Testing a Cloud-Based Model for Active Surveillance of Medical Devices with Analyses of Coronary Stent Safety Using the Data Extraction and Longitudinal Trend Analysis (DELTA) System

Joseph P Drozda Jr,1,* Henry Ssemaganda,2,* Edward A Frankenberger,3,* Eric Brandt,4,* Susan Robbins,2,* Neha Khairnar,2,* Alexandra Cha,5,* Frederic S Resnic6,* 1Retiree, Mercy, Chesterfield, MO, USA; 2Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington, MA, USA; 3Freenome, South San Francisco, CA, USA; 4Research, Mercy, Chesterfield, MO, USA; 5Booz Allen Hamilton, Bethesda, MD, USA; 6Division of Cardiovascular Medicine, Lahey Hospital and Medical Center, Burlington, MA, USA*These authors contributed equally to this workCorrespondence: Joseph P Drozda Jr, Tel +1 314 308 1732, Email [email protected]: To demonstrate the use of the Data Extraction and Longitudinal Trend Analysis (DELTA) system in the National Evaluation System for health Technology’s (NEST) medical device surveillance cloud environment by analyzing coronary stent safety using real world clinical data and comparing results to clinical trial findings.Design and Setting: Electronic health record (EHR) data from two health systems, the Social Security Death Master File, and device databases were ingested into the NEST cloud, and safety analyses of two stents were performed using DELTA.Participants and Interventions: This is an observational study of patients receiving zotarolimus drug-eluting coronary stents (ZES) or everolimus eluting coronary stents (EES) between July 1, 2015 and December 31, 2017.Results: After exclusions, 3334 patients receiving EES and 1002 receiving ZES were available for study. Analysis using inverse probability weighting showed no significant difference in one-year mortality or major adverse cardiac events (MACE) for EES compared to ZES [Mortality Odds Ratio 0.94 (95% CI 0.81– 1.175); p = 0.780] [MACE Odds Ratio 1.04 (95% CI 0.92– 1.16; p = 0.551]). Analysis using propensity matching showed no significant difference in EES one-year mortality (547 of 992 alive and available after censoring) compared to ZES (546 of 992) [Log-Rank statistic 0.3348 (p = 0.563)].Conclusion: Automated cloud-based medical device safety surveillance using EHR data is feasible and was efficiently performed using DELTA. No statistically significant differences in 1-year safety outcomes between ZES and EES were identified using two statistical approaches, consistent with randomized trial findings.Plain Language Summary: What is already known on this topic–The National Evaluation System for health Technology (NEST) Coordinating Center has developed a prototype cloud-based medical device surveillance system designed to capture clinical data obtained during routine care along with automated analysis tools to monitor device safety and performance in a real-world setting. DELTA is a suite of open-source active surveillance software tools that has been successfully deployed in other data environments and was being evaluated in the NEST cloud.What this study adds–This paper reports on the validation of DELTA methods deployed in the NEST cloud environment (appendix) as well as the initial demonstration of active safety surveillance for two commonly used coronary stent devices. Electronic health record data on the two coronary stents from two health systems were ingested into the Cloud, linked to the Social Security Death Master File along with a reference device database, and analyzed with DELTA as a prototype of an active safety surveillance system.How this study might affect research, practice, or policy–Consistent with clinical trials and a prior independent analysis, no statistically significant differences in clinical outcomes were found between the two stents. Importantly, the results support the feasibility of using the NEST multi-health system cloud for monitoring medical device safety and effectiveness. The system could minimize the risks associated with late recognition of device safety issues and support assessments of medical device value in the real world.Keywords: active surveillance, safety-based medical device withdrawals, real-world evidence, cardiac devices, outcomes assessment (health care