Improving the clinical value and utility of CGM systems: issues and recommendations : a joint statement of the European Association for the Study of Diabetes and the American Diabetes Association Diabetes Technology Working Group

The first systems for continuous glucose monitoring (CGM) became available over 15 years ago. Many then believed CGM would revolutionise the use of intensive insulin therapy in diabetes; however, progress towards that vision has been gradual. Although increasing, the proportion of individuals using CGM rather than conventional systems for self-monitoring of blood glucose on a daily basis is still low in most parts of the world. Barriers to uptake include cost, measurement reliability (particularly with earlier-generation systems), human factors issues, lack of a standardised format for displaying results and uncertainty on how best to use CGM data to make therapeutic decisions. This scientific statement makes recommendations for systemic improvements in clinical use and regulatory (pre- and postmarketing) handling of CGM devices. The aim is to improve safety and efficacy in order to support the advancement of the technology in achieving its potential to improve quality of life and health outcomes for more people with diabetes

Improving the clinical value and utility of CGM systems: issues and recommendations: a joint statement of the European Association for the Study of Diabetes and the American Diabetes Association Diabetes Technology Working Group

The first systems for continuous glucose monitoring (CGM) became available over 15 years ago. Many then believed CGM would revolutionize the use of intensive insulin therapy in diabetes; however, progress toward that vision has been gradual. Although increasing, the proportion of individuals using CGM rather than conventional systems for self-monitoring of blood glucose on a daily basis is still low in most parts of the world. Barriers to uptake include cost, measurement reliability (particularly with earlier-generation systems), human factors issues, lack of a standardized format for displaying results, and uncertainty on how best to use CGM data to make therapeutic decisions. This Scientific Statement makes recommendations for systemic improvements in clinical use and regulatory (pre- and postmarketing) handling of CGM devices. The aim is to improve safety and efficacy in order to support the advancement of the technology in achieving its potential to improve quality of life and health outcomes for more people with diabetes

Newest diabetes related technologies for pediatric type 1 diabetes and its impact on routine care : a narrative synthesis of the literature

Purpose of Review This review aims to address the actual state of the most advanced diabetes devices, as follows: continuous subcutaneous insulin infusions (CSII), continuous glucose monitoring systems (CGM), hybrid-closed loop (HCL) systems, and “Do-it-yourself” Artifcial Pancreas Systems (DIYAPS) in children, adolescents, and young adults. This review has also the objective to assess the use of telemedicine for diabetes care across three diferent areas: education, social media, and daily care. Recent Findings Recent advances in diabetes technology after integration of CSII with CGM have increased the popularity of this treatment modality in pediatric age and shifted the standard diabetes management in many countries. We found an impressive transition from the use of CSII and/or CGM only to integrative devices with automated delivery systems. Although much has changed over the past 5 years, including a pandemic period that precipitated a broader use of telemedicine in diabetes care, some advances in technology may still be an additional burden of care for providers, patients, and caregivers. The extent of a higher rate of “auto-mode” use in diabetes devices while using the HCL/DIYAPS is essential to reduce the burden of diabetes treatment. Summary More studies including higher-risk populations are needed, and eforts should be taken to ensure proper access to cost-efective advanced technology on diabetes care

Reinforcement learning application in diabetes blood glucose control: A systematic review

Background: Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. It is designed for problems which include a learning agent interacting with its environment to achieve a goal. For example, blood glucose (BG) control in diabetes mellitus (DM), where the learning agent and its environment are the controller and the body of the patient respectively. RL algorithms could be used to design a fully closed-loop controller, providing a truly personalized insulin dosage regimen based exclusively on the patient’s own data. Objective: In this review we aim to evaluate state-of-the-art RL approaches to designing BG control algorithms in DM patients, reporting successfully implemented RL algorithms in closed-loop, insulin infusion, decision support and personalized feedback in the context of DM. Methods: An exhaustive literature search was performed using different online databases, analyzing the literature from 1990 to 2019. In a first stage, a set of selection criteria were established in order to select the most relevant papers according to the title, keywords and abstract. Research questions were established and answered in a second stage, using the information extracted from the articles selected during the preliminary selection. Results: The initial search using title, keywords, and abstracts resulted in a total of 404 articles. After removal of duplicates from the record, 347 articles remained. An independent analysis and screening of the records against our inclusion and exclusion criteria defined in Methods section resulted in removal of 296 articles, leaving 51 relevant articles. A full-text assessment was conducted on the remaining relevant articles, which resulted in 29 relevant articles that were critically analyzed. The inter-rater agreement was measured using Cohen Kappa test, and disagreements were resolved through discussion. Conclusions: The advances in health technologies and mobile devices have facilitated the implementation of RL algorithms for optimal glycemic regulation in diabetes. However, there exists few articles in the literature focused on the application of these algorithms to the BG regulation problem. Moreover, such algorithms are designed for control tasks as BG adjustment and their use have increased recently in the diabetes research area, therefore we foresee RL algorithms will be used more frequently for BG control in the coming years. Furthermore, in the literature there is a lack of focus on aspects that influence BG level such as meal intakes and physical activity (PA), which should be included in the control problem. Finally, there exists a need to perform clinical validation of the algorithms

