Euglycemic Range Research Articles

Differences in Glucose Readings Between Right Arm and Left Arm Using a Continuous Glucose Monitor.

Continuous glucose monitoring (CGM) devices are used for evaluating real-time glucose levels to optimize diabetes management. There is limited information, however, on whether readings differ when a device is placed on the right versus the left arm. This study evaluated the mean difference in glucose levels between the right and left arm and the effect of unilateral arm exercise on this difference. The effect of an intermittent fasting diet on body fat percentage was also evaluated. In a prospective trial, 46 adult volunteers self-selected into the intermittent fasting (IF; N = 23) or free-living (FL; N = 23) diet group and were randomized into a unilateral arm exercise group. Volunteers had CGM sensors placed simultaneously on both arms for 12-14 days. The mean glucose level in the right arm was significantly higher than the left arm by 3.7 mg/dL (P < .001), and this result was unaffected by diet or arm exercise. Glucose levels were in euglycemic range for 75.2% of the time in the right arm and 67.5% in the left arm (P < .001). The change from baseline in body fat percentage between the IF and FL diet groups was not significant. Measured glucose level and time in euglycemic range differ per placement of the CGM device, and the implications of this difference should be considered in clinical practice and research.

Fast-acting insulin aspart (Fiasp®) improves glycemic outcomes when used with MiniMedTM 670G hybrid closed-loop system in simulated trials compared to NovoLog®

IntroductionMedtronic has developed a virtual patient simulator for modeling and predicting insulin therapy outcomes for people with type 1 diabetes (T1D). An enhanced simulator was created to estimate outcomes when using the MiniMedTM 670G system with standard NovoLog® (EU: NovoRapid, US: NovoLog) versus Fiasp ® by using clinical data. MethodsSixty-seven participants' PK profiles were generated per type of insulin (Total of 134 PK profiles). 7,485 virtual patients' PK measurements was matched with one of the 67 NovoLog® PK Tmax values. These 7,485 virtual patients were then simulated using the Medtronic MiniMed™ 670G algorithm following an in-silico protocol of 90 days: 14 days in open loop (Manual Mode) followed by 76 days in closed loop (Auto Mode). Simulation study was repeated with each NovoLog® PK profile being replaced by its corresponding Fiasp® PK profile in the same virtual patient. To validate our in-silico analysis, we compared the results of “actual” 19 “real life” patients from a clinical study ResultsSimulated overall and postprandial glycemic outcomes improved in all age groups with Fiasp®. The percentage of time in the euglycemic range increased by about ~2.2% with Fiasp®, in all age groups (p < 0.01). The percentage of time spent at <70 mg/dL was reduced by about ~0.6% with insulin Fiasp® (p < 0.01) and the mean glucose was reduced by about 1.3 mg/dL (p < 0.01), excluding those age <7 years. The simulated mean postprandial SG was reduced by ~5 mg/dL, a significant difference for all age groups. A clinical study results showed similar improvements with MiniMedTM 670G system when switching from NovoLog® to Fiasp®. ConclusionsThe simulation studies indicate that using Fiasp® in place of NovoLog® with the MiniMedTM 670G system will significantly improve the time spent in the healthy, euglycemic range and reduce exposure to hyperglycemia and hypoglycemia in most patients.

Abstract 13259: Differences in Glucose Readings Between Right Arm and Left Arm Using a Continuous Glucose Monitor

