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Pinsker JE, Lee JB, Dassau E, Seborg DE, Bradley PK, Gondhalekar R, Bevier WC, Huyett L, Zisser HC, Doyle FJ. Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas. Diabetes Care 2016; 39:1135-42. [PMID: 27289127 PMCID: PMC4915560 DOI: 10.2337/dc15-2344] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 02/18/2016] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions. RESEARCH DESIGN AND METHODS After a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.5-h CLC sessions. Challenges included overnight control after a 65-g dinner, response to a 50-g breakfast, and response to an unannounced 65-g lunch. Boluses of announced dinner and breakfast meals were given at mealtime. The primary outcome was time in glucose range 70-180 mg/dL. RESULTS Mean time in range 70-180 mg/dL was greater for MPC than for PID (74.4 vs. 63.7%, P = 0.020). Mean glucose was also lower for MPC than PID during the entire trial duration (138 vs. 160 mg/dL, P = 0.012) and 5 h after the unannounced 65-g meal (181 vs. 220 mg/dL, P = 0.019). There was no significant difference in time with glucose <70 mg/dL throughout the trial period. CONCLUSIONS This first comprehensive study to compare MPC and PID control for the AP indicates that MPC performed particularly well, achieving nearly 75% time in the target range, including the unannounced meal. Although both forms of CLC provided safe and effective glucose management, MPC performed as well or better than PID in all metrics.
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Affiliation(s)
| | - Joon Bok Lee
- William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA
| | - Eyal Dassau
- William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Dale E Seborg
- William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA
| | | | - Ravi Gondhalekar
- William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA
| | | | - Lauren Huyett
- William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA
| | - Howard C Zisser
- William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA
| | - Francis J Doyle
- William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
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53
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Richert L, Lhomme E, Fagard C, Lévy Y, Chêne G, Thiébaut R. Recent developments in clinical trial designs for HIV vaccine research. Hum Vaccin Immunother 2016; 11:1022-9. [PMID: 25751670 DOI: 10.1080/21645515.2015.1011974] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
HIV vaccine strategies are expected to be a crucial component for controlling the HIV epidemic. Despite the large spectrum of potential candidate vaccines for both prophylactic and therapeutic use, the overall development process of an efficacious HIV vaccine strategy is lengthy. The design of clinical trials and the progression of a candidate strategy through the different clinical development stages remain methodologically challenging, mainly due to the lack of validated correlates of protection. In this review, we describe recent advances in clinical trial designs to increase the efficiency of the clinical development of candidate HIV vaccine strategies. The methodological aspects of the designs for early- (phase I and II) and later -stage (phase IIB and III) development are discussed, taking into account the specificities of both prophylactic and therapeutic HIV vaccine development.
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54
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Bosley JR, Maurer TS, Musante CJ. Systems Pharmacology Modeling in Type 2 Diabetes Mellitus. SYSTEMS PHARMACOLOGY AND PHARMACODYNAMICS 2016. [DOI: 10.1007/978-3-319-44534-2_20] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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55
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Li P, Yu L, Fang Q, Lee SY. A simplification of Cobelli's glucose-insulin model for type 1 diabetes mellitus and its FPGA implementation. Med Biol Eng Comput 2016; 54:1563-77. [PMID: 26718555 DOI: 10.1007/s11517-015-1436-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 12/11/2015] [Indexed: 11/24/2022]
Abstract
Cobelli's glucose-insulin model is the only computer simulator of glucose-insulin interactions accepted by Food Drug Administration as a substitute to animal trials. However, it consists of multiple differential equations that make it hard to be implemented on a hardware platform. In this investigation, the Cobelli's model is simplified by Padé approximant method and implemented on a field-programmable gate array-based platform as a hardware model for predicting glucose changes in subjects with type 1 diabetes mellitus. Compared with the original Cobelli's model, the implemented hardware model provides a nearly perfect approximation in predicting glucose changes with rather small root-mean-square errors and maximum errors. The RMSE results for 30 subjects show that the method for simplifying and implementing Cobelli's model has good robustness and applicability. The successful hardware implementation of Cobelli's model will promote a wider adoption of this model that can substitute animal trials, provide fast and reliable glucose and insulin estimation, and ultimately assist the further development of an artificial pancreas system.
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Affiliation(s)
- Peng Li
- Department of Medical Electronics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China. .,Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China. .,University of Chinese Academy of Sciences, Beijing, China.
| | - Lei Yu
- Department of Medical Electronics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Qiang Fang
- School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia
| | - Shuenn-Yuh Lee
- Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan
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56
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Zarkogianni K, Litsa E, Mitsis K, Wu PY, Kaddi CD, Cheng CW, Wang MD, Nikita KS. A Review of Emerging Technologies for the Management of Diabetes Mellitus. IEEE Trans Biomed Eng 2015; 62:2735-49. [PMID: 26292334 PMCID: PMC5859570 DOI: 10.1109/tbme.2015.2470521] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making. METHODS A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels. RESULTS Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM. CONCLUSION Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care. SIGNIFICANCE The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.
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Affiliation(s)
| | | | | | | | | | | | - May D. Wang
- Contact information for the corresponding author: , Phone: 404-385-2954, Fax: 404-894-4243, Address: Suite 4106, UA Whitaker Building, 313 Ferst Drive, Atlanta, GA 30332, USA
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57
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De Paula M, Ávila LO, Martínez EC. Controlling blood glucose variability under uncertainty using reinforcement learning and Gaussian processes. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.06.041] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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58
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Dinani ST, Zekri M, Kamali M. Regulation of Blood Glucose Concentration in Type 1 Diabetics Using Single Order Sliding Mode Control Combined with Fuzzy On-line Tunable Gain, a Simulation Study. JOURNAL OF MEDICAL SIGNALS & SENSORS 2015; 5:131-40. [PMID: 26284169 PMCID: PMC4528351 DOI: 10.4103/2228-7477.161463] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2015] [Accepted: 05/04/2015] [Indexed: 11/23/2022]
Abstract
Diabetes is considered as a global affecting disease with an increasing contribution to both mortality rate and cost damage in the society. Therefore, tight control of blood glucose levels has gained significant attention over the decades. This paper proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on combining the fuzzy logic theory and single order sliding mode control (SOSMC) to improve the properties of sliding mode control method and to alleviate its drawbacks. The aim of the proposed controller that is called SOSMC combined with fuzzy on-line tunable gain is to tune the gain of the controller adaptively. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. As a result, this method can decline the risk of hypoglycemia, a lethal phenomenon in regulating blood glucose level in diabetics caused by a low blood glucose level. Moreover, it attenuates the chattering observed in SOSMC significantly. It is worth noting that in this approach, a mathematical model called minimal model is applied instead of the intravenously infused insulin–blood glucose dynamics. The simulation results demonstrate a good performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. In addition, this method is compared to fuzzy high-order sliding mode control (FHOSMC) and the superiority of the new method compared to FHOSMC is shown in the results.
