Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial.

BACKGROUND: The safety and effectiveness of a continuous, day-and-night automated glycaemic control system using insulin and glucagon has not been shown in a free-living, home-use setting. We aimed to assess whether bihormonal bionic pancreas initialised only with body mass can safely reduce mean glycaemia and hypoglycaemia in adults with type 1 diabetes who were living at home and participating in their normal daily routines without restrictions on diet or physical activity. METHODS: We did a random-order crossover study in volunteers at least 18 years old who had type 1 diabetes and lived within a 30 min drive of four sites in the USA. Participants were randomly assigned (1:1) in blocks of two using sequentially numbered sealed envelopes to glycaemic regulation with a bihormonal bionic pancreas or usual care (conventional or sensor-augmented insulin pump therapy) first, followed by the opposite intervention. Both study periods were 11 days in length, during which time participants continued all normal activities, including athletics and driving. The bionic pancreas was initialised with only the participant's body mass. Autonomously adaptive dosing algorithms used data from a continuous glucose monitor to control subcutaneous delivery of insulin and glucagon. The coprimary outcomes were the mean glucose concentration and time with continuous glucose monitoring (CGM) glucose concentration less than 3·3 mmol/L, analysed over days 2-11 in participants who completed both periods of the study. This trial is registered with ClinicalTrials.gov, number NCT02092220. FINDINGS: We randomly assigned 43 participants between May 6, 2014, and July 3, 2015, 39 of whom completed the study: 20 who were assigned to bionic pancreas first and 19 who were assigned to the comparator first. The mean CGM glucose concentration was 7·8 mmol/L (SD 0·6) in the bionic pancreas period versus 9·0 mmol/L (1·6) in the comparator period (difference 1·1 mmol/L, 95% CI 0·7-1·6; p<0·0001), and the mean time with CGM glucose concentration less than 3·3 mmol/L was 0·6% (0·6) in the bionic pancreas period versus 1·9% (1·7) in the comparator period (difference 1·3%, 95% CI 0·8-1·8; p<0·0001). The mean nausea score on the Visual Analogue Scale (score 0-10) was greater during the bionic pancreas period (0·52 [SD 0·83]) than in the comparator period (0·05 [0·17]; difference 0·47, 95% CI 0·21-0·73; p=0·0024). Body mass and laboratory parameters did not differ between periods. There were no serious or unexpected adverse events in the bionic pancreas period of the study. INTERPRETATION: Relative to conventional and sensor-augmented insulin pump therapy, the bihormonal bionic pancreas, initialised only with participant weight, was able to achieve superior glycaemic regulation without the need for carbohydrate counting. Larger and longer studies are needed to establish the long-term benefits and risks of automated glycaemic management with a bihormonal bionic pancreas. FUNDING: National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health, and National Center for Advancing Translational Sciences.

Predictive hyperglycemia and hypoglycemia minimization: In-home double-blind randomized controlled evaluation in children and young adolescents.

OBJECTIVE: The primary objective of this trial was to evaluate the feasibility, safety, and efficacy of a predictive hyperglycemia and hypoglycemia minimization (PHHM) system vs predictive low glucose suspension (PLGS) alone in optimizing overnight glucose control in children 6 to 14 years old. RESEARCH DESIGN AND METHODS: Twenty-eight participants 6 to 14 years old with T1D duration ≥1 year with daily insulin therapy ≥12 months and on insulin pump therapy for ≥6 months were randomized per night into PHHM mode or PLGS-only mode for 42 nights. The primary outcome was percentage of time in sensor-measured range 70 to 180 mg/dL in the overnight period. RESULTS: The addition of automated insulin delivery with PHHM increased time in target range (70-180 mg/dL) from 66 ± 11% during PLGS nights to 76 ± 9% during PHHM nights (P<.001), without increasing hypoglycemia as measured by time below various thresholds. Average morning blood glucose improved from 176 ± 28 mg/dL following PLGS nights to 154 ± 19 mg/dL following PHHM nights (P<.001). CONCLUSIONS: The PHHM system was effective in optimizing overnight glycemic control, significantly increasing time in range, lowering mean glucose, and decreasing glycemic variability compared to PLGS alone in children 6 to 14 years old.

