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Aguirre-Zapata, E., & García-Tirado, J. F. (2016). Monitoring Plasma Glucose Concentration from Interstitial Glucose Measurements for Patients at the Intensive Care Unit. Ingenierías USBmed, 7(2), 7–13. https://doi.org/10.21500/20275846.2617
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Abstract
The glucose homeostasis is responsible for regulating the blood glucose concentration around 100 mg / dl. When this physiological mechanism is broken due to the inability of the pancreas to produce insulin, an increase of the blood glucose levels is produced and patients are diagnosed with Diabetes Mellitus. In recent years, some research has directed towards the creation of an artificial pancreas that allows automatically the regulation of glucose levels in blood. However, one of the greatest difficulties in achieving this objective, is that not all internal variables of the mathematical model associated with the controller can be measured directly by physical sensors, either because there are no sensors for all variables, because existing sensors are not commercial, or because they are not viable from the economic point of view. Therefore, it is necessary to use estimation schemes to reconstruct the unknown states by measuring the interstitial glucose , in the case of the glucose-insulin system. However, the delay between plasma glucose and interstitial glucose has a negative effect on the performance of state estimators, so the treatment of this delay is necessary either from the modeling process of the glucose-insulin system or by a modification of the estimation techniques. According to the results it can be inferred that in the scenario at which the concentration of blood glucose is assumed, the estimated values have upper and lower peaks that are unrealistic from a physiological point of view, this due to the negative effect of the delay in measurement. Otherwise, in the scenario where the interstitial glucose concentration is considered as the measured variable, including dynamics of the interstitial glucose, the estimator exhibits better performance and rapid convergence to the real states.
References
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[4] V. den Berghe Greet, W. Pieter, W. Frank, V. Charles, B. Frans, S. Miet, V. Dirk, F. Patrick, L. Peter, and B. Roger, “Intensive Insulin Therapy in Critically Ill Patients,” N. Engl. J. Med., vol. 345, no. 19, pp. 1359–1367, 2001.
[5] P. Kalfon, B. Giraudeau, C. Ichai, A. Guerrini, N. Brechot, R. Cinotti, P.-F. Dequin, B. Riu-Poulenc, P. Montravers, D. Annane, H. Dupont, M. Sorine, and B. Riou, “Tight computerized versus conventional glucose control in the ICU: a randomized controlled trial,” Intensive Care Med., vol. 40, no. 2, pp. 171–181, 2014.
[6] T. N.-S. S. Investigators, “Intensive versus Conventional Glucose Control in Critically Ill Patients,” N. Engl. J. Med., vol. 360, no. 13, pp. 1283–1297, 2009.
[7] B. P. Kovatchev, M. Breton, C. Dalla Man, and C. Cobelli, “In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes,” J. Diabetes Sci. Technol., vol. 3, no. 1, pp. 44–55, 2009.
[8] L. Magni, D. M. Raimondo, C. Dalla Man, G. De Nicolao, B. Kovatchev, and C. Cobelli, “Model predictive control of glucose concentration in type I diabetic patients: An in silico trial,” Biomed. Signal Process. Control, vol. 4, no. 4, pp. 338–346, Oct. 2009.
[9] K. Lunze, T. Singh, M. Walter, M. D. Brendel, and S. Leonhardt, “Blood glucose control algorithms for type 1 diabetic patients: A methodological review,” Biomed. Signal Process. Control, vol. 8, no. 2, pp. 107–119, Mar. 2013.
[10] J. Clain, K. Ramar, S. R. Surani, W. W. Ave, and A. Pass, “Glucose control in critical care,” vol. 6, no. 9, pp. 1082–1091, 2015.
[11] C. Eberle and C. Ament, “The Unscented Kalman Filter estimates the plasma insulin from glucose measurement.,” Biosystems., vol. 103, no. 1, pp. 67–72, Jan. 2011.
[12] R. Gondhalekar, E. Dassau, and F. J. D. Iii, “Moving-horizon-like state estimation via continuous glucose monitor feedback in MPC of an artificial pancreas for type 1 diabetes,” 2014.
[13] R. Gondhalekar, E. Dassau, and F. J. Doyle, “State Estimation with Sensor Recalibrations and Asynchronous Measurements for MPC of an Artificial Pancreas to Treat T1DM,” 2014.
[14] Medtronic, “Por qué las lecturas del sensor son diferentes a las lecturas de GS.” .
[15] J. Lin, N. N. Razak, C. G. Pretty, A. Le, P. Docherty, J. D. Parente, G. M. Shaw, C. E. Hann, and J. G. Chase, “A physiological Intensive Control Insulin-Nutrition-Glucose ( ICING ) model validated in critically ill patients,” Comput. Methods Programs Biomed., vol. 102, no. 2, pp. 192–205, 2010.
[16] C. King, S. M. Anderson, M. Breton, W. L. Clarke, and B. P. Kovatchev, “Modeling of calibration effectiveness and blood-to-interstitial glucose dynamics as potential confounders of the accuracy of continuous glucose sensors during hyperinsulinemic clamp,” J. Diabetes Sci. Technol., vol. 1, no. 3, pp. 317–322, 2007.
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[18] M. Breton and B. Kovatchev, “Analysis, modeling, and simulation of the accuracy of continuous glucose sensors,” J. Diabetes Sci. Technol., vol. 2, no. 5, pp. 853–862, 2008.
[19] P. J. Stout, N. Peled, B. J. Erickson, M. E. Hilgers, J. R. Racchini, and T. B. Hoegh, “Comparison of glucose levels in dermal interstitial fluid and finger capillary blood,” Diabetes Technol. Ther., vol. 3, no. 1, pp. 81–90, 2001.
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