Skip to main content
Hauptrubriken
CME Facharzt-Training Webinare Zeitschriften Bücher e.Medpedia Podcast
Service
Jobbörse Info & Hilfe
Fachgebiete
Anästhesiologie Allgemeinmedizin Arbeitsmedizin Augenheilkunde Chirurgie Dermatologie Gynäkologie und Geburtshilfe HNO Innere Medizin Kardiologie Neurologie Onkologie und Hämatologie Orthopädie und Unfallchirurgie Pädiatrie Pathologie Psychiatrie Radiologie Rechtsmedizin Urologie Zahnmedizin
Themen
Klimawandel und Gesundheit Neues aus dem Markt COVID-19 Praxis und Beruf Seltene Erkrankungen GOÄ & EBM Panorama
Sammlungen
Kasuistiken Algorithmen & Infografiken Blickdiagnosen Arthropedia FlapFinder (für Dermatochirurgen) Videos Kongressberichterstattung Medizin für Apothekerinnen und Apotheker Für Ärztinnen und Ärzte in Weiterbildung Für Medizinstudierende
Erweiterte Suche
Anmelden
nach oben
Current Diabetes Reports
Erschienen in: Current Diabetes Reports 10/2017

01.10.2017 | Therapies and New Technologies in the Treatment of Type 1 Diabetes (M Pietropaolo, Section Editor)

Multivariable Adaptive Artificial Pancreas System in Type 1 Diabetes

Erschienen in: Current Diabetes Reports | Ausgabe 10/2017

Einloggen, um Zugang zu erhalten

Abstract

Purpose of Review

The review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems.

Recent Findings

A fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep.

