UNASSIGNED: To characterize medical device reports about elastomeric pumps delivering local anesthesia made to the US Food and Drug Administration Manufacturer and User Facility Device Experience (MAUDE) database.
UNASSIGNED: We conducted a retrospective review of medical device reports submitted to MAUDE from January 2010 to July 2018. A systematic, computerized algorithm was used to identify records pertaining to elastomeric pumps using local anesthesia. Included records indicated the use of local anesthesia or were determined to involve the use of local anesthetics (if they did not contain specific information on drug use). Reports were analyzed within the MAUDE event type categories of malfunction, injury, death, other, and missing. Possible cases of liver injury or surgical site infection were also identified. Manual review of narratives provided in MAUDE was performed by 2 reviewers to identify possible or probable cases of local anesthetic system toxicity (LAST).
UNASSIGNED: From a pool of 384,285 reports about elastomeric pumps from the MAUDE database, 4093 met inclusion criteria for involving elastomeric pumps to deliver local anesthetics, with the peak number of reports occurring in 2014. Of these identified reports, 3624 (88.5%) were categorized as malfunctions, 292 (7.1%) as injuries, and 8 (0.2%) as involving death. We identified 13 cases (0.3%) of possible liver injury and 51 cases (1.2%) of possible surgical site infection; 139 reports (3.4%) were determined to be probably (n=53) or possibly (n=86) associated with LAST.
UNASSIGNED: Malfunction of elastomeric pumps delivering local anesthetics leaves patients vulnerable to injury or death. Our study indicates that reports of malfunction, injury, and death have been reported to the MAUDE database. These reports likely reflect an underrepresentation of cases in the real-world population, emphasizing the need for more comprehensive medical device reporting.

OBJECTIVE: By introducing the Medical Device Reporting (MDR) system and related inspection practice of the US, this paper puts forward some suggestions on implementing reporting responsibility of manufactures in China.
METHODS: The MDR system and the related inspection system in the US were systematically analyzed.
RESULTS: The US had established a sound system for discovering and reporting MDR, and a mechanism for inspecting the implementing of manufactures, forming an effective post-market surveillance system.
CONCLUSIONS: By learning from the experience of the US, we can carry out the post-market surveillance of medical devices adverse events in China from the aspects of implementing the existing system, strengthening the reporting ability and perfecting the inspection mechanism.

Signal detection methods have been used extensively in post-market surveillance to identify elevated risks of adverse events. However, these statistical methods have not been widely used in detecting AE signals for medical devices. In this paper, we focused on the use of a likelihood ratio test (LRT)-based method in identifying adverse event (AE) signals associated with left ventricular assist devices (LVADs) using Medical Device Reporting (MDR) data. Among 110,927 adverse event entries identified in MDR data for LVADs, the LRT method detected 18 AE signals which included seven bleeding-related AEs such as hemolysis, thrombosis, hematuria, thrombus, blood loss, and hemorrhage. The LRT method was also applied to longitudinal data from 2007 to 2019 where a monotone alpha-spending function was used to ensure the control of type I error at each look and overall for trend analysis. Furthermore, the LRT method was compared to proportional reporting ratios (PRRs), Bayesian confidence propagation neural network (BCPNN), and simplified Bayes methods and found to be the most conservative method when examining the total number of detected signals, given its ability to control type-I error and the false discovery rate.