Background: To evaluate the efficacy of a run-to-run (R2R) adaptive wearable artificial pancreas (AP) after 1 month of closed-loop glucose control in subjects with type 1 diabetes (T1D) under free-living conditions. Methods: Eighteen adults, who had previously completed a 1-month closed-loop study with a non-adaptive artificial pancreas (NA-AP), volunteered for an additional 1-month extension study in which the AP was equipped with an adaptive model predictive control algorithm (R2R-AP). Continuous glucose monitoring data were analyzed on an intention-to-treat basis by comparing the last week of R2R-AP versus the last week of NA-AP. The primary endpoint was the time in target range (3.9–10 mmol/L) over 24 h. Results: Time in target with R2R-AP is higher than with the NA-AP, although the increase was not significant: mean 66.90% (standard deviation: 13.34) versus 61.82% (11.12), P = 0.10. The increase was significant during the night: 74.01% (14.61) versus 64.31% (15.71), P = 0.03, and at wake-up time: median 92.43% (25th; 75th percentiles: 78.22; 99.53) versus 84.54% (57.14; 88.52), P = 0.02. Time above target (>10 mmol/L) during the whole day was 30.98% (13.22) versus 36.17% (11.53), P = 0.10. The decrease was significant during the night: 24.23% (15.03) versus 34.49% (16.25), P = 0.03, and at wake-up time: 7.57% (0.00; 14.29) versus 14.29% (8.25; 42.86), P = 0.05. Time spent below target (<3.9 mmol/L) was low and similar to the two treatments. Conclusions: R2R-AP improves glucose control over NA-AP in subjects with T1D during the night, and it maintains equivalent control performance during the day in a 1-month trial under free-living conditions.
Get full access to this article
View all access options for this article.
References
1.
KovatchevB, TamborlaneWV, CefaluWT, CobelliC: The artificial pancreas in 2016: a digital treatment ecosystem for diabetes. Diabetes Care, 2016; 39:1123–1126.
2.
ThabitH, HovorkaR: Coming of age: the artificial pancreas for type 1 diabetes. Diabetologia, 2016; 59:1795–1805.
3.
PalermCC, ZisserH, JovanovičL, DoyleFJIII: A run-to-run control strategy to adjust basal insulin infusion rates in type 1 diabetes. J Process Control, 2008; 18:258–265.
4.
MagniL, ForgioneM, ToffaninC, et al.: Run-to-run tuning of model predictive control for type i diabetic subjects: an in silico trial. J Diabetes Sci Technol, 2009; 3:1091–1098.
5.
ToffaninC, SandriA, MessoriM, et al.: Automatic adaptation of basal therapy for type 1 diabetic patients: a run-to-run approach. In 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 2070–2075.
6.
ToffaninC, VisentinR, MessoriM, et al.: Towards a run-to-run adaptive artificial pancreas: in silico results. IEEE Trans Biomed Eng, 2017; [Epub ahead of print]; DOI: 10.1109/TBME.2017.2652062
7.
Dalla ManC, MichelettoF, LvD, et al.: The UVA/PADOVA type 1 diabetes simulator: new features. J Diabetes Sci Technol, 2014; 8:26–34.
8.
RenardE, FarretA, KropffJ, et al.: Day and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: results of a single-arm 1-month experience compared with a previously reported feasibility study of evening and night at home. Diabetes Care, 2016; 39:1151–1160.
9.
KropffJ, Del FaveroS, PlaceJ, et al.: 2 Month evening and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: a randomised crossover trial. Lancet Diabetes Endocrinol, 2015; 12:939–947.
10.
American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care, 2014; 37(suppl 1):S81–S90.
11.
Keith-HynesP, MizeB, RobertA, PlaceJ: The diabetes assistant: a smartphone-based system for real-time control of blood glucose. Electronics, 2014; 3:609–623.
12.
