Palabras clave
Diabetes Mellitus
Aplicación móvil
Simulación por computadora
Conteo de carbohidratos
Cómo citar
Resumen
Contexto: El conteo de carbohidratos se ha considerado la forma ideal de calcular la insulina prandial, por ende se han propuesto varias formas de mejorarlo.
Objetivo: Proponemos refinar el conteo de carbohidratos utilizando una simulación, la cual se presenta en una aplicación móvil, STUDIA, que simula en tiempo real la glucosa postprandial.
Métodos: Utilizamos un fenomenológico del tracto gastrointestinal, acoplado al modelo mínimo para la glucosa postprandial en personas con diabetes mellitus tipo 1 (DM1). Las funciones y requisitos técnicos se definieron mediante un sistema de adquisición de requerimientos. Para la caracterización de usuarios, utilizamos una aproximación basada en el individuo. El ecosistema de datos se evaluó mediante el criterio UX/UI, la curva de aprendizaje, flexibilidad y la posibilidad de ejecutar modelos matemáticos. Utilizamos datos de un paciente con DM1 para ejemplificar el uso de la aplicación y los datos del monitoreo continuo de glucosa para comparación.
Resultados: STUDIA fue construida en Android Studio® con una interfaz de usuario y un módulo administrativo basado en la web conectado a AWS®. Permite similar la glucosa basado en el conteo de carbohidratos para su refinamiento. Se utilizan los parámetros del paciente y los datos históricos de la glucosa para el ajuste de la aplicación. Esta aplicación puede ser utilizada tanto por los pacientes para comparar diferentes escenarios al igual que en la investigación clínica.
Conclusiones: Presentamos la primera aplicación para simular la glucosa postprandial basada en un modelo fenomenológico del tracto gastrointestinal para pacientes con DM1. STUDIA se probará con datos históricos de pacientes y en un ensayo clínico.
Citas
Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwinet, N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes ResClin Pract. 2019 Sept;157:107843. https://doi.org/10.1016/j.diabres.2019.107843
Klein R. Hyperglycemia and microvascular and macrovascular disease in diabetes. Diabetes care. 1995;18(2):258-68. https://doi.org/10.2337/diacare.18.2.258
Adler AI, Boyko EJ, Ahroni JH, Stensel V, Forsberg R, Smith DG. Risk factors for diabetic peripheral sensory neuropathy. Results of the Seattle Prospective Diabetic Foot Study. Diabetes care. 1997;20(7):1162-7. https://doi.org/10.2337/diacare.20.7.1162
Turner RC, Millns H, Neil HA, Stratton IM, Manley SE, Matthews DR, et al. Risk factors for coronary artery disease in non-insulin dependent diabetes mellitus: United Kingdom Prospective Diabetes Study (UKPDS: 23). BMJ (Clinical research ed). 1998;316(7134):823-8. https://doi.org/10.1136/bmj.316.7134.823
Lehto S, Ronnemaa T, Pyorala K, Laakso M. Predictors of stroke in middle-aged patients with non-insulin-dependent diabetes. Stroke. 1996;27(1):63-8. https://doi.org/10.1161/01.STR.27.1.63
Builes-Montaño CE, Chavarriaga A, Ballesteros L, Muñoz M, Medina S, Donado-Gomez JH, et al. Characteristics of hyperglycemic crises in an adult population in a teaching hospital in Colombia. Journal of Diabetes & Metabolic Disorders. 2018;17(2):143-8. https://doi.org/10.1007/s40200-018-0353-7
Nathan DM. The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study at 30 Years: Overview. Diabetes care. 2014;37(1):9-16. https://doi.org/10.2337/dc13-2112
The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus. New England Journal of Medicine. 1993;329(14):977-86. https://doi.org/10.1056/NEJM199309303291401
Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998;352(9131):854-65. https://doi.org/10.1016/S0140-6736(98)07037-8
Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HAW. 10-Year Follow-up of Intensive Glucose Control in Type 2 Diabetes. New England Journal of Medicine. 2008;359(15):1577-89. https://doi.org/10.1056/NEJMoa0806470
Pharmacologic Approaches to Glycemic Treatment: Standards of Medical Care in Diabetes-2019. Diabetes care. 2019;42(Suppl 1):S90-s102. https://doi.org/10.2337/dc19-S009
Villegas Perrasse A, Abad SB, Faciolince S, Hernández N, Maya C, Parra L, et al. Controlling diabetes mellitus and its complications in Medellin, Colombia, 2001-2003. Rev Panam Salud Publica. 2006;20(6):393-402. https://doi.org/10.1590/S1020-49892006001100005
Machado Alba JE, Moncada Escobar JC, Mesa Escobar G. Antidiabetic drugs prescription patterns among a group of patients in Colombia. Rev Panam Salud Publica. 2007;22(2):124-31. https://doi.org/10.1590/S1020-49892007000700007
Barengo NC, Camacho S, López PA, Camacho PA, García AA, Hincapié JA, et al. Patrones de prescripción de medicamentos para la diabetes mellitus tipo 2 en cinco departamentos de Colombia, en 2014. Rev Facu Nal de Salud Pública. 2018;36:58-65. https://doi.org/10.17533/udea.rfnsp.v36n2a08
Davidson PC, Hebblewhite HR, Steed RD, Bode BW. Analysis of guidelines for basal-bolus insulin dosing: basal insulin, correction factor, and carbohydrate-to-insulin ratio. Endocr Pract. 2008;14(9):1095-101. https://doi.org/10.4158/EP.14.9.1095
Evert AB, Boucher JL, Cypress M, Dunbar SA, Franz MJ, Mayer-Davis EJ, et al. Nutrition therapy recommendations for the management of adults with diabetes. Diabetes care. 2013;36(11):3821-42. https://doi.org/10.2337/dc13-2042
Souto DL, Zajdenverg L, Rodacki M, Rosado EL. Impact of advanced and basic carbohydrate counting methods on metabolic control in patients with type 1 diabetes. Nutrition. 2014;30(3):286-90. https://doi.org/10.1016/j.nut.2013.08.010
Builes-Montaño CE, Ortiz-Cano NA, Ramirez-Rincón A, Rojas-Henao NA. Efficacy and safety of carbohydrate counting versus other forms of dietary advice in patients with type 1 diabetes mellitus: A systematic review and meta-analysis of randomized clinical trials. J Hum Nutr Diet. 2022. https://doi.org/10.1111/jhn.13017
Bozzetto L, Giorgini M, Alderisio A, Costagliola L, Giacco A, Riccardi G, et al. Glycaemic load versus carbohydrate counting for insulin bolus calculation in patients with type 1 diabetes on insulin pump. Acta Diabetol. 2015;52(5):865-71. https://doi.org/10.1007/s00592-015-0716-1
Krebs JD, Parry Strong A, Cresswell P, Reynolds AN, Hanna A, Haeusler S. A randomised trial of the feasibility of a low carbohydrate diet vs standard carbohydrate counting in adults with type 1 diabetes taking body weight into account. Asia Pac J Clin Nutr. 2016;25(1):78-84. https://doi.org/10.6133/apjcn.2016.25.1.11
Holt SH, Miller JC, Petocz P. An insulin index of foods: the insulin demand generated by 1000-kJ portions of common foods. The American journal of clinical nutrition. 1997;66(5):1264-76. https://doi.org/10.1093/ajcn/66.5.1264
Bell KJ, Gray R, Munns D, Petocz P, Steil G, Howard G, et al. Clinical Application of the Food Insulin Index for Mealtime Insulin Dosing in Adults with Type 1 Diabetes: A Randomized Controlled Trial. Diabetes technology & therapeutics. 2016;18(4):218-25. https://doi.org/10.1089/dia.2015.0254
Gingras V, Haidar A, Messier V, Legault L, Ladouceur M, Rabasa-Lhoret R. A Simplified Semiquantitative Meal Bolus Strategy Combined with Single- and Dual-Hormone Closed-Loop Delivery in Patients with Type 1 Diabetes: A Pilot Study. Diabetes technology & therapeutics. 2016;18(8):464-71. https://doi.org/10.1089/dia.2016.0043
Álvarez HD, Peña M. Modelamiento de Sistemas de Inferencia Borrosa Tipo Takagi-Sugeno. Rev Avances en Sistemas Informática. 2004;1(1):1-11.