Consensus Recommendations for the Use of Automated Insulin Delivery (AID) Technologies in Clinical Practice

International audienceThe significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past six years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage

Parental evaluation of a telemonitoring service for children with Type 1 Diabetes

Introduction In the past years, we developed a telemonitoring service for young patients affected by Type 1 Diabetes. That service provides data to the clinical staff and offers an important tool to the parents, that are able to oversee in real time their children. The aim of this work was to analyze the parents' perceived usefulness of the service. Methods The service was tested by the parents of 31 children enrolled in a seven-day clinical trial during a summer camp. To study the parents' perception we proposed and analyzed two questionnaires. A baseline questionnaire focused on the daily management and implications of their children's diabetes, while a post-study one measured the perceived benefits of telemonitoring. Questionnaires also included free text comment spaces. Results Analysis of the baseline questionnaires underlined the parents' suffering and fatigue: 51% of total responses showed a negative tendency and the mean value of the perceived quality of life was 64.13 in a 0-100 scale. In the post-study questionnaires about half of the parents believed in a possible improvement adopting telemonitoring. Moreover, the foreseen improvement in quality of life was significant, increasing from 64.13 to 78.39 ( p-value\u2009=\u20090.0001). The analysis of free text comments highlighted an improvement in mood, and parents' commitment was also proved by their willingness to pay for the service (median\u2009=\u2009200\u2009euro/year). Discussion A high number of parents appreciated the telemonitoring service and were confident that it could improve communication with physicians as well as the family's own peace of mind

Evaluation of blood glucose level control in Type 1 diabetic patients using online and offline reinforcement learning

