Performance of a Wearable Ring in Controlled Hypoxia: A Prospective Observational Study.

BACKGROUND: Over recent years, technological advances in wearables have allowed for continuous home monitoring of heart rate and oxygen saturation. These devices have primarily been used for sports and general wellness and may not be suitable for medical decision-making, especially in saturations below 90% and in patients with dark skin color. Wearable clinical-grade saturation of peripheral oxygen (SpO2) monitoring can be of great value to patients with chronic diseases, enabling them and their clinicians to better manage their condition with reliable real-time and trend data. OBJECTIVE: This study aimed to determine the SpO2 accuracy of a wearable ring pulse oximeter compared with arterial oxygen saturation (SaO2) in a controlled hypoxia study based on the International Organization for Standardization (ISO) 80601-2-61:2019 standard over the range of 70%-100% SaO2 in volunteers with a broad range of skin color (Fitzpatrick I to VI) during nonmotion conditions. In parallel, accuracy was compared with a calibrated clinical-grade reference pulse oximeter (Masimo Radical-7). Acceptable medical device accuracy was defined as a maximum of 4% root mean square error (RMSE) per the ISO 80601-2-61 standard and a maximum of 3.5% RMSE per the US Food and Drug Administration guidance. METHODS: We performed a single-center, blinded hypoxia study of the test device in 11 healthy volunteers at the Hypoxia Research Laboratory, University of California at San Francisco, under the direction of Philip Bickler, MD, PhD, and John Feiner, MD. Each volunteer was connected to a breathing apparatus for the administration of a hypoxic gas mixture. To facilitate frequent blood gas sampling, a radial arterial cannula was placed on either wrist of each participant. One test device was placed on the index finger and another test device was placed on the fingertip. SaO2 analysis was performed using an ABL-90 multi-wavelength oximeter. RESULTS: For the 11 participants included in the analysis, there were 236, 258, and 313 SaO2-SpO2 data pairs for the test device placed on the finger, the test device placed on the fingertip, and the reference device, respectively. The RMSE of the test device for all participants was 2.1% for either finger or fingertip placement, while the Masimo Radical-7 reference pulse oximeter RMSE was 2.8%, exceeding the standard (4% or less) and the Food and Drug Administration guidance (3.5% or less). Accuracy of SaO2-SpO2 paired data from the 4 participants with dark skin in the study was separately analyzed for both test device placements and the reference device. The test and reference devices exceeded the minimum accuracy requirements for a medical device with RMSE at 1.8% (finger) and 1.6% (fingertip) and for the reference device at 2.9%. CONCLUSIONS: The wearable ring meets an acceptable standard of accuracy for clinical-grade SpO2 under nonmotion conditions without regard to skin color. TRIAL REGISTRATION: ClinicalTrials.gov NCT05920278; https://clinicaltrials.gov/study/NCT05920278.

The pathway to the closed-loop artificial pancreas: research and commercial perspectives.

BACKGROUND: Over the past decades, insulin pumps (CSII) and continuous glucose monitoring (CGM) systems have been combined in sensor augmented pump therapy. In addition, artificial pancreas (AP) research has progressed to clinical studies, using combinations of commercially available devices. OBJECTIVE: Sensor augmented pump therapy has been evaluated in a number of clinical trials. These studies have been designed to show glycemic outcomes. The low glucose suspend (LGS) feature of the Medtronic Paradigm Veo (Medtronic MiniMed, Northridge, CA) pump allows CGM to suspend insulin delivery if preset hypoglycemic thresholds are achieved. Evaluation of the AP has been conducted to show effect on time in target, and has involved a variety of algorithms, pumps and CGM systems. RESULTS: Multiple studies have shown that more time is spent in the target range for glucose levels and that there is less hypoglycemia with sensor augmented pump, LGS and early AP platforms. CONCLUSIONS: Current research shows that progress has been made in the iterative steps required to develop the AP.

The integrated MiniMed Paradigm REAL-Time insulin pump and glucose monitoring system: implications for improved patient outcomes.

