Algorithms for a Closed-Loop Artificial Pancreas: The Case for Proportional-Integral-Derivative Control

Closed-loop insulin delivery continues to be one of most promising strategies for achieving near-normal control of blood glucose levels in individuals with diabetes. Of the many components that need to work well for the artificial pancreas to be advanced into routine use, the algorithm used to calculate insulin delivery has received a substantial amount of attention. Most of that attention has focused on the relative merits of proportional-integral-derivative versus model-predictive control. A meta-analysis of the clinical data obtained in studies performed to date with these approaches is conducted here, with the objective of determining if there is a trend for one approach to be performing better than the other approach. Challenges associated with implementing each approach are reviewed with the objective of determining how these approaches might be improved. Results of the meta-analysis, which focused predominantly on the breakfast meal response, suggest that to date, the two approaches have performed similarly. However, uncontrolled variables among the various studies, and the possibility that future improvements could still be effected in either approach, limit the validity of this conclusion. It is suggested that a more detailed examination of the challenges associated with implementing each approach be conducted.     

Continuous glucose monitoring and closed-loop systems.

BACKGROUND: The last two decades have witnessed unprecedented technological progress in the development of continuous glucose sensors, resulting in the first generation of commercial glucose monitors. This has fuelled the development of prototypes of a closed-loop system based on the combination of a continuous monitor, a control algorithm, and an insulin pump. METHOD: A review of electromechanical closed-loop approaches is presented. This is followed by a review of existing prototypes and associated glucose sensors. A literature review was undertaken from 1960 to 2004. RESULTS: Two main approaches exist. The extracorporeal s.c.-s.c. approach employs subcutaneous glucose monitoring and subcutaneous insulin delivery. The implantable i.v.-i.p. approach adopts intravenous sampling and intraperitoneal insulin delivery. Feasibility of both solutions has been demonstrated in small-scale laboratory studies using either the classical proportional-integral-derivative controller or a model predictive controller. Performance in the home setting has yet to be demonstrated. CONCLUSIONS: The glucose monitor remains the main limiting factor in the development of a commercially viable closed-loop system, as presently available monitors fail to demonstrate satisfactory characteristics in terms of reliability and/or accuracy. Regulatory issues are the second limiting factor. Closed-loop systems are likely to be used first by health-care professionals in controlled environments such as intensive care units.     

The Impact of a Recently Approved Automated Insulin Delivery System on Glycemic,Sleep, and Psychosocial Outcomes in Older Adults With Type 1 Diabetes: A Pilot Study

Background:Older adults with type 1 diabetes (≥65 years) are often under-represented in clinical trials of automated insulin delivery (AID) systems. We sought to test the efficacy of a recently FDA-approved AID system in this population.Methods:Participants with type 1 diabetes used sensor-augmented pump (SAP) therapy for four weeks and then used an AID system (Control-IQ) for four weeks. In addition to glucose control variables, patient-reported outcomes (PRO) were assessed with questionnaires and sleep parameters were assessed by actigraphy.Results:Fifteen older adults (mean age 68.7 ± 3.3, HbA1c of 7.0 ± 0.8) completed the pilot trial. Glycemic outcomes improved during AID compared to SAP. During AID use, mean glucose was 146.0 mg/dL; mean percent time in range (TIR, 70-180 mg/dL) was 79.6%; median time below 70 mg/dL was 1.1%. The AID system was in use 92.6% ± 7.0% of the time. Compared to SAP, while participants were on AID the TIR increased significantly (+10%, P = .002) accompanied by a reduction in both time above 180 mg/dL (−6.9%, P = .005) and below 70 mg/dl (−0.4%, P = .053). Diabetes-related distress decreased significantly while using AID (P = .028), but sleep parameters remained unchanged.Conclusions:Use of this AID system in older adults improved glycemic control with high scores in ease of use, trust, and usability. Participants reported an improvement in diabetes distress with AID use. There were no significant changes in sleep.     

