Calculation of the Best Basal-Bolus Combination for Postprandial Glucose Control in Insulin Pump Therapy

"© 2011 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."[EN] Intensive insulin therapy in type 1 diabetes is based on the well-established practice of adjusting basal and bolus insulin independently. Basal insulin delivery is designed to optimize glucose concentrations between meals and overnight, while bolus insulin delivery is designed to optimize postprandial glucose concentrations. However, this strategy shows some limitations in the postprandial glucose control, especially for meals with high carbohydrate content. Strategies based on coordinating basal and bolus insulin in the postprandial period help in overcoming these limitations. An algorithm, based on mathematically guaranteed techniques (interval analysis), is presented in this paper. It determines, given the current glycemic state of the patient and the meal to be ingested, a basal-bolus combination that will yield a tight postprandial glycemic control according to the International Diabetes Federation guidelines. For a given meal, the algorithm reveals which bolus administration mode will enable a good postprandial performance: standard, square-wave, dual-wave, or temporal basal decrement. The algorithm is validated through an in silico study using the 30 subjects in the educational version of the Food and Drug Administration accepted University of Virginia simulator.This work was supported by the Spanish Government under Grants DPI-2007-66728 and DPI-2010-20764-C02, by the FEDER funds from European Union, by the Generalitat de Catalunya under Grant SGR-00296, and by the PAID-05-08 program of the Universidad Politecnica de Valencia.Revert, A.; Calm, R.; Vehí, J.; Bondía Company, J. (2011). Calculation of the Best Basal-Bolus Combination for Postprandial Glucose Control in Insulin Pump Therapy. IEEE Transactions on Biomedical Engineering. 58(2):274-281. https://doi.org/10.1109/TBME.2010.2058805S27428158

Parallel Control of an artificial pancreas with coordinated insulin, glucagon and rescue carbohydrate control actions

[EN] Background: An artificial pancreas with insulin and glucagon delivery has the potential to reduce the risk of hypo- and hyperglycemia in people with type 1 diabetes. However, a maximum dose of glucagon of 1 mg/d is recommended, potentially still requiring rescue carbohydrates in some situations. This work presents a parallel control structure with intrinsic insulin, glucagon, and rescue carbohydrates coordination to overcome glucagon limitations when needed. Methods: The coordinated controller that combines insulin, glucagon, and rescue carbohydrate suggestions (DH-CC-CHO) was compared with the insulin and glucagon delivery coordinated controller (DH-CC). The impact of carbohydrate quantization for practical delivery was also assessed. An in silico study using the UVA-Padova simulator, extended to include exercise and various sources of variability, was performed. Results: DH-CC and DH-CC-CHO performed similarly with regard to mean glucose (126.25 [123.43; 130.73] vs 127.92 [123.99; 132.97] mg/dL, P = .088), time in range (93.04 [90.00; 95.92] vs 92.91 [90.05; 95.75]%, P = .508), time above 180 mg/dL (4.94 [2.72; 7.53] vs 4.99 [2.93; 7.24]%, P = .966), time below 70 mg/dL (0.61 [0.09; 1.75] vs 0.96 [0.23; 2.17]%, P = .1364), insulin delivery (43.50 [38.68; 51.75] vs 42.86 [38.58; 51.36] U/d, P = .383), and glucagon delivery (0.75 [0.40; 1.83] vs 0.76 [0.43; 0.99] mg/d, P = .407). Time below 54 mg/dL was different (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.16]%, P = .036), although non-clinically significant. This was due to the carbs quantization effect in a specific patient, as no statistical difference was found when carbs were not quantized (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.00]%, P = .265). Conclusions: The new strategy of automatic rescue carbohydrates suggestion in coordination with insulin and glucagon delivery to overcome constraints on daily glucagon delivery was successfully evaluated in an in silico proof of concept.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) through grant number DPI2016-78831-C2-1-R and the European Union through FEDER funds. Vanessa Moscardó was recipient of an FPU grant, FPU13/04253.Moscardo-Garcia, V.; Diez, J.; Bondía Company, J. (2019). Parallel Control of an artificial pancreas with coordinated insulin, glucagon and rescue carbohydrate control actions. Journal of Diabetes Science and Technology. 13(6):1026-1034. https://doi.org/10.1177/1932296819879093S1026103413