The MiniMed Paradigm REAL-Time system (Medtronic Diabetes, Northridge, CA) was launched in 2006 and integrates real-time continuous glucose monitoring (CGM) with an insulin delivery device. The perceived advantages of such a system include the ability for patients to view and be alerted of glucose information in real-time and affords them a method to adjust insulin delivery in response to these data. Moreover, glucose, insulin, and meal information stored in the system can be uploaded by the patient to a computer using the web-based CareLink Personal Therapy Management software, or similarly by their Health Care Professional (HCP) using CareLink Pro software. The Paradigm REAL-Time system offers two distinct capabilities over conventional multiple daily injection therapy with self-monitoring of blood glucose, mainly the abilities to observe and react to glucose changes in a timely and appropriate manner using the Bolus Wizard calculator feature of the pump and to retrospectively review integrated glucose sensing and insulin delivery data. Retrospective review of uploaded data allows patients and HCPs the opportunity to modify insulin therapy and to potentially improve glycemic control without the use of a traditional glucose diary. When using CGM, the review of CareLink data can highlight interactions among meals, insulin delivery, and resulting glucose levels and supports an overall assessment of glycemic control as well as aid in the adjustment of insulin therapy regimens. Although the Paradigm REAL-Time system does not automatically adjust insulin delivery based on sensor glucose data in its current embodiment, this integrated system forms the basic platform for future generations of products in which the sensor will modify insulin dosing in semi- and ultimately fully closed-loop modalities.

The "glucose pentagon": assessing glycemic control of patients with diabetes mellitus by a model integrating different parameters from glucose profiles.

Measuring the hemoglobin A(1c) (HbA(1c)) is the standard-of-care method to assess long-term glycemic control of patients with diabetes, describing the average glycemic level. However, the HbA(1c) does not reflect acute fluctuations in glucose levels. Variability of glycemia probably has an impact on the development of diabetes-related late complications. A novel model presented in this article combines different summary measures derived from continuously recorded glucose profiles (including parameters describing glycemic variability) and the HbA(1c). The five parameters taking into account are the axes of a "glucose pentagon." Connecting the values of these parameters provided an enclosed area of a given size. For a patient with diabetes, these parameters and the connected area describe how his or her glycemia was during the monitoring period. The area of the glucose pentagon for a patient with diabetes, divided by the standard area of healthy subjects, yields a non-dimensional characteristic value defined as the glycemic risk parameter. It is assume that this risk parameter provides a more meaningful overall description of metabolic control than the HbA(1c) alone. In addition, it might also allow a better assessment of a patient's risk for developing diabetes-related late complications in comparison to the HbA(1c) alone. Of critical importance is, of course, that the clinical relevance of the glucose pentagon is verified in adequate long-term clinical studies.

The accuracy and efficacy of real-time continuous glucose monitoring sensor in patients with type 1 diabetes.

BACKGROUND: The accuracy and efficacy of the Medtronic Diabetes (Northridge, CA) Real-Time (RT)-Continuous Glucose Monitoring (CGM) sensor were analyzed in 72 subjects with type 1 diabetes. METHODS: This was a retrospective analysis of 60,050 temporally paired data points (sensor and glucose meter values) obtained during the course of an outpatient ambulatory study evaluating the efficacy of a sensor-augmented pump system in adults and adolescents. Subjects uploaded sensor values and self-monitoring blood glucose data to the CareLink Clinical Application (Medtronic Diabetes) via the Internet, every 2 weeks during the course of the study. RESULTS: The overall percentage of sensor readings within +/-20% or +/-30% agreement of reference glucose readings was 75.6% and 86.8%, respectively. The highest rate of agreement occurred in the 240-400 mg/dL range, where 79.9% of sensor readings were within +/-20% of meter values and 91.5% of sensor readings were within 30% of meter values. The mean absolute relative difference for all subjects was 15.8%, and the median absolute relative difference was 10.9%. The bias was -2.13 mg/dL. Paired glucose measurements from the RT-CGM and meter demonstrated that 95.9% of paired points in the overall subject population fell in zones A and B of the Clarke Error Grid. Consensus Error Grid Analysis established that 99.2% of paired data points were in zones A and B. CONCLUSIONS: This study reports the accuracy of a continuous glucose sensor with a large number of paired data points (60,050). RT-CGM is safe and well tolerated and provides readings that are in close agreement with glucose meter values.

