Model individualization for artificial pancreas.

BACKGROUND AND OBJECTIVE: The inter-subject variability characterizing the patients affected by type 1 diabetes mellitus makes automatic blood glucose control very challenging. Different patients have different insulin responses, and a control law based on a non-individualized model could be ineffective. The definition of an individualized control law in the context of artificial pancreas is currently an open research topic. In this work we consider two novel identification approaches that can be used for individualizing linear glucose-insulin models to a specific patient. METHODS: The first approach belongs to the class of black-box identification and is based on a novel kernel-based nonparametric approach, whereas the second is a gray-box identification technique which relies on a constrained optimization and requires to postulate a model structure as prior knowledge. The latter is derived from the linearization of the average nonlinear adult virtual patient of the UVA/Padova simulator. Model identification and validation are based on in silico data collected during simulations of clinical protocols designed to produce a sufficient signal excitation without compromising patient safety. The identified models are evaluated in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean square error. RESULTS: Both identification approaches were used to identify a linear individualized glucose-insulin model for each adult virtual patient of the UVA/Padova simulator. The resulting model simulation performance is significantly improved with respect to the performance achieved by a linear average model. CONCLUSIONS: The approaches proposed in this work have shown a good potential to identify glucose-insulin models for designing individualized control laws for artificial pancreas.

Toward a Run-to-Run Adaptive Artificial Pancreas: In Silico Results.

OBJECTIVE: Contemporary and future outpatient long-term artificial pancreas (AP) studies need to cope with the well-known large intra- and interday glucose variability occurring in type 1 diabetic (T1D) subjects. Here, we propose an adaptive model predictive control (MPC) strategy to account for it and test it in silico. METHODS: A run-to-run (R2R) approach adapts the subcutaneous basal insulin delivery during the night and the carbohydrate-to-insulin ratio (CR) during the day, based on some performance indices calculated from subcutaneous continuous glucose sensor data. In particular, R2R aims, first, to reduce the percentage of time in hypoglycemia and, secondarily, to improve the percentage of time in euglycemia and average glucose. In silico simulations are performed by using the University of Virginia/Padova T1D simulator enriched by incorporating three novel features: intra- and interday variability of insulin sensitivity, different distributions of CR at breakfast, lunch, and dinner, and dawn phenomenon. RESULTS: After about two months, using the R2R approach with a scenario characterized by a random 30% variation of the nominal insulin sensitivity the time in range and the time in tight range are increased by 11.39% and 44.87%, respectively, and the time spent above 180 mg/dl is reduced by 48.74%. CONCLUSIONS: An adaptive MPC algorithm based on R2R shows in silico great potential to capture intra- and interday glucose variability by improving both overnight and postprandial glucose control without increasing hypoglycemia. SIGNIFICANCE: Making an AP adaptive is key for long-term real-life outpatient studies. These good in silico results are very encouraging and worth testing in vivo.

Evaluating the Experience of Children With Type 1 Diabetes and Their Parents Taking Part in an Artificial Pancreas Clinical Trial Over Multiple Days in a Diabetes Camp Setting.

OBJECTIVE: To explore the experiences of children with type 1 diabetes and their parents taking part in an artificial pancreas (AP) clinical trial during a 7-day summer camp. RESEARCH DESIGN AND METHODS: A semistructured interview, composed of 14 questions based on the Technology Acceptance Model, was conducted at the end of the clinical trial. Participants also completed the Diabetes Treatment Satisfaction Questionnaire (DTSQ, parent version) and the AP Acceptance Questionnaire. RESULTS: Thirty children, aged 5-9 years, and their parents completed the study. A content analysis of the interviews showed that parents were focused on understanding the mechanisms, risks, and benefits of the new device, whereas the children were focused on the novelty of the new system. The parents' main concerns about adopting the new system seemed related to the quality of glucose control. The mean scores of DTSQ subscales indicated general parents' satisfaction (44.24 ± 5.99, range 32-53) and trustful views of diabetes control provided by the new system (7.8 ± 2.2, range 3-12). The AP Acceptance Questionnaire revealed that most parents considered the AP easy to use (70.5%), intended to use it long term (94.0%), and felt that it was apt to improve glucose control (67.0%). CONCLUSIONS: Participants manifested a positive attitude toward the AP. Further studies are required to explore participants' perceptions early in the AP development to individualize the new treatment as much as possible, and to tailor it to respond to their needs and values.

