On-line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: evaluation under fasting conditions in patients with Type 1 diabetes
Aims To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters—a model predictive control (MPC) algorithm—to control blood glucose concentration during fasting conditions in patients with Type 1 diabetes. In the subcutaneous (sc) route within a closed...
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| Veröffentlicht in: | Diabetic medicine Jg. 23; H. 1; S. 90 - 93 |
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Oxford, UK
Blackwell Science Ltd
01.01.2006
Blackwell |
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| Abstract | Aims To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters—a model predictive control (MPC) algorithm—to control blood glucose concentration during fasting conditions in patients with Type 1 diabetes. In the subcutaneous (sc) route within a closed loop system.
Methods Paired experiments were performed in six patients. Over 8 h the MPC algorithm was used to control glucose with s.c. insulin administration and two different glucose monitoring protocols: first, the algorithm was provided with intravenous (i.v.) glucose values for insulin dosage calculation directly (i.v.–s.c. route). Then, in the second experiment, i.v. glucose values were fed to the MPC with a delay of 30 min to simulate s.c. glucose measurements (‘s.c.’–s.c. route). In both experiments plasma glucose, insulin dosage, and serum insulin levels were analysed.
Results Glucose concentration was brought from hyper‐ to normoglycaemia and kept in the physiological range (6–7 mmol/l) with both routes in all subjects. Mean glucose concentration reached the threshold of 7 mmol/l approximately 2 (i.v.–s.c. route) and 3 (‘s.c.’–s.c. route) hours after the start of glucose control with the MPC. During the last 2 h of automated glucose control, mean glucose concentration was 6.3 ± 0.2 mmol/l and 6.6 ± 0.3 mmol/l for i.v.–s.c. and ‘s.c.’–s.c. route, respectively. Glucose concentration, insulin doses, and serum insulin levels did not differ significantly between routes (P > 0.05).
Conclusions The MPC algorithm is suitable for glucose control during fasting within an extracorporeal artificial β‐cell in the subcutaneous route Type 1 diabetic patients. |
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| AbstractList | To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters-a model predictive control (MPC) algorithm-to control blood glucose concentration during fasting conditions in patients with Type 1 diabetes. In the subcutaneous (sc) route within a closed loop system.
Paired experiments were performed in six patients. Over 8 h the MPC algorithm was used to control glucose with s.c. insulin administration and two different glucose monitoring protocols: first, the algorithm was provided with intravenous (i.v.) glucose values for insulin dosage calculation directly (i.v.-s.c. route). Then, in the second experiment, i.v. glucose values were fed to the MPC with a delay of 30 min to simulate s.c. glucose measurements ('s.c.'-s.c. route). In both experiments plasma glucose, insulin dosage, and serum insulin levels were analysed.
Glucose concentration was brought from hyper- to normoglycaemia and kept in the physiological range (6-7 mmol/l) with both routes in all subjects. Mean glucose concentration reached the threshold of 7 mmol/l approximately 2 (i.v.-s.c. route) and 3 ('s.c.'-s.c. route) hours after the start of glucose control with the MPC. During the last 2 h of automated glucose control, mean glucose concentration was 6.3 +/- 0.2 mmol/l and 6.6 +/- 0.3 mmol/l for i.v.-s.c. and 's.c.'-s.c. route, respectively. Glucose concentration, insulin doses, and serum insulin levels did not differ significantly between routes (P > 0.05).