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Affiliation(s)
- Soudabeh Taghian Dinani
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Maryam Zekri
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran ; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Marzieh Kamali
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
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59
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Lv D, Kulkarni SD, Chan A, Keith S, Pettis R, Kovatchev BP, Farhi LS, Breton MD. Pharmacokinetic Model of the Transport of Fast-Acting Insulin From the Subcutaneous and Intradermal Spaces to Blood. J Diabetes Sci Technol 2015; 9:831-40. [PMID: 25759184 PMCID: PMC4525663 DOI: 10.1177/1932296815573864] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Pharmacokinetic (PK) models describing the transport of insulin from the injection site to blood assist clinical decision making and are part of in silico platforms for developing and testing of insulin delivery strategies for treatment of patients with diabetes. The ability of these models to accurately describe all facets of the in vivo insulin transport is therefore critical for their application. Here, we propose a new model of fast-acting insulin analogs transport from the subcutaneous and intradermal spaces to blood that can accommodate clinically observed biphasic appearance and delayed clearance of injected insulin, 2 phenomena that are not captured by existing PK models. To develop the model we compare 9 insulin transport PK models which describe hypothetical insulin delivery pathways potentially capable of approximating biphasic appearance of exogenous insulin. The models are tested with respect to their ability to describe clinical data from 10 healthy volunteers which received 1 subcutaneous and 2 intradermal insulin injections on 3 different occasions. The optimal model, selected based on information and posterior identifiability criteria, assumes that insulin is delivered at the administrative site and is then transported to the bloodstream via 2 independent routes (1) diffusion-like process to the blood and (2) combination of diffusion-like processes followed by an additional compartment before entering the blood. This optimal model accounts for biphasic appearance and delayed clearance of exogenous insulin. It agrees better with the clinical data as compared to commonly used models and is expected to improve the in silico development and testing of insulin treatment strategies, including artificial pancreas systems.
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Affiliation(s)
- Dayu Lv
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Sandip D Kulkarni
- Department of Bioengineering, University of Maryland College Park, College Park, MD, USA
| | - Alice Chan
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Stephen Keith
- Beckton Dickinson Technologies, Research Triangle Park NC, USA
| | - Ron Pettis
- Beckton Dickinson Technologies, Research Triangle Park NC, USA
| | - Boris P Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Leon S Farhi
- Department of Medicine, Division of Endocrinology and Metabolism, University of Virginia, Charlottesville, VA, USA
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
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60
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Graham ML, Schuurman HJ. Validity of animal models of type 1 diabetes, and strategies to enhance their utility in translational research. Eur J Pharmacol 2015; 759:221-30. [DOI: 10.1016/j.ejphar.2015.02.054] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2014] [Revised: 01/15/2015] [Accepted: 02/09/2015] [Indexed: 01/22/2023]
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61
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Wang Q, Xie J, Molenaar P, Ulbrecht JS. Model Predictive Control for Type 1 Diabetes Based on Personalized Linear Time-Varying Subject Model Consisting of Both Insulin and Meal Inputs: An in Silico Evaluation. J Diabetes Sci Technol 2015; 9:941-2. [PMID: 25972282 PMCID: PMC4525652 DOI: 10.1177/1932296815586426] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Qian Wang
- Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA
| | - JinYu Xie
- Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA
| | - Peter Molenaar
- Department of Human Development and Family Studies, Pennsylvania State University, University Park, PA, USA
| | - Jan S Ulbrecht
- Departments of Biobehavioral Health and Medicine, Pennsylvania State University, University Park, PA, USA
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62
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Fang Q, Yu L, Li P. A new insulin-glucose metabolic model of type 1 diabetes mellitus: An in silico study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 120:16-26. [PMID: 25896293 DOI: 10.1016/j.cmpb.2015.03.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2014] [Revised: 03/12/2015] [Accepted: 03/30/2015] [Indexed: 06/04/2023]
Abstract
Diabetes mellitus is a serious metabolic disease that threatens people's health. The artificial pancreas system (APS) has been generally considered as the ultimate cure of type 1 diabetes mellitus (T1DM). The simulation model of insulin-glucose metabolism is an essential part of an APS as it processes the measured glucose level and generates control signal to the insulin infusion system. This paper presents a new insulin-glucose metabolic model using model reduction methods applied to the popular but complex Cobelli's model. The performances of three different model reduction methods, namely Padé approximation, Routh approximation and system identification, are compared. The results of in silico simulation based on 30 virtual patients of three groups for adults, adolescents, and children show that the approximation error between this new model and the original Cobelli's model is so small that can be neglected. It can be concluded that the proposed simplified model can describe the insulin-glucose metabolism process rather accurately as well as can be easily implemented and integrated into an APS to make the APS technology more mature and closer to clinical use. The FPGA implementation, testing and further simplification possibility will be explored in the next stage of research.
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Affiliation(s)
- Qiang Fang
- School of Electrical and Computing Engineering, RMIT University, Melbourne, VIC 3000, Australia.
| | - Lei Yu
- University of Chinese Academy of Sciences, Beijing, China
| | - Peng Li
- University of Chinese Academy of Sciences, Beijing, China
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63
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Thabit H, Hovorka R, Evans M. Artificial pancreas: the bridge to a cure for type 1 diabetes. ACTA ACUST UNITED AC 2015. [DOI: 10.1002/edn.207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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64
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León-Vargas F, Garelli F, De Battista H, Vehí J. Postprandial response improvement via safety layer in closed-loop blood glucose controllers. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2014.10.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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65
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A nonparametric approach for model individualization in an artificial pancreas∗∗This work was supported by ICT FP7-247138 Bringing the Artificial Pancreas at Home. (AP@home) project and the Fondo per gli Investimenti della Ricerca di Base project Artificial Pancreas:In Silico Development and In Vivo Validation of Algorithms forBlood Glucose Control funded by Italian Ministero dell'Istruzione,dell'Universit_a e della Ricerca. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.ifacol.2015.10.143] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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66
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Artificial Pancreas: from in-silico to in-vivo∗∗This work was supported by the Fondo per gli Investimenti della Ricerca di Base project Artificial Pancreas: In Silico Development and In Vivo Validation of Algorithms for Blood Glucose Control funded by Italian Ministero dell'Istruzione, dell'Universitä e della Ricerca. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.ifacol.2015.09.148] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Kotz K, Cinar A, Mei Y, Roggendorf A, Littlejohn E, Quinn L, Rollins DK. Multiple-Input Subject-Specific Modeling of Plasma Glucose Concentration for Feedforward Control. Ind Eng Chem Res 2014; 53:18216-18225. [PMID: 25620845 PMCID: PMC4299404 DOI: 10.1021/ie404119b] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2013] [Revised: 09/11/2014] [Accepted: 11/03/2014] [Indexed: 11/30/2022]
Abstract
The ability to accurately develop
subject-specific, input causation
models, for blood glucose concentration (BGC) for large input sets
can have a significant impact on tightening control for insulin dependent
diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead
to an effective artificial pancreas (i.e., an automatic control system
that delivers exogenous insulin) under extreme changes in critical
disturbances. These disturbances include food consumption, activity
variations, and physiological stress changes. Thus, this paper presents
a free-living, outpatient, multiple-input, modeling method for BGC
with strong causation attributes that is stable and guards against
overfitting to provide an effective modeling approach for feedforward
control (FFC). This approach is a Wiener block-oriented methodology,
which has unique attributes for meeting critical requirements for
effective, long-term, FFC.