Day-and-Night Closed-Loop Glucose Control in Patients With Type 1 Diabetes Under Free-Living Conditions: Results of a Single-Arm 1-Month Experience Compared With a Previously Reported Feasibility Study of Evening and Night at Home.

OBJECTIVE: After testing of a wearable artificial pancreas (AP) during evening and night (E/N-AP) under free-living conditions in patients with type 1 diabetes (T1D), we investigated AP during day and night (D/N-AP) for 1 month. RESEARCH DESIGN AND METHODS: Twenty adult patients with T1D who completed a previous randomized crossover study comparing 2-month E/N-AP versus 2-month sensor augmented pump (SAP) volunteered for 1-month D/N-AP nonrandomized extension. AP was executed by a model predictive control algorithm run by a modified smartphone wirelessly connected to a continuous glucose monitor (CGM) and insulin pump. CGM data were analyzed by intention-to-treat with percentage time-in-target (3.9-10 mmol/L) over 24 h as the primary end point. RESULTS: Time-in-target (mean ± SD, %) was similar over 24 h with D/N-AP versus E/N-AP: 64.7 ± 7.6 vs. 63.6 ± 9.9 (P = 0.79), and both were higher than with SAP: 59.7 ± 9.6 (P = 0.01 and P = 0.06, respectively). Time below 3.9 mmol/L was similarly and significantly reduced by D/N-AP and E/N-AP versus SAP (both P < 0.001). SD of blood glucose concentration (mmol/L) was lower with D/N-AP versus E/N-AP during whole daytime: 3.2 ± 0.6 vs. 3.4 ± 0.7 (P = 0.003), morning: 2.7 ± 0.5 vs. 3.1 ± 0.5 (P = 0.02), and afternoon: 3.3 ± 0.6 vs. 3.5 ± 0.8 (P = 0.07), and was lower with D/N-AP versus SAP over 24 h: 3.1 ± 0.5 vs. 3.3 ± 0.6 (P = 0.049). Insulin delivery (IU) over 24 h was higher with D/N-AP and SAP than with E/N-AP: 40.6 ± 15.5 and 42.3 ± 15.5 vs. 36.6 ± 11.6 (P = 0.03 and P = 0.0004, respectively). CONCLUSIONS: D/N-AP and E/N-AP both achieved better glucose control than SAP under free-living conditions. Although time in the different glycemic ranges was similar between D/N-AP and E/N-AP, D/N-AP further reduces glucose variability.

Randomized Summer Camp Crossover Trial in 5- to 9-Year-Old Children: Outpatient Wearable Artificial Pancreas Is Feasible and Safe.

OBJECTIVE: The Pediatric Artificial Pancreas (PedArPan) project tested a children-specific version of the modular model predictive control (MMPC) algorithm in 5- to 9-year-old children during a camp. RESEARCH DESIGN AND METHODS: A total of 30 children, 5- to 9-years old, with type 1 diabetes completed an outpatient, open-label, randomized, crossover trial. Three days with an artificial pancreas (AP) were compared with three days of parent-managed sensor-augmented pump (SAP). RESULTS: Overnight time-in-hypoglycemia was reduced with the AP versus SAP, median (25(th)-75(th) percentiles): 0.0% (0.0-2.2) vs. 2.2% (0.0-12.3) (P = 0.002), without a significant change of time-in-target, mean: 56.0% (SD 22.5) vs. 59.7% (21.2) (P = 0.430), but with increased mean glucose 173 mg/dL (36) vs. 150 mg/dL (39) (P = 0.002). Overall, the AP granted a threefold reduction of time-in-hypoglycemia (P < 0.001) at the cost of decreased time-in-target, 56.8% (13.5) vs. 63.1% (11.0) (P = 0.022) and increased mean glucose 169 mg/dL (23) vs. 147 mg/dL (23) (P < 0.001). CONCLUSIONS: This trial, the first outpatient single-hormone AP trial in a population of this age, shows feasibility and safety of MMPC in young children. Algorithm retuning will be performed to improve efficacy.