Sensor-augmented insulin pump therapy: results of the first randomized treat-to-target study.

BACKGROUND: The objective of the study was to evaluate the clinical effectiveness and safety of a device that combines an insulin pump with real-time continuous glucose monitoring (CGM), compared to using an insulin pump with standard blood glucose monitoring systems. METHODS: This 6-month, randomized, multicenter, treat-to-target study enrolled 146 subjects treated with continuous subcutaneous insulin infusion between the ages of 12 and 72 years with type 1 diabetes and initial A1C levels of >or=7.5%. Subjects were randomized to pump therapy with real-time CGM (sensor group [SG]) or to pump therapy and self-monitoring of blood glucose only (control group [CG]). Clinical effectiveness and safety were evaluated. RESULTS: A1C levels decreased (P<0.001) from baseline (8.44+/-0.70%) in both groups (SG, -0.71+/-0.71%; CG, -0.56+/-0.072%); however, between-group differences did not achieve significance. SG subjects showed no change in mean hypoglycemia area under the curve (AUC), whereas CG subjects showed an increase (P=0.001) in hypoglycemia AUC during the blinded periods of the study. The between-group difference in hypoglycemia AUC was significant (P<0.0002). Greater than 60% sensor utilization was associated with A1C reduction (P=0.0456). Fourteen severe hypoglycemic events occurred (11 in the SG group and three in the CG group, P=0.04). CONCLUSIONS: A1C reduction was no different between the two groups. Subjects in the CG group had increased hypoglycemia AUC and number of events during blinded CGM use; however, there was no increase in hypoglycemia AUC or number of events in the SG group. Subjects with greater sensor utilization showed a greater improvement in A1C levels.

Metabolic modelling and the closed-loop insulin delivery problem.

The approach used by Medtronic MiniMed to close the insulin delivery loop using the subcutaneous site for both glucose sensing and insulin delivery relies on modeling insulin action and beta-cell insulin secretion. This approach is contrasted with traditional control systems engineering.

Clinical experience with an integrated continuous glucose sensor/insulin pump platform: a feasibility study.

The recent US Food and Drug Administration approval of an integrated real-time continuous glucose monitoring (CGM) system and insulin pump (Medtronic Mini-Med Paradigm REAL-Time System; Medtronic MiniMed, Inc., Northridge, Calif) is the most recent breakthrough paving the way toward the development of a closedloop insulin delivery system that could revolutionize diabetes care. An early prototype of the MiniMed Paradigm REAL-Time System--which provided both realtime CGM and insulin pump therapy but was not yet fully integrated--was tested in 20 volunteer subjects with type 1 diabetes who wore the device for up to 2 y. Subjects were instructed on the technical use of the device but were provided no additional support, aside from their usual diabetes care. Participation in the trial averaged 317 d (minimum, 88 d; maximum, 618 d). Five participants prematurely dropped out of the study (2 because of the inconvenience of carrying 2 devices). Data from all subjects were analyzed. Subjects reduced their A1C by a mean of 1.1% (standard deviation, 0.8). After 3 mo of device use, the number of participants who achieved A1C <7% increased by more than 3-fold. Individuals with baseline A1C >or= 7% had a 67% likelihood of achieving an A1C <7% by the first follow-up assessment. This observational study of combined CGM and insulin pump therapy suggests that this innovative device can help people achieve improved glycemic control. Given that only about one third of individuals with diabetes are achieving glycemic control targets, more effective methods to help patients avoid the devastating consequences of uncontrolled diabetes are desperately needed.

Mobile health: the power of wearables, sensors, and apps to transform clinical trials.

Mobile technology has become a ubiquitous part of everyday life, and the practical utility of mobile devices for improving human health is only now being realized. Wireless medical sensors, or mobile biosensors, are one such technology that is allowing the accumulation of real-time biometric data that may hold valuable clues for treating even some of the most devastating human diseases. From wearable gadgets to sophisticated implantable medical devices, the information retrieved from mobile technology has the potential to revolutionize how clinical research is conducted and how disease therapies are delivered in the coming years. Encompassing the fields of science and engineering, analytics, health care, business, and government, this report explores the promise that wearable biosensors, along with integrated mobile apps, hold for improving the quality of patient care and clinical outcomes. The discussion focuses on groundbreaking device innovation, data optimization and validation, commercial platform integration, clinical implementation and regulation, and the broad societal implications of using mobile health technologies.

