Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring

[EN] The artificial pancreas (AP) system is designed to regulate blood glucose in subjects with type 1 diabetes using a continuous glucose monitor informed controller that adjusts insulin infusion via an insulin pump. However, current AP developments are mainly hybrid closed-loop systems that include feed-forward actions triggered by the announcement of meals or exercise. The first step to fully closing the loop in the AP requires removing meal announcement, which is currently the most effective way to alleviate postprandial hyperglycemia due to the delay in insulin action. Here, a novel approach to meal detection in the AP is presented using a sliding window and computing the normalized cross-covariance between measured glucose and the forward difference of a disturbance term, estimated from an augmented minimal model using an Unscented Kalman Filter. Three different tunings were applied to the samemeal detection algorithm: (1) a high sensitivity tuning, (2) a trade-off tuning that has a high amount of meals detected and a low amount of false positives (FP), and (3) a low FP tuning. For the three tunings sensitivities 99 +/- 2%, 93 +/- 5%, and 47 +/- 12% were achieved, respectively. A sensitivity analysis was also performed and found that higher carbohydrate quantities and faster rates of glucose appearance result in favorable meal detection outcomes.This work was funded by the Spanish Government through grants DPI2016-78831-C2-1-R and DPI2016-78831-C2-2-R, the University of Girona through grant BR2014/51, and the European Union through Fondo Europeo de Desarrollo Regional (FEDER) Funds.Ramkissoon, C.; Herrero, P.; Bondía Company, J.; Vehí, J. (2018). Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring. Sensors. 18(3):1-18. https://doi.org/10.3390/s18030884S11818

Guaranteed computation methods for compartmental in-series models under uncertainty

The pattern of some real phenomenon can be described by compartmental in-series models. Nevertheless, most of these processes are characterized by their variability, which produces that the exact values of the model parameters are uncertain, although they can be bounded by intervals. The aim of this paper is to compute tight solution envelopes that guarantee the inclusion of all possible behaviors of such processes. Current methods, such as monotonicity analysis, enable us to obtain guaranteed solution envelopes. However, if the model includes nonmonotone compartments or parameters, the computation of solution envelopes may produce a significant overestimation. Our proposal consists of performing a change of variables in which the output is unaltered, and the model obtained is monotone with respect to the uncertain parameters. The monotonicity of the new system allows us to compute the output bounds for the original system without overestimation. These model transformations have been developed for linear and non-linear systems. Furthermore, if the conditions are not completely satisfied, a novel method to compute tight solution envelopes is proposed. The methods exposed in this paper have been applied to compute tight solution envelopes for two different models: a linear system for glucose modeling and a non-linear system for an epidemiological model.This work was partially supported by the Spanish Ministerio de Ciencia e Innovacion through Grant DPI-2010-20764-C02-01, and by the Generalitat Valenciana through Grant GV/2012/085.De Pereda Sebastián, D.; Romero Vivó, S.; Ricarte Benedito, B.; Bondía Company, J. (2013). Guaranteed computation methods for compartmental in-series models under uncertainty. Computers and Mathematics with Applications. 66(9):1595-1605. https://doi.org/10.1016/j.camwa.2013.03.008S1595160566

On the prediction of glucose concentration under intra-patient variability in type 1 diabetes: A monotone systems approach

Insulin therapy in type 1 diabetes aims to mimic the pattern of endogenous insulin secretion found in healthy subjects. Glucose-insulin models are widely used in the development of new predictive control strategies in order to maintain the plasma glucose concentration within a narrow range, avoiding the risks of high or low levels of glucose in the blood. However, due to the high variability of this biological process, the exact values of the model parameters are unknown, but they can be bounded by intervals. In this work, the computation of tight glucose concentration bounds under parametric uncertainty for the development of robust prediction tools is addressed. A monotonicity analysis of the model states and parameters is performed. An analysis of critical points, state transformations and application of differential inequalities are proposed to deal with non-monotone parameters. In contrast to current methods, the guaranteed simulations for the glucose-insulin model are carried out by considering uncertainty in all the parameters and initial conditions. Furthermore, no time-discretisation is required, which helps to reduce the computational time significantly. As a result, we are able to compute a tight glucose envelope that bounds all the possible patient's glycemic responses with low computational effort. (C) 2012 Elsevier Ireland Ltd. All rights reserved.This work was partially supported by the Spanish Ministerio de Ciencia e Innovacion through Grant DPI-2010-20764-C02, by the Universitat Politecnica de Valencia through Grant PAID-05-09-4334, and by the Generalitat Valenciana through Grant GV/2012/085.De Pereda Sebastián, D.; Romero Vivó, S.; Ricarte Benedito, B.; Bondía Company, J. (2012). On the prediction of glucose concentration under intra-patient variability in type 1 diabetes: A monotone systems approach. Computer Methods and Programs in Biomedicine. 108(3):993-1001. https://doi.org/10.1016/j.cmpb.2012.05.012S9931001108

