Review of the Omnipod^® 5 Automated Glucose Control System Powered by Horizon™ for the Treatment of Type 1 Diabetes

References

• Pettitt DJ , TaltonJ , DabeleaDet al. Prevalence of Diabetes in U.S. Youth in 2009: the search for diabetes in youth study. Diabetes Care37(2), 402–408 (2014).
   PubMed Web of Science ®Google Scholar
• Faulds ER , ZappeJ , DunganKM. Real-world implications of hybrid close loop (HCL) insulin delivery system. Endocr. Pract. Off. J. Am. Coll. Endocrinol. Am. Assoc. Clin. Endocrinol.25(5), 477–484 (2019).
   Google Scholar
• Diabetes Control and Complications Trial Research Group Nathan DM , GenuthSet al. 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.329(14), 977–986 (1993).
   PubMed Web of Science ®Google Scholar
• Gupta A , PhatakSR , RaoYS , RameshJ , SanyalD. Consensus on choice of insulin delivery devices in routine clinical practice. Diabetes Technol. Ther. doi:10.1089/dia.2019.0494 (2020) (Epub ahead of print).
   PubMed Web of Science ®Google Scholar
• Dovc K , BoughtonC , TauschmannMet al. Young children have higher variability of insulin requirements: observations during hybrid closed-loop insulin delivery. Diabetes Care42(7), 1344–1347 (2019).
   PubMed Web of Science ®Google Scholar
• Nauck MA , LindmeyerAM , MathieuC , MeierJJ. Twenty-four hour fasting (basal rate) tests to achieve custom-tailored, hour-by-hour basal insulin infusion rates in patients with Type 1 diabetes using insulin pumps (CSII). J. Diabetes Sci. Technol. doi:10.1177/1932296819882752 (2019) (Epub ahead of print).
   PubMedGoogle Scholar
• Slattery D , AmielSA , ChoudharyP. Optimal prandial timing of bolus insulin in diabetes management: a review. Diabet. Med. J. Br. Diabet. Assoc.35(3), 306–316 (2018).
   PubMed Web of Science ®Google Scholar
• Cobry E , McFannK , MesserLet al. Timing of meal insulin boluses to achieve optimal postprandial glycemic control in patients with Type 1 diabetes. Diabetes Technol. Ther.12(3), 173–177 (2010).
   PubMed Web of Science ®Google Scholar
• Berget C , MesserLH , ForlenzaGP. A clinical overview of insulin pump therapy for the management of diabetes: past, present, and future of intensive therapy. Diabetes Spectr. Publ. Am. Diabetes Assoc.32(3), 194–204 (2019).
   PubMedGoogle Scholar
• Sora ND , ShashpalF , BondEA , JenkinsAJ. Insulin pumps: review of technological advancement in diabetes management. Am. J. Med. Sci.358(5), 326–331 (2019).
   PubMed Web of Science ®Google Scholar
• Olczuk D , PrieferR. A history of continuous glucose monitors (CGMs) in self-monitoring of diabetes mellitus. Diabetes Metab. Syndr.12(2), 181–187 (2018).
   PubMed Web of Science ®Google Scholar
• Wadwa RP , LaffelLM , ShahVN , GargSK. Accuracy of a factory-calibrated, real-time continuous glucose monitoring system during 10 days of use in youth and adults with diabetes. Diabetes Technol. Ther.20(6), 395–402 (2018).
   PubMed Web of Science ®Google Scholar
• Shah VN , LaffelLM , WadwaRP , GargSK. Performance of a factory-calibrated real-time continuous glucose monitoring system utilizing an automated sensor applicator. Diabetes Technol. Ther.20(6), 428–433 (2018).
   PubMed Web of Science ®Google Scholar
• Foster NC , BeckRW , MillerKMet al. State of Type 1 diabetes management and outcomes from the T1D exchange in 2016–2018. Diabetes Technol. Ther.21(2), 66–72 (2019).
