Skip to main content
  • More from ADA
    • Diabetes
    • Clinical Diabetes
    • Diabetes Spectrum
    • ADA Standards of Medical Care
    • ADA Scientific Sessions Abstracts
    • BMJ Open Diabetes Research & Care
  • Subscribe
  • Log in
  • Log out
  • My Cart
  • Follow ada on Twitter
  • RSS
  • Visit ada on Facebook
Diabetes Care

Advanced Search

Main menu

  • Home
  • Current
    • Current Issue
    • Online Ahead of Print
    • Special Article Collections
    • ADA Standards of Medical Care
  • Browse
    • By Topic
    • Issue Archive
    • Saved Searches
    • Special Article Collections
    • ADA Standards of Medical Care
  • Info
    • About the Journal
    • About the Editors
    • ADA Journal Policies
    • Instructions for Authors
    • Guidance for Reviewers
  • Reprints/Reuse
  • Advertising
  • Subscriptions
    • Individual Subscriptions
    • Institutional Subscriptions and Site Licenses
    • Access Institutional Usage Reports
    • Purchase Single Issues
  • Alerts
    • E­mail Alerts
    • RSS Feeds
  • Podcast
  • Submit
    • Submit a Manuscript
    • Journal Policies
    • Instructions for Authors
    • ADA Peer Review
  • More from ADA
    • Diabetes
    • Clinical Diabetes
    • Diabetes Spectrum
    • ADA Standards of Medical Care
    • ADA Scientific Sessions Abstracts
    • BMJ Open Diabetes Research & Care

User menu

  • Subscribe
  • Log in
  • Log out
  • My Cart

Search

  • Advanced search
Diabetes Care
  • Home
  • Current
    • Current Issue
    • Online Ahead of Print
    • Special Article Collections
    • ADA Standards of Medical Care
  • Browse
    • By Topic
    • Issue Archive
    • Saved Searches
    • Special Article Collections
    • ADA Standards of Medical Care
  • Info
    • About the Journal
    • About the Editors
    • ADA Journal Policies
    • Instructions for Authors
    • Guidance for Reviewers
  • Reprints/Reuse
  • Advertising
  • Subscriptions
    • Individual Subscriptions
    • Institutional Subscriptions and Site Licenses
    • Access Institutional Usage Reports
    • Purchase Single Issues
  • Alerts
    • E­mail Alerts
    • RSS Feeds
  • Podcast
  • Submit
    • Submit a Manuscript
    • Journal Policies
    • Instructions for Authors
    • ADA Peer Review
Advances in Artificial Pancreas Development

First Use of Model Predictive Control in Outpatient Wearable Artificial Pancreas

  1. Simone Del Favero1,
  2. Daniela Bruttomesso2,
  3. Federico Di Palma3,
  4. Giordano Lanzola7,
  5. Roberto Visentin1,
  6. Alessio Filippi5,
  7. Rachele Scotton2,
  8. Chiara Toffanin3,
  9. Mirko Messori3,
  10. Stefania Scarpellini3,
  11. Patrick Keith-Hynes4,
  12. Boris P. Kovatchev4,
  13. J. Hans DeVries5,
  14. Eric Renard6,
  15. Lalo Magni3,
  16. Angelo Avogaro2,
  17. Claudio Cobelli1⇑,
  18. on behalf of the AP@home Consortium
  1. 1Department of Information Engineering, University of Padova, Padova, Italy
  2. 2Department of Internal Medicine, Unit of Metabolic Diseases, University of Padova, Padova, Italy
  3. 3Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
  4. 4Center for Diabetes Technology, University of Virginia, Charlottesville, VA
  5. 5Department of Internal Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
  6. 6Department of Endocrinology, Diabetes, and Nutrition, Montpellier University Hospital, Montpellier, France
  7. 7Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
  1. Corresponding author: Claudio Cobelli, cobelli{at}dei.unipd.it.
Diabetes Care 2014 May; 37(5): 1212-1215. https://doi.org/10.2337/dc13-1631
PreviousNext
  • Article
  • Figures & Tables
  • Info & Metrics
  • PDF
Loading

Abstract

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.

