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Da Prato G, Pasquini S, Rinaldi E, Lucianer T, Donà S, Santi L, Negri C, Bonora E, Moghetti P, Trombetta M. Accuracy of CGM Systems During Continuous and Interval Exercise in Adults with Type 1 Diabetes. J Diabetes Sci Technol 2022; 16:1436-1443. [PMID: 34111989 PMCID: PMC9631517 DOI: 10.1177/19322968211023522] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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 continuous glucose monitoring systems (CGMs) play an important role in the management of T1D, but their accuracy may reduce during rapid glucose excursions. The aim of study was to assess the accuracy of recent rt-CGMs available in Italy, in subjects with T1D during 2 sessions of physical activity: moderate continuous (CON) and interval exercise (IE). METHOD we recruited 22 patients with T1D, on CSII associated or integrated with a CGM, to which a second different sensor was applied. Data recorded by CGMs were compared with the corresponding plasma glucose (PG) values, measured every 5 minutes with the glucose analyzer. To assess the accuracy of the CGMs, we evaluated the Sensor Bias (SB), the Mean Absolute Relative Difference (MARD) and the Clarke error grid (CEG). RESULTS a total of 2355 plasma-sensor glucose paired points were collected. Both average plasma and interstitial glucose concentrations did not significantly differ during CON and IE. During CON: 1. PG change at the end of exercise was greater than during IE (P = .034); 2. all sensors overestimated PG more than during IE, as shown by SB (P < .001) and MARD (P < .001) comparisons. Classifying the performance according to the CEG, significant differences were found between the 2 sessions in distribution of points in A and B zones. CONCLUSIONS the exercise affects the accuracy of currently available CGMs, especially during CON, suggesting, in this circumstance, the need to maintain blood glucose in a "prudent" range, above that generally recommended. Further studies are needed to investigate additional types of activities.
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Affiliation(s)
- G. Da Prato
- Department of Medicine, Division of
Endocrinology, Diabetes and Metabolism, University and Hospital of Verona, Verona,
Italy
| | - S. Pasquini
- Department of Medicine, Division of
Endocrinology, Diabetes and Metabolism, University and Hospital of Verona, Verona,
Italy
| | - E. Rinaldi
- Department of Medicine, Division of
Endocrinology, Diabetes and Metabolism, University and Hospital of Verona, Verona,
Italy
| | - T. Lucianer
- Department of Medicine, Division of
Endocrinology, Diabetes and Metabolism, University and Hospital of Verona, Verona,
Italy
| | - S. Donà
- Department of Medicine, Division of
Endocrinology, Diabetes and Metabolism, University and Hospital of Verona, Verona,
Italy
| | - L. Santi
- Department of Medicine, Division of
Endocrinology, Diabetes and Metabolism, University and Hospital of Verona, Verona,
Italy
| | - C. Negri
- Department of Medicine, Division of
Endocrinology, Diabetes and Metabolism, University and Hospital of Verona, Verona,
Italy
| | - E. Bonora
- Department of Medicine, Division of
Endocrinology, Diabetes and Metabolism, University and Hospital of Verona, Verona,
Italy
| | - P. Moghetti
- Department of Medicine, Division of
Endocrinology, Diabetes and Metabolism, University and Hospital of Verona, Verona,
Italy
| | - M. Trombetta
- Department of Medicine, Division of
Endocrinology, Diabetes and Metabolism, University and Hospital of Verona, Verona,
Italy
- M. Trombetta, Department of Medicine,
Section of Endocrinology, Diabetes and Metabolism, University Hospital of
Verona, Piazzale Stefani 1, Verona, 37126, Italy.
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Nimri R, Phillip M, Kovatchev B. Decision Support Systems and Closed-Loop. Diabetes Technol Ther 2022; 24:S58-S75. [PMID: 35475696 DOI: 10.1089/dia.2022.2504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Revital Nimri
- Diabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Moshe Phillip
- Diabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Boris Kovatchev
- University of Virginia Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, VA
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Fabris C, Kovatchev B. The closed‐loop artificial pancreas in 2020. Artif Organs 2020; 44:671-679. [DOI: 10.1111/aor.13704] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 04/06/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Chiara Fabris
- Center for Diabetes Technology University of Virginia Charlottesville VA USA
| | - Boris Kovatchev
- Center for Diabetes Technology University of Virginia Charlottesville VA USA
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Abstract
Over the past 50 years, the diabetes technology field progressed remarkably through self-monitoring of blood glucose (SMBG), continuous subcutaneous insulin infusion (CSII), risk and variability analysis, mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). This review follows these developments, beginning with an overview of the functioning of the human metabolic system in health and in diabetes and of its detailed quantitative network modeling. The review continues with a brief account of the first AP studies that used intravenous glucose monitoring and insulin infusion, and with notes about CSII and CGM-the technologies that made possible the development of contemporary AP systems. In conclusion, engineering lessons learned from AP research, and the clinical need for AP systems to prove their safety and efficacy in large-scale clinical trials, are outlined.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia 22908
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An in vitro model mimics the contact of biomaterials to blood components and the reaction of surrounding soft tissue. Acta Biomater 2019; 89:227-241. [PMID: 30880238 DOI: 10.1016/j.actbio.2019.03.029] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 02/20/2019] [Accepted: 03/13/2019] [Indexed: 12/21/2022]
Abstract
The therapeutic efficacy of a medical product after implantation depends strongly on the host-initiated fibrotic response (foreign body reaction). For novel biomaterials, it is of high relevance to understand this fibrotic process. As an alternative to in vivo studies, in vitro models mimic parts of the whole foreign body reaction. Aim of this study was to develop a wound model with key cells and matrix proteins in coculture. This approach combined blood components such as primary macrophages in a plasma-derived fibrin hydrogel, directly exposed to reference biomaterials (PTFE, glass, titanium). The soft tissue reaction is resembled by integrating fibroblasts in a collagen or a fibrin matrix. Those two experimental setups were conducted to show whether a long-term in vitro culture of 13 days is feasible. The response to reference biomaterials was assessed by multi-parametric analyses, comprising molecular profiling (cytokines, collagen I and ß-actin) and tissue remodeling (cell adherence, histological structure, tissue deposition). Polytetrafluorethylene (PTFE) and titanium were tested as references to correlate the in vitro evaluation to previous in vivo studies. Most striking, both model setups evaluated references' fibrotic characteristics as previously reported by in vivo studies. STATEMENT OF SIGNIFICANCE: We present a test platform applied for assessments on the foreign body reaction to biomaterials. This test system consists of blood components - macrophages and plasma-derived fibrin - as well as fibroblasts and collagen, generating a three-dimensional wound microenvironment. By this modular approach, we achieved a suitable test for long-term studies and overcame the limited short-term stability of whole blood tests. In contrast to previous models, macrophages' viability is maintained during the extended culture period and excels the quality of the model. The potential to evaluate a foreign body reaction in vitro was demonstrated with defined reference materials. This model system might be of high potential as a screening platform to identify novel biomaterial candidates.