   PubMed Web of Science ®Google Scholar
• Garg SK , WeinzimerSA , TamborlaneWVet al. 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(3), 155–163 (2017).
   PubMed Web of Science ®Google Scholar
• Forlenza GP , Pinhas-HamielO , LiljenquistDRet al. Safety evaluation of the MiniMed 670G System in children 7–13 years of age with Type 1 diabetes. Diabetes Technol. Ther.21(1), 11–19 (2018).
   PubMed Web of Science ®Google Scholar
• Brown SA , KovatchevBP , RaghinaruDet al. Six-month randomized, multicenter trial of closed-loop control in Type 1 diabetes. N. Engl. J. Med.381(18), 1707–1717 (2019).
   PubMed Web of Science ®Google Scholar
• American Diabetes Association . Standards of Medical Care in Diabetes – 2020. Diabetes Care43(S1), S1–S212 (2020).
   PubMedGoogle Scholar
• Klonoff DC , AhnD , DrincicA. Continuous glucose monitoring: a review of the technology and clinical use. Diabetes Res. Clin. Pract.133, 178–192 (2017).
   PubMed Web of Science ®Google Scholar
• Messer LH , BergetC , ForlenzaGP. A clinical guide to advanced diabetes devices and closed-loop systems using the CARES paradigm. Diabetes Technol. Ther.21(8), 462–469 (2019).
   PubMed Web of Science ®Google Scholar
• Saunders A , MesserLH , ForlenzaGP. MiniMed 670G hybrid closed loop artificial pancreas system for the treatment of Type 1 diabetes mellitus: overview of its safety and efficacy. Expert Rev. Med. Devices.16(10), 845–853 (2019).
   PubMed Web of Science ®Google Scholar
• Forlenza GP , LiZ , BuckinghamBAet al. Predictive low-glucose suspend reduces hypoglycemia in adults, adolescents, and children with Type 1 diabetes in an at-home randomized crossover study: results of the PROLOG trial. Diabetes Care41(10), 2155–2161 (2018).
   PubMed Web of Science ®Google Scholar
• Ekhlaspour L , NallyLM , El-KhatibFHet al. Feasibility studies of an insulin-only bionic pancreas in a home-use setting. J. Diabetes Sci. Technol.13(6), 1001–1007 (2019).
   PubMedGoogle Scholar
• El-Khatib FH , BalliroC , HillardMAet al. Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with Type 1 diabetes: a multicentre randomised crossover trial. Lancet Lond. Engl.389(10067), 369–380 (2017).
   PubMed Web of Science ®Google Scholar
• Russell SJ , El-KhatibFH , SinhaMet al. Outpatient glycemic control with a bionic pancreas in Type 1 diabetes. N. Engl. J. Med.371(4), 313–325 (2014).
   PubMed Web of Science ®Google Scholar
• Lewis DM . Do-It-Yourself Artificial Pancreas System and the OpenAPS Movement. Endocrinol. Metab. Clin. North Am.49(1), 203–213 (2020).
   PubMed Web of Science ®Google Scholar
• Buckingham Forlenza , SherrGalderisi , EkhlaspourGalderisi. Safety and performance of the omnipod hybrid closed-loop system in young children aged 2–6 years with Type 1 diabetes. Presented at: ADACA, USA (2019).
   Google Scholar
• Ly TT , LayneJE , HuyettLM , NazzaroD , O'ConnorJB. Novel bluetooth-enabled tubeless insulin pump: innovating pump therapy for patients in the digital age. J. Diabetes Sci. Technol.13(1), 20–26 (2019).
   PubMedGoogle Scholar
• Dassau E , ZisserH , CesarPalerm C , BuckinghamBruce A , JovanovičL , DoyleJ F III .Modular Artificial β-Cell System: a prototype for clinical research. J. Diabetes Sci. Technol. Online.2(5), 863–872 (2008).
