Multi-institutional distributed data networks for real-world evidence about medical devices: building unique device identifiers into longitudinal data (BUILD)

Lay Summary Our objective was to support FDA's efforts to build a system for using electronic health record and other data to evaluate the real-world safety and effectiveness of medical devices after their initial approval. To accomplish this while protecting patient privacy, we developed a data network of 3 major health systems and created data standards for each health system to use in building their individual research databases. A single request for summary data could then be sent to all 3 databases and the results combined without sharing individual patient information, which remained behind the health system firewalls ("distributed analysis"). As the first device to evaluate with this system, we selected coronary stents, which are inserted into heart arteries to relieve chest pain and stop heart attacks. We compared 2 types of stents and found no significant differences in safety or effectiveness between them-a result previously noted in clinical trials and supportive of the reliability of our data. Ours is the first data network established for the express purpose of evaluating medical devices using distributed analysis. We are working to extend this work to other devices and other health systems in support of the new National Evaluation System for health Technology. Objectives To support development of a robust postmarket device evaluation system using real-world data (RWD) from electronic health records (EHRs) and other sources, employing unique device identifiers (UDIs) to link to device information. Methods To create consistent device-related EHR RWD across 3 institutions, we established a distributed data network and created UDI-enriched research databases (UDIRs) employing a common data model comprised of 24 tables and 472 fields. To test the system, patients receiving coronary stents between 2010 and 2019 were loaded into each institution's UDIR to support distributed queries without sharing identifiable patient information. The ability of the system to execute queries was tested with 3 quality assurance checks. To demonstrate face validity of the data, a retrospective survival study of patients receiving zotarolimus or everolimus stents from 2012 to 2017 was performed using distributed analysis. Propensity score matching was used to compare risk of 6 cardiovascular outcomes within 12 months postimplantation. Results The test queries established network functionality. In the analysis, we identified 9141 patients (Mercy = 4905, Geisinger = 4109, Intermountain = 127); mean age 65 +/- 12 years, 69% males, 23% zotarolimus. Separate matched analyses at the 3 institutions showed hazard ratio estimates (zotarolimus vs everolimus) of 0.85-1.59 for subsequent percutaneous coronary intervention (P = .14-.52), 1.06-2.03 for death (P = .16-.78) and 0.94-1.40 for the composite endpoint (P = .16-.62). Discussion The analysis results are consistent with clinical studies comparing these devices. Conclusion This project shows that multi-institutional data networks can provide clinically relevant real-world evidence via distributed analysis while maintaining data privacy.