Summary

The development of a fully automated AP will necessitate the design of multivariable and adaptive systems that use information from wearable devices in addition to glucose sensors and modify the models used in their model-predictive alarm and control systems to adapt to the changes in the metabolic state of the user. These AP systems will also integrate modules for controller performance assessment, fault detection and diagnosis, machine learning and classification to interpret various signals and achieve fault-tolerant control. Advances in wearable devices, computational power, and safe and secure communications are enabling the development of fully automated multivariable AP systems.
Literatur
1.
Albisser AM, Leibel BS, Ewart TG, Davidovac Z, Botz CK, Zingg W. An artificial endocrine pancreas. Diabetes. 1974;23:389–96.CrossRefPubMed
2.
Haidar A, Legault L, Messier V, Mitre TM, Leroux C, Rabasa-Lhoret R. Comparison of dual-hormone artificial pancreas, single-hormone artificial pancreas, and conventional insulin pump therapy for glycaemic control in patients with type 1 diabetes: an open-label randomised controlled crossover trial. Lancet Diabetes Endocrinol. 2015;3:17–26.CrossRefPubMed
3.
Thabit H, Tauschmann M, Allen JM, Leelarathna L, Hartnell S, Wilinska ME, et al. Home use of an artificial beta cell in type 1 diabetes. N Engl J Med. 2015;373:2129–40.CrossRefPubMedPubMedCentral
4.
• Bally L, Thabit H, Kojzar H, Mader JK, Qerimi-Hyseni J, Hartnell S, 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 Diab Endocrinology. 2017;5:261–70. This study demonstrates the improvement of glycemic control with an AP over conventional insulin pump therapy.CrossRef
5.
Pinsker JE, Lee JB, Dassau E, Seborg, DE, Bradley PK, R. Gondhalekar, R, et al. Randomized crossover comparison of personalized MPC and PID control algorithms for the artificial pancreas. Diabetes Care, 2016; 39:1135–1142.
6.
Dassau E, Brown SA, Basu A, Pinsker JE, Kudva YC, Gondhalekar R, et al. Multicenter outpatient randomized crossover trial of zone-MPC artificial pancreas in type 1 diabetes: effects of initialization strategies. Diabetes. 2015;64:A59–60.
7.
Brown SA, Breton MD, Anderson S, Kollar L, Levy C, Lam D, et al. Artificial pancreas improves glycemic control in a multi-night multicenter outpatient/home study of patients with T1D. Diabetes. 2015;64:A59.CrossRef
8.
Renard E, Farret A, Kropff J, Bruttomesso D, Messori M, Place J, 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;39:1151–60.CrossRefPubMed
9.
Kropff J, Del Favero S, Place J, Toffanin C, Visentin R, Monaro M, 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;3:939–47.CrossRefPubMed
10.
Reddy M, Herrero P, El Sharkawy M, Pesl P, Jugnee N, Pavitt D, et al. Metabolic control with the bio-inspired artificial pancreas in adults with type 1 diabetes: a 24-hour randomized controlled crossover study. J. Diabetes Sci. Technol. 2015;10:405–13.CrossRefPubMedPubMedCentral
11.
Cameron F, Ly TT, Forlenza GP, Patek SD, Baysal N, Messer LH, et al. Inpatient clinical trial of a fully closed-loop artificial pancreas using only CGM and accelerometer data for insulin dosing. Diabetes Technol Ther. 2016;18:A23–4.CrossRef
12.
Ly TT, Roy A, Grosman B, Shin J, Campbell A, Monirabbasi S, et al. Day and night closed-loop control using the integrated medtronic hybrid closed-loop system in type 1 diabetes at diabetes camp. Diabetes Care. 2015;38:1205–11.CrossRefPubMed
13.
Del Favero S, Place J, Kropff J, Messori M, Keith-Hynes P, Visentin R, 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. Diabetes Obes Metab. 2015;17:468–76.CrossRefPubMed
14.
Del Favero S, Boscari F, Messori M, Rabbone I, Bonfanti R, Sabbion A, et al. Randomized summer camp crossover trial in 5-to 9-year-old children: outpatient wearable artificial pancreas is feasible and safe. Diabetes Care. 2016;39:1180–5.CrossRefPubMed
15.
Renard E, Devries JH, Cobelli C, Magni L, Place J, Kropff J, et al. Reduction of hyper-and hypoglycemia during two months with a wearable artificial pancreas from dinner to breakfast in patients with type 1 diabetes. Diabetes. 2015;64:A237–8.
16.
Russell SJ, El-Khatib FH, Sinha M, et al. Outpatient glycemic control with a bionic pancreas in type 1 diabetes. N Engl J Med. 2014;371:313–25.CrossRefPubMedPubMedCentral
17.