The Transformation of Diabetes Care Through the Use of Person-Centered Data.

The health care industry is undergoing a major transformation. Despite spending more on health care than any other country, the United States has not seen a commensurate improvement in the quality of care. Chronic disease management puts the greatest burden on the health care system with estimates suggesting that 3 of 4 health care dollars are spent on managing chronic disease. Moreover, the number of older patients with chronic conditions, like diabetes, is rising as expected, which only serves to worsen the physician shortage problem we are currently experiencing, and further increase health care costs. Unless new models of health care are established for these patients, they simply will not be served. Consistent with the message above, there are generally 3 universal health care needs, (1) improved outcomes, (2) expanded access, and (3) optimized cost and efficiency. It is likely the future state will involve value-based health care, with payment based on outcomes, not services rendered, and incentives tied more directly to the value delivered. Medical device providers will be held more accountable for positive outcomes, and to ensure success, they will need to create better solutions with their therapies. Instead of the touch point with patients being solely at the time of a procedure or sale of the device, it is likely companies will need to drive toward a more comprehensive partnership with patients, providers, and payers, extending the scope of services and interactions to provide a continuum of care. In general, companies will need to start to think of their most important customers as people living with a condition, as opposed to patients needing immediate medical devices. In this article, I discuss the challenges of health care today and present some of the opportunities to revamp health care delivery in diabetes by leveraging the pervasive use of mobile technologies and digital data.

Use of the Continuous Glucose Monitoring System to guide therapy in patients with insulin-treated diabetes: a randomized controlled trial.

OBJECTIVE: To show improved glycemic control in patients with insulin-treated diabetes after adjustments to the diabetes management plan based on either continuous glucose monitoring using the Continuous Glucose Monitoring System (CGMS) or frequent self-monitoring of blood glucose (SMBG) using a home blood glucose meter. PATIENTS AND METHODS: From January to September 2000, patients aged 19 to 76 years with insulin-treated diabetes were assigned to insulin therapy adjustments based on either CGMS or SMBG values. At the end of the study, patients in both groups used the CGMS for 3 days; these values were used to calculate measures of hypoglycemia. Repeated-measures analysis of variance with post hoc comparisons were used to test differences in hemoglobin A1c levels and hypoglycemia between the 2 study groups. RESULTS: A total of 128 patients were enrolled in the study. Nineteen discontinued study participation, leaving 51 in the CGMS group and 58 in the SMBG group. No significant differences were noted in demographics or baseline characteristics between the 2 groups. There were no significant differences in hemoglobin A1c levels between the CGMS group and the SMBG group at baseline (9.1% +/- 1.1% vs 9.0% +/- 1.0%, P = .70), and both groups showed statistically significant (P < .001) and similar (P = .95) improvement in hemoglobin A1c levels after 12 weeks of study. However, the CGMS group had a significantly shorter duration of hypoglycemia (sensor glucose, < or = 60 mg/dL) at week 12 of the study (49.4 +/- 40.8 vs 81.0 +/- 61.1 minutes per event, P = .009). CONCLUSION: Use of the CGMS to guide therapy adjustments in patients with insulin-treated diabetes reduces the duration of hypoglycemia compared with therapy adjustments guided by SMBG values alone.

Alarms based on real-time sensor glucose values alert patients to hypo- and hyperglycemia: the guardian continuous monitoring system.

