The purpose of this study was to design and evaluate a safety system for the artificial pancreas device system (APDS). Safe operation of the APDS is a critical task, where the safety system is engaged only as needed to ensure reliable operation without positive feedback to the controller.
Methods:
The Health Monitoring System (HMS) was designed as a modular system to ensure the safety of the APDS and the user. It was designed using a large set of ambulatory data and evaluated in silico by inducing hypoglycemia with a missed meal [bolus for a 65 g carbohydrate (CHO) meal] and administering rescue CHOs per HMS alerting. The HMS was validated in-clinic with a real-life challenge of a subject who overdosed insulin prior to admission.
Results:
The HMS was evaluated for clinical use with a 15 min prediction horizon. Retrospectively, 93.5% of episodes were detected with 2.9 false alarms per day. During in silico evaluation, the HMS reduced the time spent <70 mg/dl from 15% to 3%. When the HMS was first tested in-clinic, the subject overdosed ∼3 U of insulin prior to her arrival to a closed-loop session (against protocol). The controller reduced insulin delivery, and the HMS gave four alerts that were successfully received via clinical software and text and multimedia messages. Even with insulin reduction and CHO supplements, hypoglycemia was unavoidable but manageable due to the HMS, confirming that a safety system to detect adverse events is an essential part of the APDS.
Conclusions:
The ability of the HMS to be an effective alert system that provides a safety layer to the APDS controller has been demonstrated in a clinical setting.
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 (14): 977–86.
2.
ZisserHCBaileyTSSchwartzSRatnerREWiseJ.Accuracy of the SEVEN continuous glucose monitoring system: Comparison with frequently sampled venous glucose measurements. J Diabetes Sci Technol.2009;3 (5): 1146–54.
3.
KeenanDBGrosmanBClarkHWRoyAWeinzimerSAShahRVMastrototaroJJ, Continuous glucose monitoring considerations for the development of a closed-loop artificial pancreas system. J Diabetes Sci Technol.2011;5 (6): 1327–36.
4.
U.S. Department of Health and Human Services; Food and Drug Administration; Center for Devices and Radiological Health. Draft guidance for industry and Food and Drug Administration staff – the content of investigational device exemption (IDE) and premarket approval (PMA) applications for artificial pancreas device systems. http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/UCM281716.pdf.
5.
TanenbergRJNewtonCADrakeAJ, Confirmation of hypoglycemia in the “dead-in-bed” syndrome, as captured by a retrospective continuous glucose monitoring system. Endocr Pract.2010;16 (2): 244–8.
6.
KlonoffDCZimlikiCLStevensLABeastonPPinkosAChoeSYArreaza-RubinGHeetderksW.Innovations in technology for the treatment of diabetes: Clinical development of the artificial pancreas (an autonomous system). J Diabetes Sci Technol.2011;5 (3): 804–26.
7.
HovorkaRKumareswaranKHarrisJAllenJMElleriDXingDKollmanCNodaleMMurphyHRDungerDBAmielSAHellerSRWilinskaMEEvansML, Overnight closed loop insulin delivery (artificial pancreas) in adults with type 1 diabetes: Crossover randomised controlled studies. BMJ.2011;342: d1855.
8.
WilinskaMEBudimanESTaubMBElleriDAllenJMAceriniCLDungerDBHovorkaR.Overnight closed-loop insulin delivery with model predictive control: Assessment of hypoglycemia and hyperglycemia risk using simulation studies. J Diabetes Sci Technol.2009;3 (5): 1109–20.
9.
El-KhatibFHRussellSJNathanDMSutherlinRGDamianoER, A bihormonal closed-loop artificial pancreas for type 1 diabetes. Sci Transl Med.2010;2 (27): 27ra27.
10.
DassauEZisserHHarveyRAPercivalMWGrosmanBBevierWAtlasEMillerSNimriRJovanovičLDoyleFJIIIClinical Evaluation of a Personalized Artificial Pancreas. Diabetes Care (In Press).
11.
KovatchevBPatekSDassauEDoyleFJIIIMagniLDe NicolaoGCobelliC; Juvenile Diabetes Research Foundation Artificial Pancreas Consortium. Control to range for diabetes: Functionality and modular architecture. J Diabetes Sci Technol.2009;3 (5): 1058–65.
SteilGMPalermCCKurtzNVoskanyanGRoyAPazSKandeelFR, The effect of insulin feedback on closed loop glucose control. J Clin Endocrinol Metab.2011;96 (5): 1402–8.
14.
HughesCSPatekSDBretonMDKovatchevBP, Hypoglycemia prevention via pump attenuation and red-yellow-green “traffic” lights using continuous glucose monitoring and insulin pump data. J Diabetes Sci Technol.2010;4 (5): 1146–55.
