Sleep and diabetes-specific psycho-behavioral outcomes of a new automated insulin delivery system in young children with type 1 diabetes and their parents.

BACKGROUND: Data on the use of Control-IQ, the latest FDA-approved automated insulin delivery (AID) system for people with T1D 6 years of age or older is still scarce, particularly regarding nonglycemic outcomes. Children with T1D and their parents are at higher risk for sleep disturbances. This study assesses sleep, psycho-behavioral and glycemic outcomes of AID compared to sensor-augmented pump therapy (SAP) therapy in young children with T1D and their parents. METHODS: Thirteen parents and their young children (ages 7-10) on insulin pump therapy were enrolled. Children completed an initial 4-week study with SAP using their own pump and a study CGM followed by a 4-week phase of AID. Sleep outcomes for parents and children were evaluated through actigraphy watches. Several questionnaires were administered at baseline and at the end of each study phase. CGM data were used to assess glycemic outcomes. RESULTS: Actigraphy data did not show any significant change from SAP to AID, except a reduction of number of parental awakenings during the night (p = 0.036). Parents reported statistically significant improvements in Pittsburgh Sleep Quality Index total score (p = 0.009), Hypoglycemia Fear Survey total score (p = 0.011), diabetes-related distress (p = 0.032), and depression (p = 0.023). While on AID, time in range (70-180 mg/dL) significantly increased compared to SAP (p < 0.001), accompanied by a reduction in hyperglycemia (p = 0.001). CONCLUSIONS: These results suggest that use of AID has a positive impact on glycemic outcomes in young children as well as sleep and diabetes-specific quality of life outcomes in their parents.

Use of an artificial pancreas among adolescents for a missed snack bolus and an underestimated meal bolus.

OBJECTIVE: The objective of this study was to evaluate the safety and performance of the artificial pancreas (AP) in adolescents with type 1 diabetes (T1D) following insulin omission for food. RESEARCH DESIGN AND METHODS: In a randomized, cross-over trial, adolescents with T1D aged 13-18 yr were enrolled in a randomized, cross-over trial. On separate days, received either usual care (UC) through their home insulin pump or used an AP system (Diabetes Assistant platform, continuous glucose monitor, and insulin pump). Approximately 1 h after admission, participants in both groups received an unannounced snack of 30 g carbohydrate, and 4 h later they received an 80 g lunch, for which both groups only received 75% of the calculated insulin dose to cover carbohydrates. On the UC day (but not the AP day), they received their full high blood glucose (BG) correction factor at lunch. Each admission lasted approximately 8 h. RESULTS: A total of 16 participants completed the trial. On the AP day (compared to UC), mean BG was lower (197 ± 10 vs. 235 ± 14 mg/dL) and time in range 70-180 mg/dL was higher (43% ± 7 vs. 19% ± 7) (both p < 0.05) overall; these results held in the time following the snack and meal (also p < 0.05). During the trial, there were no differences between groups in the rate of hypoglycemia <70 mg/dL. CONCLUSIONS: The AP provided improvements in short-term glycemic control without increases in hypoglycemia following missed insulin for food in adolescents. Thus, the AP partly compensates for missed insulin boluses for food, a common occurrence in adolescent diabetes care. Further testing is needed in longer-term settings.

Evaluation of a Web-Based Simulation Tool for Self-Management Support in Type 1 Diabetes: A Pilot Study.

OBJECTIVE: To develop and evaluate a novel Web-based Simulation Tool (WST) that brings simulation technologies to people with Type 1 Diabetes (T1D), enabling unique patient-data interactions seamlessly on a daily basis. METHODS: A pilot clinical trial was conducted to assess system usability. The study consisted of one week of observation (Phase 1) and four weeks of interaction with WST (Phase 2). Responses to Technology Acceptance (TA) and Diabetes Distress Scale (DDS) questionnaires were collected, and follow-up interviews were conducted after Phase 2. RESULTS: Fifteen participants with T1D using Control-IQ technology (age: 36 ± 13 years, HbA1c: 6.5% ± 0.7%) completed all study procedures. Generated simulation models achieved a median Mean Absolute Relative Difference (MARD) of 6.8% [interquartile range, IQR: 5.1%, 9.1%]. A decrease in expected benefits (likely explained by issues with the third-party data collection system) and an increase in expected burdens were observed. On a 1-5 scale, ease of use, trust, and usefulness scores were 3 [3,4], 4 [3,4], and 4 [3,4], respectively. Time below 70 mg/dL decreased between Phases 1 and 2 (1.6% [0.7%,3.7%] vs 0.8% [0.5%,3.0%]). A reduction in mean emotional burden was also observed (2.5 ± 1.1 vs 2.1 ± 0.8). CONCLUSIONS: Results indicate that there was a learning curve to WST, but also that most participants trusted the system and found it useful in their diabetes care. SIGNIFICANCE: Simulation technologies like WST could be used by educators and patients to facilitate diabetes self-management, leading to better diabetes literacy and reducing associated distress.