Introduction

The reduction of postprandial glucose excursions is a major challenge for artificial pancreas (AP) systems using subcutaneous insulin infusion due to delays associated with this route, as discussed in Cobelli et al. (1). Numerous inpatient studies have shown that model predictive control (MPC) is one of the most promising control strategies to cope with this and other delays of glucose closed-loop control (1 and references cited therein). Until now, MPC has not been used in outpatient settings. Three successful outpatient studies with an AP have recently been reported, using either a heuristic algorithm (hypo/hyperglycemia-mitigation system) (2,3) or a medical-based expert system (4). The first two were 42-h studies in adults with type 1 diabetes using a wearable AP platform based on a smartphone, while the third study focused on overnight control in a camp of a large pediatric population using a laptop-based system. In this study, we report for the first time an outpatient study based on an MPC strategy and investigate specifically its ability to control postprandial glucose.

Research Design and Methods

Protocol

This study followed the same protocol as previous outpatient studies presented in Cobelli et al. (2) and Kovatchev et al. (3), to which we refer to for details.

A total of six adults (aged 21–44 years) with type 1 diabetes were studied, two and four patients simultaneously. All participants were experienced insulin pump users, and their usual pump was replaced by an Omnipod Insulin Pump (Insulet Corp., Bedford, MA) for the study. A DexCom Seven Plus sensor (DexCom, Inc., San Diego, CA) was inserted 2 to 3 days prior to trials.

Throughout the study, patients wore the DiAs platform, a portable system developed at the University of Virginia allowing outpatient closed-loop control, already used in Kovatchev et al. (3) and Keith-Hynes et al. (5). The core of the DiAs system is an off-the-shelf smartphone running an Android operating system modified for medical use. The closed-loop controller was implemented on this device. Communications between DiAs and pump/sensor were wireless, allowing the patient to move around freely. Since wireless communication is not available on the pump or the sensor themselves, the system included a connection device that communicates wirelessly with DiAs.

The study started at 18:00 on day 1 and lasted for 42 h. Standard sensor-augmented pump therapy was performed with the DiAs set in open-loop mode (i.e., patient-driven), for the first 14 h of the study (day 1 at dinner and night 1). From day 2 at breakfast to day 3 at 12:00, the closed-loop controller was active and challenged by four meals and one night. Both in open-loop and closed-loop modes, dinners were consumed in a local restaurant. Patients were asked to tell the system the estimated meal carbohydrate content. Both in open- and closed-loop modes, they were assisted in the estimation process by the attending clinician, if needed, to avoid gross estimation errors biasing the comparison. In open-loop mode, meal bolus was computed by the pump bolus calculator formula using patient-specific parameters (carbohydrate/insulin ratio and correction factor). In closed-loop mode, the DiAs-based controller computed premeal bolus according to the same patient-specific parameters but also taking into account predicted future glucose values. To avoid potential learning of optimal bolus doses from the first dinner that would have favored the closed-loop intervention, the same patient-specific carbohydrate/insulin ratio and correction factors were used in both open- and closed-loop dinners. In both cases, premeal bolus was delivered 15 min ahead of the meal.

The subjects spent the night in a hotel near the Padova University Hospital, and during the study, the subjects were free to move around the facility and its vicinity.

The subjects interacted with DiAs using a Graphical User Interface, which allows sensor calibrations, meal announcement, etc.

To enhance patient safety, patient data were streamed by DiAs in real time to a telemonitoring website (6). Accessing to the website via an ordinary PC, the study team was able to monitor from remote location the status of the patient and check the correct functioning of the system throughout the trial without interfering/interacting with the experiment unless requested by protocol safety measures or for system troubleshooting.

The study was approved by the local ethics committee and registered with ClinicalTrials.gov as NCT1447992. Written consent was obtained.

Methods

The implemented control algorithm is a modular MPC, presented in Soru et al. (7) and Patek et al. (8). It is an evolution of a previous algorithm exposed in Magni et al. (9), used in an inpatient study, as described in Breton et al. (10). A key improvement concerns meal control. The standard basal/bolus therapy is used as reference in the optimization problem, so that MPC can adapt meal bolus using information about the patient status.

Data Analysis

Data portions affected by system malfunctioning have been removed (overall, the system worked successfully 90.27% of the time). We focus on meals, particularly on dinner.

Results

Overall, the system worked successfully 94.5% during open-loop and 88.3% during closed-loop. No hypoglycemia requesting a third-party assistance and no episode with β-ketones >1.0 mmol/L or HemoCue >400 or >300 mg/dL for >1 h were recorded, and no experiment had to be discontinued due to adverse events.

The study was not designed nor powered to statistically compare open-loop versus closed-loop, but certain post hoc comparisons for a preliminary assessment of effect size can be made.

