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text/csvFDA_MAUDE_Robotic_Surgery_Raw_Data_2000_2013.csv (15MB)
The adverse events reports related to robotic surgical systems and instruments, submitted to the publicly available FDA MAUDE database between January 2000 and December 2013. We extracted this data by searching the MAUDE records which are available on the FDA website: file


text/csvFDA_MAUDE_Robotic_Surgery_Structured_Data.csv (15MB)
Structured data generated by our natural language parsing tool, after analysis of unstructured fields in the FDA MAUDE records. The new information (fields) extracted from the data include: • Patient injury (such as burns, cuts, or damage to organs) and death events that were reported under another Event Type, such as “Malfunction” or “Other”. • Surgical specialty and type of robotic procedure during which the adverse events occurred. • Major types of device or instrument malfunctions (e.g., falling of burnt/broken pieces of instruments into patients’ bodies or electrical arcing of instruments) • Adverse events that caused an interruption in the progress of surgery, by leading the surgical team to troubleshoot technical problems (e.g., restarting the system), convert the procedure to non-robotic surgical approaches (e.g., laparoscopy or open surgery), or abort the procedure and reschedule it to a later time.CSV file


Title:Adverse Events in Robotic Surgery: A Retrospective Study of 14 Years of FDA Data
Author(s):Alemzadeh, Homa
Subject(s):Robotic Surgery, Minimally invasive surgery, Patient safety, Surgery complications, Adverse Events, FDA MAUDE database, Accident Analysis, Systems Safety
Abstract:Background: Use of robotic systems for minimally invasive surgery has rapidly increased during the last decade. Yet, little is known about the safety and effectiveness of surgical robots in various surgical specialties. 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.
Issue Date:2015
Type:dataset / spreadsheet
Date Available in IDEALS:2015-11-16

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