   PubMedGoogle Scholar
• Lee JB , DassauE , GondhalekarR , SeborgDE , PinskerJE , DoyleFJ. Enhanced Model Predictive Control (eMPC) Strategy for automated glucose control. Ind. Eng. Chem. Res.55(46), 11857–11868 (2016).
   PubMed Web of Science ®Google Scholar
• Gondhalekar R , DassauE , DoyleFJ. Periodic zone-MPC with asymmetric costs for outpatient-ready safety of an artificial pancreas to treat Type 1 diabetes. Autom. J. IFAC Int. Fed. Autom. Control.71, 237–246 (2016).
   Google Scholar
• Lee JB , DassauE , SeborgDE , DoyleFJ. Model-based personalization scheme of an artificial pancreas for Type 1 diabetes applications. Presented at: 2013 American Control Conference. Washington, DC, USA (2013).
   Google Scholar
• Messer LH , ForlenzaGP , WadwaRPet al. The dawn of automated insulin delivery: a new clinical framework to conceptualize insulin administration. Pediatr. Diabetes.19(1), 14–17 (2018).
   PubMed Web of Science ®Google Scholar
• Buckingham BA , ForlenzaGP , PinskerJEet al. 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(4), 257–262 (2018).
   PubMed Web of Science ®Google Scholar
• Buckingham BA , ChristiansenMP , ForlenzaGPet al. Performance of the Omnipod Personalized Model Predictive Control Algorithm with Meal Bolus Challenges in adults with Type 1 diabetes. Diabetes Technol. Ther.20(9), 585–595 (2018).
   PubMed Web of Science ®Google Scholar
• Forlenza GP , BuckinghamBA , ChristiansenMPet al. Performance of Omnipod Personalized Model Predictive Control Algorithm with Moderate Intensity Exercise in adults with Type 1 diabetes. Diabetes Technol. Ther.21(5), 265–272 (2019).
   PubMed Web of Science ®Google Scholar
• Sherr JL , BuckinghamBA , ForlenzaGPet al. Safety and performance of the Omnipod Hybrid Closed-Loop System in adults, adolescents, and children with Type 1 diabetes over 5 days under free-living conditions. Diabetes Technol. Ther.22(3), 174–184 (2020).
   PubMed Web of Science ®Google Scholar
• Messer LH . Why expectations will determine the future of artificial pancreas. Diabetes Technol. Ther.20(S2), S2–S65 (2018).
   PubMedGoogle Scholar
• Berget C , MesserLH , VigersTet al. Six months of hybrid closed loop in the real-world: an evaluation of children and young adults using the 670G system. Pediatr. Diabetes21(2), 310–318 (2020).
   PubMed Web of Science ®Google Scholar
• Messer LH , BergetC , VigersTet al. Real world hybrid closed-loop discontinuation: predictors and perceptions of youth discontinuing the 670G system in the first 6 months. Pediatr. Diabetes21(2), 319–327 (2020).
   PubMed Web of Science ®Google Scholar
• Lal RA , BasinaM , MaahsDM , HoodK , BuckinghamB , WilsonDM. One year clinical experience of the first commercial hybrid closed-loop system. Diabetes Care42(12), 2190–2196 (2019).
   PubMed Web of Science ®Google Scholar
• Forlenza GP , CameronFM , LyTTet al. Fully closed-loop multiple model probabilistic predictive controller artificial pancreas performance in adolescents and adults in a supervised hotel setting. Diabetes Technol. Ther.20(5), 335–343 (2018).
   PubMed Web of Science ®Google Scholar
• Jacobs PG , ResalatN , ElYoussef Jet al. Incorporating an exercise detection, grading, and hormone dosing algorithm into the artificial pancreas using accelerometry and heart rate. J. Diabetes Sci. Technol.9(6), 1175–1184 (2015).
   PubMedGoogle Scholar
• Battelino T , DanneT , BergenstalRMet al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the International Consensus on time in range. Diabetes Care42(8), 1593–1603 (2019).
   PubMed Web of Science ®Google Scholar