Russell SJ, Hillard MA, Balliro C, Magyar KL, Selagamsetty R, Sinha M, et al. Day and night glycaemic control with a bionic pancreas versus conventional insulin pump therapy in preadolescent children with type 1 diabetes: a randomised crossover trial. Lancet Diabetes Endocrinol. 2016;4:233–43.CrossRefPubMedPubMedCentral
18.
Anderson SM, Raghinaru D, Pinsker JE, Boscari F, Renard E, Buckingham BA, et al. Multinational home use of closed-loop control is safe and effective. Diabetes Care. 2016;39:1143–50.CrossRefPubMed
19.
Nimri R, Muller I, Atlas E, Miller S, Kordonouri O, Bratina N, et al. Night glucose control with MD-logic artificial pancreas in home setting: a single blind, randomized crossover trial-interim analysis. Pediatr Diabetes. 2014;15:91–9.CrossRefPubMed
20.
Mauseth R, Lord SM, Hirsch IB, Kircher RC, Matheson DP, Greenbaum CJ. Stress testing of an artificial pancreas system with pizza and exercise leads to improvements in the system’s fuzzy logic controller. J. Diabetes Sci. Technol. 2015;9:1253–9.CrossRefPubMedPubMedCentral
21.
Turksoy K, Bayrak ES, Quinn L, Littlejohn E, Cinar A. Multivariable adaptive closed-loop control of an artificial pancreas without meal and activity announcement. Diabetes Technol Ther. 2013;15:386–400.CrossRefPubMedPubMedCentral
22.
•• Turksoy K, Quinn LT, Littlejohn E, Cinar A. An integrated multivariable artificial pancreas control system. J. Diabetes Sci. Technol. 2014;8:498–507. This study reports the performance of the multivariable artificial pancreas with a predictive hypoglycemia module in clinical experiments with exercise bouts and no manual entries.CrossRefPubMedPubMedCentral
23.
•• Turksoy K, Quinn LT, Littlejohn E, Cinar A. Multivariable adaptive identification and control for artificial pancreas systems. IEEE Trans Biomed Eng. 2014;61:883–91. This study provides the algorithms used in the multivariable artificial pancreas.CrossRefPubMed
24.
Kumareswaran K, Elleri D, Allen JM, Harris J, Xing D, Kollman C, et al. Meta-analysis of overnight closed-loop randomized studies in children and adults with type 1 diabetes: the Cambridge cohort. J Diabetes Sci Technol. 2011;5:1352–62.CrossRefPubMedPubMedCentral
25.
Clarke WL, Anderson S, Breton M, Patek S, Kashmer L, Kovatchev B. Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: the Virginia experience. J. Diabetes Sci. Technol. 2009;3:1031–8.CrossRefPubMedPubMedCentral
26.
Hovorka R, Allen JM, Elleri D, Chassin LJ, Harris J, Xing D, et al. Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial. Lancet. 2010;375:743–51.CrossRefPubMed
27.
Steil GM, Palerm CC, Kurtz N, Voskanyan G, Roy A, Paz S, et al. The effect of insulin feedback on closed loop glucose control. J Clin Endocrinol Metab. 2011;96:1402–8.CrossRefPubMedPubMedCentral
28.
Breton M, Farret A, Bruttomesso D, Anderson S, Magni L, Patek S, et al. Fully integrated artificial pancreas in type 1 diabetes modular closed-loop glucose control maintains near normoglycemia. Diabetes. 2012;61:2230–7.CrossRefPubMedPubMedCentral
29.
Weinzimer SA, Steil GM, Swan KL, Dziura J, Kurtz N, Tamborlane WV. Fully automated closed-loop insulin delivery versus semiautomated hybrid control in pediatric patients with type 1 diabetes using an artificial pancreas. Diabetes Care. 2008;31:934–9.CrossRefPubMed
30.
Agrawal P, Welsh JB, Kannard B, Askari S, Yang Q, Kaufman FR. Usage and effectiveness of the low glucose suspend feature of the medtronic paradigm Veo insulin pump. J Diabetes Sci Technol. 2011;5:1137–41.CrossRefPubMedPubMedCentral
31.
Cengiz E, Swan KL, Tamborlane WV, Steil GM, Steffen ST, Weinzimer SA. Is an automatic pump suspension feature safe for children with type 1 diabetes? An exploratory analysis with a closed-loop system. Diabetes Technol Ther. 2009;11:207–10.CrossRefPubMedPubMedCentral
32.
Bergenstal R, Buckingham B, Garg S, Weinzimer SA, Brazg R, Ilany J, et al. Pivotal trial of a hybrid closed-loop system in type 1 diabetes (T1D), in American Diabetes Association 2016 Meeting, 2016 (New Orleans, LA; June 10–14).
33.
34.
35.
36.
37.
38.
39.
40.
41.
• Klonoff DC, Zimliki CL, Stevens A, Beaston P, Pinkos A, Choe SY, et al. Innovations in technology for the treatment of diabetes: clinical development of the artificial pancreas (an autonomous system). J Diabetes Sci Technol. 2011;5:804–26. This publication illustrates the state-of-the-art and future challenges as the surge is AP research was accelerating.CrossRefPubMedPubMedCentral
42.
