Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: The Virginia Experience

Clarke, William L.; Anderson, Stacey M.; Breton, Marc D.; Patek, Stephen D.; Kashmer, Laurissa; Kovatchev, Boris

doi:10.1177/193229680900300506

Cited by 166 publications

(125 citation statements)

References 12 publications

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“…Peak postprandial glucose levels declined from ~250 to 180 mg/dl, and levels 3-4 h postprandial became more stable. Meal responses obtained with MPC by teams comprising investigators at the University of Virginia and Padova and at Boston University and Massachusetts General Hospital [13][14][15][16] (Figure 1B) generally showed the peak breakfast response to be similar to the initial PID study 2 . Still, comparing meal responses across studies can be confounded by differences in meal size, and averaging data inevitably results in peak postprandial values lower than the average of individual peak values.…”

Section: Review Of Available Clinical Datamentioning

confidence: 99%

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

Steil

2013

J Diabetes Sci Technol

105

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Abbreviation: (eCTR) enhanced control-to-range, (IQR) interquartile range, (MPC) model-predictive control, (PID) proportional integral derivative, (PD) pharmacodynamic, (PID IFB ) proportional integral derivative with insulin feedback, (PK) pharmacokinetic, (RLS) recursive least squares, (sCTR) standard control-to-range Journal of Diabetes Science and TechnologyVolume 7, Issue 6, November 2013 © Diabetes Technology Society AbstractClosed-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 proportionalintegral-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.

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Section: Review Of Available Clinical Datamentioning

confidence: 99%

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

Steil

2013

J Diabetes Sci Technol

105

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“…Between 2008 and 2011, promising results from inpatient closed-loop control studies were reported by several groups. [40][41][42][43][44][45] The results from these investigations were summarized in a 2011 review of the artificial pancreas field, 46 pointing out the superiority of closed-loop control over insulin pump therapy in terms of (1) increased time within target glucose range, (2) reduced incidence of hypoglycemia, and (3) better overnight control. Subsequent studies confirmed these findings in outpatient setting, 47 at diabetes camps for children, 48 and at patients' homes.…”

Section: Utility Of Cgm Informationmentioning

confidence: 99%

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Kovatchev

Patek

Ortiz

et al. 2015

Diabetes Technology & Therapeutics

184

154

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Background: The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes. Materials and Methods: A combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to ''replay'' real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows. Results: In all six scenarios, the occurrence of hypoglycemia (frequency of BG levels £ 50 mg/dL and BG levels £ 39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD = 10%. Similarly, hyperglycemia (frequency of BG levels ‡ 250 mg/dL and BG levels ‡ 400 mg/dL) increased and displayed an abrupt slope change at MARD = 10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively. Conclusions: Using CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD = 10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.

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“…The feasibility of closed-loop artificial pancreas systems and their advantage over conventional treatment has been proved in several clinical studies (Atlas et al (2010); Clarke et al (2009);Hovorka et al (2010);Steil et al (2011;; Weinzimer et al (2008)), and a wide spectrum of control algorithms has been proposed to close the control loop, including classical and modern control strategies. Many reviews on closed-loop algorithms are available, see for example (Bequette (2005); Chee & Fernando (2007); Doyle III et al (2007);El-Youssef et al (2009);Takahashi et al (2008)).…”

Section: Control Problemsmentioning

confidence: 99%

“…Earlier version of this MPC algorithm was tested in previous clinical study to evaluate its control and prediction performance during fasting conditions (Shaller et al (2006)). An automated closed-loop insulin delivery system was tested in a multinational clinical trial (Bruttomesso et al (2009);Clarke et al (2009)). The system used a personalized MPC algorithm developed in (Magni et al (2007)).…”

Section: Overnight Hypoglycemia Controlmentioning

confidence: 99%

Hypoglycemia Prevention in Closed-Loop Artificial Pancreas for Patients with Type 1 Diabetes

Abu-Rmileh¹,

Garcia-Gabin²

2011

Diabetes - Damages and Treatments

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Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: The Virginia Experience

Cited by 166 publications

References 12 publications

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

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

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Hypoglycemia Prevention in Closed-Loop Artificial Pancreas for Patients with Type 1 Diabetes

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