Comparison of interval and Monte Carlo simulation for the prediction of postprandial glucose under uncertainty in type 1 diabetes mellitus

[EN] In this paper, the problem of tackling uncertainty in the prediction of postprandial blood glucose is analyzed. Two simulation approaches, Monte Carlo and interval models, are studied and compared. Interval simulation is carried out using modal interval analysis. Simulation of a glucoregulatory model with uncertainty in insulin sensitivities, glucose absorption and food intake is carried out using both methods. Interval simulation is superior in predicting all severe and mild hyper- and hypoglycemia episodes. Furthermore, much less computational time is required for interval simulation than for Monte Carlo simulationThis work was partially supported by the Spanish Ministry of Science and Innovation and the European Union through Grant DPI-2007-66728 and by the Autonomous Government of Catalonia through SGR00523.Calm, R.; García-Jaramillo, M.; Bondía Company, J.; Sainz, M.; Vehí, J. (2011). Comparison of interval and Monte Carlo simulation for the prediction of postprandial glucose under uncertainty in type 1 diabetes mellitus. Computer Methods and Programs in Biomedicine. 104(3):325-332. https://doi.org/10.1016/j.cmpb.2010.08.008S325332104

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

[EN] Objective: Set-inversion-based prandial insulin delivery is a new model-based bolus advisor for postprandial glucose control in type 1 diabetes mellitus (T1DM). It automatically coordinates the values of basal-bolus insulin to be infused during the postprandial period so as to achieve some predefined control objectives. However, the method requires an excessive computation time to compute the solution set of feasible insulin profiles, which impedes its integration into an insulin pump. In this work, a new algorithm is presented, which reduces computation time significantly and enables the integration of this new bolus advisor into current processing features of smart insulin pumps. Methods: A new strategy was implemented that focused on finding the combined basal-bolus solution of interest rather than an extensive search of the feasible set of solutions. Analysis of interval simulations, inclusion of physiological assumptions, and search domain contractions were used. Data from six real patients with T1DM were used to compare the performance between the optimized and the conventional computations. Results: In all cases, the optimized version yielded the basal-bolus combination recommended by the conventional method and in only 0.032% of the computation time. Simulations show that the mean number of iterations for the optimized computation requires approximately 3.59 s at 20 MHz processing power, in line with current features of smart pumps. Conclusions: A computationally efficient method for basal-bolus coordination in postprandial glucose control has been presented and tested. The results indicate that an embedded algorithm within smart insulin pumps is now feasible. Nonetheless, we acknowledge that a clinical trial will be needed in order to justify this claim.This work was partially supported by the Spanish Ministry of Science and Innovation through Grant DPI-2010-20764-C02 and by the Autonomous Government of Catalonia through Grant SGR 523. Fabian León-Vargas acknowledges the FI grants of Generalitat de Catalunya.León-Vargas, F.; Calm, R.; Bondía Company, J.; Vehí, J. (2012). Improving the computational effort of set-inversion-based prandial insulin delivery for its integration in insulin pumps. Journal of Diabetes Science and Technology. 6(6):1420-1428. https://doi.org/10.1177/193229681200600623S1420142866Shetty, G., & Wolpert, H. (2010). Insulin Pump Use in Adults with Type 1 Diabetes—Practical Issues. Diabetes Technology & Therapeutics, 12(S1), S-11-S-16. doi:10.1089/dia.2010.0002Pickup, J. (2002). Glycaemic control with continuous subcutaneous insulin infusion compared with intensive insulin injections in patients with type 1 diabetes: meta-analysis of randomised controlled trials. BMJ, 324(7339), 705-705. doi:10.1136/bmj.324.7339.705Bell, MB, FACE, D. S. H., & Ovalle, MD, F. (2000). IMPROVED GLYCEMIC CONTROL WITH USE OF CONTINUOUS SUBCUTANEOUS INSULIN INFUSION COMPARED WITH MULTIPLE INSULIN INJECTION THERAPY. 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Coordinated Basal—Bolus Infusion for Tighter Postprandial Glucose Control in Insulin Pump Therapy. Journal of Diabetes Science and Technology, 3(1), 89-97. doi:10.1177/193229680900300110Revert, A., Calm, R., Vehi, J., & Bondia, J. (2011). Calculation of the Best Basal–Bolus Combination for Postprandial Glucose Control in Insulin Pump Therapy. IEEE Transactions on Biomedical Engineering, 58(2), 274-281. doi:10.1109/tbme.2010.2058805Revert, A., Rossetti, P., Calm, R., Vehí, J., & Bondia, J. (2010). Combining Basal-Bolus Insulin Infusion for Tight Postprandial Glucose Control: An in Silico Evaluation in Adults, Children, and Adolescents. Journal of Diabetes Science and Technology, 4(6), 1424-1437. doi:10.1177/193229681000400617Calm, R., García-Jaramillo, M., Bondia, J., Sainz, M. A., & Vehí, J. (2011). Comparison of interval and Monte Carlo simulation for the prediction of postprandial glucose under uncertainty in type 1 diabetes mellitus. Computer Methods and Programs in Biomedicine, 104(3), 325-332. doi:10.1016/j.cmpb.2010.08.008Zisser, H. C., Bevier, W., Dassau, E., & Jovanovič, L. (2010). Siphon Effects on Continuous Subcutaneous Insulin Infusion Pump Delivery Performance. Journal of Diabetes Science and Technology, 4(1), 98-103. doi:10.1177/19322968100040011