Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: preliminary studies in Padova and Montpellier.
New effort has been made to develop closed-loop glucose control, using subcutaneous (SC) glucose sensing and continuous subcutaneous insulin infusion (CSII) from a pump, and a control algorithm. An approach based on a model predictive control (MPC) algorithm has been utilized during closed-loop control in type 1 diabetes patients. Here we describe the preliminary clinical experience with this approach. Six type 1 diabetes patients (three in each of two clinical investigation centers in Padova and Montpellier), using CSII, aged 36 +/- 8 and 48 +/- 6 years, duration of diabetes 12 +/- 8 and 29 +/- 4 years, hemoglobin A1c 7.4% +/- 0.1% and 7.3% +/- 0.3%, body mass index 23.2 +/- 0.3 and 28.4 +/- 2.2 kg/m(2), respectively, were studied on two occasions during 22 h overnight hospital admissions 2-4 weeks apart. A Freestyle Navigator(R) continuous glucose monitor and an OmniPod insulin pump were applied in each trial. Admission 1 used open-loop control, while admission 2 employed closed-loop control using our MPC algorithm. In Padova, two out of three subjects showed better performance with the closed-loop system compared to open loop. Altogether, mean overnight plasma glucose (PG) levels were 134 versus 111 mg/dl during open loop versus closed loop, respectively. The percentage of time spent at PG > 140 mg/dl was 45% versus 12%, while postbreakfast mean PG was 165 versus 156 mg/dl during open loop versus closed loop, respectively. Also, in Montpellier, two patients out of three showed a better glucose control during closed-loop trials. Avoidance of nocturnal hypoglycemic excursions was a clear benefit during algorithm-guided insulin delivery in all cases. This preliminary set of studies demonstrates that closed-loop control based entirely on SC glucose sensing and insulin delivery is feasible and can be applied to improve glucose control in patients with type 1 diabetes, although the algorithm needs to be further improved to achieve better glycemic control.
Bruttomesso
Daniela
D
Department of Clinical and Experimental Medicine, Division of Metabolic Diseases, University of Padova, Padova, Italy.
Farret
Anne
A
Costa
Silvana
S
Marescotti
Maria Cristina
MC
Vettore
Monica
M
Avogaro
Angelo
A
Tiengo
Antonio
A
Dalla Man
Chiara
C
Place
Jerome
J
Facchinetti
Andrea
A
Guerra
Stefania
S
Magni
Lalo
L
De Nicolao
Giuseppe
G
Cobelli
Claudio
C
Renard
Eric
E
Maran
Alberto
A
D000328:Adult; D000465:Algorithms; D001786:Blood Glucose / Q000187:drug effects*; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000175:diagnosis / Q000188:drug therapy*; D019719:Diagnostic Equipment; D005240:Feasibility Studies; D005260:Female; D005602:France; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D007003:Hypoglycemia / Q000139:chemically induced / Q000517:prevention & control; D007004:Hypoglycemic Agents / Q000008:administration & dosage* / Q000009:adverse effects; D015918:Infusion Pumps, Implantable; D055104:Infusions, Subcutaneous; D007328:Insulin / Q000008:administration & dosage* / Q000009:adverse effects; D007332:Insulin Infusion Systems; D007558:Italy; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D019397:Pancreas, Artificial; D010865:Pilot Projects; D011237:Predictive Value of Tests; D013997:Time Factors
D001786:Blood Glucose; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; D007328:Insulin; C517652:hemoglobin A1c protein, human