Stochastic Seasonal Models for Glucose Prediction in the Artificial Pancreas

[EN] Background: Linear empirical dynamic models have been widely used for glucose prediction. The extension of the concept of seasonality, characteristic of other domains, is explored here for the improvement of prediction accuracy. Methods: Twenty time series of 8-hour postprandial periods (PP) for a same 60g-carbohydrate meal were collected from a closed-loop controller validation study. A single concatenated time series was produced representing a collection of data from similar scenarios, resulting in seasonality. Variability in the resulting time series was representative of worstcase intrasubject variability. Following a leave-one-out cross-validation, seasonal and nonseasonal autoregressive integrated moving average models (SARIMA and ARIMA) were built to analyze the effect of seasonality in the model prediction accuracy. Further improvement achieved from the inclusion of insulin infusion rate as exogenous variable was also analyzed. Prediction horizons (PHs) from 30 to 300 min were considered. Results: SARIMA outperformed ARIMA revealing a significant role of seasonality. For a 5-h PH, average MAPE was reduced in 26.62%. Considering individual runs, the improvement ranged from 6.3% to 54.52%. In the best-performing case this reduction amounted to 29.45%. The benefit of seasonality was consistent among different PHs, although lower PHs benefited more, with MAPE reduction over 50% for PHs of 60 and 120 minutes, and over 40% for 180 min. Consideration of insulin infusion rate into the seasonal model further improved performance, with a 61.89% reduction in MAPE for 30-min PH and reductions over 20% for PHs over 180 min. Conclusions: Seasonality improved model accuracy allowing for the extension of the PH significantly.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the Spanish Ministry of Economy and Competitiveness, grants DPI2013-46982-C2-1-R and DPI2016-78831-C2-1-R, and the European Union through FEDER funds.Montaser Roushdi Ali, E.; Diez, J.; Bondía Company, J. (2017). Stochastic Seasonal Models for Glucose Prediction in the Artificial Pancreas. Journal of Diabetes Science and Technology. 11(6):1124-1131. https://doi.org/10.1177/1932296817736074S1124113111

Insulin Estimation and Prediction A REVIEW OF THE ESTIMATION AND PREDICTION OF SUBCUTANEOUS INSULIN PHARMACOKINETICS IN CLOSED-LOOP GLUCOSE CONTROL

This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through grant DPI2013-46982-C2-1-R and the EU through FEDER funds.Bondía Company, J.; Romero Vivó, S.; Ricarte Benedito, B.; Diez, J. (2018). Insulin Estimation and Prediction A REVIEW OF THE ESTIMATION AND PREDICTION OF SUBCUTANEOUS INSULIN PHARMACOKINETICS IN CLOSED-LOOP GLUCOSE CONTROL. IEEE Control Systems. 38(1):47-66. https://doi.org/10.1109/MCS.2017.2766312S476638

Sliding-mode disturbance observers for an artificial pancreas without meal announcement

[EN] Carbohydrate counting is not only a burden for patients with type 1 diabetes, but estimation errors in meal announcement could also degrade the outcomes of the current hybrid closed-loop systems. Therefore, removing meal announcement is desirable. A novel control system is addressed here to face postprandial control without meal announcement. The proposed system grounds on two applications of the sliding mode observers in dealing with disturbances: first, the equivalent output technique is used to reconstruct the meal rate of glucose appearance via a first order sliding mode observer; second, a super-twisting -based residual generator is used to detect the meals. Subsequently, a bolusing algorithm uses the information of the two observers to trigger a series of boluses based on a proportional-derivative-like strategy. An in silico validation with 30 patients in a 30-day scenario reveals that the meal detector algorithm achieves a low rate of false positives per day (0.1 (0.1), mean (SD)) and a detection time of 28.5(6.2) min. Additionally, the bolusing algorithm fulfills a non-statistically different mean glucose than the hybrid counterpart with bolus misestimation (146.69 (12.20) mg/dLvs. 144.28 (11.01) mg/dL,p>0.05), without increasing hypoglycemia (0.029 (0.077) vs. 0.004 (0.014)%, p > 0,05), although at the expense of a slightly higher time in hyperglycemia (22.51(8.72) % vs. 18.65 (7.89)%, p <0.05) due to the conservative tuning of the bolusing algorithm for the sake of safety.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) [grant number DPI2016-78831-C2-1-12]; the European Union [FEDER funds]: and Generalitat Valenciana [grant number ACIF/2017/021]Sala-Mira, I.; Diez, J.; Ricarte Benedito, B.; Bondía Company, J. (2019). Sliding-mode disturbance observers for an artificial pancreas without meal announcement. Journal of Process Control. 78:68-77. https://doi.org/10.1016/j.jprocont.2019.03.00868777

Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring

[EN] Minimally or noninvasive continuous glucose monitors estimate plasma glucose from compartments alternative to blood, and may revolutionize the management of diabetes. However, the accuracy of current devices is still poor and it may partly depend on low performance of the implemented calibration algorithm. Here, a new adaptive calibration algorithm based on a population local-model-based intercompartmental glucose dynamic model is proposed. The novelty consists in the adaptation of data normalization parameters in real time to estimate and compensate patient's sensitivity variations. Adaptation is performed to minimize mean absolute relative deviation at the calibration points with a time window forgetting strategy. Four calibrations are used: preprandial and 1.5 h postprandial at two different meals. Two databases are used for validation: 1) a 9-hCGMSGold (Medtronic, Northridge, USA) time series with paired reference glucose values from a clinical study in 17 subjects with type 1 diabetes; 2) data from 30 virtual patients (UVa simulator, Virginia, USA), where inter-and intrasubject variability of sensor's sensitivity were simulated. Results show how the adaptation of the normalization parameters improves the performance of the calibration algorithm since it counteracts sensor sensitivity variations. This improvement is more evident in one-week simulations.Manuscript received April 17, 2012; revised September 10, 2012 and January 21, 2013; accepted March 11, 2013. Date of publication March 19, 2013; date of current version May 1, 2013. This work was supported in part by the Spanish Ministry of Science and Innovation under Project DPI2010-20764-C02 and in part by the European Union under Grant FP7-PEOPLE-2009-IEF, Ref 252085. The work of F. Barcelo-Rico was supported by the Spanish Ministry of Education (FPU AP2008-02967).Barceló-Rico, F.; Diez, J.; Rossetti, P.; Vehi, J.; Bondía Company, J. (2013). Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring. IEEE Journal of Biomedical and Health Informatics. 17(3):530-538. https://doi.org/10.1109/JBHI.2013.2253325S53053817

Artificial Pancreas System With Unannounced Meals Based on a Disturbance Observer and Feedforward Compensation

© 2021 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] This brief is focused on the closed-loop control of postprandial glucose levels of patients with type 1 diabetes mellitus after unannounced meals, which is still a major challenge toward a fully autonomous artificial pancreas. The main limitations are the delays introduced by the subcutaneous insulin pharmacokinetics and the glucose sensor, which typically leads to insulin overdelivery. Current solutions reported in the literature typically resort to meal announcement, which requires patient intervention. In this brief, a disturbance observer (DOB) is used to estimate the effect of unannounced meals, and the insulin pharmacokinetics is taken into account by means of a feedforward compensator. The proposed strategy is validated in silico with the UVa/Padova metabolic simulator. It is demonstrated how the DOB successfully estimates and counteracts not only the effect of meals but also the sudden drops in the glucose levels that may lead to hypoglycemia. For unannounced meals, results show a median time-in-range of 80% in a 30-day scenario with high carbohydrate content and large intrasubject variability. Optionally, users may decide to announce meals. In this case, considering severe bolus mismatch due to carbohydrate counting errors, the median time-in-range is increased up to 88%. In every case, hypoglycemia is avoided.This work was supported in part by the Ministerio de Economia y Competitividad under Grant DPI2016-78831-C2-1-R and in part by the European Union through FEDER Funds.Sanz Diaz, R.; García Gil, PJ.; Diez, J.; Bondía Company, J. (2021). Artificial Pancreas System With Unannounced Meals Based on a Disturbance Observer and Feedforward Compensation. IEEE Transactions on Control Systems Technology. 29(1):454-460. https://doi.org/10.1109/TCST.2020.2975147S45446029