Introduction: Poorly controlled diabetes increases the risk of cardiovascular disease and associated complications. Continuous glucose monitoring (CGM) devices are tools that allow clinicians and patients to gather real-time glucose levels to enhance glycemic control. CGM devices are increasingly used to optimize diabetes management, but there is no guidance on whether glucose readings differ when a CGM device is placed on the right versus the left arm. We assessed the hypothesis that a difference in time-matched glucose levels will be observed between the right and left arm. Methods: This prospective trial enrolled 46 adult volunteers to simultaneously wear CGM sensors on the right and left arms. Glucose readings were collected every 15 minutes over a 12-14 days study period. To assess if any observed inter-arm differences were a result of perfusion, participants were randomized to perform a daily 20-minute unilateral arm exercise using an assigned arm on Days 1-6, and to use the alternate arm from Day 7 to Days 12-14. Differences in glucose readings between the right and left arm were compared using the paired sample t-test. Results: A total of 41,288 time-matched glucose level pairs were collected and the mean glucose reading in the right arm was higher than that of the left arm by 3.7±10.6 mg/dL (p&lt;0.001). Glucose readings were in hypoglycemic range (&lt;70 mg/dL) for 22.1% of the time in the right arm and 29.8% of the time in the left arm (p&lt;0.001), in euglycemic range (70-180 mg/dL) for 75.2% of the time in the right arm and 67.5% in the left arm (p&lt;0.001), and in hyperglycemic range (&gt;180 mg/dL) for 2.8% of the time in both arms (p=0.83). The difference in glucose levels between the arms was similar before and during exercise [4.0±10.2 mg/dL vs. 4.3±10.1 mg/dL, p=0.77]. Conclusions: In conclusion, glucose levels and time in hypoglycemia and euglycemia significantly differ based on arm selection during CGM device placement. This finding is unrelated to differences in perfusion induced by unilateral arm exercise. If these findings are further validated in patients with diabetes, arm selection during CGM device placement could impact therapy decisions, clinical trial design, and interpretation.

Daily variations in sleep and glucose in adolescents with type 1 diabetes.

We used multilevel models (MLMs) to determine person (between-persons) and day level (within-person) associations between glucose variability indices and sleep characteristics in adolescents with type 1 diabetes (T1D). Adolescents with T1D (Mean age 13.4 ± 1.8 years; 37.8% male; mean HbA1c 8.2 ± 1.2%, 66 mmol/mol) monitored their sleep and glucose patterns concurrently for 3-7 days with a wrist actigraph on their non-dominant wrist and a continuous glucose monitor (CGM) (their own or a provided, blinded CGM). Glucose variability indices included J index, coefficient of variation, low and high blood glucose risk indices (LBGI and HBGI), time in range, and sleep characteristics, including bedtime, wake time, total sleep time, sleep efficiency, wake after sleep onset, awakenings, and sleep fragmentation index. More overall glucose variability was associated within person, more sleep disruptions (more awakenings and more fragmentation) or poorer sleep in our study (earlier wake time or longer wake after sleep onset). Also, more time spent in hypoglycemia <70 mg/dL and a higher LBGI was associated within person with earlier wake time indicating poorer sleep. However, a lower LBGI was associated with a later between-persons wake time. Monitoring over a longer period of time in subsequent studies would allow researchers to determine the within person association between habitual short sleep duration and glucose variability. Providers should regularly assess sleep habits in adolescents as a way to improve glycemic control. Targeting a euglycemic range overnight is also important to promote better sleep and to decrease sleep disruptions.

The Diabits App for Smartphone-Assisted Predictive Monitoring of Glycemia in Patients With Diabetes: Retrospective Observational Study.

BackgroundDiabetes mellitus, which causes dysregulation of blood glucose in humans, is a major public health challenge. Patients with diabetes must monitor their glycemic levels to keep them in a healthy range. This task is made easier by using continuous glucose monitoring (CGM) devices and relaying their output to smartphone apps, thus providing users with real-time information on their glycemic fluctuations and possibly predicting future trends.ObjectiveThis study aims to discuss various challenges of predictive monitoring of glycemia and examines the accuracy and blood glucose control effects of Diabits, a smartphone app that helps patients with diabetes monitor and manage their blood glucose levels in real time.MethodsUsing data from CGM devices and user input, Diabits applies machine learning techniques to create personalized patient models and predict blood glucose fluctuations up to 60 min in advance. These predictions give patients an opportunity to take pre-emptive action to maintain their blood glucose values within the reference range. In this retrospective observational cohort study, the predictive accuracy of Diabits and the correlation between daily use of the app and blood glucose control metrics were examined based on real app users’ data. Moreover, the accuracy of predictions on the 2018 Ohio T1DM (type 1 diabetes mellitus) data set was calculated and compared against other published results.ResultsOn the basis of more than 6.8 million data points, 30-min Diabits predictions evaluated using Parkes Error Grid were found to be 86.89% (5,963,930/6,864,130) clinically accurate (zone A) and 99.56% (6,833,625/6,864,130) clinically acceptable (zones A and B), whereas 60-min predictions were 70.56% (4,843,605/6,864,130) clinically accurate and 97.49% (6,692,165/6,864,130) clinically acceptable. By analyzing daily use statistics and CGM data for the 280 most long-standing users of Diabits, it was established that under free-living conditions, many common blood glucose control metrics improved with increased frequency of app use. For instance, the average blood glucose for the days these users did not interact with the app was 154.0 (SD 47.2) mg/dL, with 67.52% of the time spent in the healthy 70 to 180 mg/dL range. For days with 10 or more Diabits sessions, the average blood glucose decreased to 141.6 (SD 42.0) mg/dL (P<.001), whereas the time in euglycemic range increased to 74.28% (P<.001). On the Ohio T1DM data set of 6 patients with type 1 diabetes, 30-min predictions of the base Diabits model had an average root mean square error of 18.68 (SD 2.19) mg/dL, which is an improvement over the published state-of-the-art results for this data set.ConclusionsDiabits accurately predicts future glycemic fluctuations, potentially making it easier for patients with diabetes to maintain their blood glucose in the reference range. Furthermore, an improvement in glucose control was observed on days with more frequent Diabits use.