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Kovatchev B. Automated closed-loop control of diabetes: the artificial pancreas. Bioelectron Med 2018; 4:14. [PMID: 32232090 PMCID: PMC7098217 DOI: 10.1186/s42234-018-0015-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 10/08/2018] [Indexed: 12/28/2022] Open
Abstract
The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, P.O. Box 400888, Charlottesville, VA 22908 USA
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Huang Y, Mei J, Yu Y, Ding Y, Xia W, Yue T, Chen W, Zhou M, Yang Y. Comparative Decellularization and Recellularization of Normal Versus Streptozotocin‐Induced Diabetes Mellitus Rat Pancreas. Artif Organs 2018; 43:399-412. [PMID: 30182423 DOI: 10.1111/aor.13353] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Revised: 08/25/2018] [Accepted: 08/28/2018] [Indexed: 12/19/2022]
Affiliation(s)
- Ying‐Bao Huang
- Department of Radiology The First Affiliated Hospital of Wenzhou Medical University Wenzhou China
| | - Jin Mei
- Anatomy Department Wenzhou Medical University Wenzhou China
- Institute of Bioscaffold Transplantation and Immunology Wenzhou Medical University Wenzhou China
- Institute of Neuroscience Wenzhou Medical University Wenzhou China
| | - Yaling Yu
- Department of Orthopedic Surgery Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China
| | - Yuqiang Ding
- Institute of Neuroscience Wenzhou Medical University Wenzhou China
| | - Weizhi Xia
- Department of Radiology The Second Affiliated Hospital of Wenzhou Medical University Wenzhou China
| | - Ting Yue
- Department of Radiology The First Affiliated Hospital of Wenzhou Medical University Wenzhou China
| | - Weijian Chen
- Department of Radiology The First Affiliated Hospital of Wenzhou Medical University Wenzhou China
| | - Meng‐Tao Zhou
- Department of Surgery The First Affiliated Hospital of Wenzhou Medical University Wenzhou China
| | - Yun‐Jun Yang
- Department of Radiology The First Affiliated Hospital of Wenzhou Medical University Wenzhou China
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Mirshekarian S, Bunescu R, Marling C, Schwartz F. Using LSTMs to learn physiological models of blood glucose behavior. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:2887-2891. [PMID: 29060501 DOI: 10.1109/embc.2017.8037460] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
For people with type 1 diabetes, good blood glucose control is essential to keeping serious disease complications at bay. This entails carefully monitoring blood glucose levels and taking corrective steps whenever they are too high or too low. If blood glucose levels could be accurately predicted, patients could take proactive steps to prevent blood glucose excursions from occurring. However, accurate predictions require complex physiological models of blood glucose behavior. Factors such as insulin boluses, carbohydrate intake, and exercise influence blood glucose in ways that are difficult to capture through manually engineered equations. In this paper, we describe a recursive neural network (RNN) approach that uses long short-term memory (LSTM) units to learn a physiological model of blood glucose. When trained on raw data from real patients, the LSTM networks (LSTMs) obtain results that are competitive with a previous state-of-the-art model based on manually engineered physiological equations. The RNN approach can incorporate arbitrary physiological parameters without the need for sophisticated manual engineering, thus holding the promise of further improvements in prediction accuracy.
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Breton MD, Hinzmann R, Campos-Nañez E, Riddle S, Schoemaker M, Schmelzeisen-Redeker G. Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator. J Diabetes Sci Technol 2017; 11:545-552. [PMID: 28745098 PMCID: PMC5505429 DOI: 10.1177/1932296816680633] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Computer simulation has been shown over the past decade to be a powerful tool to study the impact of medical devices characteristics on clinical outcomes. Specifically, in type 1 diabetes (T1D), computer simulation platforms have all but replaced preclinical studies and are commonly used to study the impact of measurement errors on glycemia. METHOD We use complex mathematical models to represent the characteristics of 3 continuous glucose monitoring systems using previously acquired data. Leveraging these models within the framework of the UVa/Padova T1D simulator, we study the impact of CGM errors in 6 simulation scenarios designed to generate a wide variety of glycemic conditions. Assessment of the simulated accuracy of each different CGM systems is performed using mean absolute relative deviation (MARD) and precision absolute relative deviation (PARD). We also quantify the capacity of each system to detect hypoglycemic events. RESULTS The simulated Roche CGM sensor prototype (RCGM) outperformed the 2 alternate systems (CGM-1 & CGM-2) in accuracy (MARD = 8% vs 11.4% vs 18%) and precision (PARD = 6.4% vs 9.4% vs 14.1%). These results held for all studied glucose and rate of change ranges. Moreover, it detected more than 90% of hypoglycemia, with a mean time lag less than 4 minutes (CGM-1: 86%/15 min, CGM-2: 57%/24 min). CONCLUSION The RCGM system model led to strong performances in these simulation studies, with higher accuracy and precision than alternate systems. Its characteristics placed it firmly as a strong candidate for CGM based therapy, and should be confirmed in large clinical studies.
