Desarrollo de algoritmos de control para terapia MDI en pacientes con diabetes tipo 1

Diabetes Mellitus (DM) is considered one of the fastest-growing global health emergencies in the XXI century. According to the World Health Organization, it was estimated in 2021 that 537 million people were suffering from this disease, and approximately 6.7 million adults (20-70 years old) died as a result or complication of it. Additionally, it is projected that by 2030, the number of patients will reach 643 million. This disease is a severe chronic condition characterized by elevated levels of glucose in the blood due to the body's inability to produce enough insulin or effectively use the insulin it produces. In the development of treatments for DM, the use of simulators has become essential. However, the simulators currently available have limitations in terms of their complexity and capacity to represent real-life situations. These simulators often do not include enough virtual patients (VP), or these do not adequately represent a target cohort. The generation of realistic VP is key to ensuring that simulations are representative of the target population and, therefore, allow for an accurate evaluation of proposed treatment strategies. In this thesis, methodologies for the creation of virtual cohorts that are representative of a target real patient population (RP) are implemented. To validate this methodology, data from a real cohort obtained previously in a clinical trial is used. In the treatment of DM, the administration of exogenous insulin to control glucose levels is common. Currently, there are two main therapies for this purpose: multiple daily injections (MDI) and continuous subcutaneous insulin infusion (CSII). In recent years, insulin pumps have generated interest due to the development of technologies such as the artificial pancreas (AP). However, MDI therapy remains the most commonly used option by people with DM to meet daily insulin requirements. Despite the inherent limitations of MDI therapy, technical advances in continuous glucose monitoring (CGM), the creation of smart insulin pens, and the development of control strategies applied to the AP can be used to improve glycemic control in patients using this therapy. In this work, AP research is extended to MDI therapy using CGM and a smart insulin pen to "close the loop" and automatically guide the patient's manual actions when necessary to minimize hypoglycemic and hyperglycemic episodes. For this, a methodology and design of an intermittent closed-loop (CL) control system is developed specifically to improve glycemic control in T1D patients with MDI therapy. This control system uses two event-triggered model predictive controllers (MPCs). Multiple simulated tests were carried out, and the results obtained through the proposals of this research are promising. The results indicate that the proposed control system has the potential to significantly contribute to improving glycemic control and the quality of life of T1D patients using MDI therapy ​
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