Development of a classification scheme for examining adverse events associated with medical devices, specifically the Davinci surgical system as reported in the FDA MAUDE database

Priyanka Gupta, John Schomburg, Suprita Krishna, Oluwakayode Adejoro, Qi Wang, Benjamin Marsh, Andrew Nguyen, Juan Reyes Genere, Patrick Self, Erik Lund, Badrinath R. Konety

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Objective: To examine the Manufacturer and User Facility Device Experience Database (MAUDE) database to capture adverse events experienced with the Da Vinci Surgical System. In addition, to design a standardized classification system to categorize the complications and machine failures associated with the device. Summary Background Data: Overall, 1,057,000 DaVinci procedures were performed in the United States between 2009 and 2012. Currently, no system exists for classifying and comparing device-related errors and complications with which to evaluate adverse events associated with the Da Vinci Surgical System. Methods: The MAUDE database was queried for events reports related to the DaVinci Surgical System between the years 2009 and 2012. A classification system was developed and tested among 14 robotic surgeons to associate a level of severity with each event and its relationship to the DaVinci Surgical System. Events were then classified according to this system and examined by using Chi-square analysis. Results: Two thousand eight hundred thirty-seven events were identified, of which 34% were obstetrics and gynecology (Ob/Gyn); 19%, urology; 11%, other; and 36%, not specified. Our classification system had moderate agreement with a Kappa score of 0.52. Using our classification system, we identified 75% of the events as mild, 18% as moderate, 4% as severe, and 3% as life threatening or resulting in death. Seventy-seven percent were classified as definitely related to the device, 15% as possibly related, and 8% as not related. Urology procedures compared with Ob/Gyn were associated with more severe events (38% vs 26%, p < 0.0001). Energy instruments were associated with less severe events compared with the surgical system (8% vs 87%, p < 0.0001). Events that were definitely associated with the device tended to be less severe (81% vs 19%, p < 0.0001). Conclusions: Our classification system is a valid tool with moderate inter-rater agreement that can be used to better understand device-related adverse events. The majority of robotic related events were mild but associated with the device.

Original languageEnglish (US)
Pages (from-to)27-31
Number of pages5
JournalJournal of endourology
Volume31
Issue number1
DOIs
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© Mary Ann Liebert, Inc. 2017.

Keywords

  • classification of adverse events
  • FDA MAUDE
  • robotic surgery

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