Extensive assessment of blood glucose monitoring during postprandial period and its impact on closed-loop performance

[EN] Background: Closed-loop (CL) systems aims to outperform usual treatments in blood glucose control and continuous glucose monitors (CGM) are a key component in such systems. Meals represents one of the main disturbances in blood glucose control, and postprandial period (PP) is a challenging situation for both CL system and CGM accuracy. Methods: We performed an extensive analysis of sensor¿s performance by numerical accuracy and precision during PP, as well as its influence in blood glucose control under CL therapy. Results: During PP the mean absolute relative difference (MARD) for both sensors presented lower accuracy in the hypoglycemic range (19.4 ± 12.8%) than in other ranges (12.2 ± 8.6% in euglycemic range and 9.3 ± 9.3% in hyperglycemic range). The overall MARD was 12.1 ± 8.2%. We have also observed lower MARD for rates of change between 0 and 2 mg/dl. In CL therapy, the 10 trials with the best sensor spent less time in hypoglycemia (PG < 70 mg/dl) than the 10 trials with the worst sensors (2 ± 7 minutes vs 32 ± 38 minutes, respectively). Conclusions: In terms of accuracy, our results resemble to previously reported. Furthermore, our results showed that sensors with the lowest MARD spent less time in hypoglycemic range, indicating that the performance of CL algorithm to control PP was related to sensor accuracy.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has been partially supported by the Spanish Government through Grants DPI 2013-46982-C2-1-R, DPI 2016-78831-C2-1-R, DPI 2013-46982-C2-2-R, and DPI 2016-78831-C2-2-R, the National Council of Technological and Scientific Development, CNPq Brazil through Grants 202050/2015-7 and 207688/2014-1.Biagi, L.; Hirata-Bertachi, A.; Conget, I.; Quirós, C.; Giménez, M.; Ampudia-Blasco, F.; Rossetti, P.... (2017). Extensive assessment of blood glucose monitoring during postprandial period and its impact on closed-loop performance. Journal of Diabetes Science and Technology. 11(6):1089-1095. https://doi.org/10.1177/1932296817714272S10891095116Doyle, F. J., Huyett, L. M., Lee, J. B., Zisser, H. C., & Dassau, E. (2014). Closed-Loop Artificial Pancreas Systems: Engineering the Algorithms. Diabetes Care, 37(5), 1191-1197. doi:10.2337/dc13-2108Cengiz, E., & Tamborlane, W. V. (2009). A Tale of Two Compartments: Interstitial Versus Blood Glucose Monitoring. Diabetes Technology & Therapeutics, 11(S1), S-11-S-16. doi:10.1089/dia.2009.0002Cobelli, C., Schiavon, M., Dalla Man, C., Basu, A., & Basu, R. (2016). Interstitial Fluid Glucose Is Not Just a Shifted-in-Time but a Distorted Mirror of Blood Glucose: Insight from an In Silico Study. Diabetes Technology & Therapeutics, 18(8), 505-511. doi:10.1089/dia.2016.0112Castle, J. R., & Ward, W. K. (2010). Amperometric Glucose Sensors: Sources of Error and Potential Benefit of Redundancy. Journal of Diabetes Science and Technology, 4(1), 221-225. doi:10.1177/193229681000400127Basu, A., Dube, S., Veettil, S., Slama, M., Kudva, Y. C., Peyser, T., … Basu, R. (2014). Time Lag of Glucose From Intravascular to Interstitial Compartment in Type 1 Diabetes. Journal of Diabetes Science and Technology, 9(1), 63-68. doi:10.1177/1932296814554797Keenan, D. B., Grosman, B., Clark, H. W., Roy, A., Weinzimer, S. A., Shah, R. V., & Mastrototaro, J. J. (2011). Continuous Glucose Monitoring Considerations for the Development of a Closed-Loop Artificial Pancreas System. Journal of Diabetes Science and Technology, 5(6), 1327-1336. doi:10.1177/193229681100500603Van Bon, A. C., Jonker, L. D., Koebrugge, R., Koops, R., Hoekstra, J. B. L., & DeVries, J. H. (2012). Feasibility of a Bihormonal Closed-Loop System to Control Postexercise and Postprandial Glucose Excursions. Journal of Diabetes Science and Technology, 6(5), 1114-1122. doi:10.1177/193229681200600516Rossetti, P., Quirós, C., Moscardó, V., Comas, A., Giménez, M., Ampudia-Blasco, F. J., … Vehí, J. (2017). Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target. Diabetes Technology & Therapeutics, 19(6), 355-362. doi:10.1089/dia.2016.0443Bailey, T., Zisser, H., & Chang, A. (2009). New Features and Performance of a Next-Generation SEVEN-Day Continuous Glucose Monitoring System with Short Lag Time. Diabetes Technology & Therapeutics, 11(12), 749-755. doi:10.1089/dia.2009.0075Zschornack, E., Schmid, C., Pleus, S., Link, M., Klötzer, H.-M., Obermaier, K., … Freckmann, G. (2013). Evaluation of the Performance of a Novel System for Continuous Glucose Monitoring. Journal of Diabetes Science and Technology, 7(4), 815-823. doi:10.1177/193229681300700403Pleus, S., Schmid, C., Link, M., Zschornack, E., Klötzer, H.-M., Haug, C., & Freckmann, G. (2013). Performance Evaluation of a Continuous Glucose Monitoring System under Conditions Similar to Daily Life. Journal of Diabetes Science and Technology, 7(4), 833-841. doi:10.1177/193229681300700405Zisser, H. C., Bailey, T. S., Schwartz, S., Ratner, R. E., & Wise, J. (2009). Accuracy of the SEVEN® Continuous Glucose Monitoring System: Comparison with Frequently Sampled Venous Glucose Measurements. Journal of Diabetes Science and Technology, 3(5), 1146-1154. doi:10.1177/193229680900300519Obermaier, K., Schmelzeisen-Redeker, G., Schoemaker, M., Klötzer, H.-M., Kirchsteiger, H., Eikmeier, H., & del Re, L. (2013). Performance Evaluations of Continuous Glucose Monitoring Systems: Precision Absolute Relative Deviation is Part of the Assessment. Journal of Diabetes Science and Technology, 7(4), 824-832. doi:10.1177/193229681300700404Clarke, W. L., Cox, D., Gonder-Frederick, L. A., Carter, W., & Pohl, S. L. (1987). Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose. Diabetes Care, 10(5), 622-628. doi:10.2337/diacare.10.5.622Martin Bland, J., & Altman, D. (1986). STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT. The Lancet, 327(8476), 307-310. doi:10.1016/s0140-6736(86)90837-8Breton, M., & Kovatchev, B. (2008). Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors. Journal of Diabetes Science and Technology, 2(5), 853-862. doi:10.1177/193229680800200517Kropff, J., Bruttomesso, D., Doll, W., Farret, A., Galasso, S., Luijf, Y. M., … DeVries, J. H. (2014). Accuracy of two continuous glucose monitoring systems: a head‐to‐head comparison under clinical research centre and daily life conditions. Diabetes, Obesity and Metabolism, 17(4), 343-349. doi:10.1111/dom.12378Reddy, M., Herrero, P., Sharkawy, M. E., Pesl, P., Jugnee, N., Pavitt, D., … Oliver, N. S. (2015). Metabolic Control With the Bio-inspired Artificial Pancreas in Adults With Type 1 Diabetes. Journal of Diabetes Science and Technology, 10(2), 405-413. doi:10.1177/1932296815616134Pleus, S., Schoemaker, M., Morgenstern, K., Schmelzeisen-Redeker, G., Haug, C., Link, M., … Freckmann, G. (2015). Rate-of-Change Dependence of the Performance of Two CGM Systems During Induced Glucose Swings. Journal of Diabetes Science and Technology, 9(4), 801-807. doi:10.1177/193229681557871

Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target

This is a copy of an article published in the Diabetes Technology & Therapeutics © 2017 [copyright Mary Ann Liebert, Inc.]; Diabetes Technology & Therapeutics is available online at: https://www.liebertpub.com/.[EN] Background: Postprandial (PP) control remains a challenge for closed-loop (CL) systems. Few studies with inconsistent results have systematically investigated the PP period. Objective: To compare a new CL algorithm with current pump therapy (open loop [OL]) in the PP glucose control in type 1 diabetes (T1D) subjects. Methods: A crossover randomized study was performed in two centers. Twenty T1D subjects (F/M 13/7, age 40.7 -10.4 years, disease duration 22.6 +/- 9.9 years, and A1c 7.8% +/- 0.7%) underwent an 8-h mixed meal test on four occasions. In two (CL1/CL2), after meal announcement, a bolus was given followed by an algorithmdriven basal infusion based on continuous glucose monitoring (CGM). Alternatively, in OL1/OL2 conventional pump therapy was used. Main outcome measures were as follows: glucose variability, estimated with the coefficient of variation (CV) of the area under the curve (AUC) of plasma glucose (PG) and CGM values, and from the analysis of the glucose time series; mean, maximum (C-max), and time to C-max glucose concentrations and time in range (180 mg/dL). Results: CVs of the glucose AUCs were low and similar in all studies (around 10%). However, CL achieved greater reproducibility and better PG control in the PP period: CL1 = CL2 0.05) nor the need for oral glucose was significantly different (CL 40.0% vs. OL 22.5% of meals; P = 0.054). Conclusions: This novel CL algorithm effectively and consistently controls PP glucose excursions without increasing hypoglycemia. Study registered at ClinicalTrials.gov: study number NCT02100488.This work was supported by the Spanish Ministry of Economy and Competitiveness through Grants DPI2013-46982-C2-1-R and DPI2013-46982-C2-2-R, and the EU through FEDER funds. C.Q. is the recipient of a grant from the Hospital Clinic i Universitari of Barcelona ("Ajut a la recerca Josep Font 2014-2017").Rossetti, P.; Quirós, C.; Moscardo-Garcia, V.; Comas, A.; Giménez, M.; Ampudia-Blasco, F.; León, F.... (2017). Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target. Diabetes Technology & Therapeutics. 19(6):355-362. https://doi.org/10.1089/dia.2016.0443S35536219

Damage detection by using FBGs and strain field pattern recognition techniques

A novel methodology for damage detection and location in structures is proposed. The methodology is based on strain measurements and consists in the development of strain field pattern recognition techniques. The aforementioned are based on PCA (principal component analysis) and damage indices (T 2 and Q). We propose the use of fiber Bragg gratings (FBGs) as strain sensor

Soccer Team based on Agent-Oriented Programming

In this paper the analysis, design and implementation of a soccer team of micro-robots is explained. Besides the technical difficulties to develop these micro-robots, this paper also shows how to develope a multi-agent co-operative system by means of Matlab/Simulink+ a widely known Computer Aided Control System Design framework. Agent-Oriented Paradigms formalise interactions between multiple agents in terms of changing their mental states by communication between agents. Their practical implementations are usually conceived by means of Object-Oriented Paradigms. Nevertheless, the implementation of Agent-Oriented Paradigms in Matlab/Simulink is not straightforward. Thus, the obtained real implementation is an integrated system that includes several programming paradigms so as hardware platforms. Finally, the proposal of the integrated framework for the micro-robots soccer team is shown. 1. INTRODUCTION Multi-agent based mobile robotics require new examples from application and call fo..

