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

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

doi:10.4995/riai.2018.9728

Cited by 5 publications

(5 citation statements)

References 30 publications

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“…Figure 11 shows the results of this strategy under the scenario defined in Section 3.1. [22]. The iZMPC is formulated in a similar manner to the corresponding discrete formulation by using a new decision variable , which makes the cost zero when the predicted variables are inside the target zone and different from zero when they are outside it.…”

Section: Discussionmentioning

confidence: 99%

“…Insulin doses are administered as small spaced pulses, rather than a continuous input or a discrete one. Then, the consideration of it as an impulsive system is appropriate to emulate the real T1DM treatment [9,22]. Affine linear impulsive systems are described as follows:…”

Section: Impulsive Zone Model Predictive Control With Artificialmentioning

confidence: 99%

“…In this subsection, different MPC strategies for discrete and impulsive systems, applied to the T1DM treatment problem, are compared. These strategies are the standard discrete MPC (dMPC) [24], the discrete ZMPC (dZMPC) with slack variable [26], the discrete ZMPC with artificial variables (dZMPC-AV) [28,29], and the adaptation of the same formulations for impulsive systems: iMPC [30], iZMPC [22], and iZMPC-AV [10]; the description of each formulation can be seen in the appendix.…”

Section: Glycemia Control In T1dm Patients: Comparison Of Six Mpc Strmentioning

confidence: 99%

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Handling Parameter Variations during the Treatment of Type 1 Diabetes Mellitus: In Silico Results

Villa-Tamayo

Alvarez

Rivadeneira

2019

Mathematical Problems in Engineering

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Type 1 diabetic patients need a strict treatment to regulate blood glucose concentration in a target range. Despite the development of different control strategies, the model parameter variations, given by physiological changes, can generate an inaccurate treatment and in consequence hyperglycemia and hypoglycemia episodes. Therefore, it is necessary to use control techniques that compensate such effects and maintain the control goals. Here, the effect of parametric variations is examined by the sensitivity analysis from which the most influential parameters in glycemia dynamics are detected. Based on that, an offset-free MPC strategy for impulsive systems is given for the first time in literature and simulated for type 1 diabetes treatment. This scheme along with the impulsive zone MPC with artificial variables reestablishes the normoglycemia behavior since the parameter variations are adequately rejected. However, only parametric variations up to 50% from their nominal values are well compensated, which suggests that more robust formulations are needed to ensure a greater rejection of physiological variations.

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Section: Discussionmentioning

confidence: 99%

Section: Impulsive Zone Model Predictive Control With Artificialmentioning

confidence: 99%

Section: Glycemia Control In T1dm Patients: Comparison Of Six Mpc Strmentioning

confidence: 99%

See 1 more Smart Citation

Handling Parameter Variations during the Treatment of Type 1 Diabetes Mellitus: In Silico Results

Villa-Tamayo

Alvarez

Rivadeneira

2019

Mathematical Problems in Engineering

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“…Asimismo, se han desarrollado compendios para el manejo de las complicaciones y enfermedades concurrentes asociadas con la diabetes mellitus, que ayudan a los médicos en la toma de decisiones terapéuticas (Rivadeneira et al, 2019). La educación diabetológica desempeña un papel crucial en el manejo de la diabetes tipo 2, y se ha comprobado que la educación sobre hábitos de vida proporcionada por profesionales de enfermería tiene un efecto positivo en el enfoque de la enfermedad (Organización Panamericana de la Salud, 2022).…”

Section: Introducción a La Diabetes Mellitus Y Su Manejounclassified

Influencia de los factores psicosociales en la adherencia al tratamiento de la diabetes mellitus tipo 1 y 2

Samaniego Bailón,

Orbe Alvarado

2023

ConcienciaDigital

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Introducción: La diabetes mellitus, una enfermedad crónica con un manejo complejo, se ve influenciada no solo por factores biológicos, sino también por factores psicosociales. La identificación de estos factores y su papel en la adherencia al tratamiento, el control glucémico y la calidad de vida de los pacientes con diabetes es crucial para mejorar su manejo. Objetivo: Describir la influencia de los factores psicosociales en la adherencia al tratamiento de la diabetes mellitus mediante una revisión exhaustiva de la literatura científica. Metodología: Revisión bibliográfica narrativa, utilizando las bases de datos PubMed, PsycINFO y Web of Science, Latindex. Se incluirán estudios que se centren en la diabetes mellitus, examinen la relación entre uno o más factores psicosociales y el manejo de la diabetes, y proporcionen suficientes datos para evaluar los resultados de interés. Resultados: Los factores psicosociales, como la depresión y el estrés, impactan significativamente la adherencia al tratamiento de la diabetes mellitus. Los síntomas depresivos triplican la probabilidad de no adherencia, mientras que el apoyo social mejora el control glucémico. Por otro lado, las complicaciones diabéticas afectan la calidad de vida, independientemente de las políticas de tratamiento. Conclusión: Los factores psicosociales desempeñan un papel crucial en la gestión y tratamiento de la diabetes mellitus. La depresión y el estrés pueden reducir la adherencia al tratamiento, mientras que el apoyo social puede potenciarla. Las complicaciones derivadas de la diabetes impactan la calidad de vida, enfatizando la importancia de una atención integral y multidisciplinaria. Área de estudio general: Medicina. Área de estudio específica: Medicina General.

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“…Closed-loop control systems in biomedical applications is an area of increasing importance with technological advances expanding the range of possible applications. For instance, [1] analyzes closed-loop systems in applications for Type 1 diabetes management. Another example is the design of automatic control systems in the field of anesthesia.…”

Section: Introductionmentioning

confidence: 99%

Modelling the PSI response in general anesthesia

Pérez

Torres

et al. 2020

J Clin Monit Comput

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In anesthesia automation, one of the main important issues is the availability of a reliable measurement of the depth of consciousness level (hypnosis) of the patient. According to this value, the hypnotic drug dosage can be adequately calculated. One of the most studied hypnosis indexes is the bispectral index (BIS). In this article we analyzed an alternative called patient state index (PSI). The objectives of this study are, first, to validate the accuracy of the PSI describing the hypnosis level during the maintenance phase of general anesthesia, by comparing with the BIS and, second, to model the relationship between propofol infusion rate and PSI values, obtained from a SEDLine monitor. For this, real data from patients undergoing general anesthesia simultaneously monitored with both BIS and PSI signals was used. Results obtained are interesting for a correct interpretation of PSI signal in clinical practice.

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

Cited by 5 publications

References 30 publications

Handling Parameter Variations during the Treatment of Type 1 Diabetes Mellitus: In Silico Results

Handling Parameter Variations during the Treatment of Type 1 Diabetes Mellitus: In Silico Results

Influencia de los factores psicosociales en la adherencia al tratamiento de la diabetes mellitus tipo 1 y 2

Modelling the PSI response in general anesthesia

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