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Affiliation(s)
- Kaylee Kotz
- Department of Chemical and Biological Engineering, Iowa State University , Ames, Iowa 50011, United States
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology , Chicago, Illinois 60616, United States
| | - Yong Mei
- Department of Chemical and Biological Engineering, Iowa State University , Ames, Iowa 50011, United States
| | - Amy Roggendorf
- Department of Chemical and Biological Engineering, Iowa State University , Ames, Iowa 50011, United States
| | - Elizabeth Littlejohn
- Institute for Endocrine Discovery and Clinical Care, University of Chicago Medicine , Chicago, Illinois 60637, United States
| | - Laurie Quinn
- College of Nursing, University of Illinois at Chicago , Chicago, Illinois 60607, United States
| | - Derrick K Rollins
- Department of Chemical and Biological Engineering, Iowa State University , Ames, Iowa 50011, United States ; Department of Statistics, Iowa State University , Ames, Iowa 50011, United States
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68
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Zisser H, Renard E, Kovatchev B, Cobelli C, Avogaro A, Nimri R, Magni L, Buckingham BA, Chase HP, Doyle FJ, Lum J, Calhoun P, Kollman C, Dassau E, Farret A, Place J, Breton M, Anderson SM, Dalla Man C, Del Favero S, Bruttomesso D, Filippi A, Scotton R, Phillip M, Atlas E, Muller I, Miller S, Toffanin C, Raimondo DM, De Nicolao G, Beck RW. Multicenter closed-loop insulin delivery study points to challenges for keeping blood glucose in a safe range by a control algorithm in adults and adolescents with type 1 diabetes from various sites. Diabetes Technol Ther 2014; 16:613-22. [PMID: 25003311 PMCID: PMC4183913 DOI: 10.1089/dia.2014.0066] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND The Control to Range Study was a multinational artificial pancreas study designed to assess the time spent in the hypo- and hyperglycemic ranges in adults and adolescents with type 1 diabetes while under closed-loop control. The controller attempted to keep the glucose ranges between 70 and 180 mg/dL. A set of prespecified metrics was used to measure safety. RESEARCH DESIGN AND METHODS We studied 53 individuals for approximately 22 h each during clinical research center admissions. Plasma glucose level was measured every 15-30 min (YSI clinical laboratory analyzer instrument [YSI, Inc., Yellow Springs, OH]). During the admission, subjects received three mixed meals (1 g of carbohydrate/kg of body weight; 100 g maximum) with meal announcement and automated insulin dosing by the controller. RESULTS For adults, the mean of subjects' mean glucose levels was 159 mg/dL, and mean percentage of values 71-180 mg/dL was 66% overall (59% daytime and 82% overnight). For adolescents, the mean of subjects' mean glucose levels was 166 mg/dL, and mean percentage of values in range was 62% overall (53% daytime and 82% overnight). Whereas prespecified criteria for safety were satisfied by both groups, they were met at the individual level in adults only for combined daytime/nighttime and for isolated nighttime. Two adults and six adolescents failed to meet the daytime criterion, largely because of postmeal hyperglycemia, and another adolescent failed to meet the nighttime criterion. CONCLUSIONS The control-to-range system performed as expected: faring better overnight than during the day and performing with variability between patients even after individualization based on patients' prior settings. The system had difficulty preventing postmeal excursions above target range.
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Affiliation(s)
- Howard Zisser
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Eric Renard
- Montpellier University Hospital, Department of Endocrinology, Diabetes, Nutrition and INSERM 1411 Clinical Investigation Center, Institute of Functional Genomics, UMR CNRS 5203/INSERM U661, University of Montpellier, Montpellier, France
| | | | | | | | - Revital Nimri
- Jesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | | | | | - H. Peter Chase
- Barbara Davis Center for Childhood Diabetes, Aurora, Colorado
| | - Francis J. Doyle
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, California
| | - John Lum
- Jaeb Center for Health Research, Tampa, Florida
| | | | | | - Eyal Dassau
- Sansum Diabetes Research Institute, Santa Barbara, California
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, California
| | - Anne Farret
- Montpellier University Hospital, Department of Endocrinology, Diabetes, Nutrition and INSERM 1411 Clinical Investigation Center, Institute of Functional Genomics, UMR CNRS 5203/INSERM U661, University of Montpellier, Montpellier, France
| | - Jerome Place
- Montpellier University Hospital, Department of Endocrinology, Diabetes, Nutrition and INSERM 1411 Clinical Investigation Center, Institute of Functional Genomics, UMR CNRS 5203/INSERM U661, University of Montpellier, Montpellier, France
| | - Marc Breton
- University of Virginia, Charlottesville, Virginia
| | | | | | | | | | | | | | - Moshe Phillip
- Jesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Eran Atlas
- Jesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Ido Muller
- Jesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Shahar Miller
- Jesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | | | | | | | - Roy W. Beck
- Jaeb Center for Health Research, Tampa, Florida
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Chase HP, Doyle FJ, Zisser H, Renard E, Nimri R, Cobelli C, Buckingham BA, Maahs DM, Anderson S, Magni L, Lum J, Calhoun P, Kollman C, Beck RW. Multicenter closed-loop/hybrid meal bolus insulin delivery with type 1 diabetes. Diabetes Technol Ther 2014; 16:623-32. [PMID: 25188375 PMCID: PMC4183919 DOI: 10.1089/dia.2014.0050] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND This study evaluated meal bolus insulin delivery strategies and associated postprandial glucose control while using an artificial pancreas (AP) system. SUBJECTS AND METHODS This study was a multicenter trial in 53 patients, 12-65 years of age, with type 1 diabetes for at least 1 year and use of continuous subcutaneous insulin infusion for at least 6 months. Four different insulin bolus strategies were assessed: standard bolus delivered with meal (n=51), standard bolus delivered 15 min prior to meal (n=40), over-bolus of 30% delivered with meal (n=40), and bolus purposely omitted (n=46). Meal carbohydrate (CHO) intake was 1 g of CHO/kg of body weight up to a maximum of 100 g for the first three strategies or up to a maximum of 50 g for strategy 4. RESULTS Only three of 177 meals (two with over-bolus and one with standard bolus 15 min prior to meal) had postprandial blood glucose values of <60 mg/dL. Postprandial hyperglycemia (blood glucose level >180 mg/dL) was prolonged for all four bolus strategies but was shorter for the over-bolus (41% of the 4-h period) than the two standard bolus strategies (73% for each). Mean postprandial blood glucose level was 15.9 mg/dL higher for the standard bolus with meal compared with the prebolus (baseline-adjusted, P=0.07 for treatment effect over the 4-h period). CONCLUSIONS The AP handled the four bolus situations safely, but at the expense of having elevated postprandial glucose levels in most subjects. This was most likely secondary to suboptimal performance of the algorithm.
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Affiliation(s)
| | - Francis J. Doyle
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, California
| | - Howard Zisser
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Eric Renard
- Montpellier University Hospital, Department of Endocrinology, Diabetes, Nutrition and INSERM 1001 Clinical Investigation Center, the Institute of Functional Genomics, UMR CNRS 5203/INSERM U661, University of Montpellier, France
| | - Revital Nimri
- Jesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel and Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | | | | | | | | | | | - John Lum
- Jaeb Center for Health Research, Tampa, Florida
| | | | | | - Roy W. Beck
- Jaeb Center for Health Research, Tampa, Florida
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70
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Sopasakis P, Patrinos P, Sarimveis H. Robust model predictive control for optimal continuous drug administration. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 116:193-204. [PMID: 24986530 DOI: 10.1016/j.cmpb.2014.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2013] [Revised: 06/05/2014] [Accepted: 06/06/2014] [Indexed: 06/03/2023]
Abstract
In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements.
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Affiliation(s)
- Pantelis Sopasakis
- School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografou Campus, 15780 Athens, Greece; IMT Institute for Advanced Studies Lucca, Piazza San Ponziano 6, 55100 Lucca, Italy
| | - Panagiotis Patrinos
- IMT Institute for Advanced Studies Lucca, Piazza San Ponziano 6, 55100 Lucca, Italy
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografou Campus, 15780 Athens, Greece.
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71
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Fravolini ML, Fabietti PG. An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes. Comput Methods Biomech Biomed Engin 2014; 17:1464-82. [PMID: 23282162 DOI: 10.1080/10255842.2012.753064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.