Kowalski A, Lum JW. Juvenile diabetes research foundation artificial pancreas consortium update. J Diabetes Sci Tech. 2009;3:1224–6.CrossRef
43.
44.
45.
•• Kowalski A. Pathway to artificial pancreas systems revisited: moving downstream. Diabetes Care. 2015;38:1036–43. doi:10.​2337/​dc15-0364. This publication provides an excellent summary of the progress made in the past 10 years and provides a strategic road map for future research.CrossRefPubMed
46.
• Breton MD, Brown SA, Karvetski CH, Kollar L, Topchyan KA, Anderson SM, et al. Adding heart rate signal to a control-to-range artificial pancreas system improves the protection against hypoglycemia during exercise in type 1 diabetes. Diabetes Technol Ther. 2014;16:506–11. This study demonstrates the benefits of using additional measurements in enhancing the performance of an artificial pancreas during structured exercise.CrossRefPubMedPubMedCentral
47.
• Jacobs PG, Resalat N, El Youssef J, Reddy R, Branigan D, Preiser N, et al. Incorporating an exercise detection, grading, and hormone dosing algorithm into the artificial pancreas using accelerometry and heart rate. J. Diabetes Sci. Technol. 2015;9:1175–84. This study demonstrates the benefits of using additional measurements in enhancing the performance of an artificial pancreas during structured exercise.CrossRefPubMedPubMedCentral
48.
Dasanayake IS, Bevier WC, Castorino K, Pinsker JE, Seborg DE, Doyle FJ, et al. Early detection of physical activity for people type 1 diabetes mellitus. J. Diabetes Sci. Technol. 2015;9:1236–45.CrossRefPubMedPubMedCentral
49.
• Stenerson M, Cameron F, Payne SR, Payne SL, Ly TT, Wilson DM, et al. The impact of accelerometer use in exercise-associated hypoglycemia prevention in type 1 diabetes. J. Diabetes Sci. Technol. 2015;9:80–5. This study demonstrates the benefits of using additional measurements in enhancing the performance of an artificial pancreas during structured exercise.CrossRefPubMed
50.
Cichosz SL, Frystyk J, Hejlesen OK, Tarnow L, Fleischer J. A novel algorithm for prediction and detection of hypoglycemia based on continuous glucose monitoring and heart rate variability in patients with type 1 diabetes. J. Diabetes Sci. Technol. 2014;9:731–7.CrossRef
51.
Cameron F, Ly TT, Forlenza GP, Patek SD, Baysal N, Messer LH, et al. Inpatient clinical trial of a fully closed-loop artificial pancreas using only CGM and accelerometer data for insulin dosing. Diabetes Technology Therapeutics. 2016;18:A23–4.CrossRef
52.
• Turksoy K, Bayrak ES, Quinn L, Littlejohn E, Rollins D, Cinar A. Hypoglycemia early alarm systems based on multivariable models. Ind Eng Chem Res. 2013;52:12329–36. doi:10.​1021/​ie3034015.​ This study demonstrates the benefits of a hypoglycemia prediction algorithm based on recursively updated multivariable glucose concentration estimation models.CrossRef
53.
•• Turksoy K, Hajizadeh I, Samadi S, Feng J, Sevil M, Park, et al. Real-time insulin bolusing for unannounced meals with artificial pancreas. Control Eng Pract. 2017;59:159–64. This study illustrates the performance of adding an algorithm that detects rapid rises in glucose levels and provides insulin boluses when no activities that would reduce glucose levels are occurring.CrossRef
54.
55.
Srinivasan B, Primus CJ, Bonvin D, Ricker NL. Run-to-run optimization via control of generalized constraints. Control Eng Pract. 2001;9:911–9.CrossRef
56.
57.
Toffanin C, Visentin R, Messori M, Di Palma F, Magni L, Cobelli C. Towards a run-to-run adaptive artificial pancreas: in silico results. IEEE Trans Biomed Engng. 2017; doi:10.​1109/​TBME.​2017.​2652062.
58.
• Toffanin C, Messori M, Cobelli C, Magni L. Automatic adaptation of basal therapy for type 1 diabetic patients: a run-to-run approach. Biomedical Signal Processing and Control. 2017;31:539–49. This study illustrates the benefits of updating the model to improve controller performance.CrossRef
59.
Eren-Oruklu M, Cinar A, Quinn L, Smith D. Estimation of future glucose concentrations with subject-specific recursive linear models. Diabetes Technol Ther. 2009;11:243–53.CrossRefPubMedPubMedCentral
60.
Boiroux D, Duun-Henriksen AK, Schmidt S, Nørgaard K, Poulsen NK, Madsen H, et al. Adaptive control in an artificial pancreas for people with type 1 diabetes. Control Eng Pract. 2017;58:332–42.CrossRef
61.
