1,721,046 research outputs found
Open-loop insulin dosing personalization in type 1 diabetes using continuous glucose monitoring data and patient characteristics
Full text linkPatients with type 1 diabetes (T1D) require lifelong insulin therapy in order to maintain their blood glucose (BG) concentration within the euglycemic range preventing long-term complications associated with hyperglycemia and avoid- ing dangerous episodes of hypoglycemia. To achieve proper glycemic con- trol, people with T1D need to perform a constant learning process about how daily conditions (e.g. insulin administrations, meals schedule and composi- tion, physical activity, and illness) affect BG levels. More than 500,000 op- erations can be needed during the lifetime of a T1D patient to manage the therapy. For this reason, management of diabetes is burdensome for patients, and results in deteriorating their quality of life. One of the major issues in the daily management of T1D concerns with the amount of insulin that has to be administered, by a subcutaneous bolus injection, in order to compensate the increase of BG associated with meals. So far, a standard simple mathematical formula (SF), designed by clinical investigators on an empirical basis, is com- monly used by patients to calculate the size of insulin boluses. SF leverages on the current BG level obtained from self monitoring blood glucose (SMBG) samples, the estimated amount of carbohydrates (CHOs) present in the meal, and patient specific therapy parameters. While the SF is well-established in clinical practice, the insulin amount determined through its use could be sub- optimal due to several reasons, including the error patients make in estimating CHO, the intrinsical sparseness of SMBG, and the inability of accounting for many important factors such as patients’ intra-/interday variability.
Margins of improvement over the SMBG-based SF emerged in the past decade, when diabetes management has been transformed by the introduction of min- imally invasive continuous glucose monitoring (CGM) sensors, which have been recently approved by regulatory agencies, such as the Food and Drug Ad- ministration (FDA), to be usable to make treatment decisions, such as insulin dosing. Of course, CGM provides an increased amount of available features on BG, such as the rate of change (ROC), that could be exploited to improve insulin standard therapy. As a matter of fact, several attempts have been pro- posed in the literature to account for CGM-derived information and adjust SF accordingly, but unfortunately, they fall short in personalizing such an adjust- ment patient-by-patient. In this thesis we propose new methodologies for determining a dose of in- sulin bolus able to effectively account for the "dynamic" information on BG provided by the ROC and patient characteristics, the final aim being to per- sonalize the standard insulin therapy and eventually improve the glycemic control. In particular, to identify the possible margins of improvement, in the first part of the thesis we assess and analyze the criticalities of three popular literature techniques that exploit the ROC magnitude and direction to adjust the insulin bolus amount computed through SF. To such a scope, we designed ad-hoc in silico clinical trials implemented using a popular powerful simula- tion tool, i.e. the UVa/Padova T1D Simulator. Then, in the second part, we propose two novel machine learning based algorithms that, being fed by in- formation on current patient status and characteristics, provide patients with new tools to adjust SF in a personalized manner. Finally, in the third part of the thesis, we abandon the idea of using the insulin bolus provided by SF as a sort of initial estimate to be simply adjusted, and we design a brand new formula for insulin bolus determination that naturally takes into account for CGM-derived information and current patient status and characteristics. This represents an innovation in the literature because no insulin bolus formulae specifically designed for use with CGM have been proposed yet
Digital Twins in Type 1 Diabetes: A Systematic Review
Full text linkDigital twin is a new concept that is rapidly gaining recognition especially in the medical field. Indeed, being a virtual representation of real-world entities and processes, a digital twin can be used to accurately represent the patients' disease, clarify the treatment target, and realize personalized and precise therapies. However, despite being a revolutionary concept, the diffusion of digital twins in type 1 diabetes (T1D) is still limited. In this systematic review, we analyzed structure, operating conditions, and characteristics of digital twins being developed for T1D. Our search covered published documents until March 2024: 220 publications were identified, 37 of which were duplicated entries; in addition, 173 publications were removed after inspection of titles, abstracts, and keywords; and finally, 11 publications were fully reviewed, of which 8 