Evaluation of a novel continuous glucose monitoring-based method for mealtime insulin dosing - the iBolus - in subjects with type 1 diabetes using continuous subcutaneous insulin infusion therapy: a randomized controlled trial

This is a copy of an article published in the Diabetes Technology and Therapeutics © 2012 [copyright Mary Ann Liebert, Inc.]; Diabetes Technology and Therapeutics is available online at: http://online.liebertpub.com.[EN] Objective: Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy. Subjects and Methods: An individual patient¿s model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t 0) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG0¿5h) and variability as the individual coef¿cient of variation (CV) of AUC-PG0¿5h. The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t 0 (PGt0), and insulin dose to AUC-PG0¿5h and its CV was also investigated. Results: AUC-PG0¿5h was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5¿127.5 vs. 689.2¿180.7 mg/dL$h) or 100-g (752.1¿237.7 vs. 760.0¿263.2 mg/dL$h) CHO meals. A multiple regression analysis revealed a signi¿cant model only for the tBolus, with PGt0 being the best predictor of AUC-PG0¿5h explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7¿15.3% vs. 10.1¿12.5%) but independent of the factors studied. Conclusions: A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identi¿ed and analyzed for further optimization of postprandial glycemic control.We are grateful to Mrs. Sara Correa, Fundacion INCLIVA-Hospital Clinico Universitario de Valencia, and Mrs. Geles Viguer, Hospital Clinico Universitario de Valencia, for their invaluable help in conducting the study. We also thanks Dr. Carmine Fanelli, University of Perugia, Dr. Howard Zisser, Sansum Diabetes Research Institute, and Prof. Alberto Ferrer, Universitat Politecnica de Valencia, for their suggestions on study design and data analysis. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007/2013) under grant agreement 252085 and from the Spanish Ministry of Science under grants DPI2010-20764-C02-01 and DPI2011-28112-C04-01.Rossetti, P.; Ampudia Blasco, FJ.; Laguna Sanz, AJ.; Revert Tomás, A.; Vehí Casellas, J.; Ascaso, JF.; Bondía Company, J. (2012). Evaluation of a novel continuous glucose monitoring-based method for mealtime insulin dosing - the iBolus - in subjects with type 1 diabetes using continuous subcutaneous insulin infusion therapy: a randomized controlled trial. Diabetes Technology & Therapeutics. 14(11):1043-1052. https://doi.org/10.1089/dia.2012.0145S10431052141

On the computation of output bounds on parallel inputs pharmacokinetic models with parametric uncertainty

Pharmacokinetic models are of utmost importance in drug and medical research. The class of parallel inputs models consists of two or more linear chains connected together in parallel. It has been used to represent pharmacokinetic processes in which the input shows effects on the output with different delays in time. Due to physiological variability, the exact values of the model parameters are uncertain, but they can be bounded by intervals. In this case, the computation of output bounds can be posed as the solution of an initial value problem (IVP) for ordinary differential equations (ODEs) with uncertain initial conditions. However, current methods may produce a significant overestimation. In this paper, a new method to minimise overestimation when using the parallel inputs model is proposed and applied to two cases: subcutaneous insulin absorption for artificial pancreas research, and the study of the double-peak phenomenon observed for certain drugs. Our proposal consists in performing a model reduction in conjunction with analytical solutions of the input chains and a monotonicity analysis of model states and parameters. This method allows obtaining tighter output bounds with low computational cost compared to the latest techniques.This work was partially supported by the Spanish Ministerio de Ciencia e Innovacion through Grant DPI-2010-20764-C02, and by the Universitat Politecnica de Valencia through Grant PAID-05-09-4334.De Pereda Sebastián, D.; Romero Vivó, S.; Bondía Company, J. (2013). On the computation of output bounds on parallel inputs pharmacokinetic models with parametric uncertainty. Mathematical and Computer Modelling. 57:1760-1767. https://doi.org/10.1016/j.mcm.2011.11.031S176017675

Intra-articular injection of two different doses of autologous bone marrow mesenchymal stem cells versus hyaluronic acid in the treatment of knee osteoarthritis: long-term follow up of a multicenter randomized controlled clinical trial (phase I/II)