In Silico Testing of a Closed-Loop Artificial Pancreas Based on Generalized Predictive Control

The artificial pancreas (AP) system is a promising approach to maintain blood glucose concentration (BGC) of patients with type 1 diabetes (T1D) in a euglycemic range (70–180 mg/dl). A controller based on generalized predictive control (GPC) with an adaptive reference glucose trajectory and an adaptive softening factor (GPC+AA) was proposed for AP system in our previous research. It was built and tested with the University of Virginia (UVA)/Padova type 1 diabetes mellitus simulator (T1DMS), which embodies the biophysiological parameters of in-silico population accepted by the US Food and Drug Administration. It was showed that the GPC+AA controller realized effective control of the BGC of in-silico patients with T1D. Here, the effectiveness of the GPC+AA controller was further tested with strict conditions, including a pump with noise and error, a continuous glucose monitoring sensor and a high carbohydrate intake. Test results showed that the BGC of adolescent group was not effectively controlled with the GPC+AA controller. Then, the adaptive softening factor strategy was further optimized and a GPC controller with an adaptive reference glucose trajectory and an optimized adaptive softening factor (GPC+AO) was proposed here. It was demonstrated that the optimized adaptive softening factor strategy significantly improved the performance of the GPC controller, and the average BGC percentage within the euglycemic range of 9 adults and 10 adolescents increased to 80% and 70%, respectively. Thus, our GPC+AO controller is effective for patients with T1D and can be potentially applied in the AP systems.

Safety and Efficacy of Initializing the Control-IQ Artificial Pancreas System Based on Total Daily Insulin in Adolescents with Type 1 Diabetes.

Objective: To assess the safety and efficacy of a simplified initialization for the Tandem t:slim X2 Control-IQ hybrid closed-loop system, using parameters based on total daily insulin ("MyTDI") in adolescents with type 1 diabetes under usual activity and during periods of increased exercise. Research Design and Methods: Adolescents with type 1 diabetes 12-18 years of age used Control-IQ for 5 days at home using their usual parameters. Upon arrival at a 60-h ski camp, participants were randomized to either continue Control-IQ using their home settings or to reinitialize Control-IQ with MyTDI parameters. Control-IQ use continued for 5 days following camp. The effect of MyTDI on continuous glucose monitoring outcomes were analyzed using repeated measures analysis of variance (ANOVA): baseline, camp, and at home. Results: Twenty participants were enrolled and completed the study; two participants were excluded from the analysis due to absence from ski camp (1) and illness (1). Time in range was similar between both groups at home and camp. A tendency to higher time <70 mg/dL in the MyTDI group was present but only during camp (median 3.8% vs. 1.4%, P = 0.057). MyTDI users with bolus/TDI ratios >40% tended to show greater time in the euglycemic range improvements between baseline and home than users with ratios <40% (+16.3% vs. -9.0%, P = 0.012). All participants maintained an average of 95% time in closed loop (84.1%-100%). Conclusions: MyTDI is a safe, effective, and easy way to determine insulin parameters for use in the Control-IQ artificial pancreas. Future modifications to account for the influence of carbohydrate intake on MyTDI calculations might further improve time in range.

Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement.

Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses-the so-called automatic regulation of glucose (ARG)-was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size. An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only. The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fast-absorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 80.9 [77.0-85.1]%). In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.

2322-PUB: Consistency and Predictability of Control of Glycemic Variability with U200 (Concentrated r-DNA Human Insulin Premix 30/70 - 200 IU/mL) in Type 2 Diabetes Mellitus Patients

Introduction: In type 2 diabetes mellitus (T2DM) patients, U200 (concentrated r-DNA Human Insulin Premix 30/70 - 200IU/mL) provides good control of glycemic variability with minimal glycemic excursions and substantial time spent in euglycemia. Objective of this subgroup analysis was to assess consistency and predictability of control of glycemic variability with U200 by gender, body mass index (BMI) and daily insulin dose. Methods: Sixty adult patients with T2DM on treatment with human insulin (regular/NPH/Premix) on stable insulin dose were treated with U200 for 7 days. Variability in 24-hour blood glucose profile measured by CGMS device was compared between various subgroups. Results: There was no statistically significant difference between male and female, patients with BMI &amp;lt;25 kg/m2 vs. ≥ 25 kg/m2 and with daily insulin dose &amp;lt; 20 IU vs. ≥ 20 IU for mean blood glucose, mean number and duration of hyperglycemic episodes (&amp;gt;150 mg/dL), mean number and duration of hypoglycemic episodes (&amp;lt;70 mg/dL), and % of time spent in euglycemic range (70 - 150 mg/dL). Conclusion: Mean blood glucose, frequency-duration of excursions and time spent in euglycemic range with U200 were unaffected by gender, BMI and daily insulin dose thereby emphasizing consistency and predictability of control of glycemic variability across a wide range of patients. Disclosure H. Thacker: None. J. Kesavadev: None. S.A. Ingole: Employee; Self; Wockhardt. A. Shah: Employee; Self; Wockhardt. R. Jain: Employee; Self; Wockhardt. Funding Wockhardt Pharmaceuticals Ltd.

957-P: Differences in Glucose Level between Left Arm and Right Arm Using Continuous Glucose Monitors

Background: Continuous glucose monitoring (CGM) is an emerging technology that provides real-time interstitial glucose levels through a sensor with a thin filament inserted under the skin. It is customary for patients to rotate sensor application sites between arms to minimize skin irritation. However, the degree of inter-arm differences with CGM technology remains unknown. Materials and Methods: Self-proclaimed right-handed (n=5) and left-handed (n=5) subjects, regardless of concurrent comorbidities, were enrolled for CGM. A FreeStyle Libre Pro Flash Glucose Monitoring System sensor was simultaneously placed on each arm for all subjects. The sensors were worn for a maximum of 14 days with measurements taken every 15 minutes. Muscle mass and body fat analysis was conducted using a multi-frequency segmental body composition analyzer. Glucose levels from the right and left arms were time-matched with the first 12 hours eliminated from analysis. Results: A total of 9830 paired glucose levels were included for analysis. Glucose levels were outside the euglycemic range (70-140 mg/dL) for 14.5% of the time in the right arm compared to 20.0% in the left arm (P&amp;lt;0.001). The glucose level in the right arm was higher than the left arm in 67% (range 46 - 98%) of the measurements (P&amp;lt;0.001). The absolute difference in glucose level between the right and left arms was &amp;gt;10 mg/dL, &amp;gt;20 mg/dL, and &amp;gt;30 mg/dL, for 19.4%, 3.4%, and 0.32% of the time, respectively. The mean glucose level in the right arm was higher than the left arm by 3.75 mg/dL [SD, 8.51 mg/dL] in right-handed subjects, and by 3.84 mg/dL [SD, 7.09 mg/dL] in left-handed subjects (P=0.54). Conclusion: Time in euglycemic range could significantly differ based on arm selection when using a CGM. While the absolute difference in glucose levels between arms appears small, this degree of variability would have significant implications for clinical trial design using CGM technology. Arm dominance does not explain the inter-arm glucose level discordance. Disclosure N. Kim: None. K. Pham: None. A. Shek: None. J.J. Lim: Employee; Self; Genentech, Inc. Stock/Shareholder; Self; F. Hoffmann-La Roche Ltd. S.A. Shah: None. Funding University of the Pacific