The purposes of this study were to demonstrate the accuracy and effectiveness of the Guardian Continuous Monitoring System (Medtronic MiniMed, Northridge, California) and to demonstrate that the application of real-time alarms to continuous monitoring alerts users to hypo and hyperglycemia and reduces excursions in people with diabetes. A total of 71 subjects with type 1 diabetes, mean hemoglobin A1c of 7.6 +/- 1.1%, age 44.0 +/- 11.4 years, and duration of diabetes 23.6 +/- 10.6 years were enrolled in this two-period, randomized, multicenter study. Subjects were randomized into either an Alert group or a Control group. The accuracy of the Guardian was evaluated by treating the study data as a single-sample correlational design. Effectiveness of the Guardian alerts was evaluated by comparing the Alert group with the Control group. The mean (median) absolute relative error between home blood glucose meter readings and sensor values was 21.3% (17.3%), and the Guardian, on average, read 12.8 mg/dL below the concurrent home blood glucose meter readings. The hypoglycemia alert was able to distinguished glucose values < or =70 mg/dL with 67% sensitivity, 90% specificity, and 47% false alerts. The hyperglycemia alert showed a similar ability to detect sensor values > or =250 mg/dL with 63% sensitivity, 97% specificity, and 19% false alerts. The Alert group demonstrated a median decrease in the duration of hypoglycemic excursions (-27.8 min) that was significantly greater than the median decrease in the duration of hypoglycemic excursions in the Control group (-4.5 min) (P = 0.03). A marginally significant increase in the frequency of hyperglycemic excursions (P = 0.07) between Period 1 and Period 2 was accompanied by a decrease of 9.6 min in the duration of hyperglycemic excursions in the Alert group. Glucose measurements differ between blood samples taken from the finger and interstitial fluid, especially when levels are changing rapidly; however, these results demonstrate that the Guardian is reasonably accurate while performing continuous glucose monitoring. The subjects' responses to hypoglycemia alerts resulted in a significant reduction in the duration of hypoglycemic excursions; however, overtreating hypoglycemia may have resulted in a marginally significant increase in the frequency of hyperglycemic excursions.

Determination of plasma glucose during rapid glucose excursions with a subcutaneous glucose sensor.

Continuous glucose monitoring has the potential to improve glucose management and reduce the risk of hypoglycemia in individuals with diabetes. Accurate sensors may also allow the development of a closed-loop insulin delivery system. The purpose of this work was to determine the delay time associated with a subcutaneous glucose sensor during rapidly changing glucose excursions. Subcutaneous glucose sensors (Medtronic MiniMed, Inc., Northridge, CA) were inserted in five healthy men. After a 2-h stabilization period, a 3-h hyperglycemic (approximately 11 mM) clamp was performed followed by a 90-min period in which plasma glucose was allowed to decline to as low as 2.8 mM. Sensors were calibrated using two points (basal and hyperglycemia), and the calibrated sensor glucose measurements were compared with those from a reference analyzer (Beckman Instruments, Fullerton, CA). Response time was estimated from a first-order kinetic model. Plasma glucose levels, determined with the subcutaneous sensor, were highly correlated with those obtained with the reference glucose analyzer (r(2) = 0.91, p < 0.001; mean absolute difference of approximately 8%). The half-time for the sensor response was estimated to be 4.0 +/- 1.0 min. The subcutaneous glucose sensor has the potential to facilitate the detection of hypoglycemia and improve overall glycemic control when used in a real-time monitor. The rapid response should be sufficient to allow a fully automated closed-loop insulin delivery system to be developed based on the subcutaneous sensing site.

The use of a continuous glucose monitoring system in hypoglycemic disorders.

OBJECTIVE: To evaluate the use of a continuous glucose monitoring system (CGMS) in the evaluation and treatment of infants and children with hypoglycemic disorders. METHODS: Patients with hypoglycemic disorders wore the CGMS device in the Pediatric Clinic Research Center during their evaluation and treatment. Capillary blood glucose (CBG) values were obtained at least 3 times each day and entered into the device for calibration purposes. We evaluated the number of hypoglycemic episodes below 3.3 mmol/l (60 mg/dl) detected by CGMS compared to CBG values and characterized episodes by their duration and intensity. RESULTS: Five patients with hypoglycemic disorders were included in the study. There were a total of 13,369 sensor points, 343 paired sensor and CBG data points, and 57 days included. A total of 180 episodes of hypoglycemia occurred in these five patients, with an average duration of 55 +/- 13 minutes. Using a cut-off of 3.3 mmol/l (60 mg/dl) for hypoglycemia, the sensor had a sensitivity of 65.4%, specificity of 90.6%, and false positive rate of 42.9%. The positive and negative predictive values were 57.1% and 93.2%, respectively. CONCLUSION: CGMS is a useful adjunct in the diagnosis and evaluation of hypoglycemia, and for documentation of euglycemia in these patients following therapy.