Insulin Delivery Route for the Artificial Pancreas: Subcutaneous,Intraperitoneal, or Intravenous? Pros and Cons

Insulin delivery is a crucial component of a closed-loop system aiming at the development of an artificial pancreas. The intravenous route, which has been used in the bedside artificial pancreas model for 30 years, has clear advantages in terms of pharmacokinetics and pharmacodynamics, but cannot be used in any ambulatory system so far. Subcutaneous (SC) insulin infusion benefits from the broad expansion of insulin pump therapy that promoted the availability of constantly improving technology and fast-acting insulin analog use. However, persistent delays of insulin absorption and action, variability and shortterm stability of insulin infusion from SC-inserted catheters generate effectiveness and safety issues in view of an ambulatory, automated, glucose-controlled, artificial beta cell. Intraperitoneal insulin delivery, although still marginally used in diabetes care, may offer an interesting alternative because of its more-physiological plasma insulin profiles and sustained stability and reliability of insulin delivery.     

Closed-loop insulin delivery utilizing pole placement to compensate for delays in subcutaneous insulin delivery

Background

We have previously used insulin feedback (IFB) as a component of a closed-loop algorithm emulating the β cell. This was based on the observation that insulin secretion is inhibited by insulin concentration. We show here that the effect of IFB is to make a closed-loop system behave as if delays in the insulin pharmacokinetic (PK)/pharmacodynamic (PD) response are reduced. We examine whether the mechanism can be used to compensate for delays in the subcutaneous PK/PD insulin response.

Method

Closed-loop insulin delivery was performed in seven diabetic dogs using a proportional-integral-derivative model of the β cell modified by model-predicted IFB. The level of IFB was set using pole placement. Meal responses were obtained on three occasions: without IFB (NONE), reference IFB (REF), and 2xREF, with experiments performed in random order. The ability of the insulin model to predict insulin concentration was evaluated by correlation with the measured profile and results reported as R². The ability of IFB to improve the meal response was evaluated by comparing peak and nadir postprandial glucose and area under the curve (AUC; repeated measures analysis of variance with post hoc test for linear trend).

Results

Insulin concentration was well predicted by the model (median R² = 0.87, 0.79, and 0.90 for NONE, REF, and 2xREF, respectively). Peak postprandial glucose (294 ± 15, 243 ± 21, and 247 ± 16 mg/dl) and AUC (518.2 ± 36.13, 353.5 ± 45.04, and 280.3 ± 39.37 mg/dl·min) decreased with increasing IFB (p < .05, linear trend). Nadir glucose was not affected by IFB (76 ± 5.4, 68 ± 7.3, and 72 ± 4.3 mg/dl; p = .63).

Conclusions

Insulin feedback provides an effective mechanism to compensate for delay in the insulin PK/PD profile.     

Use of Microdialysis-Based Continuous Glucose Monitoring to Drive Real-Time Semi-Closed-Loop Insulin Infusion

Continuous glucose monitoring (CGM) and automated insulin delivery may make diabetes management substantially easier, if the quality of the resulting therapy remains adequate. In this study, a semi-closed-loop control algorithm was used to drive insulin therapy and its quality was compared to that of subject-directed therapy. Twelve subjects stayed at the study site for approximately 70 hours and were provided with the investigational Automated Pancreas System Test Stand (APS-TS), which was used to calculate insulin dosage recommendations automatically. These recommendations were based on microdialysis CGM values and common diabetes therapy parameters. For the first half of their stay, the subjects directed their diabetes therapy themselves, whereas for the second half, the insulin recommendations were delivered by the APS-TS (so-called algorithm-driven therapy). During subject-directed therapy, the mean glucose was 114 mg/dl compared to 125 mg/dl during algorithm-driven therapy. Time in target (90 to 150 mg/dl) was approximately 46% during subject-directed therapy and approximately 58% during algorithm-driven therapy. When subjects directed their therapy, approximately 2 times more hypoglycemia interventions (oral administration of carbohydrates) were required than during algorithm-driven therapy. No hyperglycemia interventions (delivery of addition insulin) were necessary during subject-directed therapy, while during algorithm-driven therapy, 2 hyperglycemia interventions were necessary. The APS-TS was able to adequately control glucose concentrations in the subjects. Time in target was at least comparable or moderately higher during closed-loop control and markedly fewer hypoglycemia interventions were required, thus increasing patient safety.     

Does overnight normalization of plasma glucose by insulin infusion affect assessment of glucose metabolism in Type 2 diabetes?