In-home nighttime predictive low glucose suspend experience in children and adults with type 1 diabetes.

Overnight predictive low glucose suspend (PLGS) reduces hypoglycemia across all ages; however, there are no reports on behavior or experience differences across age groups, especially in pediatrics. As run-in for a subsequent randomized clinical trial (RCT), 127 subjects (50% male) ages 4-45 yr utilized the experimental PLGS system nightly for 5-10 nights (PLGS active phase). We analyzed the number of blood glucose (BG) checks and boluses given per age group. During the subsequent 42 night RCT phase, we analyzed sensor use, skin reactions, errors, and reasons why the experimental system was not used. In 821 nights of active PLGS, subjects ages 4-6 yr (and their parents) tested BG levels 75% of nights compared with 65% of nights (7-10 yr), 53% of nights (11-14 yr), 33% of nights (15-25 yr), and 28% of nights (26-45 yr), respectively (p < 0.001). Likewise, youngest subjects (and parents) administered insulin boluses 56% of nights during active PLGS use compared with 48%, 33%, 20%, and 25%, respectively (p < 0.001). This was unrelated to study requirements. During the RCT phase, subjects 4-6 yr experienced more frequent and severe skin reactions (p = 0.02), while adult subjects (26-45 yr) wore individual sensors a median of 26 h longer than the youngest subjects (p < 0.001). Technical problems with the sensor (errors, miscalibrations, etc.), traveling, and BG levels >270 at bedtime (study requirement) were primary contributors to non-system use. Understanding the different use patterns and challenges in pediatrics and adolescence is needed to direct patient education to optimize use of PLGS and future artificial pancreas systems.

Ketone production in children with type 1 diabetes, ages 4-14 years, with and without nocturnal insulin pump suspension.

OBJECTIVE: To compare the frequency of elevated morning blood ketone levels according to age in 4-14 year olds with type 1 diabetes following overnight use of an automated low glucose insulin suspension system, or following control nights when the system was not used. RESEARCH DESIGN AND METHODS: For 28 children ages 4-9 years and 54 youth ages 10-14 years, elevation of morning blood ketone levels was assessed using the Precision Xtra Ketone meter following 1155 and 2345 nights, respectively. Repeated measures logistic regression models were used to compare age groups for blood ketone level elevation following control nights (system not activated) and following intervention nights with and without insulin suspension. RESULTS: Elevated morning blood ketones (≥0.6 mmol/L) were present following 10% of 580 control nights in the 4-9 year olds compared with 2% of 1162 control nights in 10-14 year olds (P < 0.001). Likewise, the frequency was greater following intervention nights in the younger age group (13% of 575 nights vs 2% of 1183 nights, P < 0.001). A longer duration of pump suspension resulted in a higher percentage of mornings with elevated blood ketones in the younger age group (P = 0.002), but not in the older age group (P = 0.63). The presence of elevated morning ketone levels did not progress to ketoacidosis in any subject. CONCLUSIONS: Elevated morning blood ketones are more common in younger children with type 1 diabetes with or without nocturnal insulin suspension. Care providers need to be aware of the differences in ketogenesis in younger age children relative to various clinical situations.

Preventing hypoglycemia using predictive alarm algorithms and insulin pump suspension.