Figure 1, top panel, shows the results of meal control: percent time-in-target (70–180 mg/dL, top left) and percent time-in-hypoglycemia (<70 mg/dL, top right) evaluated in the 4-h postprandial period of each meal of the study. Dinner closed-loop control was better than open-loop control of the same meal on the previous day: time-in-target increased from 68.17 to 94.84% and time-in-hypoglycemia was reduced ∼10-fold (11.95 vs. 1.25%). Lunch average control achieved by closed-loop was similar to the one achieved at dinner. No hypoglycemia was observed after lunch (12:00–16:00).

Figure 1
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1

Meal control achieved in the five meals of the study (the first dinner was handled in open-loop mode, and all other meals were handled by closed-loop). Time-in-target depicted on the left, time-in-hypoglycemia (hypo) on the right. Other meal-related outcomes are reported as mean ± SD. All metrics were evaluated in the postprandial period (i.e., 4 h after the meal) associated to each meal and based on continuous glucose monitoring sensor reading, suitably a posteriori processed to improve accuracy, as described in Beck et al. (11). Prandial excursion is defined as glucose peak minus starting glucose. AUC Hyper, area under the blood glucose curve above the hyperglycemia threshold (180 mg/dL); AUC Hypo, area under the blood glucose curve below the hypoglycemia threshold (70 mg/dL). HBGI and LBGI denote the high and low blood glucose indices, respectively (defined in Refs. 12,13). Breakf., breakfast; CL, closed-loop; OL, open-loop.

Breakfast confirmed itself as the most difficult meal to control: both breakfasts had less time-in-target than dinner and lunch (84.80 and 78.27 vs. 94.84 and 95.68%, respectively). In the first day breakfast, time-in-hypoglycemia was slightly higher than after dinner and lunch (2.44 vs. 1.25 and 0%, respectively), while no hypoglycemia was observed after the second day breakfast.

A similar picture emerges from the other meal-related metrics, reported in the bottom panel of Fig. 1.

Overnight control was also better on closed-loop versus open-loop: time-in-target, 89.40 vs. 84.97%; time-in-tight-target (80–140 mg/dL), 59.07 vs. 48.53%; and time-in-hypoglycemia, 0.00 vs. 8.19%. Although the closed-loop was challenged with more meals than open-loop, in terms of overall performance, percent time-in-target was on average >75% with both treatments (82.05 open-loop vs. 84.66% closed-loop), and a sevenfold reduction of time-in-hypo was observed with closed-loop (8.56 open-loop vs. 1.15% closed-loop).

Conclusions

Effective postprandial glycemic control is one of the major challenges to AP systems based on subcutaneous insulin infusion. To respond to this challenge, we used a meal-informed MPC strategy. In this report, we provide data on the first wearable AP outpatient study based on meal-informed MPC, showing its ability to reduce postprandial glycemic excursions. These results confirm inpatient findings of the effectiveness of MPC-based strategies and pave the way to randomized crossover outpatient studies of longer duration. The encouraging results of this report for a single meal (dinner) control needs to be confirmed in future long-term randomized studies with numerous meals, proving sustained superiority of MPC versus the commonly used bolus calculator (as those provided by pumps or meter). Because improvements in the power handling of the mobile AP platform are needed for around-the-clock experiments, the initial step may have to follow a hybrid closed-loop mode (i.e., closed-loop treatment from dinner to wake-up time and standard open-loop therapy during daytime).

Article Information

Acknowledgments. The authors thank Dexcom Inc. and Insulet Corp. for providing research material support. Neither company had any influence on trial design, analysis, or preparation of the manuscript, nor did they have access to any of the trial data.

Funding. This study was supported by the European Community Framework Programme 7 (FP7-ICT-2009-4 grant 247138).