The MPC algorithm is suitable for glucose control during fasting within an extracorporeal artificial beta-cell in the subcutaneous route Type 1 diabetic patients. Aims To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters—a model predictive control (MPC) algorithm—to control blood glucose concentration during fasting conditions in patients with Type 1 diabetes. In the subcutaneous (sc) route within a closed loop system. Methods Paired experiments were performed in six patients. Over 8 h the MPC algorithm was used to control glucose with s.c. insulin administration and two different glucose monitoring protocols: first, the algorithm was provided with intravenous (i.v.) glucose values for insulin dosage calculation directly (i.v.–s.c. route). Then, in the second experiment, i.v. glucose values were fed to the MPC with a delay of 30 min to simulate s.c. glucose measurements (‘s.c.’–s.c. route). In both experiments plasma glucose, insulin dosage, and serum insulin levels were analysed. Results Glucose concentration was brought from hyper‐ to normoglycaemia and kept in the physiological range (6–7 mmol/l) with both routes in all subjects. Mean glucose concentration reached the threshold of 7 mmol/l approximately 2 (i.v.–s.c. route) and 3 (‘s.c.’–s.c. route) hours after the start of glucose control with the MPC. During the last 2 h of automated glucose control, mean glucose concentration was 6.3 ± 0.2 mmol/l and 6.6 ± 0.3 mmol/l for i.v.–s.c. and ‘s.c.’–s.c. route, respectively. Glucose concentration, insulin doses, and serum insulin levels did not differ significantly between routes ( P > 0.05). Conclusions The MPC algorithm is suitable for glucose control during fasting within an extracorporeal artificial β‐cell in the subcutaneous route Type 1 diabetic patients. To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters-a model predictive control (MPC) algorithm-to control blood glucose concentration during fasting conditions in patients with Type 1 diabetes. In the subcutaneous (sc) route within a closed loop system.AIMSTo evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters-a model predictive control (MPC) algorithm-to control blood glucose concentration during fasting conditions in patients with Type 1 diabetes. In the subcutaneous (sc) route within a closed loop system.Paired experiments were performed in six patients. Over 8 h the MPC algorithm was used to control glucose with s.c. insulin administration and two different glucose monitoring protocols: first, the algorithm was provided with intravenous (i.v.) glucose values for insulin dosage calculation directly (i.v.-s.c. route). Then, in the second experiment, i.v. glucose values were fed to the MPC with a delay of 30 min to simulate s.c. glucose measurements ('s.c.'-s.c. route). In both experiments plasma glucose, insulin dosage, and serum insulin levels were analysed.METHODSPaired experiments were performed in six patients. Over 8 h the MPC algorithm was used to control glucose with s.c. insulin administration and two different glucose monitoring protocols: first, the algorithm was provided with intravenous (i.v.) glucose values for insulin dosage calculation directly (i.v.-s.c. route). Then, in the second experiment, i.v. glucose values were fed to the MPC with a delay of 30 min to simulate s.c. glucose measurements ('s.c.'-s.c. route). In both experiments plasma glucose, insulin dosage, and serum insulin levels were analysed.Glucose concentration was brought from hyper- to normoglycaemia and kept in the physiological range (6-7 mmol/l) with both routes in all subjects. Mean glucose concentration reached the threshold of 7 mmol/l approximately 2 (i.v.-s.c. route) and 3 ('s.c.'-s.c. route) hours after the start of glucose control with the MPC. During the last 2 h of automated glucose control, mean glucose concentration was 6.3 +/- 0.2 mmol/l and 6.6 +/- 0.3 mmol/l for i.v.