Effect of an Automated Advice Algorithm (CloudConnect) on Adolescent-Parent Diabetes-Specific Communication and Glycemic Management: A Randomized Trial.

INTRODUCTION: Because adolescence is a time of difficult management of Type 1 diabetes (T1D) in part from adolescent-parent shared responsibility of T1D management, our objective was to assess the effects of a decision support system (DSS) CloudConnect on T1D-related communication between adolescents and their parents and on glycemic management. METHODS: We followed 86 participants including 43 adolescents with T1D (not on automated insulin delivery systems, AID) and their parents/care-giver for a 12-week intervention of UsualCare + CGM or CloudConnect, which included a Weekly Report of automated T1D advice, including insulin dose adjustments, based on data from continuous glucose monitors (CGM), Fitbit and insulin use. Primary outcome was T1D-specific communication and secondary outcomes were hemoglobin A1c, time-in-target range (TIR) 70-180 mg/dl, and additional psychosocial scales. RESULTS: Adolescents and parents reported a similar amount of T1D-related communication in both the UsualCare + CGM or CloudConnect groups and had similar levels of final HbA1c. Overall blood glucose time in range 70-180 mg/dl and time below 70 mg/dl were not different between groups. Parents but not children in the CloudConnect group reported less T1D-related conflict; however, compared to the UsualCare + CGM group, adolescents and parents in the CloudConnect reported a more negative tone of T1D-related communication. Adolescent-parent pairs in the CloudConnect group reported more frequent changes in insulin dose. There were no differences in T1D quality of life between groups. CONCLUSIONS: While feasible, the CloudConnect DSS system did not increase T1D communication or provide improvements in glycemic management. Further efforts are needed to improve T1D management in adolescents with T1D not on AID systems.

Safety and Feasibility Evaluation of Step Count Informed Meal Boluses in Type 1 Diabetes: A Pilot Study.

BACKGROUND: Physical activity can cause glucose fluctuations both during and after it is performed, leading to hurdles in optimal insulin dosing in people with type 1 diabetes (T1D). We conducted a pilot clinical trial assessing the safety and feasibility of a physical activity-informed mealtime insulin bolus advisor that adjusts the meal bolus according to previous physical activity, based on step count data collected through an off-the-shelf physical activity tracker. METHODS: Fifteen adults with T1D, each using a continuous glucose monitor (CGM) and an insulin pump with carbohydrate counting, completed two randomized crossover daily visits. Participants performed a 30 to 45-minute brisk walk before lunch and lunchtime insulin boluses were calculated based on either their standard therapy (ST) or the physical activity-informed bolus method. Post-lunch glycemic excursions were assessed using CGM readings. RESULTS: There was no significant difference between visits in the time spent in hypoglycemia in the post-lunch period (median [IQR] standard: 0 [0]% vs physical activity-informed: 0 [0]%, P = NS). Standard therapy bolus yielded a higher time spent in 70 to 180 mg/dL target range (mean ± standard: 77% ± 27% vs physical activity-informed: 59% ± 31%, P = .03) yet, it was associated with a steeper negative slope in the early postprandial phase (P = .032). CONCLUSIONS: Use of step count to adjust mealtime insulin following a walking bout has proved to be safe and feasible in a cohort of 15 T1D subjects. Physical activity-informed insulin dosing of meals eaten soon after a walking bout has a potential of mitigating physical activity related glucose reduction in the early postprandial phase.