Duality of Interest. B.P.K., J.H.D., E.R., and C.C. have received research material support from Dexcom, Inc. and Insulet Corp. C.C. holds patent applications related to the study technology and is a consultant/advisor for Johnson & Johnson (research grant: Dexcom; study material support: DexCom, Insulet, and Roche Diagnostics). B.P.K. holds patent applications related to the study technology and is a consultant/advisor for Animas and DexCom (research grant: Sanofi; study material support: Abbott, DexCom, Insulet, LifeScan, and Tandem). E.R. is a consultant/advisor for Menarini Diagnostics, Abbott, Cellnovo, Dexcom, Eli Lilly, Johnson & Johnson (Animas, LifeScan), Medtronic, Novo Nordisk, Roche Diagnostics, Sanofi; research grant/material support: Abbott, Dexcom, Insulet, and Roche Diagnostics. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. All authors reviewed and provided edits and comments on manuscript drafts. S.D.F. was the senior engineer responsible for the trial, design of the protocol, data analyses, and drafting of the manuscript. D.B. was the main study physician. F.D.P. was responsible for development of the control algorithm, implementation of the controller, and telemedicine function on the DiAs system. G.L. was responsible for development of the telemedicine system. R.V. was the engineer responsible for the trial and data analyses. A.F. and R.S. were study physicians. C.T. and M.M. were responsible for development of the control algorithm. S.S. was responsible for development of the telemedicine system. P.K.-H. was the chief engineer of the DiAs smartphone-based system and user interface. B.P.K. was principal investigator at the University of Virginia and responsible for development of the DiAs system, protocol design, and drafting of the manuscript. J.H.D. and E.R. were responsible for the design of the protocol and drafting of the manuscript. L.M. was the principal investigator of the Pavia Unit and responsible for development of the algorithm and drafting of the manuscript. A.A. was chief of the metabolic diseases unit at Padova Hospital. C.C. is the principal investigator and responsible for the design of the protocol, data analysis, and drafting of the manuscript. C.C. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Footnotes

  • See accompanying articles, pp. 1182, 1184, 1191, 1198, 1204, 1216, and 1224.

  • Clinical trial reg. no. NCT1447992, clinicaltrials.gov.

  • Received July 10, 2013.
  • Accepted December 12, 2013.
  • © 2014 by the American Diabetes Association.

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

References

  1. ↵
    1. Cobelli C,
    2. Renard E,
    3. Kovatchev B
    . Artificial pancreas: past, present, future. Diabetes 2011;60:2672–2682pmid:22025773
    OpenUrlFREE Full Text
  2. ↵
    1. Cobelli C,
    2. Renard E,
    3. Kovatchev BP,
    4. et al
    . Pilot studies of wearable outpatient artificial pancreas in type 1 diabetes. Diabetes Care 2012;35:e65–e67pmid:22923687
    OpenUrlFREE Full Text
  3. ↵
    1. Kovatchev BP,
    2. Renard E,
    3. Cobelli C,
    4. et al
    . Feasibility of outpatient fully integrated closed-loop control: first studies of wearable artificial pancreas. Diabetes Care 2013;36:1851–1858pmid:23801798
    OpenUrlAbstract/FREE Full Text
  4. ↵
    1. Phillip M,
    2. Battelino T,
    3. Atlas E,
    4. et al
    . Nocturnal glucose control with an artificial pancreas at a diabetes camp. N Engl J Med 2013;368:824–833pmid:23445093
    OpenUrlCrossRefPubMedWeb of Science
  5. ↵
    1. Keith-Hynes P,
    2. Guerlain S,
    3. Mize B,
    4. et al
    . DiAs user interface: a patient-centric interface for mobile artificial pancreas systems. J Diabetes Sci Technol 2013;7:1416–1426
    OpenUrlAbstract/FREE Full Text
  6. ↵
    1. Capozzi D,
    2. Lanzola G
    . A generic telemedicine infrastructure for monitoring an artificial pancreas trial. Comput Methods Programs Biomed 2013;110:343–353pmid:23415079
    OpenUrlCrossRefPubMed
  7. ↵
    1. Soru P,
    2. De Nicolao G,
    3. Toffanin C,
    4. Dalla Man C,
    5. Cobelli C,
    6. Magni L,
    7. on behalf of the AP@home consortium
    . MPC based artificial pancreas: strategies for individualization and meal compensation. Annu Rev Contr 2012;36:118–128
    OpenUrlCrossRef
  8. ↵
    1. Patek SD,
    2. Magni L,
    3. Dassau E,
    4. et al.,
    5. International Artificial Pancreas (iAP) Study Group
    . Modular closed-loop control of diabetes. IEEE Trans Biomed Eng 2012;59:2986–2999pmid:22481809
    OpenUrlCrossRefPubMed
  9. ↵
    1. Magni L,
    2. Raimondo DM,
    3. Bossi L,
    4. et al
    . Model predictive control of type 1 diabetes: an in silico trial. J Diabetes Sci Tech 2007;1:804–812pmid:19885152
    OpenUrlAbstract/FREE Full Text
  10. ↵
    1. Breton M,
    2. Farret A,
    3. Bruttomesso D,
    4. et al.,
    5. International Artificial Pancreas Study Group
    . Fully integrated artificial pancreas in type 1 diabetes: modular closed-loop glucose control maintains near normoglycemia. Diabetes 2012;61:2230–2237pmid:22688340
    OpenUrlAbstract/FREE Full Text
  11. ↵
    1. Beck RW,
    2. Calhoun P,
    3. Kollman C
    . Challenges for outpatient closed loop studies: how to assess efficacy. Diabetes Technol Ther 2013;15:1–3pmid:23297669
    OpenUrlCrossRefPubMed
  12. ↵
    1. Kovatchev BP,
    2. Cox DJ,
    3. Gonder-Frederick LA,
    4. Young-Hyman D,
    5. Schlundt D,
    6. Clarke WL
    . Assessment of risk for severe hypoglycemia among adults with IDDM: validation of the low blood glucose index. Diabetes Care 1998;21:1870–1875pmid:9802735
    OpenUrlAbstract/FREE Full Text
  13. ↵
    1. Kovatchev BP,
    2. Cox DJ,
    3. Gonder-Frederick LA,
    4. Clarke WL
    . Methods for quantifying self-monitoring blood glucose profiles exemplified by an examination of blood glucose patterns in patients with type 1 and type 2 diabetes. Diabetes Technol Ther 2002;4:295–303pmid:12165168
    OpenUrlCrossRefPubMed
View Abstract
PreviousNext
Back to top
Diabetes Care: 37 (5)