Cameron F, Niemeyer G, Wilson DM, Bequette BW, Benassi KS, Clinton P, et al. Inpatient trial of an artificial pancreas based on multiple model probabilistic predictive control with repeated large unannounced meals. Diabetes Technol Ther. 2014;16:728–34. doi:10.​1089/​dia.​2014.​0093.CrossRefPubMedPubMedCentral
62.
• Turksoy K, Samadi S, Feng J, Littlejohn E, Quinn L, Cinar A. Meal detection in patients with type 1 diabetes: a new module for the multivariable adaptive artificial pancreas control system. IEEE J Biomed Health Inform. 2016;20:47–54. doi:10.​1109/​JBHI.​2015.​2446413. This study illustrates detection of rapid increases of glucose levels and meals based on compartmental models and reduction of peak glucose values.CrossRefPubMed
63.
Turksoy K, Kilkus J, Hajizadeh I, Samadi S, Feng J, Sevil M, et al. Hypoglycemia detection and carbohydrate suggestion in an artificial pancreas. J Diabetes Sci Technol. 2016;10:1236–44.CrossRefPubMedPubMedCentral
64.
• Samadi S, Turksoy K, Hajizadeh I, Feng J, Sevil M, Cinar A. Meal detection and carbohydrate estimation using continuous glucose sensor data. IEEE J Biomed Health Inform. 2017;21:619–27. doi:10.​1109/​JBHI.​2017.​2677953. This study illustrates detection of rapid increases of glucose levels and meals based on qualitative trend analysis and fuzzy logic, estimation of meal sizes, and reduction of peak glucose values.CrossRefPubMed
65.
Riddell MC, Zaharieva DP, Yavelberg L, Cinar A, Jamnik VK. Exercise and the development of the artificial pancreas one of the more difficult series of hurdles. J. Diabetes Sci. Technol. 2015;9:1217–26.CrossRefPubMedPubMedCentral
66.
Colberg S. Diabetic athlete’s handbook. Human Kinetics; 2009.
67.
• Riddell MC, Gallen IW, Smart CE, Taplin CE, Adolfsson P, Lumb AN, et al. Exercise management in type 1 diabetes: a consensus statement. Lancet Diabetes Endocrinol. 2017;5:377–90. This paper provides valuable information about management of glucose during and after exercise.CrossRefPubMed
68.
Yardley JE, Colberg SR. Update on Management of Type 1 diabetes and type 2 diabetes in athletes. Curr Sports Med Rep. 2017;16:38–44.CrossRefPubMed
69.
• Turksoy K, Paulino TML, Zaharieva DP, Yavelberg L, Jamnik, Riddell MC, et al. Classification of physical activity: information to artificial pancreas control systems in real time. J. Diabetes Sci. Technol. 2015;9:1200–7. This study provides an algorithm for classifying the type of exercise (aerobic or resistance) in real time to inform the artificial pancreas for proper adjustment of insulin infusion rates depending on the type of exercise.
70.
Ben Brahim N, Place J, Renard E, Breton MD. Identification of main factors explaining glucose dynamics during and immediately after moderate exercise in patients with type 1 diabetes. J. Diabetes Sci. Technol. 2015;9:1185–91.CrossRefPubMedPubMedCentral
71.
•• Turksoy K, Monforti C, Park M, Griffith G, Quinn L, Cinar A. Use of wearable sensors and biometric variables in an artificial pancreas system. Sensors. 2017;17. doi:10.​3390/​s17030532. This study provides information about informative physiological variables about physical activities for use in multivariable artificial pancreas systems that can be more effective during and after exercise.
72.
73.
Dadlani V, Levine JA, McCrady-Spitzer SK, Dassau E, Kudva YC. Physical activity capture technology with potential for incorporation into closed-loop control for type 1 diabetes. J. Diabetes Sci. Technol. 2015;9:1208–16.CrossRefPubMedPubMedCentral
74.
Kudva YC, Carter RE, Cobelli C, Basu R, Basu A. Closed-loop artificial pancreas systems: physiological input to enhance next-generation devices. Diabetes Care. 2014;37:1184–90.CrossRefPubMedPubMedCentral
75.
Fahey AJ, Paramalingam N, Davey RJ, Davis EA, Jones TW, Fournier PA. The effect of a short sprint on postexercise whole-body glucose production and utilization rates in individuals with type 1 diabetes mellitus. J Clin Endocrinol Metab. 2012;97:4193–200.CrossRefPubMed
76.
Yardley JE, Kenny GP, Perkins BA, Riddell MC, Malcolm J, Boulay P, et al. Effects of performing resistance exercise before versus after aerobic exercise on glycemia in type 1 diabetes. Diabetes Care. 2012;35:669–75.CrossRefPubMedPubMedCentral
77.
Gettman LR, Pollock ML. Circuit weight training: a critical review of its physiological benefits. Phys Sports Med. 1981;9:44–60.CrossRef
78.
Stanforth D, Stanforth PR, Hoemeke ME. Physiologic and metabolic responses to a body pump workout. J Strength Cond Res. 2000;14:144–50.