were deemed eligible for inclusion. We found that all eight methodologies are not comprehensive multi-scale virtual replicas of the individual with T1D, but they all focus on describing glucose-insulin metabolism, aiming to simulate glucose concentration resultant from therapeutic interventions. In this review, we will compare and analyze different factors characterizing these digital twins, such as operating principles (mathematical model, twinning procedure, validation and assessment) and the key aspects for practical adoption (inclusion of physical activity, data required for twinning, open-source availability). We will conclude the paper listing which, in our opinion, are the current limitations and future directives of digital twins in T1D, hoping that this article can be helpful to researchers working on diabetes technologies to further develop the use of such an important instrument
Automated pipeline for denoising, missing data processing, and feature extraction for signals acquired via wearable devices in multiple sclerosis and amyotrophic lateral sclerosis applications
Full text linkIntroduction The incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices.Methods The objective of this study is to present the automated pipeline developed to process signals and extract features from the Garmin Vivoactive 4 smartwatch, which has been chosen as the primary wearable device in the BRAINTEASER project. The proposed pipeline includes a signal processing step, which applies retiming, gap-filling, and denoising algorithms to enhance the quality of the data. The feature extraction step, on the other hand, utilizes clinical partners' knowledge and feedback to select the most relevant variables for analysis.Results The performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project.Discussion Developed in Python, this pipeline can be used by researchers for automated signal processing and feature extraction from wearable devices. It can also be easily adapted or modified to suit the specific requirements of different scenarios
Adaptive and self-learning Bayesian filtering algorithm to statistically characterize and improve signal-to-noise ratio of heart-rate data in wearable devices
Full text linkThe use of wearable sensors to monitor vital signs is increasingly important in assessing individual health. However, their accuracy often falls short of that of dedicated medical devices, limiting their usefulness in a clinical setting. This study introduces a new Bayesian filtering (BF) algorithm that is designed to learn the statistical characteristics of signal and noise, allowing for optimal smoothing. The algorithm is able to adapt to changes in the signal-to-noise ratio (SNR) over time, improving performance through windowed analysis and Bayesian criterion-based smoothing. By evaluating the algorithm on heart-rate (HR) data collected from Garmin Vivoactive 4 smartwatches worn by individuals with amyotrophic lateral sclerosis and multiple sclerosis, it is demonstrated that BF provides superior SNR tracking and smoothing compared with non-adaptive methods. The results show that BF accurately captures SNR variability, reducing the root mean square error from 2.84 bpm to 1.21 bpm and the mean absolute relative error from 3.46% to 1.36%. These findings highlight the potential of BF as a preprocessing tool to enhance signal quality from wearable sensors, particularly in HR data, thereby expanding their applications in clinical and research settings
AGATA: A Toolbox for Automated Glucose Data Analysis
No full textBackground: Analyzing continuous glucose monitoring (CGM) data is a mandatory step for multiple purposes spanning from reporting clinical trial outcomes to developing new algorithms for diabetes management. This task is repetitive, and scientists struggle in computing literature glucose control metrics and waste time in reproducing possibly complex plots and reports. For this reason, to provide the diabetes technology community a unified tool, here we present Automated Glucose dATa Analysis (AGATA), an automated glucose data analysis toolbox developed in MATLAB/Octave. Methods: Automated Glucose dATa Analysis is an open-source software program to visualize and preprocess CGM data, compute glucose control metrics, detect adverse events, evaluate the effectiveness of users' prediction algorithms, and compare study arms. Automated Glucose dATa Analysis can be used as a standalone computer application accessible through a dedicated graphical user interface, particularly suitable for clinicians, or by integrating its functionalities in user-defined MATLAB/Octave scripts, which fits the need of researchers and developers. To demonstrate its features, we used AGATA to analyze CGM data of two subjects extracted from a publicly available data set of individuals with type one diabetes. Finally, AGATA's features are compared against those of 12 noncommercial software programs for CGM data analysis. Results: Using AGATA, we easily preprocessed, analyzed, and visualized CGM data in a handy way, in compliance with the requirements and the standards defined in the literature. Compared to the other considered software programs, AGATA offers more functionalities and capabilities. Conclusion: Automated Glucose dATa Analysis is easy to use and reduces the burden of CGM data analysis. It is freely available in GitHub at https://github.com/gcappon/agata
