Comparing Clinical Accuracy and Analytical Accuracy Between Continuous Glucose Monitors.

This study compares performance between two continuous glucose monitors (CGMs). The study design contains a mix of laboratory results (CGM vs YSI) and home results (CGM vs glucose meter). Analysis is provided for both clinical accuracy and analytical accuracy of CGM glucose measurements.

Adverse Event Causes From 2022 for Four Continuous Glucose Monitors.

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 US Food and Drug Administration (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.

Retained Diabetes Devices-A Literature Review.

Background: Diabetes management and treatment requires the use of many devices that frequently must puncture the skin, creating a risk of unintentional retention in the body as a retained diabetes device. In this article, we reviewed case studies about retained diabetes devices and presented analyses of the success rate of current imaging techniques in identifying retained devices and the success rate of device removal.

Methods: PubMed and Google Scholar were searched for articles about retained diabetes devices.

Traditional Interference Experiments vs. Method Comparison Interference Experiments.

Two papers have appeared evaluating interferences in glucose meters. These studies are method comparisons with the added information of the medication(s) taken by the subjects. This paper contrasts a traditional interference study with the method comparison protocols.

Adverse Event Data for Years 2018 to 2020 for Diabetes Devices.

Unlike performance evaluations, which are often conducted under ideal conditions, adverse events occur during actual device use for people with diabetes. This report summarizes the number of adverse events for the years 2018 to 2020 for the 3 diabetes devices: blood glucose meters (BG), continuous glucose monitors (CGM), and insulin pumps. A text example of a CGM injury is provided.

More Focus is Needed to Reduce Adverse Events for Diabetes Devices.

Advances in devices for people with diabetes have demonstrated many improvements; yet, the number of adverse events has almost doubled from 2018 to 2019. It is a challenge to examine these events due to a difficult query tool on the FDA website. There are several possible reasons why effort is not devoted to decreasing the number of adverse events including the fact that user error is a common cause.

An Analysis of 2019 FDA Adverse Events for Two Insulin Pumps and Two Continuous Glucose Monitors.

US Food and Drug Administration adverse event data for 2019 were analyzed for two insulin pumps and two continuous glucose monitors (CGMs). The analyses were selective-they were guided by the text described in the adverse events. They included (1) percent using auto mode for the Medtronic 670G pump, (2) distributions of hyper and hypo glucose values for Medtronic and Tandem pumps, (3) a Parkes error grid for Dexcom CGM vs glucose meter when the complaint was inaccuracy, and (4) the most frequent events for Abbott Freestyle.

The YSI 2300 Analyzer Replacement Meeting Report.

This is a summary report of the most important aspects discussed during the YSI 2300 Analyzer Replacement Meeting. The aim is to provide the interested reader with an overview of the complex topic and propose solutions for the current issue. This solution should not only be adequate for the United States or Europe markets but also for all other countries.

Analysis of a Point-of-Care HbA1c Assay: Is It Time for an HbA1c Error Grid?

In an article in , Arnold et al have presented a thorough study of imprecision components for a point-of-care hemoglobin A1c (HbA1c) assay. An interesting and innovative approach is the combination of data from different studies to arrive at a total error estimate. But total error has the oxymoron feature of estimating performance for most (95%) but not all of the results.

Getting More Information From Glucose Meter Evaluations.

Glucose meter evaluations are common in publications and inform whether the meter meets the ISO 15197 specification. The ISO 15197 specifications, which are universally cited, leave 1% of results unspecified, which can be thought of as typical performance of results (99%) versus rare performance (1%). Suggestions are provided to extract more information from these evaluations, including rare performance, since highly discrepant results or failure to obtain a result can be observed in a glucose meter that has met the ISO 15197 specification.

Reducing Glucose Meter Adverse Events by Using Reliability Growth With the FDA MAUDE Database.

Glucose meter evaluations are common and provide important information about glucose meter performance versus standards. Although some meters meet guidelines and others fall short in these evaluations, most results are within the A and B zones of a glucose meter error grid. Another data source that is seldom used is the FDA adverse event database (MAUDE).

Why the Details of Glucose Meter Evaluations Matters.

In an article in the Journal of Diabetes Science and Technology, Macleod and coworkers describe an evaluation of LifeScan glucose meters that focus on the effects of sample types and comparison methods. They make a valid point that these factors influence the accuracy observed in evaluations and recommend the comparison method be the one recommended by the manufacturer for the sample type in the intended use statement. Yet, the recommended comparison method is not a reference method.

Analysis of "Seven Year Surveillance of the Clinical Performance of a Blood Glucose Test-Strip Product".

The article titled "Seven Year Surveillance of the Clinical Performance of a Blood Glucose Test-Strip Product" by Setford and coworkers in this issue of Journal of Diabetes Science and Technology is an impressive study showing that over 7 years in three clinics, using multiple reagent lots, a total of 73 600 samples met the ISO 15197 2015 standard with no results in the D or E zones of a Parkes glucose meter error grid. Three requirements are suggested for a clinically acceptable glucose meter. The authors provide strong evidence for meeting two requirements but fail to provide summarized data about the number of nonnumeric results.

Why the Diabetes Technology Society Surveillance Protocol for Glucose Meters Needs to Be Revised.

The Diabetes Technology Society surveillance protocol provides a seal of approval for a glucose meter if a sufficient number of a candidate glucose meter's results meet ISO 15197:2013 limits. The protocol provides clear details about how to conduct this study and analyze the data but has two flaws. There is no specification about the size of glucose meter errors that are outside of ISO limits.

The problem with total error models in establishing performance specifications and a simple remedy.

A recent issue in this journal revisited performance specifications since the Stockholm conference. Of the three recommended methods, two use total error models to establish performance specifications. It is shown that the most commonly used total error model - the Westgard model - is deficient, yet even more complete models fail to capture all errors that comprise total error.

Improving the Glucose Meter Error Grid With the Taguchi Loss Function.

Glucose meters often have similar performance when compared by error grid analysis. This is one reason that other statistics such as mean absolute relative deviation (MARD) are used to further differentiate performance. The problem with MARD is that too much information is lost.

The chronic injury glucose error grid: a tool to reduce diabetes complications.

Traditional glucose error grids provide error limits for glucose meters. These criteria help to assess the meter's suitability to prevent acute injury. We present a rationale for an error grid that provides a different set of error limits to help prevent chronic injury in diabetes.