What is it about?
Researchers developed a new method to identify possible predicate creep by using FDA data like product codes and regulatory categories. They tested this method on a real example — a robotic surgery system called the Da Vinci Si. Using their method, they found evidence of predicate creep in how the Da Vinci system was approved — meaning it had been cleared by comparing it to older devices that weren’t entirely similar.
Featured Image
Photo by Natanael Melchor on Unsplash
Why is it important?
This research offers a new tool to track and study device approval patterns, helping regulators and policymakers spot safety risks earlier. It also adds to ongoing concerns about whether the 510(k) process is being used too broadly — especially for complex or high-risk devices.
Perspectives
While our research focuses on ways to identify predicate creep, it identify if this creep is, in fact, problematic. If all the preceding predicate devices are safe, and precautions are taken to mitigate small-scale predicate creep, in many cases devices exhibiting large-scale predicate creep may still be safe. Additional research is needed to develop mechanisms to identify when this creep may pose additional risk.
Sandra Rothenberg
Rochester Institute of Technology
Read the Original
This page is a summary of: Identification of predicate creep under the 510(k) process: A case study of a robotic surgical device, PLOS One, March 2023, PLOS,
DOI: 10.1371/journal.pone.0283442.
You can read the full text:
Contributors
The following have contributed to this page