BACKGROUND: Nocturnal hypoglycemia is a significant problem. From 50% to 75% of hypoglycemia seizures occur at night. Despite the development of real-time glucose sensors (real-time continuous glucose monitor [CGM]) with hypoglycemic alarms, many patients sleep through these alarms. The goal of this pilot study was to assess the feasibility using a real-time CGM to discontinue insulin pump therapy when hypoglycemia was predicted. METHODS: Twenty-two subjects with type 1 diabetes had two daytime admissions to a clinical research center. On the first admission their basal insulin was increased until their blood glucose level was <60 mg/dL. On the second admission hypoglycemic prediction algorithms were tested to determine if hypoglycemia was prevented by a 90-min pump shutoff and to determine if the pump shutoff resulted in rebound hyperglycemia. RESULTS: Using a statistical prediction algorithm with an 80 mg/dL threshold and a 30-min projection horizon, hypoglycemia was prevented 60% of the time. Using a linear prediction algorithm with an 80 mg/dL threshold and a 45-min prediction horizon, hypoglycemia was prevented 80% of the time. There was no rebound hyperglycemia following pump suspension. CONCLUSIONS: Further development of algorithms is needed to prevent all episodes of hypoglycemia from occurring.

Feasibility Studies of an Insulin-Only Bionic Pancreas in a Home-Use Setting.

BACKGROUND: We tested the safety and performance of the "insulin-only" configuration of the bionic pancreas (BP) closed-loop blood-glucose control system in a home-use setting to assess glycemic outcomes using different static and dynamic glucose set-points. METHOD: This is an open-label non-randomized study with three consecutive intervention periods. Participants had consecutive weeks of usual care followed by the insulin-only BP with (1) an individualized static set-point of 115 or 130 mg/dL and (2) a dynamic set-point that automatically varied within 110 to 130 mg/dL, depending on hypoglycemic risk. Human factors (HF) testing was conducted using validated surveys. The last five days of each study arm were used for data analysis. RESULTS: Thirteen participants were enrolled with a mean age of 28 years, mean A1c of 7.2%, and mean daily insulin dose of 0.6 U/kg (0.4-1.0 U/kg). The usual care arm had an average glucose of 145 ± 20 mg/dL, which increased in the static set-point arm (159 ± 8 mg/dL, P = .004) but not in the dynamic set-point arm (154 ± 10 mg/dL, P = ns). There was no significant difference in time spent in range (70-180 mg/dL) among the three study arms. There was less time <70 mg/dL with both the static (1.8% ± 1.4%, P = .009) and dynamic set-point (2.7±1.5, P = .051) arms compared to the usual-care arm (5.5% ± 4.2%). HF testing demonstrated preliminary user satisfaction and no increased risk of diabetes burden or distress. CONCLUSIONS: The insulin-only configuration of the BP using either static or dynamic set-points and initialized only with body weight performed similarly to other published insulin-only systems.

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.

Fully Closed-Loop Multiple Model Probabilistic Predictive Controller Artificial Pancreas Performance in Adolescents and Adults in a Supervised Hotel Setting.

BACKGROUND: Initial Food and Drug Administration-approved artificial pancreas (AP) systems will be hybrid closed-loop systems that require prandial meal announcements and will not eliminate the burden of premeal insulin dosing. Multiple model probabilistic predictive control (MMPPC) is a fully closed-loop system that uses probabilistic estimation of meals to allow for automated meal detection. In this study, we describe the safety and performance of the MMPPC system with announced and unannounced meals in a supervised hotel setting. RESEARCH DESIGN AND METHODS: The Android phone-based AP system with remote monitoring was tested for 72 h in six adults and four adolescents across three clinical sites with daily exercise and meal challenges involving both three announced (manual bolus by patient) and six unannounced (no bolus by patient) meals. Safety criteria were predefined. Controller aggressiveness was adapted daily based on prior hypoglycemic events. RESULTS: Mean 24-h continuous glucose monitor (CGM) was 157.4 ± 14.4 mg/dL, with 63.6 ± 9.2% of readings between 70 and 180 mg/dL, 2.9 ± 2.3% of readings <70 mg/dL, and 9.0 ± 3.9% of readings >250 mg/dL. Moderate hyperglycemia was relatively common with 24.6 ± 6.2% of readings between 180 and 250 mg/dL, primarily within 3 h after a meal. Overnight mean CGM was 139.6 ± 27.6 mg/dL, with 77.9 ± 16.4% between 70 and 180 mg/dL, 3.0 ± 4.5% <70 mg/dL, 17.1 ± 14.9% between 180 and 250 mg/dL, and 2.0 ± 4.5%> 250 mg/dL. Postprandial hyperglycemia was more common for unannounced meals compared with announced meals (4-h postmeal CGM 197.8 ± 44.1 vs. 140.6 ± 35.0 mg/dL; P < 0.001). No participants met safety stopping criteria. CONCLUSIONS: MMPPC was safe in a supervised setting despite meal and exercise challenges. Further studies are needed in a less supervised environment.