79.
80.
Gonder-Frederick LA, Carter WR, Cox DJ, Clarke WL. Environmental stress and blood glucose change in insulin-dependent diabetes mellitus. Health Psychol. 1990;9:503–15.CrossRefPubMed
81.
Hanson SL, Pichert JW. Perceived stress and diabetes control in adolescents. Health Psychol. 1986;5:439–52.CrossRefPubMed
82.
Halford WK, Cuddihy S, Mortimer RH. Psychological stress and blood glucose regulation in type I diabetic patients. Health Psychol. 1990;9:516–28.CrossRefPubMed
83.
Chida Y, Hamer M. An association of adverse psychosocial factors with diabetes mellitus: a meta-analytic review of longitudinal cohort studies. Diabetologia. 2008;51:2168–78.CrossRefPubMed
84.
Hilliard ME, Joyce P, Hessler D, Butler AM, Anderson BJ, Jaser S. Stress and A1c among people with diabetes across the lifespan. Curr. Diab. Rep. 2016;16:1–10.CrossRef
85.
Baucom KJW, Queen TL, Wiebe DJ, Turner SL, Wolfe KL, Godbey EI, et al. Depressive symptoms, daily stress, and adherence in late adolescents with type 1 diabetes. Health Psychol. 2015;34:522–30.CrossRefPubMedPubMedCentral
86.
Frenzel MP, McCaul KD, Glasgow RE, Schafer LC. The relationship of stress and coping to regimen adherence and glycemic control of diabetes. J Soc Clin Psychol. 1988;6:77–87.CrossRef
87.
Surwit RS, Schneider MS. Role of stress in the etiology and treatment of diabetes mellitus. Psychosom Med. 1993;55:380–93.CrossRefPubMed
88.
Moberg E, Kollind M, Lins PE, Adamson U. Acute mental stress impairs insulin sensitivity in IDDM patients. Diabetologia. 1994;37:247–51.CrossRefPubMed
89.
Colberg SR, Edelman S. 50 secrets of the longest living people with diabetes. Da Capo Press, 2008.
90.
Riazi A, Pickup J, Bradley C. Daily stress and glycaemic control in type 1 diabetes: individual differences in magnitude, direction, and timing of stress-reactivity. Diabetes Res Clin Pract. 2004;66:237–44.CrossRefPubMed
91.
Kelly MM, Tyrka AR, Anderson GM, Price LH, Carpenter LL. Sex differences in emotional and physiological responses to the Trier Social Stress Test. J Behav Ther Exp Psychiatry. 2008;39:87–98.CrossRefPubMed
92.
Kajantie E, Phillips DIW. The effects of sex and hormonal status on the physiological response to acute psychosocial stress. Psychoneuroendocrinology. 2006;31:151–78.CrossRefPubMed
93.
Wiesli P, Schmid C, Kerwer O, Nigg-Koch C, Klaghofer R, Seifert B, et al. Acute psychological stress affects glucose concentrations in patients with type 1 diabetes following food intake but not in the fasting state. Diabetes Care. 2005;28:1910–5.CrossRefPubMed
94.
Perkins BA, Riddell MC, Campaigne BN, et al. Type 1 diabetes and exercise: using the insulin pump to maximum advantage. Can J Diabetes. 2006;30:72–9.CrossRef
95.
Gonder-Frederick LA, Grabman JH, Kovatchev B, Brown SA, Patek S, Basu A, et al. Is psychological stress a factor for incorporation into future closed-loop systems? J. Diabetes Sci. Technol. 2016;10:640–6.CrossRefPubMedPubMedCentral
96.
Perfect MM, Patel PG, Scott RE, Wheeler MD, Patel C, Griffin K, et al. Sleep, glucose, and daytime functioning in youth with type 1 diabetes. Sleep. 2012;35:81–8.CrossRefPubMedPubMedCentral
97.
Feupe SF, Frias PF, Mednick SC, Mcdevitt EA, Heintzman ND. Nocturnal continuous glucose and sleep stage data in adults with type 1 diabetes in real-world conditions. J. Diabetes Sci. Technol. 2013;7:1337–45.CrossRefPubMedPubMedCentral
98.
Jauch-Chara K, Schmid SM, Hallschmid M, Born J, Schultes B. Altered neuroendocrine sleep architecture in patients with type 1 diabetes. Diabetes Care. 2008;31:1183–8.CrossRefPubMed
99.
Yeshayahu Y, Mahmud FH. Altered sleep patterns in adolescents with type 1 diabetes: implications for insulin regimen. Diabetes Care. 2010;33:e142.CrossRefPubMed
100.
Estrada CL, Danielson KK, Drum ML, Lipton RB. Insufficient sleep in young patients with diabetes and their families. Biol Res Nurs. 2012;14:48–54.CrossRefPubMed
101.
Koren D, O’Sullivan KL, Mokhlesi B. Metabolic and glycemic sequelae of sleep disturbances in children and adults. Curr Diab Rep. 2015;15:1–10.CrossRef
102.
Jaser SS, Ellis D. Sleep in adolescents and young adults with type 1 diabetes: associations with diabetes management and glycemic control. Heal Psychol Behav Med. 2016;4:49–55.CrossRef
103.
McDonough RJ, Clements MA, DeLurgio SA, Patton SR. Sleep duration and its impact on adherence in adolescents with type 1 diabetes mellitus Pediatr. Diabetes. 2016;18:262–70.
