Nuevas Estrategias de Control Glucémico en Pacientes con Diabetes Mellitus Tipo 1

Pablo S. Rivadeneira, Juan E. Sereno, Michelle A. Caicedo

Resumen

Actualmente la diabetes mellitus es un problema de salud pública mundial. En este trabajo, se proponen estrategias de control para mantener los niveles de glucosa en sangre de pacientes diabéticos tipo 1 en los rangos ideales en pro de la salud del paciente y su calidad de vida. La primera estrategia propone una retroalimentación de estados con restricciones de positividad, que en términos médicos representa la eliminación de episodios de hipoglucemia durante períodos prolongados de ayuno. Posteriormente, se realiza una extensión para lograr el rechazo de las perturbaciones por ingesta de alimentos, mediante el acoplamiento de un control proporcional, integral y derivativo. La segunda estrategia es un control predictivo con entrada impulsiva y regulación hacia una zona objetivo. Finalmente, el desempeño de las estrategias es evaluado en 50 pacientes virtuales extraídos de la literatura y en el Simulador UVa / Padova aprobado por la Food and Drug Administration de EEUU.


Palabras clave

Sistemas de control lineal; Control predictivo basado en modelo; Control PID; Sistemas biomédicos; Control de variables fisiológicas y clínicas; páncreas artificial

Texto completo:

PDF

Referencias

Abu-Rmileh, A., Garcia-Gabin, W., 2011. Hypoglycemia prevention in closed-loop artificial pancreas for patients with type 1 diabetes. Diabetes - Damages and Treatments. InTech. https://doi.org/10.5772/22647

Bergman, R. N., 2005. Minimal model: perspective from 2005. Hormone Research 64 (3), 8–15. https://doi.org/10.1159/000089312

Bogarin-Solano, R., 2009. Diabetes mellitus tipo 1 en la edad pediátrica. Acta Pediatrica Costarricense 21, 76–85.

Bolie, V. W., 1961. Coefficients of normal blood glucose regulation. Journal of Applied Physiology 16(5), 783–788. https://doi.org/10.1152/jappl.1961.16.5.783

Bondia, J., Vehí, J., Palerm, C. C., & Herrero, P., 2010. El páncreas artificial: control automático de infusión de insulina en diabetes mellitus tipo 1. Revista Iberoamericana de Automática E Informática Industrial RIAI 7(2), 5–20. https://doi.org/10.4995/riai.2010.02.01

Camacho, E. F., Bordons, C., 2007. Model predictive control. Springer Science & Business Media. https://doi.org/10.1007/978-0-85729-398-5

Colmegna, P., Sánchez Peña, R. S., 2014. Analysis of three T1DM simulation models for evaluating robust closed-loop controllers. Computer Methods and Programs in Biomedicine 113(1), 371–382. https://doi.org/10.1016/j.cmpb.2013.09.020

De Gaetano, A., Arino, O., 2000. Mathematical modelling of the intravenous glucose tolerance test. Journal of Mathematical Biology 40(2), 136–168. DOI: 10.1007/s002850050007

De Gaetano, A., Di Martino, D., Germani, A., Manes, C., & Palumbo, P., 2005. Distributed-delay models of the glucose-insulin homeostasis and asymptotic state observation. IFAC Proceedings Volumes 38(1), 1041–1046. https://doi.org/10.1007/s002850050007

Elleri, D., Allen, J. M., Nodale, M., Wilinska, M. E., Acerini, C. L., Dunger, D. B., Hovorka, R., 2010. Suspended insulin infusion during overnight closed-loop glucose control in children and adolescents with type 1 diabetes. Diabetic Medicine 27(4), 480-484. https://doi.org/10.1111/j.1464-5491.2010.02964.x

Farina, L., Rinaldi, S., 2000. Positive Linear Systems. John Wiley & Sons, Inc. https://doi.org/10.1002/9781118033029

Forlenza, G. P., Buckingham, B., Maahs, D. M., 2016. Progress in diabetes technology: developments in insulin pumps, continuous glucose monitors, and progress towards the artificial pancreas. The Journal of Pediatrics 169, 1–8. https://doi.org/10.1016/j.jpeds.2015.10.015

Gonzalez, A. H., Rivadeneira, P. S., Ferramosca, A., Magdelaine, N., Moog, C. H., 2017. Impulsive zone mpc for type i diabetic patients based on a long-term model. IFAC-PapersOnline 50 (1), 14729-14734. https://doi.org/10.1016/j.ifacol.2017.08.2510

González, A. H., Odloak, D., 2009. A stable mpc with zone control. Journal of Process Control 19(1), 110–122. https://doi.org/10.1016/j.jprocont.2008.01.003

González, R., Cipriano, A., 2016. Control difuso con estimador de estados para sistemas de páncreas artificial. RIAI - Revista Iberoamericana de Automatica E Informatica Industrial 13(4), 393–402. https://doi.org/10.1016/j.riai.2016.09.001

Grosman, B., Dassau, E., Zisser, H. C., Jovanovič, L., Doyle, F. J., 2010. Zone model predictive control: a strategy to minimize hyper- and hypoglycemic events. Journal of Diabetes Science and Technology 4(4), 961–975. https://doi.org/10.1177/193229681000400428

