Device malfunctions and patient harm in robotic-assisted prostatectomy: a 17-year review of the FDA MAUDE database.

Robot-assisted radical prostatectomy (RARP) is the predominant surgical treatment for localized prostate cancer. However, postmarket surveillance of device-related adverse events is critical, as robotic systems entered the market through pathways requiring less extensive premarket clinical testing. This study analyzes device malfunctions and patient harm associated with RARP reported to the FDA’s MAUDE database over a 17-year period. We conducted a retrospective analysis of the MAUDE database from 2009 to March 2026. All reports related to prostatectomy using the Da Vinci surgical system were extracted and analyzed descriptively across three system models (Si, Xi, and SP). Device and patient problem frequencies were calculated, and a co-occurrence network analysis was performed to identify commonly associated device issues. Of 11,190 retrieved reports, 1,080 were related to prostatectomy and included. Across the three systems, the most common device malfunction was “Visual Prompts will not Clear,” accounting for 33.3% of Xi reports, 22.4% of SP reports, and 22.4% of Si reports, followed by “Output Problem” (17.6% for Xi, 12.9% for SP and Si). Network analysis revealed a strong co-occurrence between “Defective Component” and “Device Displays Incorrect Message” (Weight = 14 for Xi), suggesting a recurrent hardware-software failure mode consistent across platforms. Patient harms were most frequently reported as “Unspecified Tissue Injury” (31.5% for Xi, 38% for both SP and Si) and “Hemorrhage/Bleeding” (14.2% for Xi). The mortality rate among reported events was approximately 0.8–0.9%. This 17-year analysis reveals a 3.5% device malfunction rate in RARP, with a distinct pattern of software-interface and output problems persisting across the Xi, SP, and Si systems. While most patient harms align with known procedural risks, the co-occurrence of specific device failures points to systematic vulnerabilities that transcend individual system models. The Si system showed a unique profile of device problems related to electronic properties and incorrect display messages, alongside a higher proportion of severe patient outcomes in reported events. These findings highlight the need for enhanced preoperative system checks, improved surgeon training in malfunction management, and the development of more robust prospective surveillance systems to complement passive reporting.