Real-time estimation of plasma insulin concentration from continuous glucose monitor measurements.
Continuous glucose monitors can measure interstitial glucose concentration in real time for closed-loop glucose control systems, known as artificial pancreas. These control systems use an insulin feedback to maintain plasma glucose concentration within a narrow and safe range, and thus to avoid health complications. As it is not possible to measure plasma insulin concentration in real time, insulin models have been used in literature to estimate them. Nevertheless, the significant inter- and intra-patient variability of insulin absorption jeopardizes the accuracy of these estimations. In order to reduce these limitations, our objective is to perform a real-time estimation of plasma insulin concentration from continuous glucose monitoring (CGM). Hovorka's glucose-insulin model has been incorporated in an extended Kalman filter in which different selected time-variant model parameters have been considered as extended states. The observability of the original Hovorka's model and of several extended models has been evaluated by their Lie derivatives. We have evaluated this methodology with an in-silico study with 100 patients with Type 1 diabetes during 25 h. Furthermore, it has been also validated using clinical data from 12 insulin pump patients with Type 1 diabetes who underwent four mixed meal studies. Real-time insulin estimations have been compared to plasma insulin measurements to assess performance showing the validity of the methodology here used in comparison with that formerly used for insulin models. Hence, real-time estimations for plasma insulin concentration based on subcutaneous glucose monitoring can be beneficial for increasing the efficiency of control algorithms for the artificial pancreas.
a Institut Universitari d'Automàtica i Informàtica Industrial , Universitat Politècnica de València , Spain .
Diego
D
de Pereda
b Institut Universitari de Matemàtica Multidisciplinar , Universitat Politècnica de València , Spain .
Sergio
S
Romero-Vivo
b Institut Universitari de Matemàtica Multidisciplinar , Universitat Politècnica de València , Spain .
Beatriz
B
Ricarte
c Departamento de Medicina Interna , Hospital Sant Francesc de Borja , Gandía, Valencia , Spain .
Paolo
P
Rossetti
d Diabetes Reference Unit, Endocrinology and Nutrition Department , Valencia Clinic University Hospital , Valencia , Spain .
Francisco Javier
FJ
Ampudia-Blasco
a Institut Universitari d'Automàtica i Informàtica Industrial , Universitat Politècnica de València , Spain .
Jorge
J
Bondia
D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation*; D003198:Computer Simulation; D003199:Computer Systems; D003922:Diabetes Mellitus, Type 1 / Q000097:blood; D005260:Female; D006801:Humans; D007328:Insulin / Q000097:blood*; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D015203:Reproducibility of Results
D001786:Blood Glucose; D007328:Insulin
Extended Kalman filter; Type 1 diabetes; artificial pancreas; glucose–insulin models; insulin estimation