Accuracy of a Factory-Calibrated Continuous Glucose Monitor in Individuals With Diabetes on Hemodialysis.

OBJECTIVE: Continuous glucose monitoring (CGM) improves diabetes management, but its reliability in individuals on hemodialysis is poorly understood and potentially affected by interstitial and intravascular volume variations. RESEARCH DESIGN AND METHODS: We assessed the accuracy of a factory-calibrated CGM by using venous blood glucose measurements (vBGM) during hemodialysis sessions and self-monitoring blood glucose (SMBG) at home. RESULTS: Twenty participants completed the protocol. The mean absolute relative difference of the CGM was 13.8% and 14.4%, when calculated on SMBG (n = 684) and on vBGM (n = 624), and 98.7% and 100% of values in the Parkes error grid A/B zones, respectively. Throughout 181 days of CGM monitoring, the median time in range (70-180 mg/dL) was 38.5% (interquartile range 29.3-57.9), with 28.7% (7.8-40.6) of the time >250 mg/dL. CONCLUSIONS: The overall performance of a factory-calibrated CGM appears reasonably accurate and clinically relevant for use in practice by individuals on hemodialysis and health professionals to improve diabetes management.

Advanced Closed-Loop Control System Improves Postprandial Glycemic Control Compared With a Hybrid Closed-Loop System Following Unannounced Meal.

OBJECTIVE: Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement. RESEARCH DESIGN AND METHODS: Eighteen adolescents with T1D (age 15.6 ± 1.7 years; HbA1c 7.4 ± 1.5%; 9 females/9 males) participated in a randomized crossover clinical trial comparing our legacy hybrid CLC system (Unified Safety System Virginia [USS]-Virginia) with a novel fully automated CLC system (RocketAP) during two 46-h supervised admissions (each with one announced and one unannounced dinner), following 2 weeks of data collection. Primary outcome was the percentage time-in-range 70-180 mg/dL (TIR) following the unannounced meal, with secondary outcomes related to additional continuous glucose monitoring-based metrics. RESULTS: Both TIR and time-in-tight-range 70-140 mg/dL (TTR) were significantly higher using RocketAP than using USS-Virginia during the 6 h following the unannounced meal (83% [interquartile range 64-93] vs. 53% [40-71]; P = 0.004 and 49% [41-59] vs. 27% [22-36]; P = 0.002, respectively), primarily driven by reduced time-above-range (TAR >180 mg/dL: 17% [1.3-34] vs. 47% [28-60]), with no increase in time-below-range (TBR <70 mg/dL: 0% median for both). RocketAP also improved control following the announced meal (mean difference TBR: -0.7%, TIR: +7%, TTR: +6%), overall (TIR: +5%, TAR: -5%, TTR: +8%), and overnight (TIR: +7%, TTR: +19%, TAR: -5%). RocketAP delivered less insulin overall (78 ± 23 units vs. 85 ± 20 units, P = 0.01). CONCLUSIONS: A new fully automated CLC system with automatic prandial dosing was proven to be safe and feasible and outperformed our legacy USS-Virginia in an adolescent population with and without meal announcement.

Evening and overnight closed-loop control versus 24/7 continuous closed-loop control for type 1 diabetes: a randomised crossover trial.

Background: Automated closed-loop control (CLC), known as the "artificial pancreas" is emerging as a treatment option for Type 1 Diabetes (T1D), generally superior to sensor-augmented insulin pump (SAP) treatment. It is postulated that evening-night (E-N) CLC may account for most of the benefits of 24-7 CLC; however, a direct comparison has not been done. Methods: In this trial (NCT02679287), adults with T1D were randomised 1:1 to two groups, which followed different sequences of four 8-week sessions, resulting in two crossover designs comparing SAP vs E-N CLC and E-N CLC vs 24-7 CLC, respectively. Eligibility: T1D for at least 1 year, using an insulin pump for at least six months, ages 18 years or older. Primary hypothesis: E-N CLC compared to SAP will decrease percent time <70mg/dL (3.9mmol/L) measured by continuous glucose monitoring (CGM) without deterioration in HbA1c. Secondary Hypotheses: 24-7 CLC compared to SAP will increase CGM-measured time in target range (TIR, 70-180mg/dL; 3.9-10mmol/L) and will reduce glucose variability during the day. Findings: Ninety-three participants were randomised and 80 were included in the analysis, ages 18-69 years; HbA1c levels 5.4-10.6%; 66% female. Compared to SAP, E-N CLC reduced overall time <70mg/dL from 4.0% to 2.2% () resulting in an absolute difference of 1.8% (95%CI: 1.2-2.4%), p<0.0001. This was accompanied by overall reduction in HbA1c from 7.4% at baseline to 7.1% at the end of study, resulting in an absolute difference of 0.3% (95% CI: 0.1-0.4%), p<0.0001. There were 5 severe hypoglycaemia adverse events attributed to user-directed boluses without malfunction of the investigational device, and no diabetic ketoacidosis events. Interpretation: In type 1 diabetes, evening-night closed-loop control was superior to sensor-augmented pump therapy, achieving most of the glycaemic benefits of 24-7 closed-loop.