67-LB: Time Spent in Glycaemic Ranges and Carbohydrate Intake during Cycling in Professional Cyclists with Type 1 Diabetes

Aim: This observational study examined in-ride interstitial glucose data and nutritional intake in a UCI-accredited professional cycling team with type 1 diabetes (T1D) over a nine-day training camp. Methods: Sixteen male professional road cyclists with T1D (age 27±4 years, T1D duration 11±5 years, BMI 21.6±1.5 kg·min-2, HbA1c 6.8±0.6 %, O2max 71.3±3.9 ml·kg·min-1) on multiple daily injections performed eight rides over nine days lasting between 2 and 6 h, traversing 56 to 182 km/day. Cycle glycemic information (G6, Dexcom) was collected, stratified into glycemic time in ranges; hypoglycemia (Level 1: 54-70, Level 2: &amp;lt;54 mg/dL), euglycemia (71-180 mg/dL) and hyperglycemia (Level 1: 181-299, Level 2: &amp;gt;299 mg/dL) and analyzed for time spent (%) within each range. Nutritional intake during each ride was determined and reported as median (range). Results: Average percentage time spent in euglycemia during cycling was 78±6% (daily range 70-86%). Hypoglycemia during cycling was 3±4% (range 0-11%, Level 1), and 1±2% (range 0-2%, Level 2). No hypoglycemic event required assistance. Percentage time spent in hyperglycemia during cycling was 17±7% (range 8-27%, Level 1), and 1±2 % (range 0-5%, Level 2). The contribution of carbohydrates, fat and protein to total in-ride energy intake was 83.8±15.4, 12.6±8.1 and 1.2±0.6 %, respectively. Macronutrient intake was dependent on cycle duration with greater carbohydrate intake rate on longer rides (2-hour cycle: 20 (4-82) g/h vs. 6-hour cycle: 51 (17-86) g/h). Conclusions: Professional cyclists with T1D maintained a large proportion of time in a euglycemic range during intense cycling sessions over a nine-day training period. The low occurrence of in-ride hypoglycemia, mostly at Level 1, suggests a protective role for consumption of carbohydrate-rich foods to maintain glycaemia, especially during longer rides. However, time spent in Level 1 hyperglycemia during cycling was notable and should be monitored. Disclosure O. McCarthy: None. O. Moser: Research Support; Self; Abbott, Dexcom, Inc., Novo Nordisk A/S. M.L. Eckstein: Research Support; Self; Novo Nordisk A/S. S.N. Scott: None. M. Riddell: Advisory Panel; Self; Xeris Pharmaceuticals, Inc. Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc., Insulet Corporation, Novo Nordisk Inc., Sanofi. Speaker’s Bureau; Self; Medtronic MiniMed, Inc., OmniPod. Stock/Shareholder; Self; Zucara Therapeutics Inc. M. Fisher: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Mylan, NAPP Pharmaceuticals Limited, Novo Nordisk A/S. Speaker’s Bureau; Self; Eli Lilly and Company, Sanofi. F.Y. Fontana: None. K. Skroce: None. L. Festa: None. P.H. Lagrou: None. P. Southerland: Other Relationship; Self; Novo Nordisk Inc. C.A. Hayes: Other Relationship; Self; Novo Nordisk A/S. M.P. Christiansen: Research Support; Self; Abbott, Biolinq, Dexcom, Inc., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Medtronic MiniMed, Inc., Novo Nordisk A/S, Sanofi-Aventis, Xeris Pharmaceuticals, Inc. B.W. Bode: Consultant; Self; ADOCIA, Lexicon Pharmaceuticals, Inc., Novo Nordisk Inc. Research Support; Self; Becton, Dickinson and Company, Dexcom, Inc., Diasome Pharmaceuticals, Inc., Eli Lilly and Company, Eyenuk Inc., Insulet Corporation, National Institutes of Health, Novo Nordisk Inc., Sanofi Research &amp; Development, Senseonics, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., Novo Nordisk Inc., Sanofi US, Senseonics. Stock/Shareholder; Self; AgaMatrix, Glytec, LLC. C. Stettler: None. R.M. Bracken: None.

Multivariate learning framework for long-term adaptation in the artificial pancreas.

