Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms

Unsupervised Retrospective Detection of Pressure Induced Failures in Continuous Glucose Monitoring Sensors for T1D Management

Continuous Glucose Monitoring sensors (CGMs) have revolutionized type 1 diabetes (T1D) management. In particular, in several cases, the retrospective analysis of CGM recordings allows clinicians to review and adjust patients' therapy. However, in this set-up, the artifacts that are often present in CGM data could lead to incorrect therapeutic actions. To mitigate this risk, we investigate how to detect one of the most common of these artifacts, the so-called pressure induced sensor attenuations, by means of anomaly detection algorithms. Specifically, these methods belong to the class of unsupervised techniques, which is particularly appealing since it does not require a labeled dataset, hardly available in practice. After having designed five features to highlight the anomalous state of the sensor, 8 different methods (e.g. Isolation Forest and Histogram-based Outlier Score) are assessed both in silico using the UVa/Padova Type 1 Diabetes Simulator and on real data of 36 subjects monitored for about 10 days. In the in silico scenario, the best results are achieved with Isolation Forest, which recognized the 74% of the failures generating on average only 2 false alerts during the whole monitoring time. In real data, Isolation Forest is confirmed to be effective in the detection of failures, achieving a recall of 55% and generating 3 false alarms in 10 days. By allowing to detect more than 50% of the artifacts while discarding only a few portions of correct data in several days of monitoring, the proposed approach could effectively improve the quality of CGM data used by clinicians to retrospectively evaluate and adjust T1D therapy.

Hydrogel-Forming Microneedles and Applications in Interstitial Fluid Diagnostic Devices

Hydrogel-forming microneedles are constructed from or coated with polymeric, hydrophilic materials that swell upon insertion into the skin. Designed to dissolve or disintegrate postinsertion, these microneedles can deliver drugs, vaccines, or other therapeutics. Recent advancements have broadened their application scope to include the collection, transport, and extraction of dermal interstitial fluid (ISF) for medical diagnostics. This review presents a brief introduction to the characteristics of dermal ISF, methods for extraction and sampling, and critical assessment of the state-of-the-art in hydrogel-forming microneedles for ISF diagnostics. Key factors are evaluated including material composition, swelling behavior, biocompatibility, and mechanical strength necessary for effective microneedle performance and ISF collection. The review also discusses successful examples of dermal ISF assays and microneedle sensor integrations, highlighting notable achievements, identifying research opportunities, and addressing challenges with potential solutions. Despite the predominance of synthetic hydrogels in reported hydrogel-forming microneedle technologies due to their favorable swelling and gelation properties, there is a significant variety of biopolymers and composites reported in the literature. The field lacks consensus on the optimal material, composition, or fabrication methods, though emerging evidence suggests that processing and fabrication techniques are critical to the performance and utility of hydrogel-forming microneedles for ISF diagnostics.

A Passive Perspiration Inspired Wearable Platform for Continuous Glucose Monitoring

The demand for glucose monitoring devices has witnessed continuous growth from the rising diabetic population. The traditional approach of blood glucose (BG) sensor strip testing generates only intermittent glucose readings. Interstitial fluid-based devices measure glucose dynamically, but their sensing approaches remain either minimally invasive or prone to skin irritation. Here, a sweat glucose monitoring system is presented, which completely operates under rest with no sweat stimulation and can generate real-time BG dynamics. Osmotically driven hydrogels, capillary action with paper microfluidics, and self-powered enzymatic biochemical sensor are used for simultaneous sweat extraction, transport, and glucose monitoring, respectively. The osmotic forces facilitate greater flux inflow and minimize sweat rate fluctuations compared to natural perspiration-based sampling. The epidermal platform is tested on fingertip and forearm under varying physiological conditions. Personalized calibration models are developed and validated to obtain real-time BG information from sweat. The estimated BG concentration showed a good correlation with measured BG concentration, with all values lying in the A+B region of consensus error grid (MARD = 10.56% (fingertip) and 13.17% (forearm)). Overall, the successful execution of such osmotically driven continuous BG monitoring system from passive sweat can be a useful addition to the next-generation continuous sweat glucose monitors.

Calibration algorithms for continuous glucose monitoring systems based on interstitial fluid sensing

Continuous glucose monitoring (CGM) is of great importance to the treatment and prevention of diabetes. As a proven commercial technology, electrochemical glucose sensor based on interstitial fluid (ISF) sensing has high sensitivity and wide detection range. Therefore, it has good promotion prospects in noninvasive or minimally-invasive CGM system. However, since there are concentration differences and time lag between glucose in plasma and ISF, the accuracy of this type of sensors are still limited. Typical calibration algorithms rely on simple linear regression which do not account for the variability of the sensitivity of sensors. To enhance the accuracy and stability of CGM based on ISF, optimization of calibration algorithm for sensors is indispensable. While there have been considerable researches on improving calibration algorithms for CGM, they have still received less attention. This article reviews the problem of typical calibration and presents the outstanding calibration algorithms in recent years. Finally, combined with existing research and emerging sensing technologies, this paper makes an outlook on the future calibration algorithms for CGM sensors.