Accuracy of Continuous Glucose Monitoring before, during, and after Aerobic and Anaerobic Exercise in Patients with Type 1 Diabetes Mellitus

[EN] Continuous glucose monitoring (CGM) plays an important role in treatment decisions for patients with type 1 diabetes under conventional or closed-loop therapy. Physical activity represents a great challenge for diabetes management as well as for CGM systems. In this work, the accuracy of CGM in the context of exercise is addressed. Six adults performed aerobic and anaerobic exercise sessions and used two Medtronic Paradigm Enlite-2 sensors under closed-loop therapy. CGM readings were compared with plasma glucose during different periods: one hour before exercise, during exercise, and four hours after the end of exercise. In aerobic sessions, the median absolute relative difference (MARD) increased from 9.5% before the beginning of exercise to 16.5% during exercise (p < 0.001), and then decreased to 9.3% in the first hour after the end of exercise (p < 0.001). For the anaerobic sessions, the MARD before exercise was 15.5% and increased without statistical significance to 16.8% during exercise realisation (p = 0.993), and then decreased to 12.7% in the first hour after the cessation of anaerobic activities (p = 0.095). Results indicate that CGM might present lower accuracy during aerobic exercise, but return to regular operation a few hours after exercise cessation. No significant impact for anaerobic exercise was found.This project was partially supported by the Spanish Government through grants DPI2013-46982-C2-1-R, DPI2013-46982-C2-2-R, DPI2016-78831-C2-1-R, and DPI2016-78831-C2-2-R, and by the National Council of Technological and Scientific Development, CNPq Brazil through grants 202050/2015-7 and 207688/2014-1.Biagi, L.; Bertachi, A.; Quirós, C.; Giménez, M.; Conget, I.; Bondía Company, J.; Vehí, J. (2018). Accuracy of Continuous Glucose Monitoring before, during, and after Aerobic and Anaerobic Exercise in Patients with Type 1 Diabetes Mellitus. Biosensors. 8(1):1-8. https://doi.org/10.3390/bios8010022S188

Development of AIDA v4.3b diabetes simulator: Technical upgrade to support incorporation of lispro, aspart, and glargine insulin analogues