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Affiliation(s)
- Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
- Marc D. Breton, PhD, Center for Diabetes Technology, University of Virginia, PO Box 400888, Charlottesville, VA 22904-0888, USA.
| | | | - Enrique Campos-Nañez
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
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11
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Mansell EJ, Docherty PD, Chase JG. Shedding light on grey noise in diabetes modelling. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.06.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Bartlett ST, Markmann JF, Johnson P, Korsgren O, Hering BJ, Scharp D, Kay TWH, Bromberg J, Odorico JS, Weir GC, Bridges N, Kandaswamy R, Stock P, Friend P, Gotoh M, Cooper DKC, Park CG, O'Connell P, Stabler C, Matsumoto S, Ludwig B, Choudhary P, Kovatchev B, Rickels MR, Sykes M, Wood K, Kraemer K, Hwa A, Stanley E, Ricordi C, Zimmerman M, Greenstein J, Montanya E, Otonkoski T. Report from IPITA-TTS Opinion Leaders Meeting on the Future of β-Cell Replacement. Transplantation 2016; 100 Suppl 2:S1-44. [PMID: 26840096 PMCID: PMC4741413 DOI: 10.1097/tp.0000000000001055] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 10/07/2015] [Indexed: 12/11/2022]
Affiliation(s)
- Stephen T. Bartlett
- Department of Surgery, University of Maryland School of Medicine, Baltimore MD
| | - James F. Markmann
- Division of Transplantation, Massachusetts General Hospital, Boston MA
| | - Paul Johnson
- Nuffield Department of Surgical Sciences and Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Olle Korsgren
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Bernhard J. Hering
- Schulze Diabetes Institute, Department of Surgery, University of Minnesota, Minneapolis, MN
| | - David Scharp
- Prodo Laboratories, LLC, Irvine, CA
- The Scharp-Lacy Research Institute, Irvine, CA
| | - Thomas W. H. Kay
- Department of Medicine, St. Vincent’s Hospital, St. Vincent's Institute of Medical Research and The University of Melbourne Victoria, Australia
| | - Jonathan Bromberg
- Division of Transplantation, Massachusetts General Hospital, Boston MA
| | - Jon S. Odorico
- Division of Transplantation, Department of Surgery, School of Medicine and Public Health, University of Wisconsin, Madison, WI
| | - Gordon C. Weir
- Joslin Diabetes Center and Harvard Medical School, Boston, MA
| | - Nancy Bridges
- National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD
| | - Raja Kandaswamy
- Schulze Diabetes Institute, Department of Surgery, University of Minnesota, Minneapolis, MN
| | - Peter Stock
- Division of Transplantation, University of San Francisco Medical Center, San Francisco, CA
| | - Peter Friend
- Nuffield Department of Surgical Sciences and Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Mitsukazu Gotoh
- Department of Surgery, Fukushima Medical University, Fukushima, Japan
| | - David K. C. Cooper
- Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA
| | - Chung-Gyu Park
- Xenotransplantation Research Center, Department of Microbiology and Immunology, Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Phillip O'Connell
- The Center for Transplant and Renal Research, Westmead Millennium Institute, University of Sydney at Westmead Hospital, Westmead, NSW, Australia
| | - Cherie Stabler
- Diabetes Research Institute, School of Medicine, University of Miami, Coral Gables, FL
| | - Shinichi Matsumoto
- National Center for Global Health and Medicine, Tokyo, Japan
- Otsuka Pharmaceutical Factory inc, Naruto Japan
| | - Barbara Ludwig
- Department of Medicine III, Technische Universität Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden of Helmholtz Centre Munich at University Clinic Carl Gustav Carus of TU Dresden and DZD-German Centre for Diabetes Research, Dresden, Germany
| | - Pratik Choudhary
- Diabetes Research Group, King's College London, Weston Education Centre, London, United Kingdom
| | - Boris Kovatchev
- University of Virginia, Center for Diabetes Technology, Charlottesville, VA
| | - Michael R. Rickels
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Megan Sykes
- Columbia Center for Translational Immunology, Coulmbia University Medical Center, New York, NY
| | - Kathryn Wood
- Nuffield Department of Surgical Sciences and Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Kristy Kraemer
- National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD
| | - Albert Hwa
- Juvenile Diabetes Research Foundation, New York, NY
| | - Edward Stanley
- Murdoch Children's Research Institute, Parkville, VIC, Australia
- Monash University, Melbourne, VIC, Australia
| | - Camillo Ricordi
- Diabetes Research Institute, School of Medicine, University of Miami, Coral Gables, FL
| | - Mark Zimmerman
- BetaLogics, a business unit in Janssen Research and Development LLC, Raritan, NJ
| | - Julia Greenstein
- Discovery Research, Juvenile Diabetes Research Foundation New York, NY
| | - Eduard Montanya
- Bellvitge Biomedical Research Institute (IDIBELL), Hospital Universitari Bellvitge, CIBER of Diabetes and Metabolic Diseases (CIBERDEM), University of Barcelona, Barcelona, Spain
| | - Timo Otonkoski
- Children's Hospital and Biomedicum Stem Cell Center, University of Helsinki, Helsinki, Finland
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Iacovacci V, Ricotti L, Menciassi A, Dario P. The bioartificial pancreas (BAP): Biological, chemical and engineering challenges. Biochem Pharmacol 2016; 100:12-27. [DOI: 10.1016/j.bcp.2015.08.107] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2015] [Accepted: 08/26/2015] [Indexed: 01/05/2023]
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Kovatchev BP, Patek SD, Ortiz EA, Breton MD. Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring. Diabetes Technol Ther 2015; 17:177-86. [PMID: 25436913 PMCID: PMC4346608 DOI: 10.1089/dia.2014.0272] [Citation(s) in RCA: 139] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes. MATERIALS AND METHODS A combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to "replay" real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows. RESULTS In all six scenarios, the occurrence of hypoglycemia (frequency of BG levels ≤50 mg/dL and BG levels ≤39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD =10%. Similarly, hyperglycemia (frequency of BG levels ≥250 mg/dL and BG levels ≥400 mg/dL) increased and displayed an abrupt slope change at MARD=10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively. CONCLUSIONS Using CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD=10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.
