Literature DB >> 20046653

Coordinated basal-bolus infusion for tighter postprandial glucose control in insulin pump therapy.

Jorge Bondia1, Eyal Dassau, Howard Zisser, Remei Calm, Josep Vehí, Lois Jovanovič, Francis J Doyle.   

Abstract

BACKGROUND: Basal and bolus insulin determination in intensive insulin therapy for type 1 diabetes mellitus (T1DM) are currently considered independently of each other. A new strategy that coordinates basal and bolus insulin infusion to cope with postprandial glycemia in pump therapy is proposed. Superior performance of this new strategy is demonstrated through a formal analysis of attainable performances in an in silico study.
METHODS: The set inversion via interval analysis algorithm has been applied to obtain the feasible set of basal and bolus doses that, for a given meal, mathematically guarantee a postprandial response fulfilling the International Diabetes Federation (IDF) guidelines (i.e., no hypoglycemia and 2 h postprandial glucose below 140 mg/dl). Hypoglycemia has been defined as a glucose value below 70 mg/dl. A 5 h time horizon has been considered for a 70 kg in silico T1DM subject consuming meals in the range of 30 to 80 g of carbohydrates.
RESULTS: The computed feasible sets demonstrate that current separated basal/bolus strategy dramatically limits the attainable performance. For a nominal basal of 0.8 IU/h leading to a basal glucose of approximately 100 mg/dl, IDF guidelines cannot be fulfilled for meals greater than 50 g of carbohydrates, independent of the bolus insulin computed. However, coordinating the basal and bolus insulin delivery can achieve this. A decrement of basal insulin during the postprandial period is required together with an increase in bolus insulin, in appropriate percentages, which is meal dependent. After 3 h, basal insulin can be restored to its nominal value.
CONCLUSIONS: The new strategy meets IDF guidelines in a typical day, contrary to the standard basal/bolus strategy, yielding a mean 2 h postprandial glucose reduction of 36.4 mg/dl without late hypoglycemia. The application of interval analysis for the computation of feasible sets is demonstrated to be a powerful tool for the analysis of attainable performance in glucose control. © Diabetes Technology Society

Entities:  

Keywords:  glucose control; insulin pump therapy; interval analysis; set inversion; type 1 diabetes mellitus

Mesh:

Substances:

Year:  2009        PMID: 20046653      PMCID: PMC2769848          DOI: 10.1177/193229680900300110

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  5 in total

1.  Post-prandial glucose excursions following four methods of bolus insulin administration in subjects with type 1 diabetes.

Authors:  H P Chase; S Z Saib; T MacKenzie; M M Hansen; S K Garg
Journal:  Diabet Med       Date:  2002-04       Impact factor: 4.359

2.  Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes.

Authors:  Roman Hovorka; Valentina Canonico; Ludovic J Chassin; Ulrich Haueter; Massimo Massi-Benedetti; Marco Orsini Federici; Thomas R Pieber; Helga C Schaller; Lukas Schaupp; Thomas Vering; Malgorzata E Wilinska
Journal:  Physiol Meas       Date:  2004-08       Impact factor: 2.833

3.  Partitioning glucose distribution/transport, disposal, and endogenous production during IVGTT.

Authors:  Roman Hovorka; Fariba Shojaee-Moradie; Paul V Carroll; Ludovic J Chassin; Ian J Gowrie; Nicola C Jackson; Romulus S Tudor; A Margot Umpleby; Richard H Jones
Journal:  Am J Physiol Endocrinol Metab       Date:  2002-05       Impact factor: 4.310

Review 4.  Bolus calculator: a review of four "smart" insulin pumps.

Authors:  Howard Zisser; Lauren Robinson; Wendy Bevier; Eyal Dassau; Christian Ellingsen; Francis J Doyle; Lois Jovanovic
Journal:  Diabetes Technol Ther       Date:  2008-12       Impact factor: 6.118

5.  Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus.

Authors:  R Calm; M García-Jaramillo; J Vehí; J Bondia; C Tarín; W García-Gabín
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2007
  5 in total
  11 in total

Review 1.  Boluses in Insulin Therapy.

Authors:  Ralph Ziegler; Guido Freckmann; Lutz Heinemann
Journal:  J Diabetes Sci Technol       Date:  2016-07-10

2.  A novel adaptive basal therapy based on the value and rate of change of blood glucose.

Authors:  Youqing Wang; Matthew W Percival; Eyal Dassau; Howard C Zisser; Lois Jovanovic; Francis J Doyle
Journal:  J Diabetes Sci Technol       Date:  2009-09-01

3.  Probabilistic evolving meal detection and estimation of meal total glucose appearance.

Authors:  Fraser Cameron; Günter Niemeyer; Bruce A Buckingham
Journal:  J Diabetes Sci Technol       Date:  2009-09-01

Review 4.  Bolus Advisors: Sources of Error, Targets for Improvement.

Authors:  John Walsh; Ruth Roberts; Timothy S Bailey; Lutz Heinemann
Journal:  J Diabetes Sci Technol       Date:  2017-07-25

5.  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.

Authors:  Howard Zisser; Eric Renard; Boris Kovatchev; Claudio Cobelli; Angelo Avogaro; Revital Nimri; Lalo Magni; Bruce A Buckingham; H Peter Chase; Francis J Doyle; John Lum; Peter Calhoun; Craig Kollman; Eyal Dassau; Anne Farret; Jerome Place; Marc Breton; Stacey M Anderson; Chiara Dalla Man; Simone Del Favero; Daniela Bruttomesso; Alessio Filippi; Rachele Scotton; Moshe Phillip; Eran Atlas; Ido Muller; Shahar Miller; Chiara Toffanin; Davide Martino Raimondo; Giuseppe De Nicolao; Roy W Beck
Journal:  Diabetes Technol Ther       Date:  2014-07-08       Impact factor: 6.118

6.  Combining basal-bolus insulin infusion for tight postprandial glucose control: an in silico evaluation in adults, children, and adolescents.

Authors:  Ana Revert; Paolo Rossetti; Remei Calm; Josep Vehí; Jorge Bondia
Journal:  J Diabetes Sci Technol       Date:  2010-11-01

7.  Optimal control of blood glucose: the diabetic patient or the machine?

Authors:  Larry Brown; Elazer R Edelman
Journal:  Sci Transl Med       Date:  2010-04-14       Impact factor: 17.956

8.  Improving the computational effort of set-inversion-based prandial insulin delivery for its integration in insulin pumps.

Authors:  Fabian León-Vargas; Remei Calm; Jorge Bondia; Josep Vehí
Journal:  J Diabetes Sci Technol       Date:  2012-11-01

Review 9.  Systems engineering medicine: engineering the inflammation response to infectious and traumatic challenges.

Authors:  Robert S Parker; Gilles Clermont
Journal:  J R Soc Interface       Date:  2010-02-10       Impact factor: 4.118

10.  Systematically in silico comparison of unihormonal and bihormonal artificial pancreas systems.

Authors:  Xiaoteng Gao; Huangjiang Ning; Youqing Wang
Journal:  Comput Math Methods Med       Date:  2013-10-24       Impact factor: 2.238

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