The long‐term use of the artificial pancreas (AP) requires an automated insulin delivery algorithm that can learn and adapt with the growth, development, and lifestyle changes of patients. In this work, we introduce a data‐driven AP adaptation method for improved glucose management in a home environment. A two‐phase Bayesian optimization assisted parameter learning algorithm is proposed to adapt basal and carbohydrate‐ratio profile, and key feedback control parameters. The method is evaluated on the basis of the 111‐adult cohort of the FDA‐accepted UVA/Padova type 1 diabetes mellitus simulator through three scenarios with lifestyle disturbances and incorrect initial parameters. For all the scenarios, the proposed method is able to robustly adapt AP parameters for improved glycemic regulation performance in terms of percent time in the euglycemic range [70, 180] mg/dl without causing risk of hypoglycemia in terms of percent time below 70 mg/dl.

Prediction of Adverse Glycemic Events From Continuous Glucose Monitoring Signal.

The most important objective of any diabetes therapy is to maintain the blood glucose concentration within the euglycemic range, avoiding or at least mitigating critical hypo/hyperglycemic episodes. Modern continuous glucose monitoring (CGM) devices bear the promise of providing the patients with an increased and timely awareness of glycemic conditions as these get dangerously near to hypo/hyperglycemia. The challenge is to detect, with reasonable advance, the patterns leading to risky situations, allowing the patient to make therapeutic decisions on the basis of future (predicted) glucose concentration levels. We underline that a technically sound performance comparison of the approaches proposed in recent years has yet to be done, thus it is unclear which one is preferred. The aim of this study is to fill this gap by carrying out a comparative analysis among the most common methods for glucose event prediction. Both regression and classification algorithms have been implemented and analyzed, including static and dynamic training approaches. The dataset consists of 89 CGM time series measured in diabetic subjects for 7 subsequent days. Performance metrics, specifically defined to assess and compare the event-prediction capabilities of the methods, have been introduced and analyzed. Our numerical results show that a static training approach exhibits better performance, in particular when regression methods are considered. However, classifiers show some improvement when trained for a specific event category, such as hyperglycemia, achieving performance comparable to the regressors, with the advantage of predicting the events sooner.

Neonatal hypoglycemia: continuous glucose monitoring

Continuous glucose monitoring (CGM) is increasingly used in the management of diabetes in children and adults, but there are few data regarding its use in neonates. The purpose of this article is to discuss the potential benefits and limitations of CGM in neonates. Smaller electrodes in new sensors and real-time monitoring have made CGM devices more approachable for neonatal care. CGM is well tolerated in infants including very low birth weight babies, and few if any local complications have been reported. Use of CGM in newborns may reduce the frequency of blood sampling and improve glycemic stability, with more time spent in the euglycemic range. However, CGM may also lead to more intervention, with potential adverse effects on outcomes. More information is also needed about reliability, calibration and interpretation of CGM in the neonate. Although the use of CGM in neonates appears to be well tolerated, feasible and has been associated with better glycemic status, there is not yet any evidence of improved clinical outcomes. Clinical utility of CGM should be demonstrated in randomized trials prior to its introduction into regular neonatal care.

In silico testing of optimized Fuzzy P+D controller for artificial pancreas

Background and objectivesDespite therapeutic advances, a complete cure has not been found yet for patients with type 1 diabetes (T1D). Artificial pancreas (AP) is a promising approach to cope with this disease. The controller part of the AP can compute the insulin infusion rate that keeps blood glucose concentration (BGC) in normoglycemic ranges. Most controllers rely on model-based controllers and use manual meal announcements or meal detection algorithms. For a fully automated AP, a controller only using the patient's BGC data is needed. MethodsAn optimized Mamdani-type hybrid Fuzzy P+D controller was proposed. Using the University of Virginia/Padova Simulator, a 36h scenario was tested in nine virtual adult patients. To take into account the effect of continuous glucose monitor noise, the scenario was repeated 25 times for each adult. The main outcomes were the percentage of time BGC levels in the euglycemic range, low blood glucose index (LBGI), and blood glucose risk index (BGRI), respectively. ResultsThe obtained BGC values were found to be in the euglycemic range for 82.6% of the time. Moreover, the BGC values were below 50mg/dl, below 70mg/dl and above 250mg/dl for 0%, 0.35% and 0.74% of the time, respectively. The BGRI, LBGI, and high blood glucose index (HBGI) were also found as 3.75, 0.34 and 3.41, respectively. The proposed controller both increases the time the BGC levels in the euglycemic range and causes less hypoglycemia and hyperglycemia relative to the published techniques studied in a similar scenario and population.

