Acceptability of clinical assays

Acceptability can be viewed in several ways.

On one level, acceptability of an assay is decided by the FDA. The factors in their decision are the value of the information provided by the assay (the sensitivity and specificity of the test) and the potential for clinicians to make incorrect medical decisions based on test error (adverse events). The decision to accept for release for sale or reject an assay amounts to deciding which is less harmful – harm caused by adverse events for a released product or harm caused by the lack of information due to the device having been rejected and not available.

Another level of acceptability relates to released assays that have adverse events. How many adverse events are acceptable? This is often attempted to be prescribed by standards, but they often fall short of the mark. For example, in diabetes, blood glucose meters have the ISO 15197 standard that has limits for 99% of the results. But 1% of glucose meter results that are way off is a huge number.

We need new standards for assays that have goals for adverse events, and data collection methods to track adverse events.

Adverse event causes from 2022 for four continuous glucose monitors

This article has been accepted for publication in the Journal of Diabetes Science and Technology. Here is the abstract.

Background

Adverse events for continuous glucose monitors (CGMs) represent a significant issue for people with diabetes with 281,963 CGM adverse events occurring in 2022. The process to obtain adverse events and the FDA database that contains them are reviewed.

Methods

Tables were created in SQL Server for four CGM products (Dexcom G6, all versions of Abbott Libre, Medtronic Guardian 3, and Senseonics Eversense) containing either malfunction or injury adverse events sorted by the manufacturer’s chosen product code. Since the product code is not always clear (or appropriate), the causes of the events were determined from the text description of the adverse event. The resulting causes were listed in decreasing order in tables for each product and event type.

Results

A common effect of several event causes prevented the user from obtaining a result. Inaccuracy was also a frequent complaint. Other causes were specific to that device.

Conclusions

Creating tables based on manufacturer problem codes for their CGMs, followed by analysis of the adverse event text facilitates the analysis of event causes.  Analyzing adverse event data is the first step in trying to reduce the number of adverse events.