Hovorka, R., Canonico, V., Chassin, L. J., Haueter, U., Massi-Benedetti, M., Federici, M. O., Pieber, T. R., Wilinska, M. E., 2004. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiological Measurement 25(4). https://doi.org/10.1088/0967-3334/25/4/010

Kaveh, Parisa, Shtessel, Yuri., 2006. Higher order sliding mode control for blood glucose regulation. In International Workshop on Variable Structure Systems, 2006. VSS’06. 11–16. https://doi.org/10.1109/vss.2006.1644485

Loutseiko, M., Voskanyan, G., Keenan, D. B., Steil, G. M., 2011. Closed-loop insulin delivery utilizing pole placement to compensate for delays in subcutaneous insulin delivery. Journal of Diabetes Science and Technology 5(6), 1342–1351. https://doi.org/10.1177/193229681100500605

Maciejowski, J. M., 2002. Predictive control : with constraints. Prentice Hall. https://doi.org/10.1002/acs.736

Magdelaine, N., Chaillous, L., Guilhem, I., Poirier, J., Krempf, M., Moog, C. H., Carpentier, E. Le., 2015. A long-term model of the glucose – insulin dynamics of type 1 diabetes. IEEE Transactions on Bio-Medical Engineering 62(6), 1546–1552. https://doi.org/10.1109/tbme.2015.2394239

Mohammadridha, T., Rivadeneira, P. S., Sereno, J. E., Cardelli, M., & Moog, C. H. (2016). Description of the positive invariant sets of a type 1 diabetic patient model. In XVII Latin American Conference of Automatic Control, 102–108.

P. Aschner., 2010. Epidemiología de la diabetes en Colombia. Avances En Diabetología 26, 95–100. https://doi.org/10.1016/s1134-3230(10)62005-4

Palumbo, P., Pepe, P., Panunzi, S., De Gaetano, A., 2012. Time-delay model-based control of the glucose–insulin system, by means of a state observer. European Journal of Control 18(6), 591–606. https://doi.org/10.3166/ejc.18.591-606

Percival, M. W., Wang, Y., Grosman, B., Dassau, E., Zisser, H., Jovanovi Jovanovič, L., Doyle, F. J., 2011. Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters. Journal of Process Control 21(3), 391–404. https://doi.org/10.1016/j.jprocont.2010.10.003

Perrasse, A. V., Abad, S. B., Faciolince, S., Hernández, N., Maya, C., 2006. El control de la diabetes mellitus y sus complicaciones en Medellín, Colombia , 2001 – 2003. American Journal of Public Health 20(6), 2001–2003. https://doi.org/10.1590/s1020-49892006001100005

Rivadeneira, P. S., Ferramosca, A., Gonzalez, A. H., 2017a. Control strategies for non-zero set-point regulation of linear impulsive systems. IEEE Transactions on Automatic Control, 2994–3001. https://doi.org/10.1109/tac.2017.2776598

Rivadeneira, P. S., & Gonzalez, A. H., 2015. MPC with state window target control in linear impulsive systems. IFAC-PapersOnline, 507–512. https://doi.org/10.1016/j.ifacol.2015.11.329

Rivadeneira, P. S., Sereno, J. E., Magdelaine, N., & Moog, C. H. 2017b. Blood glycemia reconstruction from discrete measurements using an impulsive observer. IFAC-PapersOnLine, 50(1), 14723-14728. https://doi.org/10.1016/j.ifacol.2017.08.2509

Sereno, J. E., Gonzalez, A. H., Rivadeneira, P. S., 2017. A performance comparison between standard and impulsive zmpc on type 1 diabetic patients. IEEE 3rd Colombian Conference on Automatic Control, 1-6. https://doi.org/10.1109/ccac.2017.8276464

Sopasakis, P., Patrinos, P., Sarimveis, H., Bemporad, A., 2015. Model predictive control for linear impulsive systems. IEEE Transactions on Automatic Control 60(8), 2277–2282. https://doi.org/10.1109/tac.2014.2380672

Thabit, H., Hovorka, R., 2016. Coming of age: the artificial pancreas for type 1 diabetes. Diabetologia 59(9), 1795–1805. https://doi.org/10.1007/s00125-016-4022-4

Abstract Views

2584
Metrics Loading ...

Metrics powered by PLOS ALM


 

Citado por (artículos incluidos en Crossref)

This journal is a Crossref Cited-by Linking member. This list shows the references that citing the article automatically, if there are. For more information about the system please visit Crossref site

1. Meal detection and carbohydrate estimation based on a feedback scheme with application to the artificial pancreas
J.L. Godoy, J.E. Sereno, P.S. Rivadeneira
Biomedical Signal Processing and Control  vol: 68  primera página: 102715  año: 2021  
doi: 10.1016/j.bspc.2021.102715



Creative Commons License

Esta revista se publica bajo una Licencia Creative Commons Attribution-NonCommercial-CompartirIgual 4.0 International (CC BY-NC-SA 4.0)

Universitat Politècnica de València     https://doi.org/10.4995/riai

e-ISSN: 1697-7920     ISSN: 1697-7912