2 month evening and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: a randomised crossover trial.

BACKGROUND: An artificial pancreas (AP) that can be worn at home from dinner to waking up in the morning might be safe and efficient for first routine use in patients with type 1 diabetes. We assessed the effect on glucose control with use of an AP during the evening and night plus patient-managed sensor-augmented pump therapy (SAP) during the day, versus 24 h use of patient-managed SAP only, in free-living conditions. METHODS: In a crossover study done in medical centres in France, Italy, and the Netherlands, patients aged 18-69 years with type 1 diabetes who used insulin pumps for continuous subcutaneous insulin infusion were randomly assigned to 2 months of AP use from dinner to waking up plus SAP use during the day versus 2 months of SAP use only under free-living conditions. Randomisation was achieved with a computer-generated allocation sequence with random block sizes of two, four, or six, masked to the investigator. Patients and investigators were not masked to the type of intervention. The AP consisted of a continuous glucose monitor (CGM) and insulin pump connected to a modified smartphone with a model predictive control algorithm. The primary endpoint was the percentage of time spent in the target glucose concentration range (3·9-10·0 mmol/L) from 2000 to 0800 h. CGM data for weeks 3-8 of the interventions were analysed on a modified intention-to-treat basis including patients who completed at least 6 weeks of each intervention period. The 2 month study period also allowed us to asses HbA1c as one of the secondary outcomes. This trial is registered with ClinicalTrials.gov, number NCT02153190. FINDINGS: During 2000-0800 h, the mean time spent in the target range was higher with AP than with SAP use: 66·7% versus 58·1% (paired difference 8·6% [95% CI 5·8 to 11·4], p<0·0001), through a reduction in both mean time spent in hyperglycaemia (glucose concentration >10·0 mmol/L; 31·6% vs 38·5%; -6·9% [-9·8% to -3·9], p<0·0001) and in hypoglycaemia (glucose concentration <3·9 mmol/L; 1·7% vs 3·0%; -1·6% [-2·3 to -1·0], p<0·0001). Decrease in mean HbA1c during the AP period was significantly greater than during the control period (-0·3% vs -0·2%; paired difference -0·2 [95% CI -0·4 to -0·0], p=0·047), taking a period effect into account (p=0·0034). No serious adverse events occurred during this study, and none of the mild-to-moderate adverse events was related to the study intervention. INTERPRETATION: Our results support the use of AP at home as a safe and beneficial option for patients with type 1 diabetes. The HbA1c results are encouraging but preliminary. FUNDING: European Commission.

Safety of outpatient closed-loop control: first randomized crossover trials of a wearable artificial pancreas.

