Health Law Advisor

When I was working on my Masters in data science, one of the projects I did was to create an algorithm that would take an intended use statement for a medical device and predict whether FDA would require a clinical trial.  It worked fairly well, with accuracy of about 95%.

Since that’s a dynamic algorithm in which the user inputs an intended use statement and gets a prediction of FDA’s decision, I wanted to go about a similar task this month: create a static word cloud to show what words are most associated with intended use statements where FDA has required a clinical trial.  At least in theory, this static representation might give you a sense of words in an intended use statement that are more likely to push your device toward a clinical trial.

Introduction

Frequently, I am asked by clients to predict how long it will take for FDA to review and clear a 510(k).  At a high level, I observe that on average clearance can take 160 days according to the data.  Then, beyond that, I observe that review times are highly variable among differing product codes, and the very first Unpacking Averages post I wrote in October 2021 provided a graphic to show just how much variation there was depending on the technology.  Here, though, I want to dive into yet another separate factor that should be taken into account, the seasonality of FDA ...

Our latest focus is trying to bring data to bear on common questions we get asked by clients.  Last month the topic was: how well does my device need to perform to get premarket clearance from FDA?  This month it is: how big does my sample size need to be for any necessary clinical trial for premarket clearance? 

To date, our typical answer has been, it depends.[1]  We then explain that it’s not really a regulatory question, but a question for a statistician that is driven by the design of the clinical trial.  And the design of the clinical trial is driven by the question the clinical trial is trying ...

It’s common for a client to show up at my door and explain that they have performance data on a medical device they have been testing, and for the client to ask me if the performance they found is adequate to obtain FDA clearance through the 510(k) process. I often respond, very helpfully, “it depends.” But for some reason clients aren’t completely satisfied by that.

I then volunteer that a general rule of thumb is 95%, but that this is just a rule of thumb. For Class II medical devices undergoing review through the 510(k) process, the legal standard is that the applicant must show that ...

It is certainly easy, when writing code to accomplish some data science task, to start taking the data on face value.  In my mind, the data can simply become what they claim to be.  But it’s good to step back and remember the real world in which these data are collected, and how skeptical we need to be regarding their meaning.  I thought this month might be an opportunity to show how two different FDA databases produce quite different results when they should be the same.

The motivation for this month’s post was my frustration with the techniques for searching the FDA’s 510(k) database.  Here I’m not talking about just using the search feature that FDA provides online. Instead, I have downloaded all of the data from that database and created my own search engine, but there are still inherent limitations in what the data contain and how they are structured.  For one, if you want to submit a premarket notification for an over-the-counter product, it really isn’t easy to find predicates that are specifically cleared for over-the-counter without a lot of manual work.

To see if I could find an easier way, I decided to use the database FDA maintains for unique device identifiers, called the Global Unique Device Identification Database (GUDID).  You can search that database using the so-called AccessGUDID through an FDA link that takes you to the NIH where the database is stored. That site only allows for pretty simple search, so for what I needed to do, I downloaded the entire database so I could work directly on the data myself.

While the UDI database is enormous at this juncture (over 3 million products), what I found left me with questions about just how comprehensive and complete the data are.  At the same time, it seems like a good way to supplement the information that can be gleaned from the 510(k) database.

A private equity client asked us recently to assess a rumor that FDA was on the warpath in enforcing the 510(k) requirement on medical devices from a particular region.  Such a government initiative would significantly deter investments in the companies doing the importing.  Turns out, the agency was not.  The FDA’s recent activities in the region were well within their historical norms.

But the project got us thinking, what does the agency’s enormous database on import actions tell us about the agency’s enforcement priorities more generally?  There are literally thousands of ways to slice and dice the import data set for insights, but we picked just one as an example.  We wanted to assess, globally, over the last 20 years, in which therapeutic areas has FDA been enforcing the 510(k) requirement most often?

You might be thinking, that’s an odd title: obviously FDA’s breakthrough device designation is helpful.  However, after looking at the data, my conclusion is that I would avoid the breakthrough device designation for any product that qualifies for the 510(k) process.  The process is likely not helpful for such devices.

[Update - August 3, 2022: See the bottom of this post.]