[SPA] Los pacientes con diabetes tipo 1 deben monitorear de cerca sus niveles de glucemia y administrar insulina para controlarlos. Se han propuesto métodos de control automatizado de la glucemia que eliminan la necesidad de intervención humana, y recientemente, el aprendizaje por refuerzo, un tipo de algoritmo de aprendizaje automático, se ha utilizado como un método efectivo de control en entornos simulados. Actualmente, los métodos utilizados para los pacientes con diabetes, como el régimen basal- bolus y los monitores continuos de glucemia, tienen limitaciones y todavía requieren intervención manual. Los controladores PID se utilizan ampliamente por su simplicidad y robustez, pero son sensibles a factores externos que afectan su efectividad. Las obras existentes en la literatura de investigación se han enfocado principalmente en mejorar la precisión de estos algoritmos de control. Sin embargo, todavía hay margen para mejorar la adaptabilidad a los pacientes individuales. La siguiente fase de investigación tiene como objetivo optimizar aún más los métodos actuales y adaptar los algoritmos para controlar mejor los niveles de glucemia. Una solución potencial es usar el aprendizaje por refuerzo (RL) para entrenar los algoritmos en base a datos individuales del paciente. En esta tesis, proponemos un control en lazo cerrado para los niveles de glucemia basado en el aprendizaje profundo por refuerzo. Describimos la evaluación inicial de varias alternativas llevadas a cabo en un simulador realista del sistema glucorregulador y proponemos una estrategia de implementación particular basada en reducir la frecuencia de las observaciones y recompensas pasadas al agente, y usar una función de recompensa simple. Entrenamos agentes con esa estrategia para tres grupos de clases de pacientes, los evaluamos y los comparamos con otras alternativas. Nuestros resultados muestran que nuestro método con Proximal Policy Optimization es capaz de superar a los métodos tradicionales, así como a propuestas similares recientes, al lograr períodos más prolongados de estado glicémico seguro y de bajo riesgo. Como extensión del aporte anterior, constatamos que la aplicación práctica de los algoritmos de control de glucemia requeriría interacciones de prueba y error con los pacientes, lo que es una limitación para entrenar el sistema de manera efectiva. Como alternativa, el aprendizaje reforzado sin conexión no requiere interacción con humanos y la investigación previa sugiere que se pueden lograr resultados prometedores con conjuntos de datos obtenidos sin interacción, similar a los algoritmos de aprendizaje automático clásicos. Sin embargo, aún no se ha evaluado la aplicación del aprendizaje reforzado sin conexión al control de la glucemia. Por lo tanto, en esta tesis, evaluamos exhaustivamente dos algoritmos de aprendizaje reforzado sin conexión para el control de glucemia y examinamos su potencial y limitaciones. Evaluamos el impacto del método utilizado para generar los conjuntos de datos de entrenamiento, el tipo de trayectorias (secuencias de estados, acciones y recompensas experimentadas por un agente en un entorno,) empleadas (método único o mixto), la calidad de las trayectorias y el tamaño de los conjuntos de datos en el entrenamiento y el rendimiento, y los comparamos con las alternativas como PID y Proximal Policy Optimization. Nuestros resultados demuestran que uno de los algoritmos de aprendizaje reforzado sin conexión evaluados, Trajectory Transformer, es capaz de rendir al mismo nivel que alternativas, pero sin necesidad de interacción con pacientes reales durante el entrenamiento.[ENG] Patients with Type 1 diabetes are required to closely monitor their blood glucose levels and administer insulin to manage them. Automated glucose control methods that eliminate the need for human intervention have been proposed, and recently, reinforcement learning, a type of machine learning algorithm, has been used as an effective control method in simulated environments. Currently, the methods used for diabetes patients, such as the basal-bolus regime and continuous glucose monitors, have limitations and still require manual intervention. The PID controllers are widely used for their simplicity and robustness, but they are sensitive to external factors affecting their effectiveness. The existing works in the research literature have mainly focused on improving the accuracy of these control algorithms. However, there is still room for improvement regarding adaptability to individual patients. The next phase of research aims to further optimize the current methods and adapt the algorithms to better control blood glucose levels. Machine learning proposals have paved the way partially, but they can generate generic models with limited adaptability. One potential solution is to use reinforcement learning (RL) to train the algorithms based on individual patient data. In this thesis, we propose a closed-loop control for blood glucose levels based on Deep reinforcement learning. We describe the initial evaluation of several alternatives conducted on a realistic simulator of the glucoregulatory system and propose a particular implementation strategy based on reducing the frequency of the observations and rewards passed to the agent, and using a simple reward function. We train agents with that strategy for three groups of patient classes, evaluate and compare it with alternative control baselines. Our results show that our method with Proximal Policy Optimization is able to outperform baselines as well as similar recent proposals, by achieving longer periods of safe glycemic state and low risk. As an extension of the previous contribution, we have noticed that, practical application of blood glucose control algorithms would necessitate trial-and-error interaction with patients, which could be a limitation for effectively training the system. As an alternative, offline reinforcement learning does not require interaction with subjects and preliminary research suggests that promising results can be achieved with datasets obtained offline, similar to classical machine learning algorithms. However, application of offline reinforcement learning to glucose control has to be evaluated yet. Thus, in this thesis, we comprehensively evaluate two offline reinforcement learning algorithms for blood glucose control and examine their potential and limitations. We assess the impact of the method used to generate training datasets, the type of trajectories employed (sequences of states, actions, and rewards experienced by an agent in an environment over time), the quality of the trajectories, and the size of the datasets on training and performance, and compare them to commonly used baselines such as PID and Proximal Policy Optimization. Our results demonstrate that one of the offline reinforcement learning algorithms evaluated, Trajectory Transformer, is able to perform at the same level as the baselines, but without the need for interaction with real patients during training.Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma Doctorado en Tecnologías de la Información y las Comunicacione

The development of a glucose prediction model in critically ill patients

Purpose: The aim of the current study is to develop a prediction model for glucose levels applicable for all patients admitted to the ICU with an expected ICU stay of at least 24 h. This model will be incorporated in a closed-loop glucose system to continuously and automatically control glucose values. Methods: Data from a previous single-center randomized controlled study was used. All patients received a FreeStyle Navigator II subcutaneous CGM system from Abbott during their ICU stay. The total dataset was randomly divided into a training set and a validation set. A glucose prediction model was developed based on historical glucose data. Accuracy of the prediction model was determined using the Mean Squared Difference (MSD), the Mean Absolute Difference (MAD) and a Clarke Error Grid (CEG). Results: The dataset included 94 ICU patients with a total of 134,673 glucose measurements points that were used for modelling. MSD was 0.410 +/- 0.495 for the model, the MAD was 5.19 +/- 2.63 and in the CEG 99.8% of the data points were in the clinically acceptable regions. Conclusion: In this study a glucose prediction model for ICU patients is developed. This study shows that it is possible to accurately predict a patient's glucose 30 min ahead based on historical glucose data. This is the first step in the development of a closed-loop glucose system. (C) 2021 Elsevier B.V. All rights reserved

Type 1 diabetes

Type 1 diabetes is a chronic autoimmune disease characterised by insulin deficiency and resultant hyperglycaemia. Knowledge of type 1 diabetes has rapidly increased over the past 25 years, resulting in a broad understanding about many aspects of the disease, including its genetics, epidemiology, immune and β-cell phenotypes, and disease burden. Interventions to preserve β cells have been tested, and several methods to improve clinical disease management have been assessed. However, wide gaps still exist in our understanding of type 1 diabetes and our ability to standardise clinical care and decrease disease-associated complications and burden. This Seminar gives an overview of the current understanding of the disease and potential future directions for research and care