OBJECTIVE: We estimate the effect size of hypoglycemia risk reduction on closed-loop control (CLC) versus open-loop (OL) sensor-augmented insulin pump therapy in supervised outpatient setting. RESEARCH DESIGN AND METHODS: Twenty patients with type 1 diabetes initiated the study at the Universities of Virginia, Padova, and Montpellier and Sansum Diabetes Research Institute; 18 completed the entire protocol. Each patient participated in two 40-h outpatient sessions, CLC versus OL, in randomized order. Sensor (Dexcom G4) and insulin pump (Tandem t:slim) were connected to Diabetes Assistant (DiAs)-a smartphone artificial pancreas platform. The patient operated the system through the DiAs user interface during both CLC and OL; study personnel supervised on site and monitored DiAs remotely. There were no dietary restrictions; 45-min walks in town and restaurant dinners were included in both CLC and OL; alcohol was permitted. RESULTS: The primary outcome-reduction in risk for hypoglycemia as measured by the low blood glucose (BG) index (LGBI)-resulted in an effect size of 0.64, P = 0.003, with a twofold reduction of hypoglycemia requiring carbohydrate treatment: 1.2 vs. 2.4 episodes/session on CLC versus OL (P = 0.02). This was accompanied by a slight decrease in percentage of time in the target range of 3.9-10 mmol/L (66.1 vs. 70.7%) and increase in mean BG (8.9 vs. 8.4 mmol/L; P = 0.04) on CLC versus OL. CONCLUSIONS: CLC running on a smartphone (DiAs) in outpatient conditions reduced hypoglycemia and hypoglycemia treatments when compared with sensor-augmented pump therapy. This was accompanied by marginal increase in average glycemia resulting from a possible overemphasis on hypoglycemia safety.

First use of model predictive control in outpatient wearable artificial pancreas.

OBJECTIVE: Inpatient studies suggest that model predictive control (MPC) is one of the most promising algorithms for artificial pancreas (AP). So far, outpatient trials have used hypo/hyperglycemia-mitigation or medical-expert systems. In this study, we report the first wearable AP outpatient study based on MPC and investigate specifically its ability to control postprandial glucose, one of the major challenges in glucose control. RESEARCH DESIGN AND METHODS: A new modular MPC algorithm has been designed focusing on meal control. Six type 1 diabetes mellitus patients underwent 42-h experiments: sensor-augmented pump therapy in the first 14 h (open-loop) and closed-loop in the remaining 28 h. RESULTS: MPC showed satisfactory dinner control versus open-loop: time-in-target (70-180 mg/dL) 94.83 vs. 68.2% and time-in-hypo 1.25 vs. 11.9%. Overnight control was also satisfactory: time-in-target 89.4 vs. 85.0% and time-in-hypo: 0.00 vs. 8.19%. CONCLUSIONS: This outpatient study confirms inpatient evidence of suitability of MPC-based strategies for AP. These encouraging results pave the way to randomized crossover outpatient studies.

Artificial pancreas: model predictive control design from clinical experience.

BACKGROUND: The objective of this research is to develop a new artificial pancreas that takes into account the experience accumulated during more than 5000 h of closed-loop control in several clinical research centers. The main objective is to reduce the mean glucose value without exacerbating hypo phenomena. Controller design and in silico testing were performed on a new virtual population of the University of Virginia/Padova simulator. METHODS: A new sensor model was developed based on the Comparison of Two Artificial Pancreas Systems for Closed-Loop Blood Glucose Control versus Open-Loop Control in Patients with Type 1 Diabetes trial AP@home data. The Kalman filter incorporated in the controller has been tuned using plasma and pump insulin as well as plasma and continuous glucose monitoring measures collected in clinical research centers. New constraints describing clinical knowledge not incorporated in the simulator but very critical in real patients (e.g., pump shutoff) have been introduced. The proposed model predictive control (MPC) is characterized by a low computational burden and memory requirements, and it is ready for an embedded implementation. RESULTS: The new MPC was tested with an intensive simulation study on the University of Virginia/Padova simulator equipped with a new virtual population. It was also used in some preliminary outpatient pilot trials. The obtained results are very promising in terms of mean glucose and number of patients in the critical zone of the control variability grid analysis. CONCLUSIONS: The proposed MPC improves on the performance of a previous controller already tested in several experiments in the AP@home and JDRF projects. This algorithm complemented with a safety supervision module is a significant step toward deploying artificial pancreases into outpatient environments for extended periods of time.