BACKGROUND AND AIM: The insulin to carbohydrate ratio (CR) is a parameter used by patients with type 1 diabetes (T1D) to calculate the premeal insulin bolus. Usually, it is estimated by the physician based on patient diary, but modern diabetes technologies, such as subcutaneous glucose sensing (continuous glucose monitoring, CGM) and insulin delivery (continuous subcutaneous insulin infusion, CSII) systems, can provide important information for its optimization. In this study, a method for CR optimization based on CGM and CSII data is presented and its efficacy and robustness tested in several in silico scenarios, with the primary aim of increasing protection against hypoglycemia. METHODS: The method is based on a validated index of insulin sensitivity calculated from sensor and pump data (SISP), area under CGM and CSII curves. The efficacy and robustness of the method are tested in silico using the University of Virginia/Padova T1D simulator, in several suboptimal therapy scenarios: with nominal CR variation, over/underestimation of meal size or suboptimal basal insulin infusion. Simulated CGM and CSII data were used to calculate the optimal CR. The same scenarios were then repeated using the estimated CR and glycemic control was compared. RESULTS: The optimized CR was efficacious in protecting against hypoglycemic events caused by suboptimal therapy. The method was also robust to possible error in carbohydrate count and suboptimal basal insulin infusion. CONCLUSIONS: A novel method for CR optimization in T1D, implementable in daily life using CGM and CSII data, is proposed. The method can be used both in open- and closed-loop insulin therapy.
BACKGROUND AND AIM: The insulin to carbohydrate ratio (CR) is a parameter used by patients with type 1 diabetes (T1D) to calculate the premeal insulin bolus. Usually, it is estimated by the physician based on patient diary, but modern diabetes technologies, such as subcutaneous glucose sensing (continuous glucose monitoring, CGM) and insulin delivery (continuous subcutaneous insulin infusion, CSII) systems, can provide important information for its optimization. In this study, a method for CR optimization based on CGM and CSII data is presented and its efficacy and robustness tested in several in silico scenarios, with the primary aim of increasing protection against hypoglycemia. METHODS: The method is based on a validated index of insulin sensitivity calculated from sensor and pump data (SISP), area under CGM and CSII curves. The efficacy and robustness of the method are tested in silico using the University of Virginia/Padova T1D simulator, in several suboptimal therapy scenarios: with nominal CR variation, over/underestimation of meal size or suboptimal basal insulin infusion. Simulated CGM and CSII data were used to calculate the optimal CR. The same scenarios were then repeated using the estimated CR and glycemic control was compared. RESULTS: The optimized CR was efficacious in protecting against hypoglycemic events caused by suboptimal therapy. The method was also robust to possible error in carbohydrate count and suboptimal basal insulin infusion. CONCLUSIONS: A novel method for CR optimization in T1D, implementable in daily life using CGM and CSII data, is proposed. The method can be used both in open- and closed-loop insulin therapy.
Authors: Kirstine J Bell; Carmel E Smart; Garry M Steil; Jennie C Brand-Miller; Bruce King; Howard A Wolpert Journal: Diabetes Care Date: 2015-06 Impact factor: 19.112
Authors: Lalo Magni; Marco Forgione; Chiara Toffanin; Chiara Dalla Man; Boris Kovatchev; Giuseppe De Nicolao; Claudio Cobelli Journal: J Diabetes Sci Technol Date: 2009-09-01
Authors: Ling Hinshaw; Chiara Dalla Man; Debashis K Nandy; Ahmed Saad; Adil E Bharucha; James A Levine; Robert A Rizza; Rita Basu; Rickey E Carter; Claudio Cobelli; Yogish C Kudva; Ananda Basu Journal: Diabetes Date: 2013-02-27 Impact factor: 9.461
Authors: Nunzio Camerlingo; Martina Vettoretti; Simone Del Favero; Andrea Facchinetti; Giovanni Sparacino Journal: J Diabetes Sci Technol Date: 2020-09-17