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Affiliation(s)
- M L Fravolini
- a Department of Electronic and Information Engineering , University of Perugia , Via G. Duranti No. 93, 06125 Perugia , Italy
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72
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Liu SW, Huang HP, Lin CH, Chien IL. Modified control algorithms for patients with type 1 diabetes mellitus undergoing exercise. J Taiwan Inst Chem Eng 2014. [DOI: 10.1016/j.jtice.2014.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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73
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Sha S, Devineni D, Ghosh A, Polidori D, Hompesch M, Arnolds S, Morrow L, Spitzer H, Demarest K, Rothenberg P. Pharmacodynamic effects of canagliflozin, a sodium glucose co-transporter 2 inhibitor, from a randomized study in patients with type 2 diabetes. PLoS One 2014; 9:e105638. [PMID: 25166023 PMCID: PMC4148334 DOI: 10.1371/journal.pone.0105638] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Accepted: 07/21/2014] [Indexed: 02/06/2023] Open
Abstract
Introduction This randomized, double-blind, placebo-controlled, single and multiple ascending-dose study evaluated the pharmacodynamic effects and safety/tolerability of canagliflozin, a sodium glucose co-transporter 2 inhibitor, in patients with type 2 diabetes. Methods Patients (N = 116) discontinued their antihyperglycemic medications 2 weeks before randomization. Patients received canagliflozin 30, 100, 200, or 400 mg once daily or 300 mg twice daily, or placebo at 2 study centers in the United States and Germany, or canagliflozin 30 mg once daily or placebo at 1 study center in Korea, while maintaining an isocaloric diet for 2 weeks. On Days –1, 1, and 16, urinary glucose excretion (UGE), plasma glucose (PG), fasting PG (FPG), and insulin were measured. The renal threshold for glucose (RTG) was calculated from UGE, PG, and estimated glomerular filtration rate. Safety was evaluated based on adverse event (AE) reports, vital signs, electrocardiograms, clinical laboratory tests, and physical examinations. Results Canagliflozin increased UGE dose-dependently (∼80–120 g/day with canagliflozin ≥100 mg), with increases maintained over the 14-day dosing period with each dose. Canagliflozin dose-dependently decreased RTG, with maximal reductions to ∼4–5 mM (72–90 mg/dL). Canagliflozin also reduced FPG and 24-hour mean PG; glucose reductions were seen on Day 1 and maintained over 2 weeks. Plasma insulin reductions with canagliflozin were consistent with observed PG reductions. Canagliflozin also reduced body weight. AEs were transient, mild to moderate in intensity, and balanced across groups; 1 canagliflozin-treated female reported an episode of vaginal candidiasis. Canagliflozin did not cause hypoglycemia, consistent with the RTG values remaining above the hypoglycemia threshold. At Day 16, there were no clinically meaningful changes in urine volume, urine electrolyte excretion, renal function, or routine laboratory test values. Conclusions Canagliflozin increased UGE and decreased RTG, leading to reductions in PG, insulin, and body weight, and was generally well tolerated in patients with type 2 diabetes. Trial Registration ClinicalTrials.gov NCT00963768
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Affiliation(s)
- Sue Sha
- Janssen Research & Development, LLC, Raritan, NJ, United States of America
- * E-mail:
| | | | - Atalanta Ghosh
- Janssen Research & Development, LLC, Raritan, NJ, United States of America
| | - David Polidori
- Janssen Research & Development, LLC, San Diego, CA, United States of America
| | - Marcus Hompesch
- Profil Institute for Clinical Research, Chula Vista, CA, United States of America
| | - Sabine Arnolds
- Profil Institut für Stoffwechselforschung GmbH, Neuss, Germany
| | - Linda Morrow
- Profil Institute for Clinical Research, Chula Vista, CA, United States of America
| | - Heike Spitzer
- Profil Institut für Stoffwechselforschung GmbH, Neuss, Germany
| | - Keith Demarest
- Janssen Research & Development, LLC, Raritan, NJ, United States of America
| | - Paul Rothenberg
- Janssen Research & Development, LLC, Raritan, NJ, United States of America
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74
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Turksoy K, Quinn L, Littlejohn E, Cinar A. Multivariable adaptive identification and control for artificial pancreas systems. IEEE Trans Biomed Eng 2014; 61:883-91. [PMID: 24557689 DOI: 10.1109/tbme.2013.2291777] [Citation(s) in RCA: 129] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A constrained weighted recursive least squares method is proposed to provide recursive models with guaranteed stability and better performance than models based on regular identification methods in predicting the variations of blood glucose concentration in patients with Type 1 Diabetes. Use of physiological information from a sports armband improves glucose concentration prediction and enables earlier recognition of the effects of physical activity on glucose concentration. Generalized predictive controllers (GPC) based on these recursive models are developed. The performance of GPC for artificial pancreas systems is illustrated by simulations with UVa-Padova simulator and clinical studies. The controllers developed are good candidates for artificial pancreas systems with no announcements from patients.
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75
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Wang Y, Xie H, Jiang X, Liu B. Intelligent closed-loop insulin delivery systems for ICU patients. IEEE J Biomed Health Inform 2014; 18:290-9. [PMID: 24403427 DOI: 10.1109/jbhi.2013.2269699] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Good glycemic control through insulin administration among intensive care unit (ICU) patients can reduce mortality significantly; however, it remains a big challenge because of scarcity of individualized models for ICU patients. To deal with this challenge, a new combination of particle swarm optimization (PSO) and model predictive control (MPC) has been proposed to identify the model online as well as to optimally design the input, i.e., the insulin delivery rate automatically. According to the population distribution, ten typical linear dynamic models were selected such that any patient's model could be approximated by a linear combination of these ten typical models. PSO was used to update the weight coefficients while MPC was used to design the insulin delivery rate based on the combination model identified by using PSO. The proposed strategy was compared with the Yale protocol on 30 virtual subjects. According to the control-variability grid analysis, the percentage values in A + B zone were, respectively, 100% under the proposed strategy and while 51% under the Yale protocol, which demonstrates the superior performance of the proposed strategy. As a good candidate for the full closed-loop insulin delivery method, this new combination can control the glucose level by bringing it to a safe range promptly thereby reducing the risk of death.
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76
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Gondhalekar R, Dassau E, Doyle FJ. State Estimation with Sensor Recalibrations and Asynchronous Measurements for MPC of an Artificial Pancreas to Treat T1DM. PROCEEDINGS OF THE IFAC WORLD CONGRESS. INTERNATIONAL FEDERATION OF AUTOMATIC CONTROL. WORLD CONGRESS 2014; 47:224-230. [PMID: 28580460 PMCID: PMC5451162 DOI: 10.3182/20140824-6-za-1003.01085] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
A novel state estimation scheme is proposed for use in Model Predictive Control (MPC) of an artificial pancreas based on Continuous Glucose Monitor (CGM) feedback, for treating type 1 diabetes mellitus. The performance of MPC strategies heavily depends on the initial condition of the predictions, typically characterized by a state estimator. Commonly employed Luenberger-observers and Kalman-filters are effective much of the time, but suffer limitations. Three particular limitations are tackled by the proposed approach. First, CGM recalibrations, step changes that cause highly dynamic responses in recursive state estimators, are accommodated in a graceful manner. Second, the proposed strategy is not affected by CGM measurements that are asynchronous, i.e., neither of fixed sample-period, nor of a sample-period that is equal to the controller's. Third, the proposal suffers no offsets due to plant-model mismatches. The proposed approach is based on moving-horizon optimization.