Predictive Hyperglycemia and Hypoglycemia Minimization: In-Home Evaluation of Safety, Feasibility, and Efficacy in Overnight Glucose Control in Type 1 Diabetes.

OBJECTIVE: The objective of this study was to determine the safety, feasibility, and efficacy of a predictive hyperglycemia and hypoglycemia minimization (PHHM) system compared with predictive low-glucose insulin suspension (PLGS) alone in overnight glucose control. RESEARCH DESIGN AND METHODS: A 42-night trial was conducted in 30 individuals with type 1 diabetes in the age range 15-45 years. Participants were randomly assigned each night to either PHHM or PLGS and were blinded to the assignment. The system suspended the insulin pump on both the PHHM and PLGS nights for predicted hypoglycemia but delivered correction boluses for predicted hyperglycemia on PHHM nights only. The primary outcome was the percentage of time spent in a sensor glucose range of 70-180 mg/dL during the overnight period. RESULTS: The addition of automated insulin delivery with PHHM increased the time spent in the target range (70-180 mg/dL) from 71 ± 10% during PLGS nights to 78 ± 10% during PHHM nights (P < 0.001). The average morning blood glucose concentration improved from 163 ± 23 mg/dL after PLGS nights to 142 ± 18 mg/dL after PHHM nights (P < 0.001). Various sensor-measured hypoglycemic outcomes were similar on PLGS and PHHM nights. All participants completed 42 nights with no episodes of severe hypoglycemia, diabetic ketoacidosis, or other study- or device-related adverse events. CONCLUSIONS: The addition of a predictive hyperglycemia minimization component to our existing PLGS system was shown to be safe, feasible, and effective in overnight glucose control.

Closed-Loop Control Without Meal Announcement in Type 1 Diabetes.

OBJECTIVE: A fully closed-loop insulin-only system was developed to provide glucose control in patients with type 1 diabetes without requiring announcement of meals or activity. Our goal was to assess initial safety and efficacy of this system. RESEARCH DESIGN AND METHODS: The multiple model probabilistic controller (MMPPC) anticipates meals when the patient is awake. The controller used the subject's basal rates and total daily insulin dose for initialization. The system was tested at two sites on 10 patients in a 30-h inpatient study, followed by 15 subjects at three sites in a 54-h supervised hotel study, where the controller was challenged by exercise and unannounced meals. The system was implemented on the UVA DiAs system using a Roche Spirit Combo Insulin Pump and a Dexcom G4 Continuous Glucose Monitor. RESULTS: The mean overall (24-h basis) and nighttime (11 PM-7 AM) continuous glucose monitoring (CGM) values were 142 and 125 mg/dL during the inpatient study. The hotel study used a different daytime tuning and manual announcement, instead of automatic detection, of sleep and wake periods. This resulted in mean overall (24-h basis) and nighttime CGM values of 152 and 139 mg/dL for the hotel study and there was also a reduction in hypoglycemia events from 1.6 to 0.91 events/patient/day. CONCLUSIONS: The MMPPC system achieved a mean glucose that would be particularly helpful for people with an elevated A1c as a result of frequent missed meal boluses. Current full closed loop has a higher risk for hypoglycemia when compared with algorithms using meal announcement.

