Fully Integrated Artificial Pancreas in Type 1 Diabetes

Modular Closed-Loop Glucose Control Maintains Near Normoglycemia

Marc Breton; Anne Farret; Daniela Bruttomesso; Stacey Anderson; Lalo Magni; Stephen Patek; Chiara Dalla Man; Jerome Place; Susan Demartini; Simone Del Favero; Chiara Toffanin; Colleen Hughes-Karvetski; Eyal Dassau; Howard Zisser; Francis J. Doyle III; Giuseppe De Nicolao; Angelo Avogaro; Claudio Cobelli; Eric Renard; Boris Kovatchev

Disclosures

Diabetes. 2012;61(9):2230-2237. 

In This Article

Research Design and Methods

A total of 38 subjects with type 1 diabetes, including 11 adolescents (aged 12–18 years) and 27 adults (aged 21–65 years), were enrolled in two randomized crossover studies at the University of Virginia General Clinical Research Center (UVA), Montpellier University Hospital and Clinical Investigation Center INSERM 1001 (MON), and at the Department of Internal Medicine, University of Padova (PAD).

Study 1 enrolled 11 adolescents and 9 adults at UVA (12 adolescents and 12 adults screened) and 6 adults at MON (6 screened). Study 2 recruited 12 adults: 6 at MON (6 screened) and 6 at PAD (6 screened). Summary demographics are presented in Table 1.

All enrolled patients finished the studies, but five datasets were excluded from the analysis as follows: 1) in study 2, three datasets were excluded as a result of software malfunction and/or sensor failures and one additional dataset was partially excluded (the night portion has been removed from the overnight analysis because of extended postprandial effect in both control and treatment admission); and 2) in study 1, for unexplained reasons, one adult patient had very different metabolic characteristics between admissions 1 and 2 (doubled insulin sensitivity), precluding the comparison between the two admissions.

Protocols

Studies 1 and 2 were approved by the ethical boards of the participating institutions and by relevant regulatory agencies and were registered under the following reference numbers: 14356 and 14758 (http://www.virginia.edu/vpr/irb/hsr/index.html), 2009-A00421–56 (www.afssaps.sante.fr), and 2039P (www.sanita.padova.it). At each site, after obtaining written informed consent, patients were randomized to determine the order of open-loop CSII and CLC. Patients were equipped with two CGM devices, either Dexcom 7 (Dexcom, Inc., San Diego, CA) or Navigator (Abbott Diabetes Care, Alameda, CA), at least 24 h before admission and after careful instruction on their use. CGM devices were calibrated as per manufacturer schedule, using self-monitoring of BG measurements before admission and YSI (Yellow Spring Instruments) measurements during admission. Navigator was used at MON and Dexcom 7 was used at the other two sites; a posteriori analysis of CGM accuracy led to the conclusion that both CGMs performed similarly in terms of mean absolute difference and mean absolute relative difference (nonsignificant two-sample t test), and accuracy was improved compared with "at home" use, probably because of YSI calibrations. During the open-loop admission, patients used their own insulin pump to control BG according to capillary BG measured at patients' discretion and at least before and 2 h after meals and snacks, at bedtime, and before and after exercise. Just before the closed-loop admission, one sensor was chosen by the study physician based on accuracy and signal quality and was used thereafter to drive the CLC system; the second sensor remained as backup in case of a primary sensor failure. Patients were equipped with Omnipod Insulin Management Systems (Insulet Corporation, Bedford, MA) filled with Humalog insulin (Eli Lilly and Company, Indianapolis, IN).

Studies 1 and 2 used identical protocols, which lasted 22 h as depicted by the timeline in Fig. 1, including 30 min of moderate exercise (adults: 50% Vo2max; adolescents: OMNI rate of perceived exertion <3),[25] a patient-selected meal (1.08 ± 0.24 g carbohydrate per kg of weight, identical for both admissions), a standardized snack (20 g carbohydrate), and an 18-h CLC on the experimental days. Reference glucose values were obtained using plasma measurements (YSI 2300/2700) at least every hour over the span of the protocol (every 30 min at UVA) and more frequently during and for 1 h after exercise (every 5 and 10 min, respectively) and for an hour after meals (every 10 min). Hypoglycemia was defined as YSI reading <3.9 mmol/L or the occurrence of hypoglycemic symptoms (sweating, trembling, difficulty in thinking, dizziness, or impaired coordination). Hypoglycemia was treated with glucose tablets, the amount of which was left to physician discretion.

