Adverse Events in Robotic Surgery: A Retrospective Study of 14 Years of FDA Data

Abstract

Background: Use of robotic systems for minimally invasive surgery has rapidly increased during the last decade. Understanding the causes of adverse events and their impact on patients in robot-assisted surgery will help improve systems and operational practices to avoid incidents in the future.

Methods: By developing an automated natural language processing tool, we performed a comprehensive analysis of the adverse events reported to the publicly available MAUDE database (maintained by the U.S. Food and Drug Administration) from 2000 to 2013. We determined the number of events reported per procedure and per surgical specialty, the most common types of device malfunctions and their impact on patients, and the potential causes for catastrophic events such as patient injuries and deaths.

Results: During the study period, 144 deaths (1.4% of the 10,624 reports), 1,391 patient injuries (13.1%), and 8,061 device malfunctions (75.9%) were reported. The numbers of injury and death events per procedure have stayed relatively constant (mean = 83.4, 95% confidence interval (CI), 74.2-92.7 per 100,000 procedures) over the years. Surgical specialties for which robots are extensively used, such as gynecology and urology, had lower numbers of injuries, deaths, and conversions per procedure than more complex surgeries, such as cardiothoracic and head and neck (106.3 vs. 232.9 per 100,000 procedures, Risk Ratio = 2.2, 95% CI, 1.9-2.6). Device and instrument malfunctions, such as falling of burnt/broken pieces of instruments into the patient (14.7%), electrical arcing of instruments (10.5%), unintended operation of instruments (8.6%), system errors (5%), and video/imaging problems (2.6%), constituted a major part of the reports. Device malfunctions impacted patients in terms of injuries or procedure interruptions. In 1,104 (10.4%) of all the events, the procedure was interrupted to restart the system (3.1%), to convert the procedure to non-robotic techniques (7.3%), or to reschedule it (2.5%).

Conclusions: Despite widespread adoption of robotic systems for minimally invasive surgery in the U.S., a non-negligible number of technical difficulties and complications are still being experienced during procedures. Adoption of advanced techniques in design and operation of robotic surgical systems and enhanced mechanisms for adverse event reporting may reduce these preventable incidents in the future.

Fig 1. An example adverse event report from the publicly available FDA MAUDE database.

This report is accessible through the online FDA MAUDE database at: http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfMAUDE/Detail.cfm?MDRFOI__ID=2240665. Other MAUDE reports can also be accessed through searching the online database: http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfMAUDE/TextSearch.cfm.

Fig 2. Data extraction and analysis flow from the FDA MAUDE database.

The dictionaries of keywords for surgery types/specialties and surgical instruments were constructed based on the online information available on the da Vinci surgeries [32] and instruments catalog [33] from the manufacturer. The analysis tool was developed using open-source Python libraries for natural language processing, data analysis, and machine learning.

Fig 3. Annual Numbers of Adverse Event Reports and Rates of Events per Procedure.

The left Y-axis corresponds to the bars showing the absolute numbers of adverse events (based on the years that reports were received by the FDA). The right Y-axis corresponds to the trend lines showing (in logarithmic scale) the annual number of adverse events per 100,000 procedures (based on the year the events occurred). Numbers on the bars indicate number of deaths reported per year. Error bars represent 95% confidence intervals for the proportion estimates. Because of the small number of injury and death events reported for 2004 and 2005, a combined rate was calculated for 2004–2006. Note that of all the events, 40 were reported as part of the articles or the legal disputes received by the manufacturer.

Fig 4. Cumulative rates of malfunctions per procedure.

The rates of malfunctions per procedure were obtained for each week (see S1 Fig for more details on the estimation of the number of procedures).