-s.c. and 's.c.'-s.c. route, respectively. Glucose concentration, insulin doses, and serum insulin levels did not differ significantly between routes (P > 0.05).RESULTSGlucose concentration was brought from hyper- to normoglycaemia and kept in the physiological range (6-7 mmol/l) with both routes in all subjects. Mean glucose concentration reached the threshold of 7 mmol/l approximately 2 (i.v.-s.c. route) and 3 ('s.c.'-s.c. route) hours after the start of glucose control with the MPC. During the last 2 h of automated glucose control, mean glucose concentration was 6.3 +/- 0.2 mmol/l and 6.6 +/- 0.3 mmol/l for i.v.-s.c. and 's.c.'-s.c. route, respectively. Glucose concentration, insulin doses, and serum insulin levels did not differ significantly between routes (P > 0.05).The MPC algorithm is suitable for glucose control during fasting within an extracorporeal artificial beta-cell in the subcutaneous route Type 1 diabetic patients.CONCLUSIONSThe MPC algorithm is suitable for glucose control during fasting within an extracorporeal artificial beta-cell in the subcutaneous route Type 1 diabetic patients. Aims To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters—a model predictive control (MPC) algorithm—to control blood glucose concentration during fasting conditions in patients with Type 1 diabetes. In the subcutaneous (sc) route within a closed loop system. Methods Paired experiments were performed in six patients. Over 8 h the MPC algorithm was used to control glucose with s.c. insulin administration and two different glucose monitoring protocols: first, the algorithm was provided with intravenous (i.v.) glucose values for insulin dosage calculation directly (i.v.–s.c. route). Then, in the second experiment, i.v. glucose values were fed to the MPC with a delay of 30 min to simulate s.c. glucose measurements (‘s.c.’–s.c. route). In both experiments plasma glucose, insulin dosage, and serum insulin levels were analysed. Results Glucose concentration was brought from hyper‐ to normoglycaemia and kept in the physiological range (6–7 mmol/l) with both routes in all subjects. Mean glucose concentration reached the threshold of 7 mmol/l approximately 2 (i.v.–s.c. route) and 3 (‘s.c.’–s.c. route) hours after the start of glucose control with the MPC. During the last 2 h of automated glucose control, mean glucose concentration was 6.3 ± 0.2 mmol/l and 6.6 ± 0.3 mmol/l for i.v.–s.c. and ‘s.c.’–s.c. route, respectively. Glucose concentration, insulin doses, and serum insulin levels did not differ significantly between routes (P > 0.05). Conclusions The MPC algorithm is suitable for glucose control during fasting within an extracorporeal artificial β‐cell in the subcutaneous route Type 1 diabetic patients. |
| Author | Schaupp, L. Bodenlenz, M. Wilinska, M. E. Wach, P. Schaller, H. C. Pieber, T. R. Vering, T. Hovorka, R. Chassin, L. J. |
| Author_xml | – sequence: 1 givenname: H. C. surname: Schaller fullname: Schaller, H. C. email: schaller@microperfusion.at organization: Department of Biophysics, Institute of Biomedical Engineering, University of Technology Graz and – sequence: 2 givenname: L. surname: Schaupp fullname: Schaupp, L. organization: Department of Biophysics, Institute of Biomedical Engineering, University of Technology Graz and – sequence: 3 givenname: M. surname: Bodenlenz fullname: Bodenlenz, M. organization: Department of Biophysics, Institute of Biomedical Engineering, University of Technology Graz and – sequence: 4 givenname: M. E. surname: Wilinska fullname: Wilinska, M. E. organization: Diabetes Modelling Group, Department of Paediatrics, University of Cambridge, Cambridge, UK, and – sequence: 5 givenname: L. J. surname: Chassin fullname: Chassin, L. J. organization: Diabetes Modelling Group, Department of Paediatrics, University of Cambridge, Cambridge, UK, and – sequence: 6 givenname: P. surname: Wach fullname: Wach, P. organization: Department of Biophysics, Institute of Biomedical Engineering, University of Technology Graz and – sequence: 7 givenname: T. surname: Vering fullname: Vering, T. organization: Disetronic Medical Systems AG, Burgdorf, Switzerland – sequence: 8 givenname: R. surname: Hovorka fullname: Hovorka, R. organization: Diabetes Modelling Group, Department of Paediatrics, University of Cambridge, Cambridge, UK, and – sequence: 9 givenname: T. R. surname: Pieber fullname: Pieber, T. R. organization: Department of Internal Medicine, Diabetes and Metabolism, Medical University Graz, Graz, Austria |
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| Cites_doi | 10.1109/51.897829 10.1172/JCI117817 10.2337/diab.23.5.397 10.1007/978-1-4471-3398-8 10.1002/dmrr.205 10.2337/diacare.24.9.1696 10.1152/ajpendo.1999.277.3.E561 10.2337/diab.43.3.396 10.1109/TBME.1981.324661 10.1088/0967-3334/25/4/010 10.1055/s-2001-18594 10.1089/152091504774197990 10.1016/j.addr.2003.08.011 10.1109/10.740877 |
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| Keywords | Endocrinopathy Human Immunopathology algorithms Adaptive algorithm Subcutaneous Langerhans islet Autoimmune disease Glucose glucose monitoring continuous subcutaneous insulin infusion Surveillance Type 1 diabetes Insulin pump glucose control β Cell Predictive factor Fast Glycemia artificial β-cell Endocrine pancreas |
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| References | Camacho EF, Bordons C. Model Predictive Control. Berlin: Springer-Verlag 1999. Parker RS, Doyle FJ 3rd, Peppas NA. A model-based algorithm for blood glucose control in type I diabetic patients. IEEE Trans Biomed Eng 1999; 46: 148-157. Albisser AM, Leibel BS, Ewart TG, Davidovac Z, Botz CK, Zingg W et al. Clinical control of diabetes by the artificial pancreas. Diabetes 1974; 23: 397-404. Howey DC, Bowsher RR, Brunelle RL, Woodworth JR. [Lys(B28), Pro(B29)]-human insulin. A rapidly absorbed analogue of human insulin. Diabetes 1994; 43: 396-402. Hovorka R, Chassin LJ, Wiliniska ME, Canonico V, Akwi JA, Federici MO et al. Closing the loop: the Adicol experience. Diabetes Technol Ther 2004; 6: 307-318. Broekhuyse HM, Nelson JD, Zinman B, Albisser AM. Comparison of algorithms for the closed-loop control of blood glucose using the artificial beta cell. IEEE Trans Biomed Eng 1981; 28: 678-687. Bode BW, Sabbah HT, Gross TM, Fredrickson LP, Davidson PC. Diabetes management in the new millennium using insulin pump therapy. Diabetes Metab Res Rev 2002; 18: S14-S20. Steil GM, Panteleon AE, Rebrin K. Closed-loop insulin delivery-the path to physiological glucose control. Adv Drug Deliv Rev 2004; 56: 125-144. Hovorka R, Canonico V, Chassin LJ, Haueter U, Massi-Benedetti M, Orsini Federici M et al. Non-linear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol Meas 2004; 25: 905-920. Jungheim K, Wientjes KJ, Heinemann L, Lodwig V, Koschinsky T, Schoonen AJ. Subcutaneous continuous glucose monitoring: feasibility of a new microdialysis-based glucose sensor system. Diabetes Care 2001; 24: 1696-1697. Freckmann G, Kalatz B, Pfeiffer B, Hoss U, Haug C. Recent advances in continuous glucose monitoring. Exp Clin Endocrinol Diabetes 2001; 109: S347-S357. Sturis J, Scheen AJ, Leproult R, Polonsky KS, Van Cauter E. 24-hour glucose profiles during continuous or oscillatory insulin infusion. Demonstration of the functional significance of ultradian insulin oscillations. J Clin Invest 1995; 95: 1464-1471. Parker RS, Doyle FJ 3rd, Peppas NA. The intravenous route to blood glucose control. IEEE Eng Med Biol Mag 2001; 20: 65-73. Rebrin K, Steil GM, van Antwerp WP, Mastrototaro JJ. Subcutaneous glucose predicts plasma glucose independent of insulin: implications for continuous monitoring. Am J Physiol 1999; 277: E561-E571. 1995; 95 1974; 23 2002; 18 2001 2004; 25 2004; 56 1999; 46 2004; 6 1981; 28 1999; 277 2001; 24 2001; 109 2001; 20 1994; 43 1999 e_1_2_8_16_2 e_1_2_8_12_2 e_1_2_8_13_2 e_1_2_8_14_2 e_1_2_8_15_2 e_1_2_8_9_2 e_1_2_8_2_2 e_1_2_8_4_2 e_1_2_8_3_2 e_1_2_8_6_2 e_1_2_8_5_2 e_1_2_8_8_2 e_1_2_8_7_2 e_1_2_8_10_2 e_1_2_8_11_2 |