AIMS: In order to perform euglycaemic clamp studies in Type 2 diabetic patients, plasma glucose must be reduced to normal levels. This can be done either (i) acutely during the clamp study using high-dose insulin infusion, or (ii) slowly overnight preceding the clamp study using a low-dose insulin infusion. We assessed whether the choice of either of these methods to obtain euglycaemia biases subsequent assessment of glucose metabolism and insulin action. METHODS: We studied seven obese Type 2 diabetic patients twice: once with (+ ON) and once without (- ON) prior overnight insulin infusion. Glucose turnover rates were quantified by adjusted primed-constant 3-3H-glucose infusions, and insulin action was assessed in 4-h euglycaemic, hyperinsulinaemic (40 mU m-2 min-1) clamp studies using labelled glucose infusates (Hot-GINF). RESULTS: Basal plasma glucose levels (mean +/- sd) were 5.5 +/- 0.5 and 10.7 +/- 2.9 mmol/l in the + ON and - ON studies, respectively, and were clamped at -5.5 mmol/l. Basal rates of glucose production (GP) were similar in the + ON and - ON studies, 83 +/- 13 vs. 85 +/- 14 mg m-2 min-1 (NS), whereas basal rates of glucose disappearance (Rd) were lower in the + ON than in the - ON study, 84 +/- 8 vs. 91 +/- 11 mg m-2 min-1 (P = 0.02). During insulin infusion in the clamp period, rates of GP, 23 +/- 11 vs. 25 +/- 10 mg m-2 min-1, as well as rates of Rd, 133 +/- 32 vs. 139 +/- 37 mg m-2 min-1, were similar in the + ON and - ON studies, respectively (NS). CONCLUSIONS: Apart from basal rates of Rd, assessment of glucose turnover rates in euglycaemic clamp studies of Type 2 diabetic patients is not dependent on the method by which plasma glucose levels are lowered.     

Dynamic Insulin on Board: Incorporation of Circadian Insulin Sensitivity Variation

Background:

Insulin-on-board (IOB) estimation is used in modern insulin therapy with continuous subcutaneous insulin infusion (CSII) as well as different automatic glucose-regulating strategies (i.e., artificial pancreas products) to prevent insulin stacking that may lead to hypoglycemia. However, most of the IOB calculations are static IOB (sIOB): they are based only on approximated insulin decay and do not take into account diurnal changes in insulin sensitivity.

Methods:

A dynamic IOB (dIOB) that takes into account diurnal insulin sensitivity variation is suggested in this work and used to adjust the sIOB estimations. The dIOB function is used to correct the dosage of insulin boluses in light of this circadian variation.

Results:

Basal–bolus as applied by pump users and model predictive control therapy with and without dIOB were evaluated using the University of Virginia/Padova metabolic simulator. Three protocols with four meals of 1 g carbohydrate/kg body weight were evaluated: a nominal scenario and two robustness scenarios, one in which insulin sensitivity was 15% greater than estimated and the other where the lunch is 30% less than announced. In the nominal and robustness scenarios, respectively, the dIOB led to 6% and 24% and 40% less hypoglycemia episodes than approaches without IOB. The new approach was also compared with the sIOB to evaluate the improvements with respect to the previous approach.

Conclusions:

Improved glucose regulation was demonstrated using the dIOB where circadian insulin sensitivity is used to adjust IOB estimation. Use of diurnal variations of insulin sensitivity appears to promote effective and safe insulin therapy using CSII or artificial pancreas. Clinical trials are warranted to determine whether nocturnal hypoglycemia can be reduced using the dIOB approach.     

Combining Insulin Pumps and Continuous Glucose Monitors; Where Are We to Go from Here?

Insulin pump development started in 1978, with the first commercially available glucose sensor marketed in 1999. Combining these two instruments is a logical step toward the closed loop. This article discusses three questions: Is pump development complete? How can a pump and a sensor be combined? Can the delay problem associated with the subcutaneous–subcutaneous approach for the closed loop be overcome?     

On-line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: evaluation under fasting conditions in patients with Type 1 diabetes.

