Abstract
The automated insulin delivery (AID) device known as an artificial pancreas (AP) contains a closed-loop feedback controller that enables glucose management for those with type 1 diabetes mellitus (T1DM). The AP system is comprised of two elements: devices and software. Devices include insulin delivery devices and glucose sensors, while software includes the user interface and the control algorithm. Decision support systems (DSS) also support treatment decisions for those with diabetes mellitus if the user does not use a closed-loop system. This chapter is divided into three sections, beginning with a description of controller components, design objectives, the challenges and the limitations of AID devices. This discussion is followed by recent advances in closed-loop clinical trials, common control algorithms, and clinical challenges. Finally, general expectations for future developments within AID engineering and clinical translation are outlined.
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Bibliography
American Diabetes Association (2013) Diagnosis and classification of diabetes mellitus. Diabetes 36:S67–S74. https://doi.org/10.1016/j.autrev.2014.01.020
Battelino T, Danne T, Amiel SA et al (2019) Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time-in-range. Diabetes Care 42(8):1593–1603
Bekiari E, Kitsios K, Thabit H et al (2018) Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis. BMJ 361:k1310. https://doi.org/10.1136/bmj.k1310
Bertachi A, Ramkissoon CM, Bondia J, Vehà J (2018) Automated blood glucose control in type 1 diabetes: a review of progress and challenges. Endocrinol Diabetes y Nutr (English ed) 65:172–181. https://doi.org/10.1016/j.endinu.2017.10.011
Buckingham BA, Forlenza GP, Pinsker JE et al (2018) Safety and feasibility of the OmniPod hybrid closed-loop system in adult, adolescent, and pediatric patients with type 1 diabetes using a personalized model predictive control algorithm. Diabetes Technol Ther 20:dia.2017.0346. https://doi.org/10.1089/dia.2017.0346
Castle JR, El Youssef J, Wilson LM et al (2018) Randomized outpatient trial of single- and dual-hormone closed-loop systems that adapt to exercise using wearable sensors. Diabetes Care 41:1471–1477. https://doi.org/10.2337/dc18-0228
Chakrabarty A, Zavitsanou S, Doyle III FJ, Dassau E (2018) Event-triggered model predictive control for embedded artificial pancreas systems. IEEE Trans Biomed Eng 65:575–586. https://doi.org/10.1109/TBME.2017.2707344
Christiansen SC, Fougner AL, Stavdahl Ø et al (2017) A review of the current challenges associated with the development of an artificial pancreas by a double subcutaneous approach. Diabetes Ther 8:489–506. https://doi.org/10.1007/s13300-017-0263-6
Contreras I, Vehi J (2018) Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res 20:e10775. https://doi.org/10.2196/10775
Danne T, Nimri R, Battelino T et al (2017) International consensus on use of continuous glucose monitoring. Diabetes Care 40:1631–1640. https://doi.org/10.2337/dc17-1600
Dassau E, Brown SA, Basu A et al (2015) Adjustment of open-loop settings to improve closed-loop results in type 1 diabetes: a multicenter randomized trial. J Clin Endocrinol Metab 100:3878–3886. https://doi.org/10.1210/jc.2015-2081
Dassau E, Pinsker JE, Kudva YC et al (2017a) Twelve-week 24/7 ambulatory artificial pancreas with weekly adaptation of insulin delivery settings: effect on hemoglobin A1c and hypoglycemia. Diabetes Care 40:dc171188. https://doi.org/10.2337/dc17-1188
Dassau E, Renard E, Place J et al (2017b) Intraperitoneal insulin delivery provides superior glycaemic regulation to subcutaneous insulin delivery in model predictive control-based fully-automated artificial pancreas in patients with type 1 diabetes: a pilot study. Diabetes Obes Metab 19:1698–1705. https://doi.org/10.1111/dom.12999
Deshpande S, Pinsker JE, Zavitsanou S et al (2018) Design and clinical evaluation of the interoperable artificial pancreas system (iAPS) smartphone App: interoperable components with modular design for progressive artificial pancreas research and development. Diabetes Technol Ther 21:35–43. https://doi.org/10.1089/dia.2018.0278
Doyle III FJ, Huyett LM, Lee JB et al (2014) Closed-loop artificial pancreas systems: engineering the algorithms. Diabetes Care 37:1191–1197. https://doi.org/10.2337/dc13-2108
El-Laboudi A, Oliver NS, Cass A, Johnston D (2012) Use of microneedle array devices for continuous glucose monitoring: a review. Diabetes Technol Ther 15:101–115. https://doi.org/10.1089/dia.2012.0188
