Adverse Events Associated With Ossicular Prostheses: Utility of a Federal Database. Issue 2 (February 2022) - Tong, Jane Y.; Pasick, Luke J.; Benito, Daniel A.; Monfared, Ashkan; Sataloff, Robert T.
Abstract : Objectives: Ossicular prostheses have demonstrated utility in ossicular chain reconstruction, but their use also is associated with complications. This study aims to explore the utility of the U.S. Food and Drug Administration's Manufacturer and User Facility Device Experience (MAUDE) database for the systematic analysis of complications associated with ossicular prostheses. It also summarizes adverse events to patients (AEPs) and device malfunctions (DMs) reported in the MAUDE database, and attempts to examine the consequences brought about by these events and to identify likely root causes. Study Design: Retrospective analysis of the MAUDE database. Methods: The MAUDE database was queried for reports from January 2010 to May 2020. Data were extracted from reports involving the use of ossicular prostheses. Results: Seventy-three medical device reports, reports submitted to the Food and Drug Administration detailing suspected device-associated malfunctions, injuries, and deaths, involved ossicular prostheses and were included for analysis, from which 126 adverse events were extracted. Of these, 50 (39.7%) were AEPs, while 76 (60.3%) were DMs. The most common AEPs were foreign body and hearing loss, while the DMs reported most commonly were broken prosthesis and displaced prosthesis. Of the 76 DMs, 19 (25%) were attributed to mishandling by the operator, while 6 (7.9%) were due to a defective device. Broken prosthesis was the DM most commonly attributed to mishandling by the operator in 16 (32.7%) cases. Conclusion: Ossicular prostheses are used extensively for ossicular chain reconstruction, but also are associated with adverse events. The MAUDE database in its current form does not appear sufficient for the systematic review of adverse events associated with ossicular prostheses. A more standardized reporting format with clearly defined categories and inclusion of relevant variables might improve the quality of information provided by the MAUDE database.