Model-based sensor-augmented pump therapy.

BACKGROUND: In insulin pump therapy, optimization of bolus and basal insulin dose settings is a challenge. We introduce a new algorithm that provides individualized basal rates and new carbohydrate ratio and correction factor recommendations. The algorithm utilizes a mathematical model of blood glucose (BG) as a function of carbohydrate intake and delivered insulin, which includes individualized parameters derived from sensor BG and insulin delivery data downloaded from a patient's pump. METHODS: A mathematical model of BG as a function of carbohydrate intake and delivered insulin was developed. The model includes fixed parameters and several individualized parameters derived from the subject's BG measurements and pump data. Performance of the new algorithm was assessed using n = 4 diabetic canine experiments over a 32 h duration. In addition, 10 in silico adults from the University of Virginia/Padova type 1 diabetes mellitus metabolic simulator were tested. RESULTS: The percentage of time in glucose range 80-180 mg/dl was 86%, 85%, 61%, and 30% using model-based therapy and [78%, 100%] (brackets denote multiple experiments conducted under the same therapy and animal model), [75%, 67%], 47%, and 86% for the control experiments for dogs 1 to 4, respectively. The BG measurements obtained in the simulation using our individualized algorithm were in 61-231 mg/dl min-max envelope, whereas use of the simulator's default treatment resulted in BG measurements 90-210 mg/dl min-max envelope. CONCLUSIONS: The study results demonstrate the potential of this method, which could serve as a platform for improving, facilitating, and standardizing insulin pump therapy based on a single download of data.

Accuracy of the Enlite 6-day glucose sensor with guardian and Veo calibration algorithms.

OBJECTIVE: This study investigates the accuracy of a newly developed, next-generation subcutaneous glucose sensor, evaluated for 6-day use. RESEARCH DESIGN AND METHODS: Seventy-nine subjects (53 men, 26 women) with type 1 diabetes and 18 subjects (14 men, four women) with type 2 diabetes completed a three-center, prospective, sensor accuracy study. The mean age for the group was 42.2±15.0 years (mean±SD), ranging from 18 to 71 years, with a mean glycosylated hemoglobin level of 7.6±1.5%, ranging from 5.5% to 14%. Subjects wore Enlite™ sensors (Medtronic Diabetes, Northridge, CA) in the abdominal and buttocks region for two separate 7-day periods and calibrated with a home-use blood glucose meter. Subjects participated in an in-clinic testing day where frequent sampled plasma glucose samples were acquired every 15 min for 10 h. Sensor data was retrospectively processed with Guardian(®) REAL-Time (Medtronic) and Paradigm(®) Veo™ (Medtronic) calibration routines, and accuracy metrics were calculated for each algorithm and sensor location. Physiological time lag for each measurement site was calculated. RESULTS: Based on 6,404 plasma-sensor glucose paired points, the Enlite sensor with Veo calibration algorithm produced a mean absolute relative difference of 13.86% with 97.3% of points within the A+B zones of the Clarke error grid. Threshold-only alarms detected 90.1% of hypoglycemia and 90% of hyperglycemia. Mean time lag measured at the abdominal region was 7.94±6.48 min compared with 11.70±6.71 min (P<0.0001) at the buttocks area. CONCLUSIONS: The Enlite sensor accurately measures glucose when compared with gold standard laboratory measurements over its 6-day use. Sensors placed in the buttocks region exhibited greater time lags than sensors placed in the abdomen.

Continuous glucose monitoring considerations for the development of a closed-loop artificial pancreas system.