Forlenza GP (2017) Relevance of bolus calculators in current hybrid closed loop systems. Diabetes Technol Ther 19:400–401. https://doi.org/10.1089/dia.2017.0216
Forlenza GP, Raghinaru D, Cameron F et al (2017) Predictive hyperglycemia and hypoglycemia minimization: in-home double-blind randomized controlled evaluation in children and young adolescents. Pediatr Diabetes 19:420–428. https://doi.org/10.1111/pedi.12603
Garg SK, Weinzimer SA, Tamborlane WV et al (2017) Glucose outcomes with the in-home use of a hybrid closed-loop insulin delivery system in adolescents and adults with type 1 diabetes. Diabetes Technol Ther 19:155–163. https://doi.org/10.1089/dia.2016.0421
Gondhalekar R, Dassau E, Doyle III FJ (2016) Periodic zone-MPC with asymmetric costs for outpatient-ready safety of an artificial pancreas to treat type 1 diabetes. Automatica 71:237–246. https://doi.org/10.1016/j.automatica.2016.04.015
Haidar A, Elleri D, Kumareswaran K et al (2013) Pharmacokinetics of insulin aspart in pump-treated subjectswith type 1 diabetes: reproducibility and effect of age, weight, and duration of diabetes. Diabetes Care 36:173–174. https://doi.org/10.2337/dc13-0485
Haidar A, Legault L, Matteau-Pelletier L et al (2015) Outpatient overnight glucose control with dual-hormone artificial pancreas, single-hormone artificial pancreas, or conventional insulin pump therapy in children and adolescents with type 1 diabetes: an open-label, randomised controlled trial. Lancet Diabetes Endocrinol 3:595–604. https://doi.org/10.1016/S2213-8587(15)00141-2
Hajizadeh I, Rashid M, Samadi S et al (18AD) Adaptive and personalized plasma insulin concentration estimation for artificial pancreas systems. J Diabetes Sci Technol 12:639–649
Hajizadeh I, Turksoy K, Cengiz E, Cinar A (2017) Real-time estimation of plasma insulin concentration using continuous subcutaneous glucose measurements in people with type 1 diabetes. Proc Am Control Conf 5193–5198. https://doi.org/10.23919/ACC.2017.7963761
Kowalski A (2015) Pathway to artificial pancreas systems revisited: moving downstream. Diabetes Care 38:1036–1043. https://doi.org/10.2337/dc15-0364
Kudva YC, Carter RE, Cobelli C et al (2014) Closed-loop artificial pancreas systems: physiological input to enhance next-generation devices. Diabetes Care 37:1184–1190. https://doi.org/10.2337/dc13-2066
Lee JB, Dassau E, Seborg DE, Doyle III FJ (2013) Model-based personalization scheme of an artificial pancreas for Type 1 diabetes applications. Am Control Conf (ACC) 2013:2911–2916. https://doi.org/10.1109/ACC.2013.6580276
Lee JB, Dassau E, Gondhalekar R et al (2016) Enhanced model predictive control (eMPC) strategy for automated glucose control. Ind Eng Chem Res 55:11857–11868. https://doi.org/10.1021/acs.iecr.6b02718
Logtenberg SJJ, Kleefstra N, Houweling S et al (2009) Improved glycemia control with intraperitoneal versus subcutaneous insulin in type 1 diabetes: a randomized controlled trial. Diabetes Care 32:1372–1377. https://doi.org/10.2337/dc08-2340.Clinical
Miller PR, Narayan RJ, Polsky R (2016) Microneedle-based sensors for medical diagnosis. J Mater Chem B 4:1379–1383. https://doi.org/10.1039/c5tb02421h
Nimri R, Ochs AR, Pinsker JE et al (2019) Decision support systems and closed loop. Diabetes Technol Ther 21:S-42–S-56. https://doi.org/10.1089/dia.2019.2504
Pinsker JE, Laguna Sanz AJ, Lee JB et al (2018) Evaluation of an artificial pancreas with enhanced model predictive control and a glucose prediction trust index with unannounced exercise. Diabetes Technol Ther 20:455–464. https://doi.org/10.1089/dia.2018.0031
Rossetti P, Quirós C, Moscardó V et al (2017) Closed-loop control of postprandial glycemia using an insulin-on-board limitation through continuous action on glucose target. Diabetes Technol Ther 19:355–362. https://doi.org/10.1089/dia.2016.0443
van Dijk PR, Logtenberg SJJ, Hendriks SH et al (2015) Intraperitoneal versus subcutaneous insulin therapy in the treatment of type I diabetes mellitus. Neth J Med 73:399–409
van Dijk PR, Logtenberg SJJ, Chisalita SI et al (2016) Different effects of intraperitoneal and subcutaneous insulin administration on the GH-IGF-1 axis in type 1 diabetes. J Clin Endocrinol Metab 101:2493–2501. https://doi.org/10.1210/jc.2016-1473
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Wolkowicz, K.L., Doyle III, F.J., Dassau, E. (2021). Control of Drug Delivery for Type 1 Diabetes Mellitus. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, Cham. https://doi.org/10.1007/978-3-030-44184-5_100058
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DOI: https://doi.org/10.1007/978-3-030-44184-5_100058
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