Continuous Glucose Monitoring in Very Preterm Infants: A Randomized Controlled Trial.

Impaired glucose control in very preterm infants is associated with increased morbidity, mortality, and poor neurologic outcome. Strategies based on insulin titration have been unsuccessful in achieving euglycemia in absence of an increase in hypoglycemia and mortality. We sought to assess whether glucose administration guided by continuous glucose monitoring (CGM) is more effective than standard of care blood glucose monitoring in maintaining euglycemia in very preterm infants. Fifty newborns ≤32 weeks' gestation or with birth weight ≤1500 g were randomly assigned (1:1) within 48-hours from birth to receive computer-guided glucose infusion rate (GIR) with or without CGM. In the unblinded CGM group, the GIR adjustments were driven by CGM and rate of glucose change, whereas in the blinded CGM group the GIR was adjusted by using standard of care glucometer on the basis of blood glucose determinations. Primary outcome was percentage of time spent in euglycemic range (72-144 mg/dL). Secondary outcomes were percentage of time spent in mild (47-71 mg/dL) and severe (<47 mg/dL) hypoglycemia; percentage of time in mild (145-180 mg/dL) and severe (>180 mg/dL) hyperglycemia; and glucose variability. Neonates in the unblinded CGM group had a greater percentage of time spent in euglycemic range (median, 84% vs 68%, P < .001) and decreased time spent in mild (P = .04) and severe (P = .007) hypoglycemia and in severe hyperglycemia (P = .04) compared with the blinded CGM group. Use of CGM also decreased glycemic variability (SD: 21.6 ± 5.4 mg/dL vs 27 ± 7.2 mg/dL, P = .01; coefficient of variation: 22.8% ± 4.2% vs 27.9% ± 5.0%; P < .001). CGM-guided glucose titration can successfully increase the time spent in euglycemic range, reduce hypoglycemia, and minimize glycemic variability in preterm infants during the first week of life.

Extensive Assessment of Blood Glucose Monitoring During Postprandial Period and Its Impact on Closed-Loop Performance

Closed-loop (CL) systems aims to outperform usual treatments in blood glucose control and continuous glucose monitors (CGM) are a key component in such systems. Meals represents one of the main disturbances in blood glucose control, and postprandial period (PP) is a challenging situation for both CL system and CGM accuracy. We performed an extensive analysis of sensor's performance by numerical accuracy and precision during PP, as well as its influence in blood glucose control under CL therapy. During PP the mean absolute relative difference (MARD) for both sensors presented lower accuracy in the hypoglycemic range (19.4 ± 12.8%) than in other ranges (12.2 ± 8.6% in euglycemic range and 9.3 ± 9.3% in hyperglycemic range). The overall MARD was 12.1 ± 8.2%. We have also observed lower MARD for rates of change between 0 and 2 mg/dl. In CL therapy, the 10 trials with the best sensor spent less time in hypoglycemia (PG < 70 mg/dl) than the 10 trials with the worst sensors (2 ± 7 minutes vs 32 ± 38 minutes, respectively). In terms of accuracy, our results resemble to previously reported. Furthermore, our results showed that sensors with the lowest MARD spent less time in hypoglycemic range, indicating that the performance of CL algorithm to control PP was related to sensor accuracy.

Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control.