Background: Mesenchymal stromal cells (MSCs) are a promising option to treat knee osteoarthritis (OA). Their safety and usefulness have been reported in several short-term clinical trials but less information is available on the longterm efects of MSC in patients with osteoarthritis. We have evaluated patients included in our previous randomized clinical trial (CMM-ART, NCT02123368) to determine their long-term clinical efect. Materials: A phase I/II multicenter randomized clinical trial with active control was conducted between 2012 and 2014. Thirty patients diagnosed with knee OA were randomly assigned to Control group, intraarticularly administered hyaluronic acid alone, or to two treatment groups, hyaluronic acid together with 10×106 or 100×106 cultured autol‑ ogous bone marrow-derived MSCs (BM-MSCs), and followed up for 12 months. After a follow up of 4 years adverse efects and clinical evolution, assessed using VAS and WOMAC scorings are reported. Results: No adverse efects were reported after BM-MSCs administration or during the follow-up. BM-MSCs-adminis‑ tered patients improved according to VAS, median value (IQR) for Control, Low-dose and High-dose groups changed from 5 (3, 7), 7 (5, 8) and 6 (4, 8) to 7 (6, 7), 2 (2, 5) and 3 (3, 4), respectively at the end of follow up (Low-dose vs Control group, p=0.01; High-dose vs Control group, p=0.004). Patients receiving BM-MSCs also improved clinically accord‑ ing to WOMAC. Control group showed an increase median value of 4 points (−11;10) while Low-dose and Highdose groups exhibited values of −18 (−28;−9) and −10 (−21;−3) points, respectively (Low-dose vs Control group p=0.043). No clinical diferences between the BM-MSCs receiving groups were found. Conclusions: Single intraarticular injection of in vitro expanded autologous BM-MSCs is a safe and feasible proce‑ dure that results in long-term clinical and functional improvement of knee OA

Estimating Plasma Glucose from Interstitial Glucose: The Issue of Calibration Algorithms in Commercial Continuous Glucose Monitoring Devices

Evaluation of metabolic control of diabetic people has been classically performed measuring glucose concentrations in blood samples. Due to the potential improvement it offers in diabetes care, continuous glucose monitoring (CGM) in the subcutaneous tissue is gaining popularity among both patients and physicians. However, devices for CGM measure glucose concentration in compartments other than blood, usually the interstitial space. This means that CGM need calibration against blood glucose values, and the accuracy of the estimation of blood glucose will also depend on the calibration algorithm. The complexity of the relationship between glucose dynamics in blood and the interstitial space, contrasts with the simplistic approach of calibration algorithms currently implemented in commercial CGM devices, translating in suboptimal accuracy. The present review will analyze the issue of calibration algorithms for CGM, focusing exclusively on the commercially available glucose sensors

New possibilistic method for discovering linear local behavior using hyper-Gaussian distributed membership function

