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

Joseph P Drozda Jr; Jove Graham; Joseph B Muhlestein; James E Tcheng; James Roach; Tom Forsyth; Stacey Knight; Andrew McKinnon; Heidi May; Natalia A Wilson; Jesse A Berlin; Edgar P Simard

doi:10.1093/jamiaopen/ooac035

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

JAMIA Open. 2022 May 25;5(2):ooac035. doi: 10.1093/jamiaopen/ooac035. eCollection 2022 Jul.

Authors

Affiliations

¹ Mercy, Chesterfield, Missouri, USA.
² Center for Pharmacy Innovation and Outcomes, Geisinger, Danville, Pennsylvania, USA.
³ Intermountain Medical Center Heart Institute, Salt Lake City, Utah, USA.
⁴ Department of Medicine, Duke University, Durham, North Carolina, USA.
⁵ Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA.
⁶ Arizona State University, Phoenix, Arizona, USA.
⁷ Johnson & Johnson, Titusville, New Jersey, USA.
⁸ Medtronic, Inc., Minneapolis, Minnesota, USA.

Abstract

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.

Keywords: drug-eluting stents; medical device safety; privacy protection; real-world evidence; unique device identifier.