Artificial pancreas: automatic control of insulin infusion in type 1 diabetes mellitus

[ES] La diabetes mellitus tipo 1 es una enfermedad crónica que afecta aproximadamente a 30 millones de personas en el mundo y se caracteriza por niveles de concentración de glucosa en sangre elevados producidos por una deficiencia absoluta de insulina. Ello produce numerosas complicaciones a largo plazo como retinopatía, nefropatía y neuropatía entre otras. Las terapias actuales basadas en el suministro de insulina exógena (por inyecciones o bomba de insulina), no consiguen normalizar los niveles de glucosa de forma eficiente. Los avances tecnológicos en la última década en sistemas de medición continua de glucosa e infusión de insulina, han impulsado el desarrollo del páncreas artificial, o control automático de infusión de insulina. En este trabajo se presentará, a modo de tutorial, el pasado, presente y futuro de esta tecnología, tan esperada por el paciente diabético. Se revisará el estado actual de la tecnología para la sensorización y actuación, principales desafíos desde el punto de vista de control, las diferentes ``escuelas'' y estudios clínicos del desempeño de controladores, así como herramientas de validación de controladores mediante simulación. Dada la complejidad del problema, el desarrollo del páncreas artificial será de forma escalonada, redundando progresivamente en la mejora de la calidad de vida del paciente. Los grandes avances en los últimos cinco años hacen preveer un horizonte cercano para la primera generación de páncreas artificial.[EN] Type 1 diabetes mellitus is a chronic disease that affects approximately to 30 million people worldwide and is characterized by high blood glucose concentration levels produced by an absolute deficiency of insulin. That produces numerous long-term complications like retinopathy, nephropathy and neuropathy among others. Current therapies based on the exogenous delivery of insulin (through injections or an insulin pump), do not manage to normalize the glucose levels efficiently. Technological advances in the last decade in continuous glucose monitoring and insulin infusion have been a springboard for the development of the artificial pancreas, or automatic control of insulin infusion. In this work, the past, present and future of this technology, so long awaited by the diabetic patient, will be presented in the form of a tutorial. Current technology for sensorization and actuation will be reviewed, as well as main challenges from the control point of view, different “schools of thought” and clinical studies for controllers performance evaluation, and tools for the validation of controllers through simulation. Due to the complexity of the problem, the development of the artificial pancreas will be staggered, resulting progressively in an improvement of the patient’s quality of life. The big advances during last five years foresee a close horizon for a first generation of artificial pancreas.Este trabajo ha sido realizado parcialmente gracias al apoyo del Ministerio de Ciencia e Innovación español, a través del proyecto DPI2007-66728-C02, de la Unión Europea a través de fondos FEDER y de la Wellcome Trust.Bondía Company, J.; Vehí, J.; Palerm, CC.; Herrero, P. (2010). El Páncreas Artificial: Control Automático de Infusión de Insulina en Diabetes Mellitus Tipo 1. Revista Iberoamericana de Automática e Informática industrial. 7(2):5-20. https://doi.org/10.1016/S1697-7912(10)70021-2OJS52072Argoud, G. M., Schade, D. S., & Eaton, R. P. (1987). Insulin Suppresses Its Own Secretion In Vivo. Diabetes, 36(8), 959-962. doi:10.2337/diab.36.8.959Arleth, T., Andreassen, S., Federici, M. O., & Benedetti, M. M. (2000). A model of the endogenous glucose balance incorporating the characteristics of glucose transporters. Computer Methods and Programs in Biomedicine, 62(3), 219-234. doi:10.1016/s0169-2607(00)00069-9Atlas, E., Nimri, R., Miller, S., Grunberg, E. A., & Phillip, M. (2010). MD-Logic Artificial Pancreas System: A pilot study in adults with type 1 diabetes. Diabetes Care, 33(5), 1072-1076. doi:10.2337/dc09-1830Bailey, T., Zisser, H., & Chang, A. (2009). New Features and Performance of a Next-Generation SEVEN-Day Continuous Glucose Monitoring System with Short Lag Time. Diabetes Technology & Therapeutics, 11(12), 749-755. doi:10.1089/dia.2009.0075Basu, R., Di Camillo, B., Toffolo, G., Basu, A., Shah, P., Vella, A., … Cobelli, C. (2003). Use of a novel triple-tracer approach to assess postprandial glucose metabolism. American Journal of Physiology-Endocrinology and Metabolism, 284(1), E55-E69. doi:10.1152/ajpendo.00190.2001Bequette, B. W. (2005). A Critical Assessment of Algorithms and Challenges in the Development of a Closed-Loop Artificial Pancreas. Diabetes Technology & Therapeutics, 7(1), 28-47. doi:10.1089/dia.2005.7.28Bergman, R. N. (2003). The Minimal Model of Glucose Regulation: A Biography. Mathematical Modeling in Nutrition and the Health Sciences, 1-19. doi:10.1007/978-1-4419-9019-8_1Bevier, W. C., Zisser, H. C., Jovanovič, L., Finan, D. A., Palerm, C. C., Seborg, D. E., & Doyle, F. J. (2008). Use of Continuous Glucose Monitoring to Estimate Insulin Requirements in Patients with Type 1 Diabetes Mellitus during a Short Course of Prednisone. Journal of Diabetes Science and Technology, 2(4), 578-583. doi:10.1177/193229680800200408Bliss, M. (2007). The discovery of insulin. 