Heterogeneity and nearest-neighbor coupling can explain small-worldness and wave properties in pancreatic islets
Full text linkDesign and Usability Assessment of a User-Centered, Modular Platform for Real-World Data Acquisition in Clinical Trials involving Post-bariatric Surgery Patients
No full textBackground: Clinical trials often face challenges in efficient data collection and participant monitoring. To address these issues, we developed the IMPACT platform, comprising a real-time mobile application for data collection and a web-based dashboard for remote monitoring and management. Methods: This article presents the design, development, and usability assessment of the IMPACT platform customized for patients with post-bariatric surgery hypoglycemia (PBH). We focus on adapting key IMPACT components, including continuous glucose monitoring (CGM), symptom tracking, and meal logging, as crucial elements for user-friendly and efficient PBH monitoring. Results: The adapted IMPACT platform demonstrated effectiveness in data collection and remote participant monitoring. The mobile application allowed patients to easily track their data, while the clinician dashboard provided a comprehensive overview of enrolled patients, featuring filtering options and alert mechanisms for identifying data collection issues. The platform incorporated various visual representations, including time plots and category-based visualizations, which greatly facilitated data interpretation and analysis. The System Usability Scale questionnaire results indicated a high level of usability for the web dashboard, with an average score of 86.3 out of 100. The active involvement of clinicians throughout the development process ensured that the platform allowed for the collection and visualization of clinically meaningful data. Conclusions: By leveraging IMPACT's existing features and infrastructure, the adapted version streamlined data collection, analysis, and trial customization for PBH research. The platform's high usability underscores its alignment with the requirements for conducting research using continuous real-world data in PBH patients and other populations of interest
A Deep-Learning Based Algorithm for the Management of Hyperglycemia in Type 1 Diabetes Therapy
No full textdrCORRECT: An Algorithm for the Preventive Administration of Postprandial Corrective Insulin Boluses in Type 1 Diabetes Management
No full textBackground: In type 1 diabetes therapy, precise tuning of postprandial corrective insulin boluses (CIBs) is crucial to mitigate hyperglycemia without inducing dangerous hypoglycemic events. Several heuristic formulas accounting for continuous glucose monitoring (CGM) trend have been proposed in the literature. However, these formulas suggest a lot of quantized CIB adjustments, and they lack personalization. Method: drCORRECT algorithm proposed in this work employs a patient-specific time parameter and the "dynamic risk" (DR) measure to determine postprandial CIB suggestion. The expected benefits include the reduction of time in hyperglycemia, thanks to the preventive action exploited through DR. drCORRECT has been assessed retrospectively vs the literature methods proposed by Aleppo et al (AL), Bruttomesso et al (BR), and Ziegler et al (ZI) using a data set of 49 CGM daily traces recorded in free-living conditions. Retrospective evaluation of the algorithms is made possible by the use of ReplayBG, a digital twin-based tool that allows assessing alternative insulin therapies on already collected glucose data. Efficacy in terms of glucose control was measured by temporal, risk indicators, and dedicated hyperglycemic/hypoglycemic events metrics. Results: drCORRECT significantly reduces time spent in hyperglycemia when compared with AL and BR (33.52 [24.16, 39.89]% vs 39.76 [22.54, 48.15]% and 36.32 [26.91, 45.93]%, respectively); significantly reduces daily injected insulin (5.97 [3.80, 8.06] U vs 7.5 [5.21, 10.34] U), glycemia risk index (38.78 [26.58, 55.39] vs 40.78 [27.95, 70.30]), and time spent in hypoglycemia (0.00 [0.00, 1.74]% vs 0.00 [0.00, 10.23]%) when compared with ZI, resulting overall in a safer strategy. Conclusions: The proposed drCORRECT algorithm allows preventive actions thanks to the personalized timing configuration and the introduction of the innovative DR-based CIB threshold, proving to be a valid alternative to the available heuristic literature methods
Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications
Full text linkBy providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors
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