BACKGROUND: Commercialization of a closed-loop artificial pancreas system that employs continuous subcutaneous insulin infusion and interstitial fluid glucose sensing has been encumbered by state-of-the-art technology. Continuous glucose monitoring (CGM) devices with improved accuracy could significantly advance development efforts. However, the current accuracy of CGM devices might be adequate for closed-loop control. METHODS: The influence that known CGM limitations have on closed-loop control was investigated by integrating sources of sensor inaccuracy with the University of Virginia Padova Diabetes simulator. Non-glucose interference, physiological time lag and sensor error measurements, selected from 83 Enlite™ glucose sensor recordings with the Guardian® REAL-Time system, were used to modulate simulated plasma glucose signals. The effect of sensor accuracy on closed-loop controller performance was evaluated in silico, and contrasted with closed-loop clinical studies during the nocturnal control period. RESULTS: Based on n = 2472 reference points, a mean sensor error of 14% with physiological time lags of 3.28 ± 4.62 min (max 13.2 min) was calculated for simulation. Sensor bias reduced time in target for both simulation and clinical experiments. In simulation, additive error increased time <70 mg/dl and >180 mg/dl by 0.2% and 5.6%, respectively. In-clinic, the greatest low blood glucose index values (max = 5.9) corresponded to sensor performance. CONCLUSION: Sensors have sufficient accuracy for closed-loop control, however, algorithms are necessary to effectively calibrate and detect erroneous calibrations and failing sensors. Clinical closed-loop data suggest that control with a higher target of 140 mg/dl during the nocturnal period could significantly reduce the risk for hypoglycemia.

Accuracy of a new real-time continuous glucose monitoring algorithm.

BACKGROUND: Through minimally invasive sensor-based continuous glucose monitoring (CGM), individuals can manage their blood glucose (BG) levels more aggressively, thereby improving their hemoglobin A1c level, while reducing the risk of hypoglycemia. Tighter glycemic control through CGM, however, requires an accurate glucose sensor and calibration algorithm with increased performance at lower BG levels. METHODS: Sensor and BG measurements for 72 adult and adolescent subjects were obtained during the course of a 26-week multicenter study evaluating the efficacy of the Paradigm REAL-Time (PRT) sensor-augmented pump system (Medtronic Diabetes, Northridge, CA) in an outpatient setting. Subjects in the study arm performed at least four daily finger stick measurements. A retrospective analysis of the data set was performed to evaluate a new calibration algorithm utilized in the Paradigm Veo insulin pump (Medtronic Diabetes) and to compare these results to performance metrics calculated for the PRT. RESULTS: A total of N = 7193 PRT sensor downloads for 3 days of use, as well as 90,472 temporally and nonuniformly paired data points (sensor and meter values), were evaluated, with 5841 hypoglycemic and 15,851 hyperglycemic events detected through finger stick measurements. The Veo calibration algorithm decreased the overall mean absolute relative difference by greater than 0.25 to 15.89%, with hypoglycemia sensitivity increased from 54.9% in the PRT to 82.3% in the Veo (90.5% with predictive alerts); however, hyperglycemia sensitivity was decreased only marginally from 86% in the PRT to 81.7% in the Veo. CONCLUSIONS: The Veo calibration algorithm, with sensor error reduced significantly in the 40- to 120-mg/dl range, improves hypoglycemia detection, while retaining accuracy at high glucose levels.

Practical considerations in the use of real-time continuous glucose monitoring alerts.

BACKGROUND: The safety and efficacy of real-time (RT) continuous glucose monitoring (CGM) systems in the management of type 1 diabetes are increasingly apparent. Clinical trials have demonstrated the utility of these systems in lowering hemoglobin A1c, minimizing hypoglycemia, and reducing glycemic variability. These RT systems allow patients to conveniently monitor their glucose levels by displaying concentration and trending information. Several of these RT systems provide preset alerts that sound when absolute glucose thresholds are reached. Additionally, some systems allow for predictive algorithm-based alerts that incorporate rates of change. However, clinical trials have identified significant noncompliance in the use of these devices, most notably in the pediatric and adolescent populations. A retrospective review of CGM reports shows that many patients set high and low alert thresholds at levels that result in frequent alerts, potentially resulting in patient nuisance, dismissal of consequential alerts, and eventual product abandonment. Therefore, setting the alert thresholds at appropriate high and low settings can determine the balance between either a perceived benefit by the patient and their long-term use of CGM systems or annoyance to the patient and discontinuation. CONCLUSION: Care should be taken to set CGM alerts at levels that result in a manageable number of notifications per day. In some cases, providers should consider not using alerts at all or consider using broad targets when initiating CGM to maximize alert specificity. Real-time CGM is safe and generally well tolerated; however, individualization of alert settings is necessary maximize the system's benefits and patient adherence.