Objective A variety of post-marketing surveillance strategies to monitor the safety of medical devices have been supported by the Food and Drug Administration, but there are few systems to automate surveillance. Our objective was to develop a system to perform real-time monitoring of safety data using a variety of process control techniques.

Design The web-based Data Extraction and Longitudinal Time Analysis (DELTA) system imports clinical data in real-time from an electronic database, and generates alerts for potentially unsafe devices or procedures. The statistical techniques used are statistical process control (SPC), logistic regression (LR), and Bayesian Updating Statistics (BUS).

Measurements We selected in-patient mortality following implantation of the Cypher drug eluting coronary stent to evaluate our system. Data from the University of Michigan consortium bare-metal stent study was used to calculate the event rate alerting boundaries. Data analysis was performed on local catheterization data from Brigham and Women's Hospital from July 01, 2003, shortly after the CYPHER release, to December 31, 2004, including 2270 cases with 27 observed deaths.

Results The single-stratum SPC had alerts in months 4 and 10. The multi-strata SPC had alerts in months 5, 10 and 18 in the moderate-risk stratum, and months 1, 4, 7, and 10 in the high-risk stratum. The only cumulative alerts were in the first month for the high-risk stratum of the multi-strata SPC. The LR method showed no monthly or cumulative alerts. The BUS method showed an alert in the first month for the high-risk stratum.

Conclusions The system performed adequately within the Brigham's hospital intranet environment based on the design goals. All three cumulative methods agreed that the overall observed event rates were not significantly higher for the new medical device than for a closely related medical device, and were consistent with the observation that the initial concerns about this device dissipated as more data accumulated.