Automated Overnight Closed-Loop Control Using a Proportional-Integral-Derivative Algorithm with Insulin Feedback in Children and Adolescents with Type 1 Diabetes at Diabetes Camp.

OBJECTIVE: This study determined the feasibility and efficacy of an automated proportional-integral-derivative with insulin feedback (PID-IFB) controller in overnight closed-loop (OCL) control of children and adolescents with type 1 diabetes over multiple days in a diabetes camp setting. RESEARCH DESIGN AND METHODS: The Medtronic (Northridge, CA) Android™ (Google, Mountain View, CA)-based PID-IFB system consists of the Medtronic Minimed Revel™ 2.0 pump and Enlite™ sensor, a control algorithm residing on an Android phone, a translator, and remote monitoring capabilities. An inpatient study was completed for 16 participants to determine feasibility. For the camp study, subjects with type 1 diabetes were randomized to either OCL or sensor-augmented pump therapy (control conditions) per night for up to 6 nights at diabetes camp. RESULTS: During the camp study, 21 subjects completed 50 OCL nights and 52 control nights. Based on intention to treat, the median time spent in range, from 70 to 150 mg/dL, was greater during OCL at 66.4% (n = 55) versus 50.6% (n = 52) during the control period (P = 0.004). A per-protocol analysis allowed for assessment of algorithm performance with the median percentage time in range, 70-150 mg/dL, being 75.5% (n = 37) for OCL versus 47.6% (n = 32) for the control period (P < 0.001). There was less time spent in the hypoglycemic ranges <60 mg/dL and <70 mg/dL during OCL compared with the control period (P = 0.003 and P < 0.001, respectively). CONCLUSIONS: The PID-IFB controller is effective in improving time spent in range as well as reducing nocturnal hypoglycemia during the overnight period in children and adolescents with type 1 diabetes in a diabetes camp setting.

Efficacy of an Overnight Predictive Low-Glucose Suspend System in Relation to Hypoglycemia Risk Factors in Youth and Adults With Type 1 Diabetes.

BACKGROUND: We developed a system to suspend insulin pump delivery overnight when the glucose trend predicts hypoglycemia. This predictive low-glucose suspend (PLGS) system substantially reduces nocturnal hypoglycemia without an increase in morning ketosis. Evaluation of hypoglycemia risk factors that could potentially influence the efficacy of the system remains critical for understanding possible problems with the system and identifying patients that may have the greatest benefit when using the system. METHODS: The at-home randomized trial consisted of 127 study participants with hemoglobin A1c (A1C) of ≤8.5% (mmol/mol) for patients aged 4-14 years and ≤8.0% for patient aged 15-45 years. Factors assessed included age, gender, A1C, diabetes duration, daily percentage basal insulin, total daily dose of insulin (units/kg-day), bedtime BG, bedtime snack, insulin on board, continuous glucose monitor (CGM) rate of change (ROC), day of the week, time system activated, daytime exercise intensity, and daytime CGM-measured hypoglycemia. RESULTS: The PLGS system was effective in preventing hypoglycemia for each factor subgroup. There was no evidence that the PLGS system was more or less effective in preventing hypoglycemia in any one subgroup compared with the other subgroups based on that factor. In addition, the effect of the system on overnight hyperglycemia did not differ in subgroups. CONCLUSIONS: The PLGS system tested in this study effectively reduced hypoglycemia without a meaningful increase in hyperglycemia across a variety of factors.

Duration of Infusion Set Survival in Lipohypertrophy Versus Nonlipohypertrophied Tissue in Patients with Type 1 Diabetes.