Introduction. AIDA is an interactive educational diabetes simulator available on the Internet without charge since 1996 (accessible at: http://www.2aida.org/). Since the program’s original release, users have developed new requirements, with new operating systems coming into use and more complex insulin management regimens being adopted. The current work has aimed to design a comprehensive diabetes simulation system from both a clinical and information technology perspective. Methods. A collaborative development is taking place with a new generic model of subcutaneous insulin absorption, permitting the simulation of rapidly-acting and very long-acting insulin analogues, as well as insulin injections larger than 40 units. This novel, physiological insulin absorption model has been incorporated into AIDA v4. Technical work has also been undertaken to install and operate the AIDA software within a DOSBox emulator, to ensure compatibility with Windows XP, Vista and 7 operating systems as well as Apple Macintosh computers running Parallels PC emulation software. Results. Plasma insulin simulations are demonstrated following subcutaneous injections of a rapidly-acting insulin analogue, a short-acting insulin preparation, intermediate-acting insulin, and a very long-acting insulin analogue for injected insulin doses up to 60 units of insulin. Discussion. The current work extends the useful life of the existing AIDA v4 program.Lehmann, ED.; Tarín, C.; Bondía Company, J.; Teufel, E.; Deutsch, T. (2011). Development of AIDA v4.3b diabetes simulator: Technical upgrade to support incorporation of lispro, aspart, and glargine insulin analogues. Journal of Electrical and Computer Engineering. 2011:1-17. doi:10.1155/2011/427196S1172011Lehmann, E. D. (1996). Application of information technology in clinical diabetes care—A Special Issue. Part 1. Databases, algorithms and decision support. Medical Informatics, 21(4), 255-258. doi:10.3109/14639239608999287Lehmann, E. D. (1997). Application of information technology in clinical diabetes care–A Special Issue. Part 2. Models and education. Medical Informatics, 22(1), 1-3. doi:10.3109/14639239709089830The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus. (1993). New England Journal of Medicine, 329(14), 977-986. doi:10.1056/nejm199309303291401Mühlhauser, I., Bruckner, I., Berger, M., Cheţa, D., Jörgens, V., Ionescu-Tîrgovişte, C., … Mincu, I. (1987). Evaluation of an intensified insulin treatment and teaching programme as routine management of Type 1 (insulin-dependent) diabetes. Diabetologia, 30(9), 681-690. doi:10.1007/bf00296989DAFNE Study Group. (2002). Training in flexible, intensive insulin management to enable dietary freedom in people with type 1 diabetes: dose adjustment for normal eating (DAFNE) randomised controlled trial. BMJ, 325(7367), 746-746. doi:10.1136/bmj.325.7367.746Howorka, K., Pumprla, J., Wagner-Nosiska, D., Grillmayr, H., Schlusche, C., & Schabmann, A. (2000). Empowering diabetes out-patients with structured education: Journal of Psychosomatic Research, 48(1), 37-44. doi:10.1016/s0022-3999(99)00074-4Lehmann, E. . (1998). Preliminary experience with the Internet release of AIDA—an interactive educational diabetes simulator. Computer Methods and Programs in Biomedicine, 56(2), 109-132. doi:10.1016/s0169-2607(98)00019-4Lehmann, E. D. (1999). Experience with the Internet Release of AIDA v4.0 - http://www.diabetic.org.uk/aida.htm - An Interactive Educational Diabetes Simulator. Diabetes Technology & Therapeutics, 1(1), 41-54. doi:10.1089/152091599317567Lehmann, E. D. (1997). Interactive educational simulators in diabetes care. Medical Informatics, 22(1), 47-76. doi:10.3109/14639239709089834Lehmann, E. D., Deutsch, T., Carson, E. R., & Sönksen, P. H. (1994). Combining rule-based reasoning and mathematical modelling in diabetes care. Artificial Intelligence in Medicine, 6(2), 137-160. doi:10.1016/0933-3657(94)90042-6Lehmann, E. D., Deutsch, T., Carson, E. R., & Sönksen, P. H. (1994). AIDA: an interactive diabetes advisor. Computer Methods and Programs in Biomedicine, 41(3-4), 183-203. doi:10.1016/0169-2607(94)90054-xLehmann, E. D., Hermanyi, I., & Deutsch, T. (1994). Retrospective validation of a physiological model of glucose-insulin interaction in type 1 diabetes mellitus. Medical