Lab on skin: real-time metabolite monitoring with polyphenol film based subdermal wearable patches

The advent of digital technologies has spurred the development of wearable sensing devices marking a significant shift in obtaining real-time physiological information. The principal objective is to transition from blood-centric monitoring to minimally invasive modalities, which will enable movement from specialised settings to more accessible environments such as the practices of general practitioners or even home settings. While subcutaneously implanted continuous monitoring devices have demonstrated this transition, detection of analytes from sample matrices like skin interstitial fluid (ISF), is a frontier that offers attractive minimally invasive routes for detection of biomarkers. This manuscript presents a comprehensive overview of our work in subdermal wearable biosensing patches for the simultaneous monitoring of glucose and lactate from ISF in ambulatory conditions. The performance of the subdermal wearable glucose monitoring patch was evaluated over a duration of three days, which is the longest reported duration reported till date. The subdermal wearable lactate sensing patch was worn for the duration of the exercise. Our findings highlight a critical observation that biofouling effects become apparent after a 24 h period. The data presented in this manuscript extends on the knowledge in the areas of continuous metabolite monitoring by introducing multifunctional polyphenol polymer films that can be used for both glucose and lactate monitoring with appropriate modifications. This study underscores the potential of subdermal wearable patches as versatile tools for real-time metabolite monitoring, positioning them as valuable assets in the evolution of personalised healthcare in diverse settings.

Wearing the Lab: Advances and Challenges in Skin-Interfaced Systems for Continuous Biochemical Sensing

Continuous, on-demand, and, most importantly, contextual data regarding individual biomarker concentrations exemplify the holy grail for personalized health and performance monitoring. This is well-illustrated for continuous glucose monitoring, which has drastically improved outcomes and quality of life for diabetic patients over the past 2 decades. Recent advances in wearable biosensing technologies (biorecognition elements, transduction mechanisms, materials, and integration schemes) have begun to make monitoring of other clinically relevant analytes a reality via minimally invasive skin-interfaced devices. However, several challenges concerning sensitivity, specificity, calibration, sensor longevity, and overall device lifetime must be addressed before these systems can be made commercially viable. In this chapter, a logical framework for developing a wearable skin-interfaced device for a desired application is proposed with careful consideration of the feasibility of monitoring certain analytes in sweat and interstitial fluid and the current development of the tools available to do so. Specifically, we focus on recent advancements in the engineering of biorecognition elements, the development of more robust signal transduction mechanisms, and novel integration schemes that allow for continuous quantitative analysis. Furthermore, we highlight the most compelling and promising prospects in the field of wearable biosensing and the challenges that remain in translating these technologies into useful products for disease management and for optimizing human performance.

The role of continuous glucose monitoring in physical activity and nutrition management: perspectives on present and possible uses

PURPOSE

Continuous glucose monitoring (CGM) is on the rise as the prevalence of obesity and diabetes increases. This review aimed to explore the use of CGM and its potential novel applications in physical activity and nutrition management.

METHODS

We searched PubMed, Web of Science, and Wiley Online Library databases using the keywords 'continuous glucose monitor,' 'nutrition,' 'physical activity,' and 'numerical modeling.'

RESULTS

Continuous blood glucose measurement is useful for individuals with obesity and diabetes. Long-term blood glucose data allow for personalized planning of nutritional composition, meal timing, and physical activity type and intensity, as well as help prevent hypoglycemia and hyperglycemia. Thus, understanding the limitations of CGM is important for its effective use.

CONCLUSION

CGM systems are being increasingly used to monitor and identify appropriate blood glucose controlling interventions. Blood glucose level is influenced by various factors such as nutrient composition, meal timing, physical activity, circadian rhythm, and cortisol levels. Numerical modeling can be used to analyze the complex relationship between stress, sleep, nutrition, and physical activity, which affect blood glucose levels. In future, blood glucose, sleep, and stress data will be integrated to predict appropriate lifestyle levels for blood glucose management. This integrated approach improves glucose control and overall wellbeing, potentially reducing societal costs.

Super-twisting-based meal detector for type 1 diabetes management: Improvement and assessment in a real-life scenario

BACKGROUND AND OBJECTIVE

Hybrid automated insulin delivery systems rely on carbohydrate counting to improve postprandial control in type 1 diabetes. However, this is an extra burden on subjects, and it introduces a source of potential errors that could impact control performances. In fact, carbohydrates estimation is challenging, prone to errors, and it is known that subjects sometimes struggle to adhere to this requirement, forgetting to perform this task. A possible solution is the use of automated meal detection algorithms. In this work, we extended a super-twisting-based meal detector suggested in the literature and assessed it on real-life data.