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Affiliation(s)
- Boris P Kovatchev
- 1 University of Virginia Center for Diabetes Technology , Charlottesville, Virginia
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15
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Scuffi C. Interstitium versus Blood Equilibrium in Glucose Concentration and its Impact on Subcutaneous Continuous Glucose Monitoring Systems. EUROPEAN ENDOCRINOLOGY 2014; 10:36-42. [PMID: 29872462 PMCID: PMC5983095 DOI: 10.17925/ee.2014.10.01.36] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2013] [Accepted: 02/13/2014] [Indexed: 12/18/2022]
Abstract
The relationship between both interstitial and blood glucose remains a debated topic, on which there is still no consensus. The experimental evidence suggests that blood and interstitial fluid glucose levels are correlated by a kinetic equilibrium, which as a consequence has a time and magnitude gradient in glucose concentration between blood and interstitium. Furthermore, this equilibrium can be perturbed by several physiological effects (such as foreign body response, wound-healing effect, etc.), with a consequent reduction of interstitial fluid glucose versus blood glucose correlation. In the present study, the impact of operating in the interstitium on continuous glucose monitoring systems (CGMs) will be discussed in depth, both for the application of CGMs in the management of diabetes and in other critical areas, such as tight glycaemic control in critically ill patients.
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Affiliation(s)
- Cosimo Scuffi
- Scientist, Scientific and Technology Affairs Department, A. Menarini Diagnostics, Florence, Italy
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16
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Artificial Pancreas Coupled Vital Signs Monitoring for Improved Patient Safety. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2013. [DOI: 10.1007/s13369-012-0456-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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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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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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Patek SD, Magni L, Dassau E, Karvetski C, Toffanin C, De Nicolao G, Del Favero S, Breton M, Man CD, Renard E, Zisser H, Doyle FJ, Cobelli C, Kovatchev BP. Modular closed-loop control of diabetes. IEEE Trans Biomed Eng 2012; 59:2986-99. [PMID: 22481809 DOI: 10.1109/tbme.2012.2192930] [Citation(s) in RCA: 137] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Modularity plays a key role in many engineering systems, allowing for plug-and-play integration of components, enhancing flexibility and adaptability, and facilitating standardization. In the control of diabetes, i.e., the so-called "artificial pancreas," modularity allows for the step-wise introduction of (and regulatory approval for) algorithmic components, starting with subsystems for assured patient safety and followed by higher layer components that serve to modify the patient's basal rate in real time. In this paper, we introduce a three-layer modular architecture for the control of diabetes, consisting in a sensor/pump interface module (IM), a continuous safety module (CSM), and a real-time control module (RTCM), which separates the functions of insulin recommendation (postmeal insulin for mitigating hyperglycemia) and safety (prevention of hypoglycemia). In addition, we provide details of instances of all three layers of the architecture: the APS© serving as the IM, the safety supervision module (SSM) serving as the CSM, and the range correction module (RCM) serving as the RTCM. We evaluate the performance of the integrated system via in silico preclinical trials, demonstrating 1) the ability of the SSM to reduce the incidence of hypoglycemia under nonideal operating conditions and 2) the ability of the RCM to reduce glycemic variability.
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Affiliation(s)
- S D Patek
- Center for Diabetes Technology and Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904-4747, USA.
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ALANIS ALMAY, LEON BLANCAS, SANCHEZ EDGARN, RUIZ-VELAZQUEZ EDUARDO. BLOOD GLUCOSE LEVEL NEURAL MODEL FOR TYPE 1 DIABETES MELLITUS PATIENTS. Int J Neural Syst 2012; 21:491-504. [DOI: 10.1142/s0129065711003000] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper deals with the blood glucose level modeling for Type 1 Diabetes Mellitus (T1DM) patients. The model is developed using a recurrent neural network trained with an extended Kalman filter based algorithm in order to develop an affine model, which captures the nonlinear behavior of the blood glucose metabolism. The goal is to derive a dynamical mathematical model for the T1DM as the response of a patient to meal and subcutaneous insulin infusion. Experimental data given by continuous glucose monitoring system is utilized for identification and for testing the applicability of the proposed scheme to T1DM subjects.
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Affiliation(s)
- ALMA Y. ALANIS
- CUCEI, Universidad de Guadalajara, Apartado Postal 51–71, Col. las Aguilas, C.P. 45080, Zapopan, Jalisco, Mexico
| | - BLANCA S. LEON
- CINVESTAV, Unidad Guadalajara, Apartado Postal 31–438, Plaza La Luna, Guadalajara, Jalisco, C.P. 45091, Mexico
| | - EDGAR N. SANCHEZ
- CINVESTAV, Unidad Guadalajara, Apartado Postal 31–438, Plaza La Luna, Guadalajara, Jalisco, C.P. 45091, Mexico
| | - EDUARDO RUIZ-VELAZQUEZ
- Division de Electronica y Computacion, CUCEI, Universidad de Guadalajara, Av. Revolucion 1500, Guadalajara, Jal., Mexico
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Affiliation(s)
- Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy.
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Affiliation(s)
- David C Klonoff
- Mills-Peninsula Health Services, San Mateo, California, USA.