Hybrid Closed-Loop Control Is Safe and Effective for People with Type 1 Diabetes Who Are at Moderate to High Risk for Hypoglycemia.

Background: Typically, closed-loop control (CLC) studies excluded patients with significant hypoglycemia. We evaluated the effectiveness of hybrid CLC (HCLC) versus sensor-augmented pump (SAP) in reducing hypoglycemia in this high-risk population. Methods: Forty-four subjects with type 1 diabetes, 25 women, 37 ± 2 years old, HbA1c 7.4% ± 0.2% (57 ± 1.5 mmol/mol), diabetes duration 19 ± 2 years, on insulin pump, were enrolled at the University of Virginia (N = 33) and Stanford University (N = 11). Eligibility: increased risk of hypoglycemia confirmed by 1 week of blinded continuous glucose monitor (CGM); randomized to 4 weeks of home use of either HCLC or SAP. Primary/secondary outcomes: risk for hypoglycemia measured by the low blood glucose index (LBGI)/CGM-based time in ranges. Results: Values reported: mean ± standard deviation. From baseline to the final week of study: LBGI decreased more on HCLC (2.51 ± 1.17 to 1.28 ± 0.5) than on SAP (2.1 ± 1.05 to 1.79 ± 0.98), P < 0.001; percent time below 70 mg/dL (3.9 mmol/L) decreased on HCLC (7.2% ± 5.3% to 2.0% ± 1.4%) but not on SAP (5.8% ± 4.7% to 4.8% ± 4.5%), P = 0.001; percent time within the target range 70-180 mg/dL (3.9-10 mmol/L) increased on HCLC (67.8% ± 13.5% to 78.2% ± 10%) but decreased on SAP (65.6% ± 12.9% to 59.6% ± 16.5%), P < 0.001; percent time above 180 mg/dL (10 mmol/L) decreased on HCLC (25.1% ± 15.3% to 19.8% ± 10.1%) but increased on SAP (28.6% ± 14.6% to 35.6% ± 17.6%), P = 0.009. Mean glucose did not change significantly on HCLC (144.9 ± 27.9 to 143.8 ± 14.4 mg/dL [8.1 ± 1.6 to 8.0 ± 0.8 mmol/L]) or SAP (152.5 ± 24.3 to 162.4 ± 28.2 [8.5 ± 1.4 to 9.0 ± 1.6]), P = ns. Conclusions: Compared with SAP therapy, HCLC reduced the risk and frequency of hypoglycemia, while improving time in target range and reducing hyperglycemia in people at moderate to high risk of hypoglycemia.

Continuous Glucose Monitoring and Insulin Informed Advisory System with Automated Titration and Dosing of Insulin Reduces Glucose Variability in Type 1 Diabetes Mellitus.

