Combining basal-bolus insulin infusion for tight postprandial glucose control: an in silico evaluation in adults, children, and adolescents.
BACKGROUND
Achieving good postprandial glycemic control, without triggering hypoglycemia events, is a challenge of treatment strategies for type 1 diabetes subjects. Continuous subcutaneous insulin infusion, the gold standard of therapy, is based on heuristic adjustments of both basal and prandial insulin. Some tools, such as bolus calculators, are available to aid patients in selecting a meal-related insulin dose. However, they are still based on empiric parameters such as the insulin-to-carbohydrate ratio and on the physicians' and patients' ability to fit bolus mode to meal composition.


METHODS
In this article, a nonheuristic method for assessment of prandial insulin administration is presented and evaluated. An algorithm based on set inversion via interval analysis is used to coordinate basal and bolus insulin infusions to deal with postprandial glucose excursions. The evaluation is carried out through an in silico study using the 30 virtual patients available in the educational version of the Food and Drug Administration-accepted University of Virginia simulator. Results obtained using the standard bolus strategy and different coordinated basal-bolus solutions provided by the algorithm are compared.


RESULTS
Coordinated basal-bolus solutions improve postprandial glucose performance in most cases, mainly in terms of reducing hypoglycemia risk, but also increasing the percentage of time in normoglycemia. Moreover, glycemic variability is reduced considerably by using these innovative solutions.


CONCLUSIONS
The algorithm presented here is a robust nonheuristic alternative to deal with postprandial glycemic control. It is shown as a powerful tool that could be integrated in future smart insulin pumps.
Instituto Universitario de Automática e Informática Industrial, Universidad Politécnica de Valencia, Camino de Vera s/n, Valencia, Spain.
Ana
A
Revert
Paolo
P
Rossetti
Remei
R
Calm
Josep
J
Vehí
Jorge
J
Bondia
D000293:Adolescent; D000465:Algorithms; D001331:Automation; D001786:Blood Glucose / Q000187:drug effects* / Q000378:metabolism; D002648:Child; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000188:drug therapy*; D054796:Drug Dosage Calculations; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000139:chemically induced / Q000517:prevention & control; D007004:Hypoglycemic Agents / Q000008:administration & dosage* / Q000009:adverse effects; D007328:Insulin / Q000008:administration & dosage* / Q000009:adverse effects; D007332:Insulin Infusion Systems; D008875:Middle Aged; D008954:Models, Biological; D019518:Postprandial Period; D013997:Time Factors; D016896:Treatment Outcome
D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin