OBJECTIVES: The goal of this study was to develop a system model of type 1 diabetes for the purpose of in silico simulation for the prediction of long-term glycemic control outcomes. METHODS: The system model was created and identified on a physiological cohort of virtual type 1 diabetes patients (n = 40). Integral-based identification was used to develop (n = 40) insulin sensitivity profiles. RESULTS: The n = 40 insulin sensitivity profiles provide a driving input for virtual patient trials using the models developed. The identified models have a median (90% range) absolute percentage error of 1.33% (0.08-7.20%). The median (90% range) absolute error was 0.12 mmol/liter (0.01-0.56 mmol/liter). The model and integral-based identification of SI captured all patient dynamics with low error, which would lead to more physiological behavior simulation. CONCLUSIONS: A simulation tool incorporating n = 40 virtual patient data sets to predict long-term glycemic control outcomes from clinical interventions was developed based on a physiological type 1 diabetes metabolic system model. The overall goal is to utilize this model and insulin sensitivity profiles to develop and optimize self-monitoring blood glucose and multiple daily injection therapy.
OBJECTIVES: The goal of this study was to develop a system model of type 1 diabetes for the purpose of in silico simulation for the prediction of long-term glycemic control outcomes. METHODS: The system model was created and identified on a physiological cohort of virtual type 1 diabetespatients (n = 40). Integral-based identification was used to develop (n = 40) insulin sensitivity profiles. RESULTS: The n = 40 insulin sensitivity profiles provide a driving input for virtual patient trials using the models developed. The identified models have a median (90% range) absolute percentage error of 1.33% (0.08-7.20%). The median (90% range) absolute error was 0.12 mmol/liter (0.01-0.56 mmol/liter). The model and integral-based identification of SI captured all patient dynamics with low error, which would lead to more physiological behavior simulation. CONCLUSIONS: A simulation tool incorporating n = 40 virtual patient data sets to predict long-term glycemic control outcomes from clinical interventions was developed based on a physiological type 1 diabetes metabolic system model. The overall goal is to utilize this model and insulin sensitivity profiles to develop and optimize self-monitoring blood glucose and multiple daily injection therapy.
Authors: Joseph El Youssef; Jessica R Castle; Deborah L Branigan; Ryan G Massoud; Matthew E Breen; Peter G Jacobs; B Wayne Bequette; W Kenneth Ward Journal: J Diabetes Sci Technol Date: 2011-11-01
Authors: Xing-Wei Wong; J Geoffrey Chase; Christopher E Hann; Thomas F Lotz; Jessica Lin; Aaron J Le Compte; Geoffrey M Shaw Journal: J Diabetes Sci Technol Date: 2008-05
Authors: J Geoffrey Chase; Aaron LeCompte; Geoffrey M Shaw; Amy Blakemore; Jason Wong; Jessica Lin; Christopher E Hann Journal: J Diabetes Sci Technol Date: 2008-07
Authors: James A Revie; David J Stevenson; J Geoffrey Chase; Christopher E Hann; Bernard C Lambermont; Alexandre Ghuysen; Philippe Kolh; Philippe Morimont; Geoffrey M Shaw; Thomas Desaive Journal: Ann Intensive Care Date: 2011-08-11 Impact factor: 6.925
Authors: Paul D Docherty; J Geoffrey Chase; Christopher E Hann; Thomas F Lotz; J Lin; Kirsten A McAuley; Geoffrey M Shaw Journal: Open Med Inform J Date: 2010-07-27