104.
Turner SL, Queen TL, Butner J, Wiebe D, Berg CA. Variations in daily sleep quality and type 1 diabetes management in late adolescents. J Pediatr Psychol. 2016;41:661–9.CrossRefPubMedPubMedCentral
105.
Barnard K, James J, Kerr D, Adolfsson P, Runion A, Serbedzija G. Impact of chronic sleep disturbance for people living with type 1 diabetes. J Diabetes Sci Technol. 2016;10:762–7.CrossRefPubMed
106.
Sherr JL, Collazo MP, Cengiz E, Michaud C, Carria L, Steffen AT, et al. Safety of nighttime 2-hour suspension of basal insulin in pump-treated type 1 diabetes even in the absence of low glucose. Diabetes Care. 2014;37:773–9.CrossRefPubMedPubMedCentral
107.
Buckingham B, Chase HP, Dassau E, Cobry E, Clinton P, Gage V, et al. Prevention of nocturnal hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Care. 2010;33:1013–7.CrossRefPubMedPubMedCentral
108.
Beck RW, Raghinaru D, Wadwa RP, Chase HP, Maahs DM, Buckingham BA. Frequency of morning ketosis after overnight insulin suspension using an automated nocturnal predictive low glucose suspend system. Diabetes Care. 2014;37:1224–9.CrossRefPubMedPubMedCentral
109.
Buckingham BA, Raghinaru D, Cameron F, Bequette BW, Chase HP, Maahs DM, et al. Predictive low-glucose insulin suspension reduces duration of nocturnal hypoglycemia in children without increasing ketosis. Diabetes Care. 2015;38:1197–204.CrossRefPubMedPubMedCentral
110.
111.
Feng J, Turksoy K, Samadi S, Hajizadeh I, Littlejohn E, Cinar A. Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters. J Process Control. 2017. Accepted for publication.
112.
Turksoy K. Iman Hajizadeh I, Littlejohn E, Cinar A. Multivariate statistical monitoring of sensor faults of a multivariable artificial pancreas. IFAC World Congress, 2017.
113.
Forlenza G, Chernavvsky D, DeBoer M, Robic J, Kovatchev B, Wadwa RP, et al. The artificial pancreas improves glycemic control during extended exercise at ski camp in adolescents with type 1 diabetes. Diabetes Technol Ther. 2017 (2017 ATTD Supplement) 19:A-47.
114.
Sevil M, Hajizadeh I, Samadi S, Feng J, Lazaro C, Frantz N, et al. Social and competition stress detection with wristband physiological signals. 2017 I.E. 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2017. 39–42.
115.
• Haidar A, Legault L, Matteau-Pelletier L, Messier V, Dallaire M, Ladouceur M, et al. 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. 2015;3:595–604. This study provides a good comparison of single (insulin) and dual-hormone (insulin/glucagon) artificial pancreas performance.CrossRefPubMed
116.
Castle JR, El Youssef J, Bakhtiani PA, Cai Y, Stobbe JM, Branigan D, et al. Effect of repeated glucagon doses on hepatic glycogen in type 1 diabetes: implications for a bihormonal closed-loop system. Diabetes Care. 2015;38:2115–9.CrossRefPubMedPubMedCentral
117.
Feng J, Turksoy K, Cinar A. Performance assessment of model-based artificial pancreas control systems. In: Kirchsteiger H, Jorgensen JB, Renard E, del Re L, editors. Prediction methods for blood glucose concentration. New York: Springer; 2016. p. 243–65.CrossRef
118.
• Hajizadeh I, Rashid M, Turksoy K, Samadi S, Frantz N, Sevil M, Cengiz E, et al. Plasma insulin estimation in people with type 1 diabetes mellitus. Ind Eng Chem Res. 2017. Accepted for publication. This study provides an algorithm to estimate plasma insulin concentration from CGM readings in real time to replace insulin-on-board estimates based on aggregate patient data.
119.
• Gondhalekar R, Dassau E, Doyle FJ. Periodic zone-MPC with asymmetric costs for outpatient-ready safety of an artificial pancreas to treat type 1 diabetes. Automatica. 2016;71:237–46. This study provides an enhancement of model-predictive artificial pancreas control systems by adjusting the cost function for different ranges of glucose concentrations and modifying the aggressiveness of the control actions as needed.CrossRefPubMedPubMedCentral
120.
Chakrabarty A, Zavitsanou S, Doyle FJ, Dassau E. Event-triggered model predictive control for embedded artificial pancreas systems, IEEE Trans BME, 2017.
121.
122.
123.
124.
125.
126.
127.
128.
Metadaten
Titel
Multivariable Adaptive Artificial Pancreas System in Type 1 Diabetes
Publikationsdatum
01.10.2017
Erschienen in
Current Diabetes Reports / Ausgabe 10/2017
Print ISSN: 1534-4827
Elektronische ISSN: 1539-0829
DOI
https://doi.org/10.1007/s11892-017-0920-1