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Kovatchev B, Cobelli C, Renard E, Anderson S, Breton M, Patek S, Clarke W, Bruttomesso D, Maran A, Costa S, Avogaro A, Dalla Man C, Facchinetti A, Magni L, De Nicolao G, Place J, Farret A. Multinational study of subcutaneous model-predictive closed-loop control in type 1 diabetes mellitus: summary of the results. J Diabetes Sci Technol 2010; 4:1374-81. [PMID: 21129332 PMCID: PMC3005047 DOI: 10.1177/193229681000400611] [Citation(s) in RCA: 171] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND In 2008-2009, the first multinational study was completed comparing closed-loop control (artificial pancreas) to state-of-the-art open-loop therapy in adults with type 1 diabetes mellitus (T1DM). METHODS The design of the control algorithm was done entirely in silico, i.e., using computer simulation experiments with N=300 synthetic "subjects" with T1DM instead of traditional animal trials. The clinical experiments recruited 20 adults with T1DM at the Universities of Virginia (11); Padova, Italy (6); and Montpellier, France (3). Open-loop and closed-loop admission was scheduled 3-4 weeks apart, continued for 22 h (14.5 h of which were in closed loop), and used a continuous glucose monitor and an insulin pump. The only difference between the two sessions was that insulin dosing was performed by the patient under a physician's supervision during open loop, whereas insulin dosing was performed by a control algorithm during closed loop. RESULTS In silico design resulted in rapid (less than 6 months compared to years of animal trials) and cost-effective system development, testing, and regulatory approvals in the United States, Italy, and France. In the clinic, compared to open-loop, closed-loop control reduced nocturnal hypoglycemia (blood glucose below 3.9 mmol/liter) from 23 to 5 episodes (p<.01) and increased the amount of time spent overnight within the target range (3.9 to 7.8 mmol/liter) from 64% to 78% (p=.03). CONCLUSIONS In silico experiments can be used as viable alternatives to animal trials for the preclinical testing of insulin treatment strategies. Compared to open-loop treatment under identical conditions, closed-loop control improves the overnight regulation of diabetes.
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Affiliation(s)
- Boris Kovatchev
- Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, Virginia 22908, USA.
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Klueh U, Kaur M, Qiao Y, Kreutzer DL. Critical role of tissue mast cells in controlling long-term glucose sensor function in vivo. Biomaterials 2010; 31:4540-51. [PMID: 20226521 PMCID: PMC2850116 DOI: 10.1016/j.biomaterials.2010.02.023] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2009] [Accepted: 02/10/2010] [Indexed: 10/19/2022]
Abstract
Little is known about the specific cells, mediators and mechanisms involved in the loss of glucose sensor function (GSF) in vivo. Since mast cells (MC) are known to be key effector cells in inflammation and wound healing, we hypothesized that MC and their products are major contributors to the skin inflammation and wound healing that controls GSF at sites of sensor implantation. To test this hypothesis we utilized a murine model of continuous glucose monitoring (CGM) in vivo in both normal C57BL/6 mice (mast cell sufficient), as well as mast cell deficient B6.Cg-Kit(W-sh)/HNihrJaeBsmJ (Sash) mice over a 28 day CGM period. As expected, both strains of mice displayed excellent CGM for the first 7 days post sensor implantation (PSI). CGM in the mast cell sufficient C57BL/6 mice was erratic over the remaining 21 days PSI. CGM in the mast cell deficient Sash mice displayed excellent sensor function for the entire 28 day of CGM. Histopathologic evaluation of implantation sites demonstrated that tissue reactions in Sash mice were dramatically less compared to the reactions in normal C57BL/6 mice. Additionally, mast cells were also seen to be consistently associated with the margins of sensor tissue reactions in normal C57BL/6 mice. Finally, direct injection of bone marrow derived mast cells at sites of sensor implantation induced an acute and dramatic loss of sensor function in both C57BL/6 and Sash mice. These results demonstrate the key role of mast cells in controlling glucose sensor function in vivo.
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Affiliation(s)
- Ulrike Klueh
- Center for Molecular Tissue Engineering, University of Connecticut, School of Medicine, 263 Farmington Ave., Farmington, CT 06030, USA.
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Quiroz G, Femat R. Theoretical blood glucose control in hyper- and hypoglycemic and exercise scenarios by means of an algorithm. J Theor Biol 2010; 263:154-60. [DOI: 10.1016/j.jtbi.2009.11.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2009] [Revised: 10/10/2009] [Accepted: 11/18/2009] [Indexed: 10/20/2022]
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Rodríguez-Herrero A, Pérez-Gandía C, Rigla M, de Leiva A, Gómez EJ, Hernando ME. A simulation study of an inverse controller for closed- and semiclosed-loop control in type 1 diabetes. Diabetes Technol Ther 2010; 12:95-104. [PMID: 20105038 DOI: 10.1089/dia.2009.0093] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND Closed-loop control algorithms in diabetes aim to calculate the optimum insulin delivery to maintain the patient in a normoglycemic state, taking the blood glucose level as the algorithm's main input. The major difficulties facing these algorithms when applied subcutaneously are insulin absorption time and delays in measurement of subcutaneous glucose with respect to the blood concentration. METHODS This article presents an inverse controller (IC) obtained by inversion of an existing mathematical model and validated with synthetic patients simulated with a different model and is compared with a proportional-integral-derivative controller. RESULTS Simulated results are presented for a mean patient and for a population of six simulated patients. The IC performance is analyzed for both full closed-loop and semiclosed-loop control. The IC is tested when initialized with the heuristic optimal gain, and it is compared with the performance when the initial gain is deviated from the optimal one (+/-10%). CONCLUSIONS The simulation results show the viability of using an IC for closed-loop diabetes control. The IC is able to achieve normoglycemia over long periods of time when the optimal gain is used (63% for the full closed-loop control, and it is increased to 96% for the semiclosed-loop control).
