7. Diabetes Technology: Standards of Medical Care in Diabetes-2020.

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc20-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc20-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

System Accuracy Assessment of a Blood Glucose Meter With Wireless Internet Access Associated With Unusual Hypoglycemia Patterns in Clinical Trials.

BACKGROUND: In recent randomized clinical trials, an unusual reporting pattern of glycemic data and hypoglycemic events potentially related to an internet enabled blood glucose meter (MyGlucoHealth, BGM) was observed. Therefore, this clinical study was conducted to evaluate the system accuracy of the BGM in accordance with the ISO15197:2015 guidelines with additional data collection. METHODS: To investigate system accuracy, 10 of 3088 devices and 6 of 23 strip lots, used in the trials, were selected by a randomization procedure and a standard repeatability assessment. YSI 2300 STAT Plus was used as the standard reference method. The samples were distributed as per the ISO15197:2015 recommendations with 20 additional samples in the hypoglycemic range. Each sample was tested with 6 devices and 6 strip lots with double determinations. RESULTS: Overall, 121 subjects with blood glucose values 26-423 mg/dL were analyzed, resulting in 1452 data points. In all, 186/1452 readings (12.8%) did not meet the ISO acceptance criteria. Data evaluated according to the FDA guidelines showed that 336/1452 (23.1%) readings did not meet the acceptance criteria. A clear bias toward elevated values was observed for BG <100 mg/dL (MARD: 11.0%). CONCLUSIONS: The results show that the BGM, although approved according to standard regulatory guidelines, did not meet the level of analytical accuracy required for clinical treatment decisions according to ISO 15197:2015 and FDA requirements. In general, caution should be exercised before selection of BGMs for patients and in clinical trials.

A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations.

Numerous accelerometers and prediction methods are used to estimate energy expenditure (EE). Validation studies have been limited to small sample sizes in which participants complete a narrow range of activities and typically validate only one or two prediction models for one particular accelerometer. The purpose of this study was to evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Two hundred and seventy-seven participants completed an average of six treadmill (TRD) (1.34, 1.56, 2.23 ms(-1) each at 0 and 3% grade) and five self-paced activities of daily living (ADLs). EE estimates were compared with indirect calorimetry. Accelerometers were worn while EE was measured using a portable metabolic unit. To estimate EE, 4 ActiGraph prediction models were used, 5 Actical models, and 2 RT3 proprietary models. Across all activities, each equation underestimated EE (bias -0.1 to -1.4 METs and -0.5 to -1.3 kcal, respectively). For ADLs EE was underestimated by all prediction models (bias -0.2 to -2.0 and -0.2 to -2.8, respectively), while TRD activities were underestimated by seven equations, and overestimated by four equations (bias -0.8 to 0.2 METs and -0.4 to 0.5 kcal, respectively). Misclassification rates ranged from 21.7 (95% CI 20.4, 24.2%) to 34.3% (95% CI 32.3, 36.3%), with vigorous intensity activities being most often misclassified. Prediction equations did not yield accurate point estimates of EE across a broad range of activities nor were they accurate at classifying activities across a range of intensities (light <3 METs, moderate 3-5.99 METs, vigorous ≥ 6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE.

Clinical review: Realistic expectations and practical use of continuous glucose monitoring for the endocrinologist.

CONTEXT: Real-time continuous glucose monitoring (CGM) has been available for type 1 diabetes for several years. This paper is a status report on our early experiences with this next technology. EVIDENCE ACQUISITION: The two major sources of data acquisition included PubMed search strategies and personal experience of the author from clinical experience. EVIDENCE SYNTHESIS: Data assessing CGM accuracy, short-term outcomes (12 wk), and longer term outcomes (6 months) are reported. Potential strategies for successful and efficient use in an office or clinic setting are also discussed. Practical aspects of CGM use (alarm settings, using glycemic trending information) are also reviewed. CONCLUSIONS: Accuracy of this technology has improved in the short amount of time it has been available. Six-month data suggest that patient selection is a key for success. Patients who do not understand or practice the basics of intensive insulin therapy have the greatest challenges. Those who do best watch the receiver frequently, continue with frequent home blood glucose monitoring, use the trending information to make insulin adjustments, and understand the limitations of the technology. With insurance reimbursement improving, CGM is gaining acceptance as an important tool for the management of type 1 diabetes. Like home blood glucose monitoring and insulin pump therapy, this technology by itself is not a panacea for diabetes control. However, it further adds to our ability to improve the lives of people with diabetes. Long-term, the hope is that this technology will pave the way for a "closed-loop" device.

Non-invasive glucose monitoring: assessment of technologies and devices according to quantitative criteria.

Aim of this review was to describe the main technologies for non-invasive glucose monitoring and the corresponding most relevant devices. The review tries to overcome the limitations of previous reviews on this topic, such as the lack of objective criteria for inclusion or exclusion of technologies or devices, and the poor organization of the information, which often does not allow easy comparison between technologies and devices. In this review, the information is concise and organized into specific categories, and hence it becomes easy to compare advantages and disadvantages of the different technologies and devices. For technologies, the categories of information considered are the technology name, the underlying physical principle, the technology limitations and the measurement sites on the human body. For devices, the categories of information are the device name, its approval condition (FDA Approval and/or CE Mark), the technology on which it is based, a device general description, the tests performed on the device, the corresponding results, safety information, aspects affecting usability, current status of the device and the manufacturer, an Internet reference for the device. A total of 14 technologies and 16 devices are included. Conclusions of the review were that, despite some interesting and promising technologies and devices, a satisfactory solution to the non-invasive glucose monitoring problem still requires further efforts.

The market for new diabetes products and the role of third-party payors.

Diabetes devices and supplies ("Products") are paid for differently when used in different patient care settings, and when paid for by different third-party payors ("Payors"). Manufacturers of a Product need to understand the different markets defined by the Payors. Payors cover different population groups with different risks of experiencing diabetes. The Payors determine whether or not particular products will be made available to their eligible population. The Payors decide how much to pay for products that they decide to cover. Medicare is a good example of how product markets are determined by Payor decisions. Medicare makes national coverage decisions, binding across the United States, and permits local coverage decisions when there is no national decision. Manufacturers need to understand this process in order to determine whether to try to obtain national decisions or local decisions. Manufacturers also need to understand the payment methodologies used in different patient care settings in order to identify how they should market their Products in each of those settings.

How to get paid for a new diabetes product.

Manufacturers of diabetes devices and supplies ("Products") should consider developing information during the Products' clinical trials that will assist in the later marketing of their Products. Manufacturers should engage in the clinical trials of their Products physicians who may be able to provide future assistance in obtaining appropriate reimbursement codes for the Products. Manufacturers should develop during the clinical trial process cost-effectiveness information that can be used to pursue coverage and reimbursement decisions from third-party payors in the future. Different Product reimbursement codes are developed by the American Medical Association and by the Centers for Medicare and Medicaid Services. For both these organizations there is a long time frame to get any code established. Manufacturers can develop information in a way that will make these time periods shorter if the appropriate strategy is developed early in the Product development process.