BACKGROUND: Glucose variability (GV) remains a key limiting factor in the success of diabetes management. While new technologies, for example, accurate continuous glucose monitoring (CGM) and connected insulin delivery devices, are now available, current treatment standards fail to leverage the wealth of information generated. Expert systems, from automated insulin delivery to advisory systems, are a key missing element to richer, more personalized, glucose management in diabetes. METHODS: Twenty four subjects with type 1 diabetes mellitus (T1DM), 15 women, 37 ± 11 years of age, hemoglobin A1c 7.2% ± 1%, total daily insulin (TDI) 46.7 ± 22.3 U, using either an insulin pump or multiple daily injections with carbohydrate counting, completed two randomized crossover 48-h visits at the University of Virginia, wearing Dexcom G4 CGM, and using either usual care or the UVA decision support system (DSS). DSS consisted of a combination of automated insulin titration, bolus calculation, and CHO treatment advice. During each admission, participants were exposed to a variety of meal sizes and contents and two 45-min bouts of exercise. GV and glucose control were assessed using CGM. RESULTS: The use of DSS significantly reduced GV (coefficient of variation: 0.36 ± 08. vs. 0.33 ± 0.06, P = 0.045) while maintaining glycemic control (average CGM: 155.2 ± 27.1 mg/dL vs. 155.2 ± 23.2 mg/dL), by reducing hypoglycemia exposure (%<70 mg/dL: 3.8% ± 4.6% vs. 1.8% ± 2%, P = 0.018), with nonsignificant trends toward reduction of significant hyperglycemia overnight (%>250 mg/dL: 5.3% ± 9.5% vs. 1.9% ± 4.6%) and at mealtime (11.3% ± 14.8% vs. 5.8% ± 9.1%). CONCLUSIONS: A CGM/insulin informed advisory system proved to be safe and feasible in a cohort of 24 T1DM subjects. Use of the system may result in reduced GV and improved protection against hypoglycemia.

Performance of an Artificial Pancreas System for Young Children with Type 1 Diabetes.

BACKGROUND: Young children 5-8 years old with type 1 diabetes (T1D) exhibit clear needs for improved glycemic control but may be limited in their ability to safely interact with an artificial pancreas system. Our goal was to evaluate the safety and performance of an artificial pancreas (AP) system among young children with T1D. RESEARCH DESIGN AND METHODS: In a randomized, crossover trial, children with T1D age 5-8 years were enrolled to receive on separate study periods (in random order) either the UVa AP using the DiAs Control Platform software with child-resistant lock-out screens (followed as an out-patient admission) or their usual insulin pump+continuous glucose monitor (CGM) care at home. Hypoglycemic events and CGM tracings were compared between the two 68-h study periods. All analyses were adjusted for level of physical activity as tracked using Fitbit devices. RESULTS: Twelve participants (median age 7 years, n = 6 males) completed the trial. Compared to home care, the AP admission resulted in increased time with blood glucose (BG) 70-180 mg/dL (73% vs. 47%) and lower mean BG (152 mg/dL vs. 190 mg/dL), both P < 0.001 after adjustment for activity. Occurrence of hypoglycemia was similar between sessions without differences in time <70 mg/dL (AP 1.1% ± 1.1%; home 1.6% ± 1.2%). There were no adverse events during the AP or home study periods. CONCLUSIONS: Use of an AP in young children was safe and resulted in improved mean BG without increased hypoglycemia. This suggests that AP use in young children is safe and improves overall diabetes control. ClinicalTrials.gov registration number: NCT02750267.

Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems?

BACKGROUND: The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings. METHODS: A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses. RESULTS: Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r(2) = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r(2) = 2.33%) and decreased carbohydrate consumption (r = -.157, P = .031, r(2) = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices. CONCLUSIONS: These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals.

Fluctuations of Hyperglycemia and Insulin Sensitivity Are Linked to Menstrual Cycle Phases in Women With T1D.

BACKGROUND: Factors influencing glycemic variability in type 1 diabetes (T1D) may play a significant role in the refinement of closed loop insulin administration. Phase of menstrual cycle is one such factor that has been inadequately investigated. We propose that unique individual patterns can be constructed and used as parameters of closed loop systems. METHOD: Women with T1D on continuous subcutaneous insulin infusion and continuous glucose monitoring were studied for 3 consecutive menstrual cycles. Ovulation prediction kits and labs were used to confirm phase of menstrual cycle. Glycemic risks were assessed using the low- and high blood glucose indices (LBGI and HBGI). Insulin sensitivity (SI) was estimated using a Kalman filtering method from meal and insulin data. Overall change significance for glycemic risks was assessed by repeated measures ANOVA, with specific phases emphasized using contrasts. RESULTS: Ovulation was confirmed in 33/36 cycles studied in 12 subjects (age = 33.1 ± 7.0 years, BMI = 25.7 ± 2.9 kg/m(2), A1c = 6.8 ± 0.7%). Risk for hyperglycemia changed significantly during the cycle (P = .023), with HBGI increasing until early luteal phase and returning to initial levels thereafter. LBGI was steady in the follicular phase, decreasing thereafter but not significantly. SI was depressed during the luteal phase when compared to the early follicular phase (P ≤ .05). Total daily insulin, carbohydrates, or calories did not show any significant fluctuations. CONCLUSIONS: Women with T1D have glycemic variability changes that are specific to the individual and are linked to phase of cycle. An increased risk of hyperglycemia was observed during periovulation and early luteal phases compared to the early follicular phase; these changes appear to be associated with decreased insulin sensitivity during the luteal phase.