Weitere Artikel der Ausgabe 10/2017

Current Diabetes Reports 10/2017 Zur Ausgabe

Pathogenesis of Type 1 Diabetes (A Pugliese, Section Editor)

Tolerogenic Nanoparticles to Treat Islet Autoimmunity

Diabetes and Pregnancy (M-F Hivert, Section Editor)

Application of One-Step IADPSG Versus Two-Step Diagnostic Criteria for Gestational Diabetes in the Real World: Impact on Health Services, Clinical Care, and Outcomes

Microvascular Complications—Retinopathy (JK Sun and PS Silva, Section Editors)

Diabetic Eye Screening: Knowledge and Perspectives from Providers and Patients

Microvascular Complications—Retinopathy (JK Sun and PS Silva, Section Editors)

Animal Models of Diabetic Retinopathy

Immunology, Transplantation, and Regenerative Medicine (L Piemonti and V Sordi, Section Editors)

Heterogeneity in the Beta-Cell Population: a Guided Search Into Its Significance in Pancreas and in Implants

Pathogenesis of Type 2 Diabetes and Insulin Resistance (RM Watanabe, Section Editor)

Re-visiting the Endocannabinoid System and Its Therapeutic Potential in Obesity and Associated Diseases

Leitlinien kompakt für die Innere Medizin

Mit medbee Pocketcards sicher entscheiden.

Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag

Kostenlos registrieren

Neu im Fachgebiet Innere Medizin

Notfall-TEP der Hüfte ist auch bei 90-Jährigen machbar

26.04.2024 Hüft-TEP Nachrichten

Ob bei einer Notfalloperation nach Schenkelhalsfraktur eine Hemiarthroplastik oder eine totale Endoprothese (TEP) eingebaut wird, sollte nicht allein vom Alter der Patientinnen und Patienten abhängen. Auch über 90-Jährige können von der TEP profitieren.

Niedriger diastolischer Blutdruck erhöht Risiko für schwere kardiovaskuläre Komplikationen

25.04.2024 Hypotonie Nachrichten

Wenn unter einer medikamentösen Hochdrucktherapie der diastolische Blutdruck in den Keller geht, steigt das Risiko für schwere kardiovaskuläre Ereignisse: Darauf deutet eine Sekundäranalyse der SPRINT-Studie hin.

Bei schweren Reaktionen auf Insektenstiche empfiehlt sich eine spezifische Immuntherapie

25.04.2024 Allergien und Intoleranzreaktionen Nachrichten

Insektenstiche sind bei Erwachsenen die häufigsten Auslöser einer Anaphylaxie. Einen wirksamen Schutz vor schweren anaphylaktischen Reaktionen bietet die allergenspezifische Immuntherapie. Jedoch kommt sie noch viel zu selten zum Einsatz.

Therapiestart mit Blutdrucksenkern erhöht Frakturrisiko

25.04.2024 Hypertonie Nachrichten

Beginnen ältere Männer im Pflegeheim eine Antihypertensiva-Therapie, dann ist die Frakturrate in den folgenden 30 Tagen mehr als verdoppelt. Besonders häufig stürzen Demenzkranke und Männer, die erstmals Blutdrucksenker nehmen. Dafür spricht eine Analyse unter US-Veteranen.

Update Innere Medizin

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen
Bildnachweise