AIMS: To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters-a model predictive control (MPC) algorithm-to control blood glucose concentration during fasting conditions in patients with Type 1 diabetes. In the subcutaneous (sc) route within a closed loop system. METHODS: Paired experiments were performed in six patients. Over 8 h the MPC algorithm was used to control glucose with s.c. insulin administration and two different glucose monitoring protocols: first, the algorithm was provided with intravenous (i.v.) glucose values for insulin dosage calculation directly (i.v.-s.c. route). Then, in the second experiment, i.v. glucose values were fed to the MPC with a delay of 30 min to simulate s.c. glucose measurements ('s.c.'-s.c. route). In both experiments plasma glucose, insulin dosage, and serum insulin levels were analysed. RESULTS: Glucose concentration was brought from hyper- to normoglycaemia and kept in the physiological range (6-7 mmol/l) with both routes in all subjects. Mean glucose concentration reached the threshold of 7 mmol/l approximately 2 (i.v.-s.c. route) and 3 ('s.c.'-s.c. route) hours after the start of glucose control with the MPC. During the last 2 h of automated glucose control, mean glucose concentration was 6.3 +/- 0.2 mmol/l and 6.6 +/- 0.3 mmol/l for i.v.-s.c. and 's.c.'-s.c. route, respectively. Glucose concentration, insulin doses, and serum insulin levels did not differ significantly between routes (P > 0.05). CONCLUSIONS: The MPC algorithm is suitable for glucose control during fasting within an extracorporeal artificial beta-cell in the subcutaneous route Type 1 diabetic patients.     

Rapid healing of diabetic foot ulcers with meticulous blood glucose control

Summary Fifteen diabetic patients, with neuropathic food ulcers refractory to conventional treatment, were found to be poorly balanced and were put on meticulous regimens; some on continuous subcutaneous insulin infusion and others on split mixed doses. Once diabetes was controlled, the wound healed rapidly in 11 of the patients within 4 to 13 weeks. In 4 patients amputation was necessary. The outcome was better in patients with good peripheral pulses. We suggest that tight control of diabetes promotes healing of diabetic foot lesions.     

Siphon Effects on Continuous Subcutaneous Insulin Infusion Pump Delivery Performance

Background

The objective was to quantify hydrostatic effects on continuous subcutaneous insulin infusion (CSII) pumps during basal and bolus insulin delivery.

Methods

We tested CSII pumps from Medtronic Diabetes (MiniMed 512 and 515), Smiths Medical (Deltec Cozmo 1700), and Insulet (OmniPod) using insulin aspart (Novolog, Novo Nordisk). Pumps were filled and primed per manufacturer''s instructions. The fluid level change was measured using an inline graduated glass pipette (100 μl) when the pipette was moved in relation to the pump (80 cm Cosmo and 110 cm Medtronics) and when level. Pumps were compared during 1 and 5 U boluses and basal insulin delivery of 1.0 and 1.5 U/h.

Results

Pronounced differences were seen during basal delivery in pumps using 80–100 cm tubing. For the 1 U/h rate, differences ranged from 74.5% of the expected delivery when the pumps were below the pipettes and pumping upward to 123.3% when the pumps were above the pipettes and pumping downward. For the 1.5 U/h rate, differences ranged from 86.7% to 117.0% when the pumps were below or above the pipettes, respectively. Compared to pumps with tubing, OmniPod performed with significantly less variation in insulin delivery.

Conclusions

Changing position of a conventional CSII pump in relation to its tubing results in significant changes in insulin delivery. The siphon effect in the tubing may affect the accuracy of insulin delivery, especially during low basal rates. This effect has been reported when syringe pumps were moved in relation to infusion sites but has not been reported with CSII pumps.     

Use of subcutaneous interstitial fluid glucose to estimate blood glucose: revisiting delay and sensor offset

Background

Estimates for delays in the interstitial fluid (ISF) glucose response to changes in blood glucose (BG) differ substantially among research groups. We review these findings along with arguments that continuous glucose monitoring (CGM) devices used to measure ISF delay contribute to the variability. We consider the impact of the ISF delay and review approaches to correct for it, including strategies pursued by the manufacturers of these devices. The focus on how the manufacturers have approached the problem is motivated by the observation that clinicians and researchers are often unaware of how the existing CGM devices process the ISF glucose signal.

Methods

Numerous models and simulations were used to illustrate problems related to measurement and correction of ISF glucose delay.