Adjustment of Open-Loop Settings to Improve Closed-Loop Results in Type 1 Diabetes: A Multicenter Randomized Trial.

CONTEXT: Closed-loop control (CLC) relies on an individual's open-loop insulin pump settings to initialize the system. Optimizing open-loop settings before using CLC usually requires significant time and effort. OBJECTIVE: The objective was to investigate the effects of a one-time algorithmic adjustment of basal rate and insulin to carbohydrate ratio open-loop settings on the performance of CLC. DESIGN: This study reports a multicenter, outpatient, randomized, crossover clinical trial. PATIENTS: Thirty-seven adults with type 1 diabetes were enrolled at three clinical sites. INTERVENTIONS: Each subject's insulin pump settings were subject to a one-time algorithmic adjustment based on 1 week of open-loop (i.e., home care) data collection. Subjects then underwent two 27-hour periods of CLC in random order with either unchanged (control) or algorithmic adjusted basal rate and carbohydrate ratio settings (adjusted) used to initialize the zone-model predictive control artificial pancreas controller. Subject's followed their usual meal-plan and had an unannounced exercise session. MAIN OUTCOMES AND MEASURES: Time in the glucose range was 80-140 mg/dL, compared between both arms. RESULTS: Thirty-two subjects completed the protocol. Median time in CLC was 25.3 hours. The median time in the 80-140 mg/dl range was similar in both groups (39.7% control, 44.2% adjusted). Subjects in both arms of CLC showed minimal time spent less than 70 mg/dl (median 1.34% and 1.37%, respectively). There were no significant differences more than 140 mg/dL. CONCLUSIONS: A one-time algorithmic adjustment of open-loop settings did not alter glucose control in a relatively short duration outpatient closed-loop study. The CLC system proved very robust and adaptable, with minimal (<2%) time spent in the hypoglycemic range in either arm.

Computing the surveillance error grid analysis: procedure and examples.

The surveillance error grid (SEG) analysis is a tool for analysis and visualization of blood glucose monitoring (BGM) errors, based on the opinions of 206 diabetes clinicians who rated 4 distinct treatment scenarios. Resulting from this large-scale inquiry is a matrix of 337 561 risk ratings, 1 for each pair of (reference, BGM) readings ranging from 20 to 580 mg/dl. The computation of the SEG is therefore complex and in need of automation. The SEG software introduced in this article automates the task of assigning a degree of risk to each data point for a set of measured and reference blood glucose values so that the data can be distributed into 8 risk zones. The software's 2 main purposes are to (1) distribute a set of BG Monitor data into 8 risk zones ranging from none to extreme and (2) present the data in a color coded display to promote visualization. Besides aggregating the data into 8 zones corresponding to levels of risk, the SEG computes the number and percentage of data pairs in each zone and the number/percentage of data pairs above/below the diagonal line in each zone, which are associated with BGM errors creating risks for hypo- or hyperglycemia, respectively. To illustrate the action of the SEG software we first present computer-simulated data stratified along error levels defined by ISO 15197:2013. This allows the SEG to be linked to this established standard. Further illustration of the SEG procedure is done with a series of previously published data, which reflect the performance of BGM devices and test strips under various environmental conditions. We conclude that the SEG software is a useful addition to the SEG analysis presented in this journal, developed to assess the magnitude of clinical risk from analytically inaccurate data in a variety of high-impact situations such as intensive care and disaster settings.