Engineering & Physics, 16(3), 193-202. doi:10.1016/1350-4533(94)90038-8Lehmann, E. D., Hermanyi, I., & Deutsch, T. (1994). Retrospective validation of a physiological model of glucose-insulin interaction in type 1 diabetes mellitus. Medical Engineering & Physics, 16(4), 351-352. doi:10.1016/1350-4533(94)90064-7Lehmann, E. D., & Deutsch, T. (1996). Computer assisted diabetes care: a 6-year retrospective. Computer Methods and Programs in Biomedicine, 50(3), 209-230. doi:10.1016/0169-2607(96)01751-8Lehmann, E. D. (2001). Simulating Glycosylated Hemoglobin (HbA1c) Levels in Diabetes Using an Interactive Educational Virtual Diabetes Patient Simulator. Diabetes Technology & Therapeutics, 3(3), 517-524. doi:10.1089/15209150152607268Lehmann, E. D., & Deutsch, T. (1992). A physiological model of glucose-insulin interaction in type 1 diabetes mellitus. Journal of Biomedical Engineering, 14(3), 235-242. doi:10.1016/0141-5425(92)90058-sLehmann, E. D., Chatu, S. S., & Hashmy, S. S. H. (2006). Retrospective Pilot Feedback Survey of 200 Users of the AIDA Version 4 Educational Diabetes Program. 17—Quantitative Survey Data. Diabetes Technology & Therapeutics, 8(3), 419-432. doi:10.1089/dia.2006.8.419Lehmann, E. D., Chatu, S. S., & Hashmy, S. H. (2006). Retrospective Pilot Feedback Survey of 200 Users of the AIDA Version 4 Educational Diabetes Program. 2—Qualitative Feedback Data. Diabetes Technology & Therapeutics, 8(5), 602-608. doi:10.1089/dia.2006.8.602Lehmann, E. D., Chatu, S. S., & Hashmy, S. S. H. (2007). Retrospective Pilot Feedback Survey of 200 Users of the AIDA Version 4 Educational Diabetes Program. 3—Discussion. Diabetes Technology & Therapeutics, 9(1), 122-132. doi:10.1089/dia.2006.0065Binder, C., Lauritzen, T., Faber, O., & Pramming, S. (1984). Insulin Pharmacokinetics. Diabetes Care, 7(2), 188-199. doi:10.2337/diacare.7.2.188Plougmann, S., Hejlesen, O. K., & Cavan, D. A. (2001). DiasNet—a diabetes advisory system for communication and education via the internet. International Journal of Medical Informatics, 64(2-3), 319-330. doi:10.1016/s1386-5056(01)00214-3Storm, M. C., & Dunn, M. F. (1985). The Glu(B13) carboxylates of the insulin hexamer form a cage for cadmium and calcium ions. Biochemistry, 24(7), 1749-1756. doi:10.1021/bi00328a027Kurtzhals, P., & Ribel, U. (1995). Action Profile of Cobalt(III)-Insulin: A Novel Principle of Protraction of Potential Use for Basal Insulin Delivery. Diabetes, 44(12), 1381-1385. doi:10.2337/diab.44.12.1381Kobayashi, T., Sawano, S., Itoh, T., Kosaka, K., Hirayama, H., & Kasuya, Y. (1983). The Pharmacokinetics of Insulin After Continuous Subcutaneous Infusion or Bolus Subcutaneous Injection in Diabetic Patients. Diabetes, 32(4), 331-336. doi:10.2337/diab.32.4.331Berger, M., & Rodbard, D. (1989). Computer Simulation of Plasma Insulin and Glucose Dynamics After Subcutaneous Insulin Injection. Diabetes Care, 12(10), 725-736. doi:10.2337/diacare.12.10.725Mosekilde, E., Jensen, K. S., Binder, C., Pramming, S., & Thorsteinsson, B. (1989). Modeling absorption kinetics of subcutaneous injected soluble insulin. Journal of Pharmacokinetics and Biopharmaceutics, 17(1), 67-87. doi:10.1007/bf01059088Trajanoski, Z., Wach, P., Kotanko, P., Ott, A., & Skraba, F. (1993). Pharmacokinetic Model for the Absorption of Subcutaneously Injected Soluble Insulin and Monomeric Insulin - Analogues - Pharmakokinetisches Modell für die Absorption von subkutan injiziertem löslichem Insulin und monomeren Insulinanaloga. Biomedizinische Technik/Biomedical Engineering, 38(9), 224-231. doi:10.1515/bmte.1993.38.9.224Wach, P., Trajanoski, Z., Kotanko, P., & Skrabal, F. (1995). Numerical approximation of mathematical model for absorption of subcutaneously injected insulin. Medical & Biological Engineering & Computing, 33(1), 18-23. doi:10.1007/bf02522939Tarin, C., Teufel, E., Pico, J., Bondia, J., & Pfleiderer, H.