In this Issue

May 2014, 37(5)
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by Author
  • Masthead (PDF)
Sign up to receive current issue alerts
View Selected Citations (0)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word about Diabetes Care.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
First Use of Model Predictive Control in Outpatient Wearable Artificial Pancreas
(Your Name) has forwarded a page to you from Diabetes Care
(Your Name) thought you would like to see this page from the Diabetes Care web site.
Citation Tools
First Use of Model Predictive Control in Outpatient Wearable Artificial Pancreas
Simone Del Favero, Daniela Bruttomesso, Federico Di Palma, Giordano Lanzola, Roberto Visentin, Alessio Filippi, Rachele Scotton, Chiara Toffanin, Mirko Messori, Stefania Scarpellini, Patrick Keith-Hynes, Boris P. Kovatchev, J. Hans DeVries, Eric Renard, Lalo Magni, Angelo Avogaro, Claudio Cobelli, on behalf of the AP@home Consortium
Diabetes Care May 2014, 37 (5) 1212-1215; DOI: 10.2337/dc13-1631

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Add to Selected Citations
Share

First Use of Model Predictive Control in Outpatient Wearable Artificial Pancreas
Simone Del Favero, Daniela Bruttomesso, Federico Di Palma, Giordano Lanzola, Roberto Visentin, Alessio Filippi, Rachele Scotton, Chiara Toffanin, Mirko Messori, Stefania Scarpellini, Patrick Keith-Hynes, Boris P. Kovatchev, J. Hans DeVries, Eric Renard, Lalo Magni, Angelo Avogaro, Claudio Cobelli, on behalf of the AP@home Consortium
Diabetes Care May 2014, 37 (5) 1212-1215; DOI: 10.2337/dc13-1631
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Research Design and Methods
    • Results
    • Conclusions
    • Article Information
    • Footnotes
    • References
  • Figures & Tables
  • Info & Metrics
  • PDF

Related Articles

Cited By...

More in this TOC Section

  • Feasibility of Closed-Loop Insulin Delivery in Type 2 Diabetes: A Randomized Controlled Study
  • The Artificial Pancreas: Are We There Yet?
  • Closed-Loop Artificial Pancreas Systems: Physiological Input to Enhance Next-Generation Devices
Show more Advances in Artificial Pancreas Development

Similar Articles

Navigate

  • Current Issue
  • Standards of Care Guidelines
  • Online Ahead of Print
  • Archives
  • Submit
  • Subscribe
  • Email Alerts
  • RSS Feeds

More Information

  • About the Journal
  • Instructions for Authors
  • Journal Policies
  • Reprints and Permissions
  • Advertising
  • Privacy Policy: ADA Journals
  • Copyright Notice/Public Access Policy
  • Contact Us

Other ADA Resources

  • Diabetes
  • Clinical Diabetes
  • Diabetes Spectrum
  • BMJ Open - Diabetes Research & Care
  • Standards of Medical Care in Diabetes
  • Scientific Sessions Abstracts
  • Professional Books
  • Diabetes Forecast

 

  • DiabetesJournals.org
  • Diabetes Core Update
  • ADA's DiabetesPro
  • ADA Member Directory
  • Diabetes.org

© 2019 by the American Diabetes Association. Diabetes Care Print ISSN: 0149-5992, Online ISSN: 1935-5548.