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Hoekstra M, Vogelzang M, Verbitskiy E, Nijsten MWN. Health technology assessment review: Computerized glucose regulation in the intensive care unit--how to create artificial control. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2009; 13:223. [PMID: 19849827 PMCID: PMC2784347 DOI: 10.1186/cc8023] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Current care guidelines recommend glucose control (GC) in critically ill patients. To achieve GC, many ICUs have implemented a (nurse-based) protocol on paper. However, such protocols are often complex, time-consuming, and can cause iatrogenic hypoglycemia. Computerized glucose regulation protocols may improve patient safety, efficiency, and nurse compliance. Such computerized clinical decision support systems (Cuss) use more complex logic to provide an insulin infusion rate based on previous blood glucose levels and other parameters. A computerized CDSS for glucose control has the potential to reduce overall workload, reduce the chance of human cognitive failure, and improve glucose control. Several computer-assisted glucose regulation programs have been published recently. In order of increasing complexity, the three main types of algorithms used are computerized flowcharts, Proportional-Integral-Derivative (PID), and Model Predictive Control (MPC). PID is essentially a closed-loop feedback system, whereas MPC models the behavior of glucose and insulin in ICU patients. Although the best approach has not yet been determined, it should be noted that PID controllers are generally thought to be more robust than MPC systems. The computerized Cuss that are most likely to emerge are those that are fully a part of the routine workflow, use patient-specific characteristics and apply variable sampling intervals.
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Affiliation(s)
- Miriam Hoekstra
- Departments of Anesthesiology and Cardiology, University Medical Center Groningen, 9700 RB Groningen, the Netherlands.
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Magni L, Raimondo D, Dalla Man C, De Nicolao G, Kovatchev B, Cobelli C. Model predictive control of glucose concentration in type I diabetic patients: An in silico trial. Biomed Signal Process Control 2009. [DOI: 10.1016/j.bspc.2009.04.003] [Citation(s) in RCA: 145] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Hoshino M, Haraguchi Y, Mizushima I, Sakai M. Recent progress in mechanical artificial pancreas. J Artif Organs 2009; 12:141-9. [PMID: 19894087 DOI: 10.1007/s10047-009-0463-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2009] [Indexed: 12/14/2022]
Affiliation(s)
- Masami Hoshino
- Department of Surgery, Shisei Hospital, Sayama-shi, Saitama, Japan.
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Abstract
This issue of Journal of Diabetes Science and Technology contains a collection of 12 original articles describing the latest advances in the development of algorithms for controlling insulin delivery in an artificial pancreas. Algorithms presented in this issue are affected by numerous quantifiable factors, including insulin pharmaco-kinetics, timing of meal carbohydrate appearance, meal size, amount of exercise, presence of stress, day-to-day variations in insulin sensitivity, insulin time-activity profiles, accuracy of glucose monitor calibration, metabolic profiles of both adults and neonates, and risks of hypoglycemia/hyperglycemia. These articles present theoretical advances in insulin delivery algorithms from modeled in silico patients, as well as clinical data from actual patients who have used closed loop systems. The novel approaches described in these articles are expected to bring us much closer to realization of a commercially available closed loop system for controlling glucose levels in patients with diabetes.
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Affiliation(s)
- David C Klonoff
- Mills-Peninsula Health Services, San Mateo, California 94401, USA.
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Magni L, Forgione M, Toffanin C, Dalla Man C, Kovatchev B, De Nicolao G, Cobelli C. Run-to-run tuning of model predictive control for type 1 diabetes subjects: in silico trial. J Diabetes Sci Technol 2009; 3:1091-8. [PMID: 20144422 PMCID: PMC2769897 DOI: 10.1177/193229680900300512] [Citation(s) in RCA: 91] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The technological advancements in subcutaneous continuous glucose monitoring and insulin pump delivery systems have paved the way to clinical testing of artificial pancreas devices. The experience derived by clinical trials poses technological challenges to the automatic control expert, the most notable being the large interpatient and intrapatient variability and the inherent uncertainty of patient information. METHODS A new model predictive control (MPC) glucose control system is proposed. The starting point is an MPC algorithm applied in 20 type 1 diabetes mellitus (T1DM) subjects. Three main changes are introduced: individualization of the ARX model used for prediction; synthesis of the MPC law on top of the open-loop basal/bolus therapy; and a run-to-run approach for implementing day-by-day tuning of the algorithm. In order to individualize the ARX model, a sufficiently exciting insulin profile is imposed by splitting the premeal bolus into two smaller boluses (40% and 60%) injected 30 min before and 30 min after the meal. RESULTS The proposed algorithm was tested on 100 virtual subjects extracted from an in silico T1DM population. The trial simulates 44 consecutive days, during which the patient receives breakfast, lunch, and dinner each day. For 10 days, meals are multiplied by a random variable uniformly distributed in [0.5, 1.5], while insulin delivery is based on nominal meals. Moreover, for 10 days, either a linear increase or decrease of insulin sensitivity (+/-25% of nominal value) is introduced. CONCLUSIONS The ARX model identification procedure offers an automatic tool for patient model individualization. The run-to-run approach is an effective way to auto-tune the aggressiveness of the closed-loop control law, is robust to meal variation, and is also capable of adapting the regulator to slow parameter variations, e.g., on insulin sensitivity.
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Affiliation(s)
- Lalo Magni
- Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy
| | - Marco Forgione
- Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy
| | - Chiara Toffanin
- Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Boris Kovatchev
- Department of Psychiatry and Neurobehavioral Science, University of Virginia Health System, Charlottesville, Virginia
| | - Giuseppe De Nicolao
- Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
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Abstract
Blood glucose control performed by intensive care unit (ICU) nurses is becoming standard practice for critically ill patients. New algorithms, ranging from basic protocols to elementary computerized protocols to advanced computerized protocols, have been presented during the last years aiming to reduce the workload of the medical team. This paper gives an overview of the different types of algorithms and their features. Performance comparisons between different algorithms are avoided as blood glucose sampling frequencies and protocol durations were not similar among different studies and even within studies. Particularly advanced computerized protocols can potentially be introduced as fully-automated blood glucose algorithms when accurate and reliable near-continuous glucose sensor devices are available. Furthermore, it is surprising to consider in some of the described protocols that the original blood glucose target ranges (80-110 mg/dl) were increased (due to fear of hypoglycaemia) and/or that glycaemia levels were determined in capillary blood samples.