BACKGROUND: Improved insulin infusion set survival and faster insulin action are important issues for pump users and for the development of an artificial pancreas. The current recommendation is to change infusion sets every 3 days. Our objectives were to determine the effect of lipohypertrophy (LH) on infusion set survival and continuous glucose monitoring glucose levels. RESEARCH DESIGN AND METHODS: In this multicenter crossover trial, we recruited 20 subjects (age 28.1 ± 9.0 years) with type 1 diabetes (duration 17.5 ± 8.8 years) and an area of lipohypertrophied tissue >3 cm. Subjects alternated weekly wearing a Teflon infusion set in an area of either LH or non-LH for 4 weeks. Sets were changed after (a) failure or (b) surviving 7 days of use. RESULTS: The least-squares mean duration of infusion set survival for sets that lasted <7 days in lipohypertrophied tissue was 4.31 days compared with 4.12 days in nonlipohypertrophied tissue (P = 0.71). The average duration of set survival for individual subjects ranged from 2.2 to 7.0 days. Infusion sets in lipohypertrophied tissue failed due to hyperglycemia in 35% of subjects compared with 23% in nonlipohypertrophied tissue (P = 0.22). Both lipohypertrophied and nonlipohypertrophied tissues displayed a general increase in mean daily glucose after the third day of infusion set wear, but daily mean glucose did not differ by tissue type (P > 0.38 on each day). CONCLUSION: LH did not significantly affect infusion set survival or mean glucose. Achieving optimal infusion set performance requires research into factors affecting set survival. Additionally, the recommendation for duration of set change may need to be individualized.

Day and Night Closed-Loop Control Using the Integrated Medtronic Hybrid Closed-Loop System in Type 1 Diabetes at Diabetes Camp.

OBJECTIVE: To evaluate the feasibility and efficacy of a fully integrated hybrid closed-loop (HCL) system (Medtronic MiniMed Inc., Northridge, CA), in day and night closed-loop control in subjects with type 1 diabetes, both in an inpatient setting and during 6 days at diabetes camp. RESEARCH DESIGN AND METHODS: The Medtronic MiniMed HCL system consists of a fourth generation (4S) glucose sensor, a sensor transmitter, and an insulin pump using a modified proportional-integral-derivative (PID) insulin feedback algorithm with safety constraints. Eight subjects were studied over 48 h in an inpatient setting. This was followed by a study of 21 subjects for 6 days at diabetes camp, randomized to either the closed-loop control group using the HCL system or to the group using the Medtronic MiniMed 530G with threshold suspend (control group). RESULTS: The overall mean sensor glucose percent time in range 70-180 mg/dL was similar between the groups (73.1% vs. 69.9%, control vs. HCL, respectively) (P = 0.580). Meter glucose values between 70 and 180 mg/dL were also similar between the groups (73.6% vs. 63.2%, control vs. HCL, respectively) (P = 0.086). The mean absolute relative difference of the 4S sensor was 10.8 ± 10.2%, when compared with plasma glucose values in the inpatient setting, and 12.6 ± 11.0% compared with capillary Bayer CONTOUR NEXT LINK glucose meter values during 6 days at camp. CONCLUSIONS: In the first clinical study of this fully integrated system using an investigational PID algorithm, the system did not demonstrate improved glucose control compared with sensor-augmented pump therapy alone. The system demonstrated good connectivity and improved sensor performance.

Factors associated with nocturnal hypoglycemia in at-risk adolescents and young adults with type 1 diabetes.