| References_xml | – reference: Hovorka R, Canonico V, Chassin LJ, Haueter U, Massi-Benedetti M, Orsini Federici M et al. Non-linear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol Meas 2004; 25: 905-920. – reference: Parker RS, Doyle FJ 3rd, Peppas NA. A model-based algorithm for blood glucose control in type I diabetic patients. IEEE Trans Biomed Eng 1999; 46: 148-157. – reference: Parker RS, Doyle FJ 3rd, Peppas NA. The intravenous route to blood glucose control. IEEE Eng Med Biol Mag 2001; 20: 65-73. – reference: Albisser AM, Leibel BS, Ewart TG, Davidovac Z, Botz CK, Zingg W et al. Clinical control of diabetes by the artificial pancreas. Diabetes 1974; 23: 397-404. – reference: Howey DC, Bowsher RR, Brunelle RL, Woodworth JR. [Lys(B28), Pro(B29)]-human insulin. A rapidly absorbed analogue of human insulin. Diabetes 1994; 43: 396-402. – reference: Rebrin K, Steil GM, van Antwerp WP, Mastrototaro JJ. Subcutaneous glucose predicts plasma glucose independent of insulin: implications for continuous monitoring. Am J Physiol 1999; 277: E561-E571. – reference: Steil GM, Panteleon AE, Rebrin K. Closed-loop insulin delivery-the path to physiological glucose control. Adv Drug Deliv Rev 2004; 56: 125-144. – reference: Broekhuyse HM, Nelson JD, Zinman B, Albisser AM. Comparison of algorithms for the closed-loop control of blood glucose using the artificial beta cell. IEEE Trans Biomed Eng 1981; 28: 678-687. – reference: Camacho EF, Bordons C. Model Predictive Control. Berlin: Springer-Verlag 1999. – reference: Bode BW, Sabbah HT, Gross TM, Fredrickson LP, Davidson PC. Diabetes management in the new millennium using insulin pump therapy. Diabetes Metab Res Rev 2002; 18: S14-S20. – reference: Jungheim K, Wientjes KJ, Heinemann L, Lodwig V, Koschinsky T, Schoonen AJ. Subcutaneous continuous glucose monitoring: feasibility of a new microdialysis-based glucose sensor system. Diabetes Care 2001; 24: 1696-1697. – reference: Hovorka R, Chassin LJ, Wiliniska ME, Canonico V, Akwi JA, Federici MO et al. Closing the loop: the Adicol experience. Diabetes Technol Ther 2004; 6: 307-318. – reference: Sturis J, Scheen AJ, Leproult R, Polonsky KS, Van Cauter E. 24-hour glucose profiles during continuous or oscillatory insulin infusion. Demonstration of the functional significance of ultradian insulin oscillations. J Clin Invest 1995; 95: 1464-1471. – reference: Freckmann G, Kalatz B, Pfeiffer B, Hoss U, Haug C. Recent advances in continuous glucose monitoring. 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| Snippet | Aims To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters—a model predictive control (MPC) algorithm—to... Aims To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters—a model predictive control (MPC) algorithm—to... To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters-a model predictive control (MPC) algorithm-to control... |
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| SubjectTerms | Administration, Cutaneous Algorithms artificial β-cell Biological and medical sciences Blood Glucose - analysis Computer Simulation continuous subcutaneous insulin infusion Diabetes Mellitus, Type 1 - blood Diabetes. Impaired glucose tolerance Drug Administration Schedule Endocrine pancreas. Apud cells (diseases) Endocrinopathies Etiopathogenesis. Screening. Investigations. Target tissue resistance Fasting Female glucose control glucose monitoring Humans Insulin - administration & dosage Insulin - blood Insulin Infusion Systems Male Medical sciences Models, Biological |
| Title | On-line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: evaluation under fasting conditions in patients with Type 1 diabetes |
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