Lema-Perez L, Garcia-Tirado J, Builes-Montano C, Alvarez H. Phenomenological-Based model of human stomach and its role in glucose metabolism. J Theor Biol. 2019;460:88-100. https://doi.org/10.1016/j.jtbi.2018.10.024
Blandón LA, Gallego MSC, Patino SIO, Pérez LL. A Phenomenological-Based Model for the Small Intestine Role in Human Glucose Homeostasis. 2019 IEEE 4th Colombian Conference on Automatic Control (CCAC); 2019 15-18 Oct. 2019. https://doi.org/10.1109/CCAC.2019.8920843
Builes-Montaño CE, Lema-Perez L, Garcia-Tirado J, Alavarez H. Main glucose hepatic fluxes in healthy subjects predicted from a phenomenological-based model. Comput Biol Med. 2022;142:105232. https://doi.org/10.1016/j.compbiomed.2022.105232
Garcia-Tirado J, Colmegna P, Corbett JP, Ozaslan B, Breton MD. In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System. Journal of diabetes science and technology. 2019;13(6):1054-64. https://doi.org/10.1177/1932296819879084
Meade LT, Rushton WE. Accuracy of Carbohydrate Counting in Adults. Clin Diabetes. 2016;34(3):142-7. https://doi.org/10.2337/diaclin.34.3.142
Kawamura T, Takamura C, Hirose M, Hashimoto T, Higashide T, Kashihara Y, et al. The factors affecting on estimation of carbohydrate content of meals in carbohydrate counting. Clin Pediatr Endocrinol. 2015;24(4):153-65. https://doi.org/10.1297/cpe.24.153
Wolpert HA, Atakov-Castillo A, Smith SA, Steil G. Dietary fat acutely increases glucose concentrations and insulin requirements in patients with type 1 diabetes: implications for carbohydrate-based bolus dose calculation and intensive diabetes management. Diabetes care. 2013;36(4):810-6. https://doi.org/10.2337/dc12-0092
Garcia-Lopez JM, Gonzalez-Rodriguez M, Pazos-Couselo M, Gude F, Prieto-Tenreiro A, Casanueva F.. Should the amounts of fat and protein be taken into consideration to calculate the lunch prandial insulin bolus? Results from a randomized crossover trial. Diabetes technology & therapeutics. 2013;15(2):166-71. https://doi.org/10.1089/dia.2012.0149
Rizun N, Taranenko Y. Simulation models of human decision-making processes. Management Dynamics in the Knowledge Economy. 2014;2(2):241-64.
Pagé C, Bernier P-M, Trempe M. Using video simulations and virtual reality to improve decision-making skills in basketball. Journal of sports sciences. 2019;37(21):2403-10. https://doi.org/10.1080/02640414.2019.1638193
Benjamin P, Koola P, Akella K, Graul M, Painter M. Using Simulation Based Training Methods for Improved Warfighter Decision Making. International Conference on Augmented Cognition; 2013: Springer. https://doi.org/10.1007/978-3-642-39454-6_2
Larkin A, Hanley KL, Warters M, Littman G. Virtual Simulation Improves Clinical Decision-Making in Managing Type 2 Diabetes. Diabetes. 2018;67(Supplement 1):688-P. https://doi.org/10.2337/db18-688-P
Sperl-Hillen J, O'Connor P, Ekstrom H, Rush W, Asche S, Fernandes O, et al. Using Simulation Technology to Teach Diabetes Care Management Skills to Resident Physicians. Journal of diabetes science and technology. 2013;7(5):1243-54. https://doi.org/10.1177/193229681300700514
Ajmera I, Swat M, Laibe C, Le Novère N, Chelliah V. The impact of mathematical modeling on the understanding of diabetes and related complications. CPT Pharmacometrics Syst Pharmacol. 2013;2:e54. https://doi.org/10.1038/psp.2013.30
Lema-Perez L, Aguirre-Zapata E, Garcia-Tirado J. Recent advances in mathematical models for the understanding and treatment of Type 1 Diabetes Mellitus. 2015 IEEE 2nd Colombian Conference on Automatic Control (CCAC); 2015 14-16 Oct. 2015.
Fritzen K, Heinemann L, Schnell O. Modeling of Diabetes and Its Clinical Impact. Journal of diabetes science and technology. 2018;12(5):976-84. https://doi.org/10.1177/1932296818785642
Colmegna P, Bisio A, Mcfadden R, Wakeman CA, Oliveri RN, Breton M. 97-LB: Bringing Simulation Technologies to People with T1D: A Pilot Study. Diabetes. 2021;70(Supplement_1). https://doi.org/10.2337/db21-97-LB
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