-J. (2005). Comprehensive Pharmacokinetic Model of Insulin Glargine and Other Insulin Formulations. IEEE Transactions on Biomedical Engineering, 52(12), 1994-2005. doi:10.1109/tbme.2005.857681Lehmann, E. D., Tarín, C., Bondia, J., Teufel, E., & Deutsch, T. (2007). Incorporating a Generic Model of Subcutaneous Insulin Absorption into the AIDA v4 Diabetes Simulator. Journal of Diabetes Science and Technology, 1(3), 423-435. doi:10.1177/193229680700100317Lehmann, E. D., Tarín, C., Bondia, J., Teufel, E., & Deutsch, T. (2007). Incorporating a Generic Model of Subcutaneous Insulin Absorption into the AIDA v4 Diabetes Simulator 2. Preliminary Bench Testing. Journal of Diabetes Science and Technology, 1(5), 780-793. doi:10.1177/193229680700100525Lehmann, E. D., Tarín, C., Bondia, J., Teufel, E., & Deutsch, T. (2009). Incorporating a Generic Model of Subcutaneous Insulin Absorption into the AIDA v4 Diabetes Simulator 3. Early Plasma Insulin Determinations. Journal of Diabetes Science and Technology, 3(1), 190-201. doi:10.1177/193229680900300123Wong, J., Chase, J. G., Hann, C. E., Shaw, G. M., Lotz, T. F., Lin, J., & Le Compte, A. J. (2008). A Subcutaneous Insulin Pharmacokinetic Model for Computer Simulation in a Diabetes Decision Support Role: Model Structure and Parameter Identification. Journal of Diabetes Science and Technology, 2(4), 658-671. doi:10.1177/193229680800200417Wong, J., Chase, J. G., Hann, C. E., Shaw, G. M., Lotz, T. F., Lin, J., & Le Compte, A. J. (2008). A Subcutaneous Insulin Pharmacokinetic Model for Computer Simulation in a Diabetes Decision Support Role: Validation and Simulation. Journal of Diabetes Science and Technology, 2(4), 672-680. doi:10.1177/193229680800200418Kuang, Y., & Li, J. (2008). Systemically modeling the dynamics of plasma insulin in subcutaneous injection of insulin analogues for type 1 diabetes. Mathematical Biosciences and Engineering, 6(1), 41-58. doi:10.3934/mbe.2009.6.41Kang, S., Brange, J., Burch, A., Volund, A., & Owens, D. R. (1991). Subcutaneous Insulin Absorption Explained by Insulin’s Physicochemical Properties: Evidence From Absorption Studies of Soluble Human Insulin and Insulin Analogues in Humans. Diabetes Care, 14(11), 942-948. doi:10.2337/diacare.14.11.942Robertson, D. A., Singh, B. M., Hale, P. J., Jensen, I., & Nattrass, M. (1992). Metabolic Effects of Monomeric Insulin Analogues of Different Receptor Affinity. Diabetic Medicine, 9(3), 240-246. doi:10.1111/j.1464-5491.1992.tb01769.xKang, S., Owens, D. R., Vora, J. P., & Brange, J. (1990). Comparison of insulin analogue B9AspB27Glu and soluble human insulin in insulin-treated diabetes. The Lancet, 335(8685), 303-306. doi:10.1016/0140-6736(90)90602-2Bergman, R. N. (1989). Toward Physiological Understanding of Glucose Tolerance: Minimal-Model Approach. Diabetes, 38(12), 1512-1527. doi:10.2337/diab.38.12.1512Dalla Man, C., Rizza, R. A., & Cobelli, C. (2007). Meal Simulation Model of the Glucose-Insulin System. IEEE Transactions on Biomedical Engineering, 54(10), 1740-1749. doi:10.1109/tbme.2007.893506Sivitz, W. I., Davidson, P. C., Steed, D., Bode, B., & Richardson, P. (1989). Computer-Assisted Instruction in Intense Insulin Therapy Using a Mathematical Model for Clinical Simulation With a Clinical Algorithm and Flow Sheet. The Diabetes Educator, 15(1), 77-79. doi:10.1177/014572178901500120Hedbrant, J., Ludvigsson, J., & Nordenskjöld, K. (1991). Särimner: a computer model of diabetes physiology for education of physicians and patients. Diabetes Research and Clinical Practice, 14(2), 113-122. doi:10.1016/0168-8227(91)90117-vHedbrant, J., & Ludvigsson, J. (1995). Use of computer simulator training in the education of diabetic teenagers. Practical Diabetes International, 12(1), 18-21. doi:10.1002/pdi.1960120109Rutscher, A., Salzsieder, E., & Fischer, U. (1994). KADIS: model-aided education in type I diabetes. Computer Methods and Programs in Biomedicine, 41(3-4), 205-215. doi:10.1016/0169-2607(94)90055-8Lehmann, E. D. (2003). Usage of a diabetes simulation system for education via the internet. International Journal of Medical Informatics, 69(1), 63-69. doi:10.1016/s1386-5056(02)00015-1Biermann, E., & Mehnert, H. (1990). DIABLOG: a simulation program of insulin-glucose dynamics for education of diabetics. Computer Methods and Programs in Biomedicine, 32(3-4), 311-318. doi:10.1016/0169-2607(90)90114-oBiermann, E. (1994). DIACATOR: simulation of metabolic abnormalities of type II diabetes mellitus by use of a personal computer. Computer Methods and Programs in Biomedicine, 41(3-4), 217-229. doi:10.1016/0169-2607(94)90056-6Wilson, D. M. (1999). Diabetes Simulators: Ready for Prime Time? Diabetes Technology & Therapeutics, 1(1), 55-56. doi:10.1089/15209159931757

A multiple local models approach to accuracy improvement in continuous glucose monitoring

This is a copy of an article published in the Diabetes Technology & Therapeutics © 2012 copyright Mary Ann Liebert, Inc.; Diabetes Technology & Therapeutics is available online at: http://online.liebertpub.com/toc/dia/14/1[EN] Background: Continuous glucose monitoring (CGM) devices estimate plasma glucose (PG) from measurements in compartments alternative to blood. The accuracy of currently available CGM is yet unsatisfactory and may depend on the implemented calibration algorithms, which do not compensate adequately for the differences of glucose dynamics between the compartments. Here we propose and validate an innovative calibration algorithm for the improvement of CGM performance. Methods: CGM data from GlucoDay (R) (A. Menarini, Florence, Italy) and paired reference PG have been obtained from eight subjects without diabetes during eu-, hypo-, and hyperglycemic hyperinsulinemic clamps. A calibration algorithm based on a dynamic global model (GM) of the relationship between PG and CGM in the interstitial space has been obtained. The GM is composed by independent local models (LMs) weighted and added. LMs are defined by a combination of inputs from the CGM and by a validity function, so that each LM represents to a variable extent a different metabolic condition and/or sensor-subject interaction. The inputs best suited for glucose estimation were the sensor current I and glucose estimations (G) over cap, at different time instants [I-k, Ik-1, (G) over cap (k-1)] (IIG). In addition to IIG, other inputs have been used to obtain the GM, achieving different configurations of the calibration algorithm. Results: Even in its simplest configuration considering only IIG, the new calibration algorithm improved the accuracy of the estimations compared with the manufacturer's estimate: mean absolute relative difference (MARD) = 10.8 +/- 1.5% versus 14.7 +/- 5.4%, respectively (P = 0.012, by analysis of variance). When additional exogenous signals were considered, the MARD improved further (7.8 +/- 2.6%, P<0.05). Conclusions: The LM technique allows for the identification of intercompartmental glucose dynamics. Inclusion of these dynamics into the calibration algorithm improves the accuracy of PG estimations.The authors acknowledge the partial funding of this work by the Spanish Ministry of Science and Innovation projects DPI2007-66728-C02-01 and DPI2010-20764-C02-01 and by the European Union through FEDER funds and grant FP7-PEOPLE-2009-IEF, Reference 252085. F.B.R. is the recipient of a fellowship (FPU AP2008-02967) from the Spanish Ministry of Education.Barceló Rico, F.; Bondía Company, J.; Diez Ruano, JL.; Rossetti ., P. (2012). A multiple local models approach to accuracy improvement in continuous glucose monitoring. Diabetes Technology & Therapeutics. 14(1):74-82. https://doi.org/10.1089/dia.2011.0138S748214