Early Detection of an Underperforming Implantable Cardiovascular Device Using an Automated Safety Surveillance Tool

Abstract

Background— Postmarket medical device surveillance in the United States depends largely on voluntary reporting of adverse events. Consequently, early safety signals may be missed, exposing patients to potentially hazardous products. The aim of this study was to assess the feasibility of using an automated safety surveillance tool to detect early signals that a marketed implantable cardiac device was underperforming. Methods and Results— For this purpose, we performed simulated prospective monthly full-cohort and propensity-matched comparative survival analyses on our 3-center database of Sprint Fidelis and Quattro Secure implantable cardioverter-defibrillator leads, using a commercially available automated surveillance tool that was preset to trigger an alert if the log rank probability value was <0.05. During the study, 84 of 1035 Fidelis (8.1%) and 23 of 1675 Quattro (1.4%) leads failed. The simulated full-cohort analysis triggered a sustained alert for Fidelis leads beginning 13 months after the first implant and 2 years before Fidelis leads were removed from the market. Of the 1035 patients who had Fidelis leads, up to 969 (93.6%) were successfully matched to Quattro patients. In the propensity-matched analysis, the alert triggered 22 months after the first Fidelis implant and more than 1 year before the lead was recalled. Conclusions— An active automated safety surveillance system could have identified this implantable cardiovascular device problem substantially sooner than was achieved through existing postmarket surveillance methods. Such a tool, when applied to clinical registries and remote monitoring databases, may limit the exposure of large populations to underperforming and potentially hazardous cardiovascular devices.

Transvenous Implantable Cardioverter‐Defibrillator Lead Reliability: Implications for Postmarket Surveillance

“…In addition, modeling failure rates derived from our observational findings indicated that traditional approaches to ICD lead surveillance—even if bolstered by unique device identifiers—will need robust sample sizes with long follow-up to demonstrate meaningful deviations in performance within the normal range. However, our simulation results also support efforts to identify seriously flawed leads, such as Sprint Fidelis, in near real time using multicenter implant and follow-up databases 17. In sum, these data should buttress confidence in current standards for transvenous lead design, while also providing essential clinical and statistical context for ongoing discussions regarding postmarket surveillance and comparative effectiveness research in this area.…”

Approval of High-Risk Medical Devices in the US: Implications for Clinical Cardiology

“…However, this piecemeal approach to data collection and the limitations of passive adverse event reporting in particular can lead to gaps in the availability of data and inconsistent analysis of data that are available. Hauser and colleagues simulated a prospective postmarket evaluation of the Sprint Fidelis ICD lead using a three-center database, finding that such active investigation may have raised red flags about early lead failure two years before Fidelis was actually taken off the market [34]. In the case of the recalled Riata ICD lead, the FDA did not mandate a postmarket study until after the lead was recalled in 2011 [33•].…”

Registry-Based Prospective, Active Surveillance of Medical-Device Safety

“…We have previously validated an active clinical-data-surveillance system, denoted DELTA (Data Extraction and Longitudinal Trend Analysis), capable of prospectively monitoring clinical registries and other detailed clinical data sources for safety signals 13,15,18–22 . DELTA is a collection of integrated software components linking open-source database management and statistical analysis tools, and is designed to simultaneously support multiple risk-adjusted prospective safety-surveillance analyses of complex clinical datasets 13,15 ‎(See Supplementary Appendix for additional information regarding DELTA).…”