15.
DassauEZisserHPercivalMWGrosmanBJovanovičLDoyleFJIIIClinical results of automated artificial pancreatic β-cell system with unannounced meal using multiparametric MPC and insulin-on-board. Diabetes.2010;59 (Suppl 1): A94.
16.
ZisserHDassauEBevierWHarveyRAPercivalMWGrosmanBSeborgDEJovanovičLDoyleFJIIIInitial evaluation of a fully automated artificial pancreas. Presented at: 71st American Diabetes Association Meeting, 2011, San Diego, CA.
17.
BuckinghamBChaseHPDassauECobryEClintonPGageVCaswellKWilkinsonJCameronFLeeHBequetteBWDoyleFJIIIPrevention of nocturnal hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Care.2010;33 (5): 1013–7.
18.
PalermCCBequetteBW, Hypoglycemia detection and prediction using continuous glucose monitoring-a study on hypoglycemic clamp data. J Diabetes Sci Technol.2007;1 (5): 624–9.
19.
McGarraughGBergenstalR.Detection of hypoglycemia with continuous interstitial and traditional blood glucose monitoring using the freestyle navigator continuous glucose monitoring system. Diabetes Technol Ther.2009;11 (3): 145–50.
20.
CryerPEAxelrodLGrossmanABHellerSRMontoriVMSeaquistERServiceFJ; Endocrine Society. Evaluation and management of adult hypoglycemic disorders: An Endocrine Society clinical practice guideline. J Clin Endocrinol Metab.2009;94 (3): 709–28.
21.
BuckinghamBWilsonDMLecherTHanasRKaisermanKCameronF.Duration of nocturnal hypoglycemia before seizures. Diabetes Care.2008;31 (11): 2110–2.
22.
DassauEBequetteBWBuckinghamBADoyleFJIIIDetection of a meal using continuous glucose monitoring: Implications for an artificial beta-cell. Diabetes Care.2008;31 (2): 295–300.
23.
GrosmanBDassauEZisserHCJovanovičLDoyleFJIIIZone model predictive control: A strategy to minimize hyper- and hypoglycemic events. J Diabetes Sci Technol.2010;4 (4): 961–75.
24.
American Institute of Chemical Engineers, Center for Chemical Process Safety. Layer of protection analysis: Simplified process risk assessment. New York: Center for Chemical Process Safety of the American Institute of Chemical Engineers; 2001.
25.
DassauEJovanovicLDoyleFJIIIZisserHC, Enhanced 911/ global position system wizard: A telemedicine application for the prevention of severe hypoglycemia–monitor, alert, and locate. J Diabetes Sci Technol.2009;3 (6): 1501–6.
26.
PedersenJS, The nature of amyloid-like glucagon fibrils. J Diabetes Sci Technol.2010;4 (6): 1357–67.
27.
PearsonT.Glucagon as a treatment of severe hypoglycemia: Safe and efficacious but underutilized. Diabetes Educ.2008;34 (1): 128–34.
28.
DunnTCEastmanRCTamadaJA, Rates of glucose change measured by blood glucose meter and the glucowatch biographer during day, night, and around mealtimes. Diabetes Care.2004;27 (9): 2161–5.
29.
SeborgDEEdgarTFMellichampDADoyleFJIIIProcess dynamics and control. 3rd ed.Hoboken: John Wiley and Sons; 2011.
DassauECameronFLeeHBequetteBWZisserHJovanovičLChaseHPWilsonDMBuckinghamBADoyleFJIIIRealtime hypoglycemia prediction suite using continuous glucose monitoring: A safety net for the artificial pancreas. Diabetes Care.2010;33 (6): 1249–54.
32.
KovatchevBPBretonMDManC DallaCobelliC.In silico model and computer simulation environment approximating the human glucose/insulin utilization. Food and Drug Administration Master File MAF 1521; 2008.
33.
American Diabetes Association. Standards of medical care in diabetes–2012. Diabetes Care.2012;35Suppl 1: S11–63.
34.
ZisserHCDassauEBevierWHarveyRJovanovičLDoyleFJ3rd.Clinical evaluation of a fully-automated artificial pancreas using zone-model predictive control with health monitoring system. Presented at: American Diabetes Association 72nd Scientific Sessions, Philadelphia, PA, 2012.
35.
ParkerRSDoyleFJIIIHartingJEPeppasNA, Model predictive control for infusion pump insulin delivery. Presented at: 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1996.
36.
ParkerRSDoyleFJIIIPeppasNA, A model-based algorithm for blood glucose control in type I diabetic patients. IEEE Trans Biomed Eng.1999;46 (2): 148–57.