25th anniversary ed. The University of Chicago Press.Bruttomesso, D., Farret, A., Costa, S., Marescotti, M. C., Vettore, M., Avogaro, A., … Maran, A. (2009). Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: Preliminary Studies in Padova and Montpellier. 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Control oriented model of insulin and glucose dynamics in type 1 diabetics. Medical & Biological Engineering & Computing, 44(1-2), 69-78. doi:10.1007/s11517-005-0012-2Facchinetti, A., Sparacino, G., & Cobelli, C. (2010). Modeling the Error of Continuous Glucose Monitoring Sensor Data: Critical Aspects Discussed through Simulation Studies. Journal of Diabetes Science and Technology, 4(1), 4-14. doi:10.1177/193229681000400102Fatourechi, M. M., Kudva, Y. C., Murad, M. H., Elamin, M. B., Tabini, C. C., & Montori, V. M. (2009). Hypoglycemia with Intensive Insulin Therapy: A Systematic Review and Meta-Analyses of Randomized Trials of Continuous Subcutaneous Insulin Infusion Versus Multiple Daily Injections. The Journal of Clinical Endocrinology & Metabolism, 94(3), 729-740. doi:10.1210/jc.2008-1415FDA (2002). General principles of software validation; final guidance for industry and FDA staff. 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Combined improvements in implantable pump technology and insulin stability allow safe and effective long term intraperitoneal insulin delivery in type 1 diabetic patients: the EVADIAC experience. Diabetes & Metabolism, 29(6), 602-607. doi:10.1016/s1262-3636(07)70075-7Guilhem, I., Leguerrier, A., Lecordier, F., Poirier, J., & Maugendre, D. (2006). Technical risks with subcutaneous insulin infusion. Diabetes & Metabolism, 32(3), 279-284. doi:10.1016/s1262-3636(07)70281-1Guyton, J. R., Foster, R. O., Soeldner, J. S., Tan, M. H., Kahn, C. B., Koncz, L., & Gleason, R. E. (1978). A Model of Glucose-insulin Homeostasis in Man that Incorporates the Heterogeneous Fast Pool Theory of Pancreatic Insulin Release. Diabetes, 27(10), 1027-1042. doi:10.2337/diab.27.10.1027Herman, W. H., & Eastman, R. C. (1998). The Effects of Treatment on the Direct Costs of Diabetes. Diabetes Care, 21(Supplement_3), C19-C24. doi:10.2337/diacare.21.3.c19Herrero, P., J. Vehí, R. Corcoy, A. Chico, B. Pons and A. de Leiva (2008). Model based fault detection in the artificial β-cell framework. In: Eighth Diabetes Technology Meeting.Hoshino, M., Haraguchi, Y., Mizushima, I., & Sakai, M. (2009). Recent progress in mechanical artificial pancreas. Journal of Artificial Organs, 12(3), 141-149. doi:10.1007/s10047-009-0463-6Hovorka, R. (2006). Continuous glucose monitoring and closed-loop systems. Diabetic Medicine, 23(1), 1-12. doi:10.1111/j.1464-5491.2005.01672.xHovorka, R. (2008). The Future of Continuous Glucose Monitoring: Closed Loop. Current Diabetes Reviews, 4(3), 269-279. doi:10.2174/157339908785294479Hovorka, R., Shojaee-Moradie, F., Carroll, P. V., Chassin, L. J., Gowrie, I. J., Jackson, N. C., … Jones, R. H. (2002). Partitioning glucose distribution/transport, disposal, and endogenous production during IVGTT. American Journal of Physiology-Endocrinology and Metabolism, 282(5), E992-E1007. doi:10.1152/ajpendo.00304.2001Hovorka, R., Allen, J. M., Elleri, D., Chassin, L. J., Harris, J., Xing, D., … Dunger, D. B. (2010). Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial. The Lancet, 375(9716), 743-751. doi:10.1016/s0140-6736(09)61998-xHovorka, R., Canonico, V., Chassin, L. J., Haueter, U., Massi-Benedetti, M., Federici, M. O., … Wilinska, M. E. (2004). Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiological Measurement, 25(4), 905-920. doi:10.1088/0967-3334/25/4/010Ibbini, M., & Masadeh, M. (2005). A fuzzy logic based closed-loop control system for blood glucose level regulation in diabetics. Journal of Medical Engineering & Technology, 29(2), 64-69. doi:10.1080/03091900410001709088JDRF: Artificial Pancreas Project (n.d.).http://jdrf.org. Accessed on March 8 2010.Jeitler, K., Horvath, K., Berghold, A., Gratzer, T. W., Neeser, K., Pieber, T. R., & Siebenhofer, A. (2008). 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Prediction of postprandial blood glucose under uncertainty and intra-patient variability in type 1 diabetes: a comparative study of three interval models,