Figure 1.

Design and profile of randomized clinical trials and timeline of inpatient admissions.

Figure 2.

Modular architecture of CTR.

Control Algorithms

The control algorithms used by studies 1 and 2—sCTR and eCTR, respectively—were designed and tested in silico using computer simulation,[26] and each algorithm had a different focus: in study 1, the emphasis was on safety and prevention of hypo- and hyperglycemia, while in study 2, the emphasis was on tight glycemic control. Nevertheless, as outlined above, both control algorithms relied on the same modular architecture and belonged to the same CTR class.[23]

Modular Architecture

The modular architecture of the CTR system comprises 1) the bottom system layer (SSM), which operates continuously and is in charge of prevention of hypoglycemia—the major barrier to tight glycemic control,[27]2) the middle layer (range control module), which is responsible for real-time correction of insulin dosing and is different in sCTR and eCTR; and 3) the top layer, which tunes the real-time control layer using clinical parameters and historical data, which was done off-line in this implementation. The communications between the CGM sensor, the CTR system, and the insulin pump were handled by the artificial pancreas system (APS) software.[28]

The two algorithms presented below include the SSM (bottom layer) and different range controllers (middle layer).

sCTR: Study 1

The two modules of sCTR are the SSM and a standard range control module that avoids prolonged hyperglycemic excursions. Both modules use a real-time estimate of the patient's metabolic state based on CGM and insulin infusion data. This estimate is used for prediction of the risks of hypo- and hyperglycemia 30–45 min ahead of the event. If a risk for hypoglycemia is predicted, the SSM attenuates automatically any insulin requests proportionally to the predicted risk level. How aggressively the system attenuates insulin is determined with readily available patient characteristics (e.g., body weight, insulin-to-carbohydrate ratio, and basal insulin delivery).[24] If a risk for hyperglycemia is predicted, the range controller introduces a correction bolus using the predicted plasma glucose and the patient's CSII parameters to reach a predetermined glucose target (8.3 mmol/L); the system injects only half of the computed bolus and can do so once every hour. Premeal boluses are calculated by the patients, based on their usual routine. The meal carbohydrate content was provided to the patient as measured in the clinical research center (CRC) kitchen.

eCTR: Study 2

The two modules of the eCTR are the SSM and an enhanced range control module based on an MPC algorithm that aims to maintain glycemia in a target range. eCTR also uses insulin-on-board constraints[29] intended to prevent insulin overdose during intensified therapy.

The rationale behind MPC was presented in detail in a recent review.[7] Controller aggressiveness was individualized for each subject based on readily available patient characteristics (e.g., body weight, insulin-to-carbohydrate ratio, and basal insulin delivery).[30] In this application, the MPC worked using information from the individual's conventional therapy. Premeal boluses were triggered by the patient, with the carbohydrate amount measured in the CRC kitchen but automatically calculated by eCTR.

Technical details for the sCTR and eCTR algorithms can be found in a previous publication[30] where they are tested and compared in silico.

Performance Indices

Algorithm performance has been assessed by calculating several indices derived from the measured BG profiles: the percent time spent in near normoglycemia (3.9–10 mmol/L), the percent time in tight glycemic range (4.4–7.8 mmol/L), mean glucose, intrasubject glucose variability (calculated as SD), and the number of hypoglycemic events per subject. In addition, the low blood glucose index (LBGI) and high blood glucose index (HBGI), together with the BG Risk Index, were used as metrics of risk for hypo- and hyperglycemia and overall glucose variability.[9] For detailed analysis, the full admission was segmented into four time windows: controller warm-up (2 p.m. to 4 p.m.), exercise and recovery (4 p.m. to 7 p.m.), dinner and snack (7 p.m. to midnight), and overnight (midnight to 8 a.m., no large disturbances). The warm-up period was excluded from the overall analysis.

Statistical Analysis

Comparison between open- and closed-loop admissions was performed using paired t tests and ANOVA with covariates when necessary; all results are provided as mean ± SE of the mean, open-loop versus closed-loop admission, unless specified otherwise.

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