Context: Initial studies of closed-loop proportional integral derivative control in individuals with type 1 diabetes showed good overnight performance, but with breakfast meal being the hardest to control and requiring supplemental carbohydrate to prevent hypoglycemia. Objective: The aim of this study was to assess the ability of insulin feedback to improve the breakfast-meal profile. Design and Setting: We performed a single center study with closed-loop control over approximately 30 h at an inpatient clinical research facility. Patients: Eight adult subjects with previously diagnosed type 1 diabetes participated. Intervention: Subjects received closed-loop insulin delivery with supplemental carbohydrate as needed. Main Outcome Measures: Outcome measures were plasma insulin concentration, model-predicted plasma insulin concentration, 2-h postprandial and 3- to 4-h glucose rate-of-change following breakfast after 1 d of closed-loop control, and the need for supplemental carbohydrate in response to nadir hypoglycemia. Results: Plasma insulin levels during closed loop were well correlated with model predictions (R = 0.86). Fasting glucose after 1 d of closed loop was not different from nighttime target (118 ± 9 vs. 110 mg/dl; P = 0.38). Two-hour postbreakfast glucose was 132 ± 16 mg/dl with stable values 3–4 h after the meal (0.03792 ± 0.0884 mg/dl · min, not different from 0; P = 0.68) and at target (97 ± 6 mg/dl, not different from 90; P = 0.28). Three subjects required supplemental carbohydrates after breakfast on d 2 of closed loop. Conclusions/Interpretation: Insulin feedback can be implemented using a model estimate of concentration. Proportional integral derivative control with insulin feedback can achieve a desired breakfast response but still requires supplemental carbohydrate to be delivered in some instances. Studies assessing more optimal control configurations and safeguards need to be conducted.

OBJECTIVE Attempts to build an artificial pancreas by using subcutaneous insulin delivery from a portable pump guided by an subcutaneous glucose sensor have encountered delays and variability of insulin absorption. We tested closed-loop intraperitoneal insulin infusion from an implanted pump driven by an subcutaneous glucose sensor via a proportional-integral-derivative (PID) algorithm. RESEARCH DESIGN AND METHODS Two-day closed-loop therapy (except for a 15-min premeal manual bolus) was compared with a 1-day control phase with intraperitoneal open-loop insulin delivery, according to randomized order, in a hospital setting in eight type 1 diabetic patients treated by implanted pumps. The percentage of time spent with blood glucose in the 4.4–6.6 mmol/l range was the primary end point. RESULTS During the closed-loop phases, the mean ± SEM percentage of time spent with blood glucose in the 4.4–6.6 mmol/l range was significantly higher (39.1 ± 4.5 vs. 27.7 ± 6.2%, P = 0.05), and overall dispersion of blood glucose values was reduced among patients. Better closed-loop glucose control came from the time periods excluding the two early postprandial hours with a higher percentage of time in the 4.4–6.6 mmol/l range (46.3 ± 5.3 vs. 28.6 ± 7.4, P = 0.025) and lower mean blood glucose levels (6.9 ± 0.3 vs. 7.9 ± 0.6 mmol/l, P = 0.036). Time spent with blood glucose <3.3 mmol/l was low and similar for both investigational phases. CONCLUSIONS Our results demonstrate the feasibility of intraperitoneal insulin delivery for an artificial β-cell and support the need for further study. Moreover, according to a semiautomated mode, the features of the premeal bolus in terms of timing and amount warrant further research.

OBJECTIVE Afternoon exercise increases the risk of nocturnal hypoglycemia (NH) in subjects with type 1 diabetes. We hypothesized that automated feedback-controlled closed-loop (CL) insulin delivery would be superior to open-loop (OL) control in preventing NH and maintaining a higher proportion of blood glucose levels within the target blood glucose range on nights with and without antecedent afternoon exercise. RESEARCH DESIGN AND METHODS Subjects completed two 48-h inpatient study periods in random order: usual OL control and CL control using a proportional-integrative-derivative plus insulin feedback algorithm. Each admission included a sedentary day and an exercise day, with a standardized protocol of 60 min of brisk treadmill walking to 65–70% maximum heart rate at 3:00 p.m. RESULTS Among 12 subjects (age 12–26 years, A1C 7.4 ± 0.6%), antecedent exercise increased the frequency of NH (reference blood glucose <60 mg/dL) during OL control from six to eight events. In contrast, there was only one NH event each on nights with and without antecedent exercise during CL control (P = 0.04 vs. OL nights). Overnight, the percentage of glucose values in target range was increased with CL control (P < 0.0001). Insulin delivery was lower between 10:00 p.m. and 2:00 a.m. on nights after exercise on CL versus OL, P = 0.008. CONCLUSIONS CL insulin delivery provides an effective means to reduce the risk of NH while increasing the percentage of time spent in target range, regardless of activity level in the mid-afternoon. These data suggest that CL control could be of benefit to patients with type 1 diabetes even if it is limited to the overnight period.

Patients with diabetes play with a double-edged sword when it comes to deciding glucose and A_1c target levels. On the one side, tight control has been shown to be crucial in avoiding long-term complications; on the other, tighter control leads to an increased risk of iatrogenic hypoglycemia, which is compounded when hypoglycemia unawareness sets in. Development of continuous glucose monitoring systems has led to the possibility of being able not only to detect hypoglycemic episodes, but to make predictions based on trends that would allow the patient to take preemptive action to entirely avoid the condition. Using an optimal estimation theory approach to hypoglycemia prediction, we demonstrate the effect of measurement sampling frequency, threshold level, and prediction horizon on the sensitivity and specificity of the predictions. We discuss how optimal estimators can be tuned to trade-off the false alarm rate with the rate of missed predicted hypoglycemic episodes. We also suggest the use of different alarm levels as a function of current and future estimates of glucose and the hypoglycemic threshold and prediction horizon.

Motivation: The fear of hypoglycemia remains an important limiting factor in the ability of an individual with type 1 diabetes to tightly regulate glycemia. Continuous glucose monitors provide important feedback to improve glycemic control, but there remains a need for these devices to better alarm of possible impending hypoglycemia, particularly overnight or other periods when the individual is engaged in activities that take their focus away from glucose monitoring. Methods: We have previously proposed an algorithm, based on the use of real-time glucose sensor signals and optimal estimation theory (Kalman filtering), to predict hypoglycemia; the algorithm was validated in simulation-based studies. In this article we further refine and validate the prediction algorithm based on the analysis of clinical hypoglycemic clamp data from 13 subjects. The sensitivity and specificity of the predictions are calculated with respect to reference blood glucose values obtained at the same sampling rate of the sensor. Results: For a 30-minute prediction horizon and alarm threshold of 70 mg/dl, the sensitivity and specificity were 90 and 79%, respectively, indicating that a 21% false alarm rate must be tolerated to predict 90% of the hypoglycemic events 30 minutes ahead of time. Shorter prediction horizons yield a significant improvement in sensitivity and specificity. Discussion: Sensitivity and specificity data as a function of prediction horizon and alarm threshold enable an individual to adjust the alarm to best meet their needs. Such decisions can be made depending on the subject's risk for hypoglycemia, for example.