Development of an effective artificial pancreas (AP) controller to deliver insulin autonomously to people with type 1 diabetes mellitus is a difficult task. In this paper, three enhancements to a clinically validated AP model predictive controller (MPC) are proposed that address major challenges facing automated blood glucose control, and are then evaluated by both in silico tests and clinical trials. First, the core model of insulin-blood glucose dynamics utilized in the MPC is expanded with a medically inspired personalization scheme to improve controller responses in the face of inter- and intra-individual variations in insulin sensitivity. Next, the asymmetric nature of the short-term consequences of hypoglycemia versus hyperglycemia is incorporated in an asymmetric weighting of the MPC cost function. Finally, an enhanced dynamic insulin-on-board algorithm is proposed to minimize the likelihood of controller-induced hypoglycemia following a rapid rise of blood glucose due to rescue carbohydrate load with accompanying insulin suspension. Each advancement is evaluated separately and in unison through in silico trials based on a new clinical protocol, which incorporates induced hyper- and hypoglycemia to test robustness. The advancements are also evaluated in an advisory mode (simulated) testing of clinical data. The combination of the three proposed advancements show statistically significantly improved performance over the nonpersonalized controller without any enhancements across all metrics, displaying increased time in the 70-180 mg/dL safe glycemic range (76.9 versus 68.8%) and the 80-140 mg/dL euglycemic range (48.1 versus 44.5%), without a statistically significant increase in instances of hypoglycemia. The proposed advancements provide safe control action for AP applications, personalizing and improving controller performance without the need for extensive model identification processes.

Accuracy of Continuous Glucose Monitoring During Three Closed-Loop Home Studies Under Free-Living Conditions.

Objectives: Closed-loop (CL) systems modulate insulin delivery based on glucose levels measured by a continuous glucose monitor (CGM). Accuracy of the CGM affects CL performance and safety. We evaluated the accuracy of the Freestyle Navigator® II CGM (Abbott Diabetes Care, Alameda, CA) during three unsupervised, randomized, open-label, crossover home CL studies.Materials and Methods: Paired CGM and capillary glucose values (10,597 pairs) were collected from 57 participants with type 1 diabetes (41 adults [mean±SD age, 39±12 years; mean±SD hemoglobin A1c, 7.9±0.8%] recruited at five centers and 16 adolescents [mean±SD age, 15.6±3.6 years; mean±SD hemoglobin A1c, 8.1±0.8%] recruited at two centers). Numerical accuracy was assessed by absolute relative difference (ARD) and International Organization for Standardization (ISO) 15197:2013 15/15% limits, and clinical accuracy was assessed by Clarke error grid analysis.Results: Total duration of sensor use was 2,002 days (48,052 h). Overall sensor accuracy for the capillary glucose range (1.1–27.8 mmol/L) showed mean±SD and median (interquartile range) ARD of 14.2±15.5% and 10.0% (4.5%, 18.4%), respectively. Lowest mean ARD was observed in the hyperglycemic range (9.8±8.8%). Over 95% of pairs were in combined Clarke error grid Zones A and B (A, 80.1%, B, 16.2%). Overall, 70.0% of the sensor readings satisfied ISO criteria. Mean ARD was consistent (12.3%; 95% of the values fall within ±3.7%) and not different between participants (P=0.06) within the euglycemic and hyperglycemic range, when CL is actively modulating insulin delivery.Conclusions: Consistent accuracy of the CGM within the euglycemic–hyperglycemic range using the Freestyle Navigator II was observed and supports its use in home CL studies. Our results may contribute toward establishing normative CGM performance criteria for unsupervised home use of CL.

Patient decision-making of CGM sensor driven insulin therapies in type 1 diabetes: In silico assessment.

In type 1 diabetes (T1D) therapy, continuous glucose monitoring (CGM) sensors, which provide glucose concentration in the subcutis every 1-5 min for 7 consecutive days, should allow in principle a more efficient insulin dosing than that based on the conventional 3-4 self-monitoring of blood glucose (SMBG) measurements per day. However, CGM, at variance with SMBG, is still not approved for insulin dosing in T1D management because regulatory agencies, e.g. FDA, are looking for more factual evidence on its safety. An in silico assessment of SMBG- vs CGM-driven insulin therapy can be a first step. Here we present a simulation model of T1D patient decision-making obtained by interconnecting models of glucose-insulin dynamics, SMBG and CGM measurement errors, carbohydrates-counting errors, insulin boluses time variability and forgetfulness, and subcutaneous insulin pump delivery. Inter- and intra- patient variability of model parameters are considered. The T1D patient decision-making model allows to run realistic multi-day simulations scenarios in a population of virtual subjects. We present the first results of simulations run in 20 virtual subjects over a 7-day period, which demonstrates that additional information brought by CGM (trend and hypo/hyperglycemic warnings) with respect to SMBG produces a statistically significant increment (about of 9%) of time spent by the patient in the euglycemic range (70-180 mg/dl).