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Kovatchev B, Breton M, Clarke W. Chapter 3 Analytical Methods for the Retrieval and Interpretation of Continuous Glucose Monitoring Data in Diabetes. Methods Enzymol 2009; 454:69-86. [DOI: 10.1016/s0076-6879(08)03803-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Kovatchev BP, Breton M, Man CD, Cobelli C. In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. J Diabetes Sci Technol 2009; 3:44-55. [PMID: 19444330 PMCID: PMC2681269 DOI: 10.1177/193229680900300106] [Citation(s) in RCA: 382] [Impact Index Per Article: 25.5] [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
Arguably, a minimally invasive system using subcutaneous (s.c.) continuous glucose monitoring (CGM) and s.c. insulin delivery via insulin pump would be a most feasible step to closed-loop control in type 1 diabetes mellitus (T1DM). Consequently, diabetes technology is focusing on developing an artificial pancreas using control algorithms to link CGM with s.c. insulin delivery. The future development of the artificial pancreas will be greatly accelerated by employing mathematical modeling and computer simulation. Realistic computer simulation is capable of providing invaluable information about the safety and the limitations of closed-loop control algorithms, guiding clinical studies, and out-ruling ineffective control scenarios in a cost-effective manner. Thus computer simulation testing of closed-loop control algorithms is regarded as a prerequisite to clinical trials of the artificial pancreas. In this paper, we present a system for in silico testing of control algorithms that has three principal components: (1) a large cohort of n=300 simulated "subjects" (n=100 adults, 100 adolescents, and 100 children) based on real individuals' data and spanning the observed variability of key metabolic parameters in the general population of people with T1DM; (2) a simulator of CGM sensor errors representative of Freestyle Navigator™, Guardian RT, or Dexcom™ STS™, 7-day sensor; and (3) a simulator of discrete s.c. insulin delivery via OmniPod Insulin Management System or Deltec Cozmo(®) insulin pump. The system has been shown to represent adequate glucose fluctuations in T1DM observed during meal challenges, and has been accepted by the Food and Drug Administration as a substitute to animal trials in the preclinical testing of closed-loop control strategies.
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Renard E. Insulin delivery route for the artificial pancreas: subcutaneous, intraperitoneal, or intravenous? Pros and cons. J Diabetes Sci Technol 2008; 2:735-8. [PMID: 19885254 PMCID: PMC2769765 DOI: 10.1177/193229680800200429] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Insulin delivery is a crucial component of a closed-loop system aiming at the development of an artificial pancreas. The intravenous route, which has been used in the bedside artificial pancreas model for 30 years, has clear advantages in terms of pharmacokinetics and pharmacodynamics, but cannot be used in any ambulatory system so far. Subcutaneous (SC) insulin infusion benefits from the broad expansion of insulin pump therapy that promoted the availability of constantly improving technology and fast-acting insulin analog use. However, persistent delays of insulin absorption and action, variability and shortterm stability of insulin infusion from SC-inserted catheters generate effectiveness and safety issues in view of an ambulatory, automated, glucose-controlled, artificial beta cell. Intraperitoneal insulin delivery, although still marginally used in diabetes care, may offer an interesting alternative because of its more-physiological plasma insulin profiles and sustained stability and reliability of insulin delivery.
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Affiliation(s)
- Eric Renard
- Endocrinology Department, Lapeyronie Hospital, CHU Montpellier, Université Montpellier 1, Montepellier, France.
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Magni L, Raimondo DM, Man CD, Breton M, Patek S, Nicolao GD, Cobelli C, Kovatchev BP. Evaluating the efficacy of closed-loop glucose regulation via control-variability grid analysis. J Diabetes Sci Technol 2008; 2:630-5. [PMID: 19885239 PMCID: PMC2769756 DOI: 10.1177/193229680800200414] [Citation(s) in RCA: 105] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Advancements in subcutaneous continuous glucose monitoring and subcutaneous insulin delivery are stimulating the development of a minimally invasive artificial pancreas that facilitates optimal glycemic regulation in diabetes. The key component of such a system is the blood glucose controller for which different design strategies have been investigated in the literature. In order to evaluate and compare the efficacy of the various algorithms, several performance indices have been proposed. METHODS A new tool-control-variability grid analysis (CVGA)-for measuring the quality of closed-loop glucose control on a group of subjects is introduced. It is a method for visualization of the extreme glucose excursions caused by a control algorithm in a group of subjects, with each subject presented by one data point for any given observation period. A numeric assessment of the overall level of glucose regulation in the population is given by the summary outcome of the CVGA. RESULTS It has been shown that CVGA has multiple uses: comparison of different patients over a given time period, of the same patient over different time periods, of different control laws, and of different tuning of the same controller on the same population. CONCLUSIONS Control-variability grid analysis provides a summary of the quality of glycemic regulation for a population of subjects and is complementary to measures such as area under the curve or low/high blood glucose indices, which characterize a single glucose trajectory for a single subject.
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Affiliation(s)
- Lalo Magni
- Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy.