BACKGROUND: Hypoglycemia remains an impediment to good glycemic control, with nocturnal hypoglycemia being particularly dangerous. Information on major contributors to nocturnal hypoglycemia remains critical for understanding and mitigating risk. MATERIALS AND METHODS: Continuous glucose monitoring (CGM) data for 855 nights were studied, generated by 45 subjects 15-45 years of age with hemoglobin A1c (HbA1c) levels of ≤8.0% who participated in a larger randomized study. Factors assessed for potential association with nocturnal hypoglycemia (CGM measurement of <60 mg/dL for ≥30 min) included bedtime blood glucose (BG), exercise intensity, bedtime snack, insulin on board, day of the week, previous daytime hypoglycemia, age, gender, HbA1c level, diabetes duration, daily basal insulin, and daily insulin dose. RESULTS: Hypoglycemia occurred during 221 of 885 (25%) nights and was more frequent with younger age (P<0.001), lower HbA1c levels (P=0.006), medium/high-intensity exercise during the preceding day (P=0.003), and the occurrence of antecedent daytime hypoglycemia (P=0.001). There was a trend for lower bedtime BG levels to be associated with more frequent nocturnal hypoglycemia (P=0.10). Bedtime snack, before bedtime insulin bolus, weekend versus weekday, gender, and daily basal and bolus insulin were not associated with nocturnal hypoglycemia. CONCLUSIONS: Awareness that HbA1c level, exercise, bedtime BG level, and daytime hypoglycemia are all modifiable factors associated with nocturnal hypoglycemia may help patients and providers decrease the risk of hypoglycemia at night. Risk for nocturnal hypoglycemia increased in a linear fashion across the range of variables, with no clear-cut thresholds to guide clinicians or patients for any particular night.

Inpatient trial of an artificial pancreas based on multiple model probabilistic predictive control with repeated large unannounced meals.

BACKGROUND: Closed-loop control of blood glucose levels in people with type 1 diabetes offers the potential to reduce the incidence of diabetes complications and reduce the patients' burden, particularly if meals do not need to be announced. We therefore tested a closed-loop algorithm that does not require meal announcement. MATERIALS AND METHODS: A multiple model probabilistic predictive controller (MMPPC) was assessed on four patients, revised to improve performance, and then assessed on six additional patients. Each inpatient admission lasted for 32 h with five unannounced meals containing approximately 1 g/kg of carbohydrate per admission. The system used an Abbott Diabetes Care (Alameda, CA) Navigator(®) continuous glucose monitor (CGM) and Insulet (Bedford, MA) Omnipod(®) insulin pump, with the MMPPC implemented through the artificial pancreas system platform. The controller was initialized only with the patient's total daily dose and daily basal pattern. RESULTS: On a 24-h basis, the first cohort had mean reference and CGM readings of 179 and 167 mg/dL, respectively, with 53% and 62%, respectively, of readings between 70 and 180 mg/dL and four treatments for glucose values <70 mg/dL. The second cohort had mean reference and CGM readings of 161 and 142 mg/dL, respectively, with 63% and 78%, respectively, of the time spent euglycemic. There was one controller-induced hypoglycemic episode. For the 30 unannounced meals in the second cohort, the mean reference and CGM premeal, postmeal maximum, and 3-h postmeal values were 139 and 132, 223 and 208, and 168 and 156 mg/dL, respectively. CONCLUSIONS: The MMPPC, tested in-clinic against repeated, large, unannounced meals, maintained reasonable glycemic control with a mean blood glucose level that would equate to a mean glycated hemoglobin value of 7.2%, with only one controller-induced hypoglycemic event occurring in the second cohort.

Remote glucose monitoring in camp setting reduces the risk of prolonged nocturnal hypoglycemia.