[EN] This paper presents a method to find a model of a system based on the integration of a set of local models. Mainly, properties are sought for the local models: independence of clusters and interpretability of their validity. This has been achieved through the introduction of a possibilistic clustering for the first property and a pre-fixed shape of the membership functions for the second one. A new cost index for the clustering optimization problem has been defined consisting of two terms: one for global error and another for local errors. By giving higher importance to the local errors term, local models valid regionally can be found. To avoid local optima and numerical issues, the parameters of the models are found using global optimization. This new method has been applied to several data sets, and results show how the desired characteristics can be achieved in the resulting models.The authors acknowledge the partial funding of this work by the projects: the national projects DPI2007-66728-C02-01 and DPI2008-06737-C02-01. The first author is recipient of a fellowship from the Spanish Ministry of Education (FPU AP2008-02967). The translation of this paper was funded by the Universidad Politecnica de Valencia, Spain.Barcelo-Rico, F.; Diez, J.; Bondia Company, J. (2012). New possibilistic method for discovering linear local behavior using hyper-Gaussian distributed membership function. Knowledge and Information Systems. 30(2):377-403. https://doi.org/10.1007/s10115-011-0385-5S377403302Aronovich L, Splieger I (2010) Bulk construction of dynamic clustered metric trees. Knowl Inf Syst 22(2): 211–244Barcelo-Rico F, Diez J, Bondia J (2010) A comparative study of codification techniques for clustering heart disease database. Biomed Signal Process Control. doi: 10.1016/j.bspc.2010.07.004Bezdek JC (1981) Pattern recognition with fuzzy objective functions algorithms. Plenum Press, New YorkBezdek JC, Pal NR (1998) Some new indexes of cluster validity. IEEE Trans Syst Man Cybern Part B Cybern 28: 301–315Bezdek J, Ehrlich R, Full W (1984) Fcm: The fuzzy c-means clustering algorithm. Comput Geosci 10: 191–203Cheng K, Liu L (2009) “best k”: critical clustering structures in categorical datasets. Knowl Inf Syst 20(1): 1–33de Oliveira J, Pedrycz W (2007) Advances in fuzzy clustering and its applications. Wiley, New YorkDe Carlo LT (1997) On the meaning and use of Kurtosis. Psychol Methods 2: 292–307Diez JL (2003) Técnicas de agrupamiento para identificacin y control por modelos locales. PhD thesis, Universitat Politècnica de ValènciaDiez JL, Navarro JL, Sala A (2007) A fuzzy clustering algorithm enhancing local model interpretability. Soft Comput Fusion Found Method Appl 11: 973–983Diez JL, Sala A, Navarro JL (2005) Target shape possibilistic clustering applied to local-model identification. Engineering Applications of Artificial Intelligence 4thKim EY, Kim SY, Ashlock D, Nam D (2009) Multi-k: accurate classification of microarray subtypes using ensemble k-means clustering. BMC Bioinform 10: 260Egea JA, Rodriguez-Fernandez M, Banga JR, Mart R (2007) Scatter search for chemical and bio-process optimization. J Global Optim 37: 481–503Egea-Larrosa JA (2008) New Heuristics for Global Optimization of Complex bioprocesses. PhD thesis, Universidade de VigoGustafson EE, Kessel WC (1978) Fuzzy clustering with a fuzzy covariance matrix. In: IEEE conference on decision and control, pp 761–766Hartigan J, Wong MA (1979) A K-means clustering algorithm. JR Stat Soc Ser C 28: 100–108Hathaway R, Bezdek J (1993) Switching regression models and fuzzy clustering. IEEE Trans Fuzzy Syst 1(3): 195–204Abonyi J, Babuska R, Szeifert F (2002) Modified gath-geva fuzzy clustering for identification of takagi-sugeno fuzzy models. IEEE Trans Syst Man Cybern Part B Cybern 32(5): 612–621Krishnapuram R, Keller JM (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1: 98–110Emami MR, Turksen IB, Goldenberg AA (1998) Development of a systematic methodology of fuzzy logic modeling. Trans Fuzzy Syst 6: 346–366Goebel M, Gruenwald L (1999) A survey of data mining and knowledge discovery software tools. SIGKDD Explor Newsl 1(1): 20–33Ryoke M, Nakamori Y, Suzuki K (1995) Adaptive fuzzy clustering and fuzzy prediction models. In: Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE International Conference on vol 4Sugeno M, Yasukawa T (1993) A fuzzy-logic based approach to qualitative modelling. Trans Fuzzy Syst 1: 7–31Chaoji V, Hasan MA, Salem S, Zaki M (2009) Sparcl: an effective and efficient algorithm for mining arbitrary shape-based clusters. Knowl Inf Syst 21(2):201–22

Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring

The artificial pancreas (AP) system is designed to regulate blood glucose in subjects with type 1 diabetes using a continuous glucose monitor informed controller that adjusts insulin infusion via an insulin pump. However, current AP developments are mainly hybrid closed-loop systems that include feed-forward actions triggered by the announcement of meals or exercise. The first step to fully closing the loop in the AP requires removing meal announcement, which is currently the most effective way to alleviate postprandial hyperglycemia due to the delay in insulin action. Here, a novel approach to meal detection in the AP is presented using a sliding window and computing the normalized cross-covariance between measured glucose and the forward difference of a disturbance term, estimated from an augmented minimal model using an Unscented Kalman Filter. Three different tunings were applied to the same meal detection algorithm: (1) a high sensitivity tuning, (2) a trade-off tuning that has a high amount of meals detected and a low amount of false positives (FP), and (3) a low FP tuning. For the three tunings sensitivities 99 ± 2%, 93 ± 5%, and 47 ± 12% were achieved, respectively. A sensitivity analysis was also performed and found that higher carbohydrate quantities and faster rates of glucose appearance result in favorable meal detection outcomesThis work was funded by the Spanish Government through grants DPI2016-78831-C2-1-R and DPI2016-78831-C2-2-R, the University of Girona through grant BR2014/51, and the European Union through Fondo Europeo de Desarrollo Regional (FEDER) Fund