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Magni L, Raimondo D, Man CD, De Nicolao G, Kovatchev B, Cobelli C. Model Predictive Control of glucose concentration in subjects with type 1 diabetes: an in silico trial. ACTA ACUST UNITED AC 2008. [DOI: 10.3182/20080706-5-kr-1001.00714] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Kovatchev B, Clarke W. Peculiarities of the continuous glucose monitoring data stream and their impact on developing closed-loop control technology. J Diabetes Sci Technol 2008; 2:158-63. [PMID: 19578532 PMCID: PMC2705169 DOI: 10.1177/193229680800200125] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Therapeutic advances in type 1 diabetes (T1DM) are currently focused on developing a closed-loop control system using a continuous glucose monitor (CGM), subcutaneous insulin delivery, and a control algorithm. Because a CGM assesses blood glucose indirectly (and therefore often inaccurately), it limits the effectiveness of the controller. In order to improve the quality of CGM data, a series of analyses are suggested. These analyses evaluate and compensate for CGM errors, assess risks associated with glucose variability, predict glucose fluctuation, and forecast hypo- and hyperglycemia. These analyses are illustrated with data collected using the MiniMed CGMS® (Medtronic, Northridge, CA) and Freestyle Navigator(™) (Abbott Diabetes Care, Alameda, CA). It is important to remember that traditional statistics do not work with CGM data because consecutive CGM readings are highly interdependent.
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Affiliation(s)
- Boris Kovatchev
- Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, Virginia
| | - William Clarke
- Department of Pediatrics, University of Virginia, Charlottesville, Virginia
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Abstract
Creating a wearable artificial pancreas (AP) by closing the loop between a glucose sensor and an insulin infusion pump has the potential to significantly impact the complications associated with and improve the quality of life of diabetic individuals. Despite recent progress on glucose sensor and insulin infusion technologies, control algorithms built on the simple glucose value efferent and insulin dose afferent model are not efficient and reliable. Based on glucose regulatory mechanisms known to date, their impairment in the diabetic state, and fundamental principles of control theory, some corrections to the present course of research are proposed to facilitate the removal of this barrier. A greater emphasis on model predictive controllers or controllers that exploit a mathematical representation, or model, of the patient's own physiology is proposed. Whole-body physiologically based pharmacokinetics-pharmacodynamics-type models hold the best odds for enabling a successful closed-loop AP. However, two major improvements to the diabetes modeling state of the art are required to make them practical for daily care: integrating hypothalamus-pituitary-adrenal axis and gastrointestinal tract submodels. Although there are simple representations of these in current existence, large concerted efforts between experimentalists and modelers will be required to enhance their accuracy. Finally, changes in hardware that complements controller performance are suggested. For instance, the development of dual control inputs of insulin and glucagon could relax tolerances on controller accuracy.
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Magni L, Raimondo DM, Bossi L, Man CD, De Nicolao G, Kovatchev B, Cobelli C. Model predictive control of type 1 diabetes: an in silico trial. J Diabetes Sci Technol 2007; 1:804-12. [PMID: 19885152 PMCID: PMC2769684 DOI: 10.1177/193229680700100603] [Citation(s) in RCA: 228] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The development of artificial pancreas has received a new impulse from recent technological advancements in subcutaneous continuous glucose monitoring and subcutaneous insulin pump delivery systems. However, the availability of innovative sensors and actuators, although essential, does not guarantee optimal glycemic regulation. Closed-loop control of blood glucose levels still poses technological challenges to the automatic control expert, most notable of which are the inevitable time delays between glucose sensing and insulin actuation. METHODS A new in silico model is exploited for both design and validation of a linear model predictive control (MPC) glucose control system. The starting point is a recently developed meal glucose-insulin model in health, which is modified to describe the metabolic dynamics of a person with type 1 diabetes mellitus. The population distribution of the model parameters originally obtained in healthy 204 patients is modified to describe diabetic patients. Individual models of virtual patients are extracted from this distribution. A discrete-time MPC is designed for all the virtual patients from a unique input-output-linearized approximation of the full model based on the average population values of the parameters. The in silico trial simulates 4 consecutive days, during which the patient receives breakfast, lunch, and dinner each day. RESULTS Provided that the regulator undergoes some individual tuning, satisfactory results are obtained even if the control design relies solely on the average patient model. Only the weight on the glucose concentration error needs to be tuned in a quite straightforward and intuitive way. The ability of the MPC to take advantage of meal announcement information is demonstrated. Imperfect knowledge of the amount of ingested glucose causes only marginal deterioration of performance. In general, MPC results in better regulation than proportional integral derivative, limiting significantly the oscillation of glucose levels. CONCLUSIONS The proposed in silico trial shows the potential of MPC for artificial pancreas design. The main features are a capability to consider meal announcement information, delay compensation, and simplicity of tuning and implementation.
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Affiliation(s)
- Lalo Magni
- Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy.
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Kondepati VR, Heise HM. Recent progress in analytical instrumentation for glycemic control in diabetic and critically ill patients. Anal Bioanal Chem 2007; 388:545-63. [PMID: 17431594 DOI: 10.1007/s00216-007-1229-8] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2006] [Revised: 02/16/2007] [Accepted: 02/22/2007] [Indexed: 01/08/2023]
Abstract
Implementing strict glycemic control can reduce the risk of serious complications in both diabetic and critically ill patients. For this reason, many different analytical, mainly electrochemical and optical sensor approaches for glucose measurements have been developed. Self-monitoring of blood glucose (SMBG) has been recognised as being an indispensable tool for intensive diabetes therapy. Recent progress in analytical instrumentation, allowing submicroliter samples of blood, alternative site testing, reduced test time, autocalibration, and improved precision, is comprehensively described in this review. Continuous blood glucose monitoring techniques and insulin infusion strategies, developmental steps towards the realization of the dream of an artificial pancreas under closed loop control, are presented. Progress in glucose sensing and glycemic control for both patient groups is discussed by assessing recent published literature (up to 2006). The state-of-the-art and trends in analytical techniques (either episodic, intermittent or continuous, minimal-invasive, or noninvasive) detailed in this review will provide researchers, health professionals and the diabetic community with a comprehensive overview of the potential of next-generation instrumentation suited to either short- and long-term implantation or ex vivo measurement in combination with appropriate body interfaces such as microdialysis catheters.
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Affiliation(s)
- Venkata Radhakrishna Kondepati
- ISAS--Institute for Analytical Sciences at the University of Dortmund, Bunsen-Kirchhoff-Strasse 11, 44139, Dortmund, Germany
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