OBJECTIVE: This study tested the feasibility and effectiveness of remote continuous glucose monitoring (CGM) in a diabetes camp setting. SUBJECTS AND METHODS: Twenty campers (7-21 years old) with type 1 diabetes were enrolled at each of three camp sessions lasting 5-6 days. On alternating nights, 10 campers were randomized to usual wear of a Dexcom (San Diego, CA) G4™ PLATINUM CGM system, and 10 were randomized to remote monitoring with the Dexcom G4 PLATINUM communicating with the Diabetes Assistant, a cell phone platform, to allow wireless transmission of CGM values. Up to 15 individual graphs and sensor values could be displayed on a single remote monitor or portable tablet. An alarm was triggered for values <70 mg/dL, and treatment was given for meter-confirmed hypoglycemia. The primary end point was to decrease the duration of hypoglycemic episodes <50 mg/dL. RESULTS: There were 320 nights of CGM data and 197 hypoglycemic events. Of the remote monitoring alarms, 79% were true (meter reading of <70 mg/dL). With remote monitoring, 100% of alarms were responded to, whereas without remote monitoring only 54% of alarms were responded to. The median duration of hypoglycemic events <70 mg/dL was 35 min without remote monitoring and 30 min with remote monitoring (P=0.078). Remote monitoring significantly decreased prolonged hypoglycemic events, eliminating all events <50 mg/dL lasting longer than 30 min as well as all events <70 mg/dL lasting more than 2 h. CONCLUSIONS: Remote monitoring is feasible at diabetes camps and effective in reducing the risk of prolonged nocturnal hypoglycemia. This technology will facilitate forthcoming studies to evaluate the efficacy of automated closed-loop systems in the camp setting.

Overnight glucose control with an automated, unified safety system in children and adolescents with type 1 diabetes at diabetes camp.

OBJECTIVE: To determine the safety and efficacy of an automated unified safety system (USS) in providing overnight closed-loop (OCL) control in children and adolescents with type 1 diabetes attending diabetes summer camps. RESEARCH DESIGN AND METHODS: The Diabetes Assistant (DIAS) USS used the Dexcom G4 Platinum glucose sensor (Dexcom) and t:slim insulin pump (Tandem Diabetes Care). An initial inpatient study was completed for 12 participants to evaluate safety. For the main camp study, 20 participants with type 1 diabetes were randomized to either OCL or sensor-augmented therapy (control conditions) per night over the course of a 5- to 6-day diabetes camp. RESULTS: Subjects completed 54 OCL nights and 52 control nights. On an intention-to-treat basis, with glucose data analyzed regardless of system status, the median percent time in range, from 70-150 mg/dL, was 62% (29, 87) for OCL nights versus 55% (25, 80) for sensor-augmented pump therapy (P = 0.233). A per-protocol analysis allowed for assessment of algorithm performance. The median percent time in range, from 70-150 mg/dL, was 73% (50, 89) for OCL nights (n = 41) versus 52% (24, 83) for control conditions (n = 39) (P = 0.037). There was less time spent in the hypoglycemic range <50, <60, and <70 mg/dL during OCL compared with the control period (P = 0.019, P = 0.009, and P = 0.023, respectively). CONCLUSIONS: The DIAS USS algorithm is effective in improving time spent in range as well as reducing nocturnal hypoglycemia during the overnight period in children and adolescents with type 1 diabetes in a diabetes camp setting.

Inpatient studies of a Kalman-filter-based predictive pump shutoff algorithm.

BACKGROUND: An insulin pump shutoff system can prevent nocturnal hypoglycemia and is a first step on the pathway toward a closed-loop artificial pancreas. In previous pump shutoff studies using a voting algorithm and a 1 min continuous glucose monitor (CGM), 80% of induced hypoglycemic events were prevented. METHODS: The pump shutoff algorithm used in previous studies was revised to a single Kalman filter to reduce complexity, incorporate CGMs with different sample times, handle sensor signal dropouts, and enforce safety constraints on the allowable pump shutoff time. RESULTS: Retrospective testing of the new algorithm on previous clinical data sets indicated that, for the four cases where the previous algorithm failed (minimum reference glucose less than 60 mg/dl), the mean suspension start time was 30 min earlier than the previous algorithm. Inpatient studies of the new algorithm have been conducted on 16 subjects. The algorithm prevented hypoglycemia in 73% of subjects. Suspension-induced hyperglycemia is not assessed, because this study forced excessive basal insulin infusion rates. CONCLUSIONS: The new algorithm functioned well and is flexible enough to handle variable sensor sample times and sensor dropouts. It also provides a framework for handling sensor signal attenuations, which can be challenging, particularly when they occur overnight.