William L. Clarke

Closed-loop (CL) therapy systems should be safe, efficacious, and easily manageable for type 1 diabetes mellitus patient use. For the first two clinical requirements, noninferiority and superiority criteria must be determined based on current conventional and intensive therapy outcomes. Current frequencies of hypoglycemia and diabetic ketoacidosis are reviewed and safety expectations for CL therapy systems are proposed. Glycosylated hemoglobin levels lower than current American Diabetes Association recommendations for different age groups are proposed as superiority criteria. Measures of glycemic variability are described and the recording of blood glucose levels as percentages within, above, and below a target range are suggested as reasonable alternatives to sophisticated statistical analyses. It is also suggested that Diabetes Quality of Life and Fear of Hypoglycemia surveys should be used to track psychobehavioral outcomes. Manageability requirements for safe and effective clinical management of CL systems are worth being underscored. The weakest part of the infusion system remains the catheter, which is exposed to variable and under-delivery incidents. Detection methods are needed to warn both the system and the patient about altered insulin delivery, including internal pressure and flow alarms. Glucose monitor sensor accuracy is another requirement; it includes the definition of conditions that lead to capillary glucose measurement, eventually followed by sensor recalibration or replacement. The crucial clinical requirement will be a thorough definition of the situations when the patient needs to move from CL to manual management of insulin delivery, or inversely can switch back to CL after a requested interruption. Instructions about these actions will constitute a major part of the education process of the patients before using CL systems and contribute to the manageability of these systems.

Background: The objective of this study was to understand the level of performance of blood glucose monitors as assessed in the published literature. Methods: Medline from January 2000 to October 2009 and reference lists of included articles were searched to identify eligible studies. Key information was abstracted from eligible studies: blood glucose meters tested, blood sample, meter operators, setting, sample of people (number, diabetes type, age, sex, and race), duration of diabetes, years using a glucose meter, insulin use, recommendations followed, performance evaluation measures, and specific factors affecting the accuracy evaluation of blood glucose monitors. Results: Thirty-one articles were included in this review. Articles were categorized as review articles of blood glucose accuracy (6 articles), original studies that reported the performance of blood glucose meters in laboratory settings (14 articles) or clinical settings (9 articles), and simulation studies (2 articles). A variety of performance evaluation measures were used in the studies. The authors did not identify any studies that demonstrated a difference in clinical outcomes. Examples of analytical tools used in the description of accuracy (e.g., correlation coefficient, linear regression equations, and International Organization for Standardization standards) and how these traditional measures can complicate the achievement of target blood glucose levels for the patient were presented. The benefits of using error grid analysis to quantify the clinical accuracy of patient-determined blood glucose values were discussed. Conclusions: When examining blood glucose monitor performance in the real world, it is important to consider if an improvement in analytical accuracy would lead to improved clinical outcomes for patients. There are several examples of how analytical tools used in the description of self-monitoring of blood glucose accuracy could be irrelevant to treatment decisions.

Recent progress in the development of clinically accurate continuous glucose monitors (CGMs), automated continuous insulin infusion pumps, and control algorithms for calculating insulin doses from CGM data have enabled the development of prototypes of subcutaneous closed-loop systems for controlling blood glucose (BG) levels in type 1 diabetes. The use of a new personalized model predictive control (MPC) algorithm to determine insulin doses to achieve and maintain BG levels between 70 and 140 mg/dl overnight and to control postprandial BG levels is presented. Eight adults with type 1 diabetes were studied twice, once using their personal open-loop systems to control BG overnight and for 4 h following a standardized meal and once using a closed-loop system that utilizes the MPC algorithm to control BG overnight and for 4 h following a standardized meal. Average BG levels, percentage of time within BG target of 70–140 mg/dl, number of hypoglycemia episodes, and postprandial BG excursions during both study periods were compared. With closed-loop control, once BG levels achieved the target range (70–140 mg/dl), they remained within that range throughout the night in seven of the eight subjects. One subject developed a BG level of 65 mg/dl, which was signaled by the CGM trend analysis, and the MPC algorithm directed the discontinuance of the insulin infusion. The number of overnight hypoglycemic events was significantly reduced (p = .011) with closed-loop control. Postprandial BG excursions were similar during closed-loop and open-loop control Model predictive closed-loop control of BG levels can be achieved overnight and following a standardized breakfast meal. This “artificial pancreas” controls BG levels as effectively as patient-directed open-loop control following a morning meal but is significantly superior to open-loop control in preventing overnight hypoglycemia.