Results

We find that (1) there is no evidence that the true physiologic ISF glucose delay is longer than 5–10 min and that the values longer than this can be explained by delays in CGM filtering routines; (2) the primary impact of the true ISF delay is on sensor calibration algorithms, making it difficult to estimate calibration factors and offset (OS) currents; (3) inaccurate estimates of the sensor OS current result in overestimation of sensor glucose at low values, making it difficult to detect hypoglycemia; (4) many device companies introduce nonlinear components into their filters, which can be expected to confound attempts by investigators to reconstruct BG using linear deconvolution; and (5) algorithms advocated by academic groups are seldom compared to algorithms pursued by industry, making it difficult to ascertain their value.

Conclusions

The absence of any direct comparisons between existing and new algorithms for correcting ISF delay and sensor OS current is, in part, due to the difficulty in extracting relevant details from industry patents and/or extracting unfiltered sensor signals from industry products. The model simulation environment, where all aspects of the signal can be derived, may be more appropriate for developing new filtering and calibration strategies. Nevertheless, clinicians, academic researchers, and the industry would benefit from collaborating when evaluating those strategies.     

Continuous glucose monitoring considerations for the development of a closed-loop artificial pancreas system

Background

Commercialization of a closed-loop artificial pancreas system that employs continuous subcutaneous insulin infusion and interstitial fluid glucose sensing has been encumbered by state-of-the-art technology. Continuous glucose monitoring (CGM) devices with improved accuracy could significantly advance development efforts. However, the current accuracy of CGM devices might be adequate for closed-loop control.

Methods

The influence that known CGM limitations have on closed-loop control was investigated by integrating sources of sensor inaccuracy with the University of Virginia Padova Diabetes simulator. Non-glucose interference, physiological time lag and sensor error measurements, selected from 83 Enlite™ glucose sensor recordings with the Guardian® REAL-Time system, were used to modulate simulated plasma glucose signals. The effect of sensor accuracy on closed-loop controller performance was evaluated in silico, and contrasted with closed-loop clinical studies during the nocturnal control period.

Results

Based on n = 2472 reference points, a mean sensor error of 14% with physiological time lags of 3.28 ± 4.62 min (max 13.2 min) was calculated for simulation. Sensor bias reduced time in target for both simulation and clinical experiments. In simulation, additive error increased time <70 mg/dl and >180 mg/dl by 0.2% and 5.6%, respectively. In-clinic, the greatest low blood glucose index values (max = 5.9) corresponded to sensor performance.

Conclusion

Sensors have sufficient accuracy for closed-loop control, however, algorithms are necessary to effectively calibrate and detect erroneous calibrations and failing sensors. Clinical closed-loop data suggest that control with a higher target of 140 mg/dl during the nocturnal period could significantly reduce the risk for hypoglycemia.     

Determinants of glycaemic control in type 1 diabetes during intensified therapy with multiple daily insulin injections or continuous subcutaneous insulin infusion: importance of blood glucose variability

BACKGROUND AND METHODS: We investigated the factors that determine the best glycaemic control on multiple daily insulin (MDI) injections and continuous subcutaneous insulin infusion (CSII), and the hypothesis that blood glucose variability is a major determinant of control and that the resultant HbA(1c) on MDI correlates with the improvement achieved by CSII. We studied 30 type 1 diabetic subjects already receiving MDI. Renewed attempts to improve control on MDI were made for a median of five months, and then the subjects were switched to CSII. The variability of within-day and between-day blood glucose concentrations was calculated from blood glucose self-monitoring data. RESULTS: HbA(1c) during MDI varied from 5.7 to 11.7% (mean +/- SD, 8.5 +/- 1.4%). Within- and between-day blood glucose variability correlated with HbA(1c) on MDI (r = 0.59, p < 0.001; r = 0.48, p < 0.03). Within-day variability remained an independent predictor of HbA(1c) on MDI. Mean HbA(1c) improved with CSII (to 7.3 +/- 0.9%, p < 0.001), but reduction in HbA(1c) was variable and was related to the HbA(1c) on MDI (r = 0.79, p < 0.001) and within-day variability (r = 0.56, p < 0.01). Similar results were observed for subjects treated only with glargine-based MDI. CONCLUSIONS: The best glycaemic control achievable on MDI is related to blood glucose variability-those with the largest swings in blood glucose retaining the highest HbA(1c). The improvement in control achieved by CSII is related to HbA(1c) and blood glucose variability on MDI. Pump therapy is most effective in those worst controlled on MDI.