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Affiliation(s)
- Ravi Gondhalekar
- Dept. Chemical Engineering, University of California Santa Barbara (UCSB), USA
| | - Eyal Dassau
- Dept. Chemical Engineering, University of California Santa Barbara (UCSB), USA
| | - Francis J Doyle
- Dept. Chemical Engineering, University of California Santa Barbara (UCSB), USA
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77
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Greenwood NJC, Gunton JE. A computational proof of concept of a machine-intelligent artificial pancreas using Lyapunov stability and differential game theory. J Diabetes Sci Technol 2014; 8:791-806. [PMID: 25562888 PMCID: PMC4764243 DOI: 10.1177/1932296814536271] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND This study demonstrated the novel application of a "machine-intelligent" mathematical structure, combining differential game theory and Lyapunov-based control theory, to the artificial pancreas to handle dynamic uncertainties. METHODS Realistic type 1 diabetes (T1D) models from the literature were combined into a composite system. Using a mixture of "black box" simulations and actual data from diabetic medical histories, realistic sets of diabetic time series were constructed for blood glucose (BG), interstitial fluid glucose, infused insulin, meal estimates, and sometimes plasma insulin assays. The problem of underdetermined parameters was side stepped by applying a variant of a genetic algorithm to partial information, whereby multiple candidate-personalized models were constructed and then rigorously tested using further data. These formed a "dynamic envelope" of trajectories in state space, where each trajectory was generated by a hypothesis on the hidden T1D system dynamics. This dynamic envelope was then culled to a reduced form to cover observed dynamic behavior. A machine-intelligent autonomous algorithm then implemented game theory to construct real-time insulin infusion strategies, based on the flow of these trajectories through state space and their interactions with hypoglycemic or near-hyperglycemic states. RESULTS This technique was tested on 2 simulated participants over a total of fifty-five 24-hour days, with no hypoglycemic or hyperglycemic events, despite significant uncertainties from using actual diabetic meal histories with 10-minute warnings. In the main case studies, BG was steered within the desired target set for 99.8% of a 16-hour daily assessment period. Tests confirmed algorithm robustness for ±25% carbohydrate error. For over 99% of the overall 55-day simulation period, either formal controller stability was achieved to the desired target or else the trajectory was within the desired target. CONCLUSIONS These results suggest that this is a stable, high-confidence way to generate closed-loop insulin infusion strategies.
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Affiliation(s)
- Nigel J C Greenwood
- School of Mathematics and Physics, University of Queensland, Brisbane, Australia Neuromathix, NeuroTech Research Pty Ltd
| | - Jenny E Gunton
- Westmead Clinical School, University of Sydney, Sydney, Australia Diabetes and Transcription Factors Group, Garvan Institute of Medical Research, Darlinghurst, Australia St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Kensington, Australia Diabetes and Endocrinology, Westmead Hospital, Sydney, Australia
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Jacobs PG, El Youssef J, Castle J, Bakhtiani P, Branigan D, Breen M, Bauer D, Preiser N, Leonard G, Stonex T, Ward WK. Automated control of an adaptive bihormonal, dual-sensor artificial pancreas and evaluation during inpatient studies. IEEE Trans Biomed Eng 2014; 61:2569-81. [PMID: 24835122 DOI: 10.1109/tbme.2014.2323248] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Automated control of blood glucose in patients with type-1 diabetes has not yet been fully implemented. The aim of this study was to design and clinically evaluate a system that integrates a control algorithm with off-the-shelf subcutaneous sensors and pumps to automate the delivery of the hormones glucagon and insulin in response to continuous glucose sensor measurements. The automated component of the system runs an adaptive proportional derivative control algorithm which determines hormone delivery rates based on the sensed glucose measurements and the meal announcements by the patient. We provide details about the system design and the control algorithm, which incorporates both a fading memory proportional derivative controller (FMPD) and an adaptive system for estimating changing sensitivity to insulin based on a glucoregulatory model of insulin action. For an inpatient study carried out in eight subjects using Dexcom SEVEN PLUS sensors, prestudy HbA1c averaged 7.6, which translates to an estimated average glucose of 171 mg/dL. In contrast, during use of the automated system, after initial stabilization, glucose averaged 145 mg/dL and subjects were kept within the euglycemic range (between 70 and 180 mg/dL) for 73.1% of the time, indicating improved glycemic control. A further study on five additional subjects in which we used a newer and more reliable glucose sensor (Dexcom G4 PLATINUM) and made improvements to the insulin and glucagon pump communication system resulted in elimination of hypoglycemic events. For this G4 study, the system was able to maintain subjects' glucose levels within the near-euglycemic range for 71.6% of the study duration and the mean venous glucose level was 151 mg/dL.
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79
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Ghorbani M, Bogdan P. Reducing risk of closed loop control of blood glucose in artificial pancreas using fractional calculus. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2014; 2014:4839-4842. [PMID: 25571075 DOI: 10.1109/embc.2014.6944707] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Healthcare costs in the US are among the highest in the world. Chronic diseases such as diabetes significantly contribute to these extensive costs. Despite technological advances to improve sensing and actuation devices, we still lack a coherent theory that facilitates the design and optimization of efficient and robust medical cyber-physical systems for managing chronic diseases. In this paper, we propose a mathematical model for capturing the complex dynamics of blood glucose time series (e.g., time dependent and fractal behavior) observed in real world measurements via fractional calculus concepts. Building upon our time dependent fractal model, we propose a novel model predictive controller for an artificial pancreas that regulates insulin injection. We verify the accuracy of our controller by comparing it to conventional non-fractal models using real world measurements and show how the nonlinear optimal controller based on fractal calculus concepts is superior to non-fractal controllers in terms of average risk index and prediction accuracy.
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80
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Tárník M, Miklovičová E, Murgaš J, Ottinger I, Ludwig T. Model Reference Adaptive Control of Glucose in Type 1 Diabetics: A Simulation Study. ACTA ACUST UNITED AC 2014. [DOI: 10.3182/20140824-6-za-1003.00321] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Del Favero S, Bruttomesso D, Di Palma F, Lanzola G, Visentin R, Filippi A, Scotton R, Toffanin C, Messori M, Scarpellini S, Keith-Hynes P, Kovatchev BP, Devries JH, Renard E, Magni L, Avogaro A, Cobelli C. First use of model predictive control in outpatient wearable artificial pancreas. Diabetes Care 2014; 37:1212-5. [PMID: 24757228 DOI: 10.2337/dc13-1631] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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.
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Hashimoto S, Noguchi CCY, Furutani E. Postprandial blood glucose control in type 1 diabetes for carbohydrates with varying glycemic index foods. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2014; 2014:4835-4838. [PMID: 25571074 DOI: 10.1109/embc.2014.6944706] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Treatment of type 1 diabetes consists of maintaining postprandial normoglycemia using the correct prandial insulin dose according to food intake. Nonetheless, it is hardly achieved in practice, which results in several diabetes-related complications. In this study we present a feedforward plus feedback blood glucose control system that considers the glycemic index of foods. It consists of a preprandial insulin bolus whose optimal bolus dose and timing are stated as a minimization problem, which is followed by a postprandial closed-loop control based on model predictive control. Simulation results show that, for a representative carbohydrate intake of 50 g, the present control system is able to maintain postprandial glycemia below 140 mg/dL while preventing postprandial hypoglycemia as well.