Keith-HynesP, GuerlainS, MizeB, et al.: DiAs user interface: a patient-centric interface for mobile artificial pancreas systems. J Diabetes Sci Technol, 2013; 7:1416–1426.
13.
LanzolaG, ScarpelliniS, Di PalmaF, et al.: Monitoring artificial pancreas trials through agent-based technologies: a case report. J Diabetes Sci Technol, 2014; 8:216–224.
14.
PatekSD, MagniL, DassauE, et al.: Modular closed-loop control of diabetes. IEEE Trans Biomed Eng, 2012; 59:2986–2999.
15.
KovatchevB, PatekS, DassauE, et al.Control to range for diabetes: functionality and modular architecture. J Diabetes Sci Technol, 2009; 3:1058–1065.
16.
ToffaninC, MessoriM, Di PalmaF, et al.: Artificial pancreas: model predictive control design from clinical experience. J Diabetes Sci Technol, 2013; 7:1470–1483.
17.
Del FaveroS, PlaceJ. KropffJ, et al.: Multicenter outpatient dinner/overnight reduction of hypoglycemia and increased time of glucose in target with a wearable artificial pancreas using modular model predictive control in adults with type 1 diabetes. Diab Obes Metab, 2015; 17:468–476.
18.
MaahsDM, BuckinghamBA, CastleJR, et al.: Outcome measures for artificial pancreas clinical trials: a consensus report. Diabetes Care, 2016; 39:1175–1179.
19.
IBM Knowledge Center.. Hodges-Lehmann Estimates, Related Samples. 2012. http://01.ibm.com/support/knowledgecenter/SSLVMB_21.0.0/com.ibm.spss.statistics.help/alg_nonparametric_related_hodges-lehman.htm (accessed June10, 2015).
20.
KovatchevBP, ClarkeWL, BretonM, et al.: Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application. Diabetes Technol Ther, 2005; 7:849–862.
21.
The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med, 1993; 329:977–986.
22.
El-KhatibFH, RussellJ, MagyarKL, et al.: Autonomous and continuous adaptation of a bihormonal bionic pancreas in adults and adolescents with type 1 diabetes. J Clin Endocrinol Metab, 2014; 99:1701–1711.
23.
Eren-OrukluM, CinarA, QuinnL, SmithD: Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes. J Process Control, 2009; 19:1333–1346.
24.
ZisserH, JovanovičL, Doyle IIIFJ, et al.: Run-to- run control of meal-related insulin dosing. Diabetes Technol Ther, 2005; 7:48–57.
25.
PalermCC, ZisserH, BevierW, et al.: Prandial insulin dosing using run-to-run control application of clinical data and medical expertise to define a suitable performance metric. Diabetes Care, 2007; 30:1131–1136.
26.
KovatchevB, ChengP, AndersonSM, et al.: Feasibility of long-term closed-loop control: a multicenter 6-month trial of 24/7 automated insulin delivery. Diabetes Technol Ther, 2017; 19:18–24.
27.
BallyL, ThabitH, KojzarH, et al.: Day-and-night glycaemic control with closed-loop insulin delivery versus conventional insulin pump therapy in free-living adults with well controlled type 1 diabetes: an open-label, randomised, crossover study. Lancet Diabetes Endocrinol, 2017; 5:261–270.
28.
TauschmannM, AllenJM, WilinskaME, et al.: Home use of day-and-night hybrid closed-loop insulin delivery in suboptimally controlled adolescents with type 1 diabetes: a 3-week, free-living, randomized crossover trial. Diabetes Care, 2016; 39:2019–2025.
29.
ThabitH, TauschmannM, AllenJM, et al.: Home use of an artificial beta cell in type 1 diabetes. N Engl J Med, 2015; 373:2129–2140.
30.
NimriR, MullerI, AtlasE, et al.: MD-Logic Overnight control for 6 weeks of home use in patients with type 1 diabetes: randomized crossover trial. Diabetes Care, 2014; 37:3025–3032.