The behavior of three insulin action and glucose kinetics models was assessed for an insulin therapy regime in the presence of patient variability. For this purpose, postprandial glucose in patients with type 1 diabetes was predicted by considering intra- and inter-patient variability using modal interval analysis. Equations to achieve optimal prediction are presented for models 1, 2 and 3, which are of increasing complexity. The model parameters were adjusted to reflect the “same” patient in the presence of variability. The glucose response envelope for model 1, the simplest insulin–glucose model assessed, included the responses of the other two models when a good fit of the model parameters was achieved. Thus, under variability, simple glucose–insulin models may be sufficient to describe patient dynamics in most situations.This work was partially supported by the Spanish Ministry of Science and Innovation through Grant DPI-2010-20764-C02, and by the Autonomous Government of Catalonia through Grant SGR 523.García Jaramillo, MA.; Calm, R.; Bondía Company, J.; Vehí, J. (2012). Prediction of postprandial blood glucose under uncertainty and intra-patient variability in type 1 diabetes: a comparative study of three interval models,. Computer Methods and Programs in Biomedicine. 108(1):993-1001. doi:10.1016/j.cmpb.2012.04.003S9931001108

Comparison of interval and Monte Carlo simulation for the prediction of postprandial glucose under uncertainty in type 1 diabetes mellitus

[EN] In this paper, the problem of tackling uncertainty in the prediction of postprandial blood glucose is analyzed. Two simulation approaches, Monte Carlo and interval models, are studied and compared. Interval simulation is carried out using modal interval analysis. Simulation of a glucoregulatory model with uncertainty in insulin sensitivities, glucose absorption and food intake is carried out using both methods. Interval simulation is superior in predicting all severe and mild hyper- and hypoglycemia episodes. Furthermore, much less computational time is required for interval simulation than for Monte Carlo simulationThis work was partially supported by the Spanish Ministry of Science and Innovation and the European Union through Grant DPI-2007-66728 and by the Autonomous Government of Catalonia through SGR00523.Calm, R.; García-Jaramillo, M.; Bondía Company, J.; Sainz, M.; Vehí, J. (2011). Comparison of interval and Monte Carlo simulation for the prediction of postprandial glucose under uncertainty in type 1 diabetes mellitus. Computer Methods and Programs in Biomedicine. 104(3):325-332. https://doi.org/10.1016/j.cmpb.2010.08.008S325332104