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83
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Luijf YM, DeVries JH, Zwinderman K, Leelarathna L, Nodale M, Caldwell K, Kumareswaran K, Elleri D, Allen JM, Wilinska ME, Evans ML, Hovorka R, Doll W, Ellmerer M, Mader JK, Renard E, Place J, Farret A, Cobelli C, Del Favero S, Dalla Man C, Avogaro A, Bruttomesso D, Filippi A, Scotton R, Magni L, Lanzola G, Di Palma F, Soru P, Toffanin C, De Nicolao G, Arnolds S, Benesch C, Heinemann L. Day and night closed-loop control in adults with type 1 diabetes: a comparison of two closed-loop algorithms driving continuous subcutaneous insulin infusion versus patient self-management. Diabetes Care 2013; 36:3882-7. [PMID: 24170747 PMCID: PMC3836152 DOI: 10.2337/dc12-1956] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To compare two validated closed-loop (CL) algorithms versus patient self-control with CSII in terms of glycemic control. RESEARCH DESIGN AND METHODS This study was a multicenter, randomized, three-way crossover, open-label trial in 48 patients with type 1 diabetes mellitus for at least 6 months, treated with continuous subcutaneous insulin infusion. Blood glucose was controlled for 23 h by the algorithm of the Universities of Pavia and Padova with a Safety Supervision Module developed at the Universities of Virginia and California at Santa Barbara (international artificial pancreas [iAP]), by the algorithm of University of Cambridge (CAM), or by patients themselves in open loop (OL) during three hospital admissions including meals and exercise. The main analysis was on an intention-to-treat basis. Main outcome measures included time spent in target (glucose levels between 3.9 and 8.0 mmol/L or between 3.9 and 10.0 mmol/L after meals). RESULTS Time spent in the target range was similar in CL and OL: 62.6% for OL, 59.2% for iAP, and 58.3% for CAM. While mean glucose level was significantly lower in OL (7.19, 8.15, and 8.26 mmol/L, respectively) (overall P = 0.001), percentage of time spent in hypoglycemia (<3.9 mmol/L) was almost threefold reduced during CL (6.4%, 2.1%, and 2.0%) (overall P = 0.001) with less time ≤2.8 mmol/L (overall P = 0.038). There were no significant differences in outcomes between algorithms. CONCLUSIONS Both CAM and iAP algorithms provide safe glycemic control.
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84
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Steil GM. Algorithms for a closed-loop artificial pancreas: the case for proportional-integral-derivative control. J Diabetes Sci Technol 2013; 7:1621-31. [PMID: 24351189 PMCID: PMC3876341 DOI: 10.1177/193229681300700623] [Citation(s) in RCA: 85] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
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.
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Affiliation(s)
- Garry M Steil
- Children's Hospital Boston, 300 Longwood Ave., Boston, MA 02215. garry.steil@childrens/harvard.edu
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85
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Abstract
The relative merits of model predictive control (MPC) and proportional-integral-derivative (PID) control are discussed, with the end goal of a closed-loop artificial pancreas (AP). It is stressed that neither MPC nor PID are single algorithms, but rather are approaches or strategies that may be implemented very differently by different engineers. The primary advantages to MPC are that (i) constraints on the insulin delivery rate (and/or insulin on board) can be explicitly included in the control calculation; (ii) it is a general framework that makes it relatively easy to include the effect of meals, exercise, and other events that are a function of the time of day; and (iii) it is flexible enough to include many different objectives, from set-point tracking (target) to zone (control to range). In the end, however, it is recognized that the control algorithm, while important, represents only a portion of the effort required to develop a closed-loop AP. Thus, any number of algorithms/approaches can be successful--the engineers involved in the design must have experience with the particular technique, including the important experience of implementing the algorithm in human studies and not simply through simulation studies.
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Affiliation(s)
- B Wayne Bequette
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180-3590.
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Keith-Hynes P, Guerlain S, Mize B, Hughes-Karvetski C, Khan M, McElwee-Malloy M, Kovatchev BP. DiAs user interface: a patient-centric interface for mobile artificial pancreas systems. J Diabetes Sci Technol 2013; 7:1416-26. [PMID: 24351168 PMCID: PMC3876320 DOI: 10.1177/193229681300700602] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Recent in-hospital studies of artificial pancreas (AP) systems have shown promising results in improving glycemic control in patients with type 1 diabetes mellitus. The next logical step in AP development is to conduct transitional outpatient clinical trials with a mobile system that is controlled by the patient. In this article, we present the user interface (UI) of the Diabetes Assistant (DiAs), an experimental smartphone-based mobile AP system, and describe the reactions of a round of focus groups to the UI. This work is an initial inquiry involving a relatively small number of potential users, many of whom had never seen an AP system before, and the results should be understood in that light. METHODS We began by considering how the UI of an AP system could be designed to make use of the familiar touch-based graphical UI of a consumer smartphone. After developing a working prototype UI, we enlisted a human factors specialist to perform a heuristic expert analysis. Next we conducted a formative evaluation of the UI through a series of three focus groups with N = 13 potential end users as participants. The UI was modified based upon the results of these studies, and the resulting DiAs system was used in transitional outpatient AP studies of adults in the United States and Europe. RESULTS The DiAs UI was modified based on focus group feedback from potential users. The DiAs was subsequently used in JDRF- and AP@Home-sponsored transitional outpatient AP studies in the United States and Europe by 40 subjects for 2400 h with no adverse events. CONCLUSIONS Adult patients with type 1 diabetes mellitus are able to control an AP system successfully using a patient-centric UI on a commercial smartphone in a transitional outpatient environment.
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Affiliation(s)
- Patrick Keith-Hynes
- Center for Diabetes Technology Research, University of Virginia, 617 West Main St., 4th Floor, Charlottesville, VA 22903.
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87
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Toffanin C, Messori M, Palma FD, Nicolao GD, Cobelli C, Magni L. Artificial pancreas: model predictive control design from clinical experience. J Diabetes Sci Technol 2013; 7:1470-83. [PMID: 24351173 PMCID: PMC3876325 DOI: 10.1177/193229681300700607] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Chiara Toffanin
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Mirko Messori
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Federico Di Palma
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Giuseppe De Nicolao
- Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Lalo Magni
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
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88
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Place J, Robert A, Ben Brahim N, Keith-Hynes P, Farret A, Pelletier MJ, Buckingham B, Breton M, Kovatchev B, Renard E. DiAs web monitoring: a real-time remote monitoring system designed for artificial pancreas outpatient trials. J Diabetes Sci Technol 2013; 7:1427-35. [PMID: 24351169 PMCID: PMC3876321 DOI: 10.1177/193229681300700603] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Developments in an artificial pancreas (AP) for patients with type 1 diabetes have allowed a move toward performing outpatient clinical trials. "Home-like" environment implies specific protocol and system adaptations among which the introduction of remote monitoring is meaningful. We present a novel tool allowing multiple patients to monitor AP use in home-like settings. METHODS We investigated existing systems, performed interviews of experienced clinical teams, listed required features, and drew several mockups of the user interface. The resulting application was tested on the bench before it was used in three outpatient studies representing 3480 h of remote monitoring. RESULTS Our tool, called DiAs Web Monitoring (DWM), is a web-based application that ensures reception, storage, and display of data sent by AP systems. Continuous glucose monitoring (CGM) and insulin delivery data are presented in a colored chart to facilitate reading and interpretation. Several subjects can be monitored simultaneously on the same screen, and alerts are triggered to help detect events such as hypoglycemia or CGM failures. In the third trial, DWM received approximately 460 data per subject per hour: 77% for log messages, 5% for CGM data. More than 97% of transmissions were achieved in less than 5 min. CONCLUSIONS Transition from a hospital setting to home-like conditions requires specific AP supervision to which remote monitoring systems can contribute valuably. DiAs Web Monitoring worked properly when tested in our outpatient studies. It could facilitate subject monitoring and even accelerate medical and technical assessment of the AP. It should now be adapted for long-term studies with an enhanced notification feature.
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Affiliation(s)
- Jérôme Place
- Département d'Endocrinologie, Diabète et Nutrition, Hôpital Lapeyronie, 371 Avenue du Doyen Gaston Giraud, 34295 Montpellier, France.
