo Clinic
Andrew Wen, Mayo Clinic
Nansu Zong, Mayo Clinic
Shintaro Tsuji, Mayo Clinic
Jun Chen, Mayo Clinic
Nilay Shah, Mayo Clinic
Guoqian Jiang, Mayo Clinic