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. Both types of accuracy are important. Error grid analysis informs about clinical accuracy. Analytical error is important as most users would prefer a CGM with a smaller spread of CGM versus reference differences. The authors provide the percentage of time that no result was obtained. Study design, data analysis, and editorial support were provided by a manufacturer of one of the products studied. This study provides a template for comparisons.

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. As 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.

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. Unlike the advice in CLSI EP7, a substance that interferes should be reported even if the level of interference is clinically acceptable. The evidence of no clinically important interference in the method comparison protocol is very weak, and there is no possibility to detect statistically significant interferences. I provide an example where vitamin C at a therapeutic level was within clinical error limits, but when the concentration was at levels used to treat cancer, there was bias well above clinically acceptable limits.

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. Relevant articles that included sufficient details about discovery and removal of the device were included. The success rate of identification and the success rate of removal of retained devices were both calculated as percentages. RESULTS: Sixteen case studies of retained diabetes devices were identified. These devices included parts of continuous glucose monitors and infusion sets, a lancet, and various types of needles for insulin injection. Each case is presented with details about the year of publication, the retained diabetes device, the company that produced the device, the age and gender of the patient, the type of diabetes that the patient had, the location of the device, the reason for initial discovery of the retained device, the process of locating the device, the success rate for removal of the device, and the removal procedure of the device. Analysis revealed a 100% success rate for the use of imaging technology including X-rays and computed tomography to identify a retained diabetes device. The patients with retained diabetes devices had a 62.5% success rate for eventual removal of the device. CONCLUSIONS: With the increasing use of injected, inserted, and implanted diabetes wearables for digital health, it is likely that some of the devices will detach, break apart, or otherwise become retained in the body. It is important to be aware of available technologies to identify retained diabetes devices so that it will be possible in most cases to surgically remove these devices if they detach or become retained.

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. We found that for the 670G pump, there were more hypo events when auto mode was on than when auto mode was off. With Dexcom CGMs, users complained about inaccurate result when most results were in the B zone. With the Abbott Freestyle, the most frequent adverse event was an allergic skin reaction.

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. This commentary serves to raise awareness of the adverse event problem.

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. Possible reasons are suggested for trends. Whereas the rate per test result (events/usage) is exceedingly small, the rate per patient (events/people with diabetes that use insulin) is of concern. Hence, it is important to determine event causes and provide corrective actions. The first step is to put in place routine analysis of adverse event data for diabetes devices.

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

In an article in Journal of Diabetes Science and Technology, 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. An HbA1c error grid would provide the severity for results that exceed the 6% requirement. Since this device is intended for Clinical Laboratory Improvement Amendments waived labs and allows for finger-stick samples, monitoring the Food and Drug Administration adverse event database (MAUDE, Manufacturer and User Facility Device Experience) is recommended.

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. It is also recommended that when manufacturers perform evaluations, they analyze adverse events contained in the FDA MAUDE database. Finally, we point out an important problem with the ISO 15197 specifications.

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. The accuracy hierarchy of definitive, reference, and field methods originally described by Tietz should remind one that virtually all glucose meter evaluations use commercially available field methods as the comparison method. Finally, one should not neglect the FDA adverse event database as a way to assess glucose meter performance.

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). This database describes glucose meter malfunctions and injury as reported by actual users and returned 10 837 adverse events across all meters for the first 7 months of 2018. Reliability growth management is an established tool to reduce failure rates. A reliability growth example is presented followed by a discussion of how this tool could be applied to reduce glucose meter failure events using the MAUDE database.

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. Finally, the authors overstate some results, called "spin" by some which is not necessary. The superb results should stand on their own.

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. A meter that has a result in the E zone of a glucose meter error grid could receive the DTS seal of approval. In addition, the protocol uses the ISO standard, which could be considered a "state of the art" standard instead of an error grid, which is a clinical standard. Remedies for these problems are to replace the ISO standard with an error grid and to include requirements for errors found in C or higher zones of an error grid.

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. Moreover, total error models are often set at 95% of results, which leave 5% of results as unspecified. Glucose meter performance standards are used to illustrate these problems. The Westgard model is useful to asses assay performance but not to set performance specifications. Total error can be used to set performance specifications if the specifications include 100% of the results.

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. But additional information is available within the A zone of an error grid by using the Taguchi loss function. Applying the Taguchi loss function gives each glucose meter difference from reference a value ranging from 0 (no error) to 1 (error reaches the A zone limit). Values are averaged over all data which provides an indication of risk of an incorrect medical decision. This allows one to differentiate glucose meter performance for the common case where meters have a high percentage of values in the A zone and no values beyond the B zone. Examples are provided using simulated data.