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89
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Schaller S, Willmann S, Lippert J, Schaupp L, Pieber TR, Schuppert A, Eissing T. A Generic Integrated Physiologically based Whole-body Model of the Glucose-Insulin-Glucagon Regulatory System. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2013; 2:e65. [PMID: 23945606 PMCID: PMC3828004 DOI: 10.1038/psp.2013.40] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2013] [Accepted: 06/03/2013] [Indexed: 11/23/2022]
Abstract
Models of glucose metabolism are a valuable tool for fundamental and applied medical research in diabetes. Use cases range from pharmaceutical target selection to automatic blood glucose control. Standard compartmental models represent little biological detail, which hampers the integration of multiscale data and confines predictive capabilities. We developed a detailed, generic physiologically based whole-body model of the glucose-insulin-glucagon regulatory system, reflecting detailed physiological properties of healthy populations and type 1 diabetes individuals expressed in the respective parameterizations. The model features a detailed representation of absorption models for oral glucose, subcutaneous insulin and glucagon, and an insulin receptor model relating pharmacokinetic properties to pharmacodynamic effects. Model development and validation is based on literature data. The quality of predictions is high and captures relevant observed inter- and intra-individual variability. In the generic form, the model can be applied to the development and validation of novel diabetes treatment strategies.
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Affiliation(s)
- S Schaller
- 1] Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, Germany [2] Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen, Aachen, Germany
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90
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Schnell O, Erbach M, Wintergerst E. Higher accuracy of self-monitoring of blood glucose in insulin-treated patients in Germany: clinical and economical aspects. J Diabetes Sci Technol 2013; 7:904-12. [PMID: 23911172 PMCID: PMC3879755 DOI: 10.1177/193229681300700413] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND Accuracy standards of blood glucose (BG) meters are currently under review. Revised standards are expected to tighten accuracy requirements. Regarding clinical and financial impact of BG meter accuracy, very little data are available. The aim of this study was to analyze potential cost savings related to higher accuracy of glucose meters in Germany. METHODS As a model for calculation, a reduction of meter error from 20% to 5% was applied. The health economic analysis was based on four main pillars: (1) number of insulin-treated patients; (2) costs for glucose monitoring in Germany; (3) data of a modeling analysis on the impact on hypoglycemic episodes, glycosylated hemoglobin (HbA1c), and, subsequently, myocardial infarctions; and (4) costs of diabetes-related complications in Germany. A reduction of meter error from 20% to 5% was identified to be associated with a 10% reduction in severe hypoglycemic episodes and a 0.39% reduction in HbA1c, which translates into a 0.5% reduction of myocardial infarctions. RESULTS According to the health economic analysis, the reduction in severe hypoglycemic episodes and myocardial infarctions led to cost savings of €24.14 per patient per year. Considering 390,000 type 1 diabetes patients or 2.3 million insulin-treated patients in Germany, these savings could be equal to a reduction in health care expenditures of more than €9.4 million and €55.5 million, respectively. CONCLUSIONS Potential cost savings and clinical effects due to higher accuracy of BG meters should provide an impetus to implementation of tighter accuracy standards and development of glucose meters that provide highest possible accuracy.
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Affiliation(s)
- Oliver Schnell
- Forschergruppe Diabetes e.V., Helmholtz Center Munich, Munich-Neuherberg, Germany.
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91
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McCall AL, Farhy LS. Treating type 1 diabetes: from strategies for insulin delivery to dual hormonal control. MINERVA ENDOCRINOL 2013; 38:145-63. [PMID: 23732369 PMCID: PMC4220674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Type 1 diabetes is a disorder where slow destruction of pancreatic β-cells occurs through autoimmune mechanisms. The result is a progressive and ultimately complete lack of endogenous insulin. Due to β-cell lack, secondary abnormalities in glucagon and likely in incretins occur. These multiple hormonal abnormalities cause metabolic instability and extreme glycemic variability, which is the primary phenotype. As the disease progresses patients often develop hypoglycemia unawareness and defects in their counterregulatory defenses. Intensive insulin therapy may thus lead to 3-fold excess of severe hypoglycemia and severely hinder the effective and safe control of hyperglycemia. The main goal of the therapy for type 1 diabetes has long been physiological mimicry of normal insulin secretion based on monitoring which requires considerable effort and understanding of the underlying physiology. Attainment of this goal is challenged by the nature of the disease and our current lack of means to fully repair the abnormal endocrine pancreas interactive functions. As a result, various insulin preparations have been developed to partially compensate for the inability to deliver timely exogenous insulin directly to the portal/intrapancreatic circulation. It remains an ongoing task to identify the ideal routes and regimens of their delivery and potentially that of other hormones to restore the deficient and disordered hormonal environment of the pancreas to achieve a near normal metabolic state. Several recent technological advances help addressing these goals, including the rapid progress in insulin pumps, continuous glucose sensors, and ultimately the artificial pancreas closed-loop technology and the recent start of dual-hormone therapies.
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Affiliation(s)
- A L McCall
- Department of Medicine and Center for Diabetes Technology, University of Virginia, Charlottesville, VA 22908, USA.
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92
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Polidori D, Sha S, Ghosh A, Plum-Mörschel L, Heise T, Rothenberg P. Validation of a novel method for determining the renal threshold for glucose excretion in untreated and canagliflozin-treated subjects with type 2 diabetes mellitus. J Clin Endocrinol Metab 2013; 98:E867-71. [PMID: 23585665 PMCID: PMC3706739 DOI: 10.1210/jc.2012-4205] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
CONTEXT The stepwise hyperglycemic clamp procedure (SHCP) is the gold standard for measuring the renal threshold for glucose excretion (RT(G)), but its use is limited to small studies in specialized laboratories. OBJECTIVE The objective of the study was to validate a new method for determining RT(G) using data obtained during a mixed-meal tolerance test (MMTT) in untreated and canagliflozin-treated subjects with type 2 diabetes mellitus (T2DM). DESIGN This was an open-label study with 2 sequential parts. SETTING The study was performed at a single center in Germany. PATIENTS Twenty-eight subjects with T2DM were studied. INTERVENTIONS No treatment intervention was given in part 1. In part 2, subjects were treated with canagliflozin 100 mg/d for 8 days. In each part, subjects underwent an MMTT and a 5-step SHCP on consecutive days. MAIN OUTCOME MEASURES For both methods, RT(G) was estimated using measured blood glucose (BG) and urinary glucose excretion (UGE); estimated glomerular filtration rates were also used to determine RT(G) during the MMTT. The methods were compared using the concordance correlation coefficient and geometric mean ratios. RESULTS In untreated and canagliflozin-treated subjects, the relationship between UGE rate and BG was well described by a threshold relationship. Good agreement was obtained between the MMTT-based and SHCP-derived RT(G) values. The concordance correlation coefficient (for all subjects) was 0.94; geometric mean ratios (90% confidence intervals) for RT(G) values (MMTT/SHCP) were 0.93 (0.89-0.96) in untreated subjects and 1.03 (0.78-1.37) in canagliflozin-treated subjects. Study procedures and treatments were generally well tolerated in untreated and canagliflozin-treated subjects. CONCLUSIONS In both untreated and canagliflozin-treated subjects with T2DM, RT(G) can be accurately estimated from measured BG, UGE, and estimated glomerular filtration rates using an MMTT-based method.
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Affiliation(s)
- David Polidori
- Janssen Research & Development, LLC, 3210 Merryfield Row, San Diego, CA 92121, USA.
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93
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Abbes IB, Richard PY, Lefebvre MA, Guilhem I, Poirier JY. A closed-loop artificial pancreas using a proportional integral derivative with double phase lead controller based on a new nonlinear model of glucose metabolism. J Diabetes Sci Technol 2013; 7:699-707. [PMID: 23759403 PMCID: PMC3869138 DOI: 10.1177/193229681300700315] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
BACKGROUND Most closed-loop insulin delivery systems rely on model-based controllers to control the blood glucose (BG) level. Simple models of glucose metabolism, which allow easy design of the control law, are limited in their parametric identification from raw data. New control models and controllers issued from them are needed. METHODS A proportional integral derivative with double phase lead controller was proposed. Its design was based on a linearization of a new nonlinear control model of the glucose-insulin system in type 1 diabetes mellitus (T1DM) patients validated with the University of Virginia/Padova T1DM metabolic simulator. A 36 h scenario, including six unannounced meals, was tested in nine virtual adults. A previous trial database has been used to compare the performance of our controller with their previous results. The scenario was repeated 25 times for each adult in order to take continuous glucose monitoring noise into account. The primary outcome was the time BG levels were in target (70-180 mg/dl). RESULTS Blood glucose values were in the target range for 77% of the time and below 50 mg/dl and above 250 mg/dl for 0.8% and 0.3% of the time, respectively. The low blood glucose index and high blood glucose index were 1.65 and 3.33, respectively. CONCLUSION The linear controller presented, based on the linearization of a new easily identifiable nonlinear model, achieves good glucose control with low exposure to hypoglycemia and hyperglycemia.