Continuous glucose monitors (CGMs) generate data streams that are both complex and voluminous. The analyses of these data require an understanding of the physical, biochemical, and mathematical properties involved in this technology. This article describes several methods that are pertinent to the analysis of CGM data, taking into account the specifics of the continuous monitoring data streams. These methods include: (1) evaluating the numerical and clinical accuracy of CGM. We distinguish two types of accuracy metrics—numerical and clinical—each having two subtypes measuring point and trend accuracy. The addition of trend accuracy, e.g., the ability of CGM to reflect the rate and direction of blood glucose (BG) change, is unique to CGM as these new devices are capable of capturing BG not only episodically, but also as a process in time. (2) Statistical approaches for interpreting CGM data. The importance of recognizing that the basic unit for most analyses is the glucose trace of an individual, i.e., a time-stamped series of glycemic data for each person, is stressed. We discuss the use of risk assessment, as well as graphical representation of the data of a person via glucose and risk traces and Poincaré plots, and at a group level via Control Variability-Grid Analysis. In summary, a review of methods specific to the analysis of CGM data series is presented, together with some new techniques. These methods should facilitate the extraction of information from, and the interpretation of, complex and voluminous CGM time series.

Continuous Glucose Sensors (CGS) generate rich and informative continuous data streams which have the potential to improve the glycemic condition of the patient with diabetes. Such data are critical to the development of closed loop systems for automated glycemic control. Thus the numerical and clinical accuracy of such must be assured. Although numerical point accuracy of these systems has been described using traditional statistics, there are no requirements, as of yet, for determining and reporting the rate (trend) accuracy of the data generated. In addition, little attention has been paid to the clinical accuracy. of these systems. Continuous Glucose-Error Grid Analysis (CG-EGA) is the only method currently available for assessing the clinical accuracy of such data and reporting this accuracy for each of the relevant glycemic ranges, - hypoglycemia, euglycemia, hyperglycemia. This manuscript reviews the development of the original Error Grid Analysis (EGA) and describes its inadequacies when used to determine point accuracy of CGS systems. The development of CG-EGA as a logical extension of EGA for use with CGS is described in detail and examples of how it can be used to describe the clinical accuracy of several CGS are shown. Information is presented on how to obtain assistance with the use of CG-EGA.

Continuous glucose sensors (CGS) offer the potential to greatly change the lives of people with diabetes. Even though two of these systems (Guardian RT, Medtronic, Northridge, CA, and DexCom STS, DexCom, San Diego, CA) have been approved by the Food and Drug Administration for use as adjuncts to self-blood glucose monitoring (SBGM), questions remain concerning the accuracy of these devices. When considering accuracy, two distinct approaches should be emphasized: (1) numerical and (2) clinical. Because CGS data are a process in time, each of these two approaches includes two subtypes of accuracy: point and rate. Conventional statistics such as correlation coefficients, mean and median relative absolute differences, and International Standards Organization criteria are measures of numerical point accuracy. A new measure, the R deviation, is introduced to quantify numerical rate accuracy. Error-grid analysis (Clarke EGA) measures clinical point accuracy. The only measure of both clinical point accuracy and rate accuracy is continuous glucose error-grid analysis. This analysis is a combination of two components, P-EGA measuring point accuracy and R-EGA measuring rate accuracy, which are designed to assess the information that distinguishes continuous glucose measurements from intermittent SBGM determinations. Further, a better understanding of the source of the error associated with time lag and its effect on CGS readings may improve sensor output. Finally, the reliability of the CGS sensors, in terms of initial calibration and long-term application, needs to be assessed carefully if current CGS systems are to be used as hypoglycemia monitors or incorporated in the future design of closed loop (artificial pancreas) systems.

OBJECTIVE—The Commonwealth of Virginia passed legislation in 1999 requiring nonmedical school personnel to assist students with the management of type 1 diabetes when school nurses were unavailable. This study was designed to determine which school personnel in Virginia currently assist type 1 diabetic students with insulin administration and management of hypoglycemia and to determine if these students are being cared for in a safe manner.

Thyroid dysfunction is recognized in the newborns of mothers affected by Graves' disease during pregnancy. We describe the development of concurrent hyperthyroidism and hypothyroidism in the twin infants of a mother with Graves' disease diagnosed during pregnancy.