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Affiliation(s)
- Ilham Ben Abbes
- Supelec/I.E.T.R., Avenue de la Boulaie, Cesson-Sévigné Cedex, France.
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94
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Abstract
Bioresponsive hydrogels are emerging with technological significance in targeted drug delivery, biosensors, and regenerative medicine. Their ability to respond to specific biologically derived stimuli creates a design challenge in effectively linking the conferred biospecificity with an engineered response tailored to the needs of a particular application. Moreover, the fundamental phenomena governing the response must support an appropriate dynamic range, limit of detection, and the potential for feedback control. The design of these systems is inherently complicated due to the high interdependency of the governing phenomena that guide sensing, transduction, and actuation of the hydrogel. Future advancements in bioresponsive hydrogels will out of necessity contain control loops similar to synthetic metabolic pathways. The use of these materials will continue to expand as they become coupled and integrated with new technologies.
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95
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Blood glucose control algorithms for type 1 diabetic patients: A methodological review. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2012.09.003] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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96
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Revert A, Garelli F, Pico J, De Battista H, Rossetti P, Vehi J, Bondia J. Safety auxiliary feedback element for the artificial pancreas in type 1 diabetes. IEEE Trans Biomed Eng 2013; 60:2113-22. [PMID: 23428611 DOI: 10.1109/tbme.2013.2247602] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The artificial pancreas aims at the automatic delivery of insulin for glycemic control in patients with type 1 diabetes, i.e., closed-loop glucose control. One of the challenges of the artificial pancreas is to avoid controller overreaction leading to hypoglycemia, especially in the late postprandial period. In this study, an original proposal based on sliding mode reference conditioning ideas is presented as a way to reduce hypoglycemia events induced by a closed-loop glucose controller. The method is inspired in the intuitive advantages of two-step constrained control algorithms. It acts on the glucose reference sent to the main controller shaping it so as to avoid violating given constraints on the insulin-on-board. Some distinctive features of the proposed strategy are that 1) it provides a safety layer which can be adjusted according to medical criteria; 2) it can be added to closed-loop controllers of any nature; 3) it is robust against sensor failures and overestimated prandial insulin doses; and 4) it can handle nonlinear models. The method is evaluated in silico with the ten adult patients available in the FDA-accepted UVA simulator.
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Affiliation(s)
- A Revert
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia 46022, Spain.
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97
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Liu SW, Huang HP, Lin CH, Chien IL. Fuzzy-Logic-Based Supervisor of Insulin Bolus Delivery for Patients with Type 1 Diabetes Mellitus. Ind Eng Chem Res 2013. [DOI: 10.1021/ie301621u] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Shih-Wei Liu
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Hsiao-Ping Huang
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Chia-Hung Lin
- Division of Endocrinology and
Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan
| | - I-Lung Chien
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
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98
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Bequette BW. Challenges and Recent Progress in the Development of a Closed-loop Artificial Pancreas. ANNUAL REVIEWS IN CONTROL 2012; 36:255-266. [PMID: 23175620 PMCID: PMC3501007 DOI: 10.1016/j.arcontrol.2012.09.007] [Citation(s) in RCA: 85] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Pursuit of a closed-loop artificial pancreas that automatically controls the blood glucose of individuals with type 1 diabetes has intensified during the past six years. Here we discuss the recent progress and challenges in the major steps towards a closed-loop system. Continuous insulin infusion pumps have been widely available for over two decades, but "smart pump" technology has made the devices easier to use and more powerful. Continuous glucose monitoring (CGM) technology has improved and the devices are more widely available. A number of approaches are currently under study for fully closed-loop systems; most manipulate only insulin, while others manipulate insulin and glucagon. Algorithms include on-off (for prevention of overnight hypoglycemia), proportional-integral-derivative (PID), model predictive control (MPC) and fuzzy logic based learning control. Meals cause a major "disturbance" to blood glucose, and we discuss techniques that our group has developed to predict when a meal is likely to be consumed and its effect. We further examine both physiology and device-related challenges, including insulin infusion set failure and sensor signal attenuation. Finally, we discuss the next steps required to make a closed-loop artificial pancreas a commercial reality.
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99
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Kovatchev BP. Diabetes technology: markers, monitoring, assessment, and control of blood glucose fluctuations in diabetes. SCIENTIFICA 2012; 2012:283821. [PMID: 24278682 PMCID: PMC3820631 DOI: 10.6064/2012/283821] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2012] [Accepted: 10/02/2012] [Indexed: 06/02/2023]
Abstract
People with diabetes face a life-long optimization problem: to maintain strict glycemic control without increasing their risk for hypoglycemia. Since the discovery of insulin in 1921, the external regulation of diabetes by engineering means has became a hallmark of this optimization. Diabetes technology has progressed remarkably over the past 50 years-a progress that includes the development of markers for diabetes control, sophisticated monitoring techniques, mathematical models, assessment procedures, and control algorithms. Continuous glucose monitoring (CGM) was introduced in 1999 and has evolved from means for retroactive review of blood glucose profiles to versatile reliable devices, which monitor the course of glucose fluctuations in real time and provide interactive feedback to the patient. Technology integrating CGM with insulin pumps is now available, opening the field for automated closed-loop control, known as the artificial pancreas. Following a number of in-clinic trials, the quest for a wearable ambulatory artificial pancreas is under way, with a first prototype tested in outpatient setting during the past year. This paper discusses key milestones of diabetes technology development, focusing on the progress in the past 10 years and on the artificial pancreas-still not a cure, but arguably the most promising treatment of diabetes to date.
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Affiliation(s)
- Boris P. Kovatchev
- Department of Psychiatry and Neurobehavioral Sciences, Department of Systems and Information Engineering, Center for Diabetes Technology, and University of Virginia Health System, University of Virginia, P.O. Box 400888, Charlottesville, VA 22908, USA
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100
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García-Jaramillo M, Calm R, Bondia J, Vehí J. Prediction of postprandial blood glucose under uncertainty and intra-patient variability in type 1 diabetes: a comparative study of three interval models. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 108:224-233. [PMID: 22677264 DOI: 10.1016/j.cmpb.2012.04.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2011] [Revised: 04/04/2012] [Accepted: 04/11/2012] [Indexed: 06/01/2023]
Abstract
The behavior of three insulin action and glucose kinetics models was assessed for an insulin therapy regime in the presence of patient variability. For this purpose, postprandial glucose in patients with type 1 diabetes was predicted by considering intra- and inter-patient variability using modal interval analysis. Equations to achieve optimal prediction are presented for models 1, 2 and 3, which are of increasing complexity. The model parameters were adjusted to reflect the "same" patient in the presence of variability. The glucose response envelope for model 1, the simplest insulin-glucose model assessed, included the responses of the other two models when a good fit of the model parameters was achieved. Thus, under variability, simple glucose-insulin models may be sufficient to describe patient dynamics in most situations.
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Affiliation(s)
- M García-Jaramillo
- Institut d'Informatica i Aplicacions, University of Girona, Campus de Montilivi, Edifici P4, 17071 Girona, Spain.
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