METHODS

To reduce the false detections in the original meal detector, we implemented an implicit discretization of the super-twisting and replaced the Euler approximation of the glucose derivative with a Kalman filter. The modified meal detector is retrospectively evaluated in a challenging real-life dataset corresponding to a 2-week trial with 30 subjects using sensor-augmented pump control. The assessment includes an analysis of the nature and riskiness of false detections.

RESULTS

The proposed algorithm achieved a recall of 70 [13] % (median [interquartile range]), a precision of 73 [26] %, and had 1.4 [1.4] false positives-per-day. False positives were related to rising glucose conditions, whereas false negatives occurred after calibrations, missing samples, or hypoglycemia treatments.

CONCLUSIONS

The proposed algorithm achieves encouraging performance. Although false positives and false negatives were not avoided, they are related to situations with a low risk of hypoglycemia and hyperglycemia, respectively.

Physiological reconstruction of blood glucose level using CGMS-signals only

Patient with diabetes must regularly monitor blood glucose level. Drawing a blood sample is a painful and discomfort experience. Alternatively, the patient measures interstitial fluid glucose level with a sensor installed in subcutaneous tissue. Then, a model of glucose dynamics calculates blood glucose level from the sensor-measured, i.e., interstitial fluid glucose level of subcutaneous tissue. Interstitial fluid glucose level can significantly differ from blood glucose level. The sensor is either factory-calibrated, or the patient calibrates the sensor periodically by drawing blood samples, when glucose levels of both compartments are steady. In both cases, the sensor lifetime is limited up to 14 days. This is the present state of the art. With a physiological model, we would like to prolong the sensor lifetime with an adaptive approach, while requiring no additional blood sample. Prolonging sensor’s lifetime, while reducing the associated discomfort, would considerably improve patient’s quality of life. We demonstrate that it is possible to determine personalized model parameters from multiple CGMS-signals only, using an animal experiment with a hyperglycemic clamp. The experimenter injected separate glucose and insulin boluses to trigger rapid changes, on which we evaluated the ability to react to non-steady glucose levels in different compartments. With the proposed model, 70%, 80% and 95% of the calculated blood glucose levels had relative error less than or equal to 21.9%, 32.5% and 43.6% respectively. Without the model, accuracy of the sensor-estimated blood glucose level decreased to 39.4%, 49.9% and 99.0% relative errors. This confirms feasibility of the proposed method.

Artificial intelligence perspective in the future of endocrine diseases

In recent years, artificial intelligence (AI) shows promising results in the diagnosis, prediction, and management of diseases. The move from handwritten medical notes to electronic health records and a huge number of digital data commenced in the era of big data in medicine. AI can improve physician performance and help better clinical decision making which is called augmented intelligence. The methods applied in the research of AI and endocrinology include machine learning, artificial neural networks, and natural language processing. Current research in AI technology is making major efforts to improve decision support systems for patient use. One of the best-known applications of AI in endocrinology was seen in diabetes management, which includes prediction, diagnosis of diabetes complications (measuring microalbuminuria, retinopathy), and glycemic control. AI-related technologies are being found to assist in the diagnosis of other endocrine diseases such as thyroid cancer and osteoporosis. This review attempts to provide insight for the development of prospective for AI with a focus on endocrinology.

Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments

The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.

Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction

BACKGROUND

Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures.

METHODS

A machine learning model is developed for probabilistic prediction of hypoglycemia (<70 mg/dL) in 30- and 60-minute time horizons based on CGM datasets obtained from 112 patients over a range of 90 days consisting of over 1.6 million CGM values under normal living conditions. A comprehensive set of features relevant for hypoglycemia are developed and a parsimonious subset with most influence on predicting hypoglycemic risk is identified. Model performance is evaluated both with and without contextual information on insulin and carbohydrate intake.

RESULTS

The model predicted hypoglycemia with >91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate data yielded performance improvement for 60-minute but not for 30-minute predictions. Model performance was highest for nocturnal hypoglycemia (~95% sensitivity). Shortterm (less than one hour) and medium-term (one to four hours) features for good prediction performance are identified.

CONCLUSIONS

Innovative feature identification facilitated high performance for hypoglycemia risk prediction in pediatric youth with T1D. Timely alerts of impending hypoglycemia may enable proactive measures to avoid severe hypoglycemia and achieve optimal glycemic control. The model will be deployed on a patient-facing smartphone application in an upcoming pilot study.

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

BACKGROUND

Predictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences.

OBJECTIVE

This study aimed to develop a prediction model for hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods.

METHODS

Performance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods).

RESULTS

This study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patient- and time-based validation strategies.

CONCLUSIONS

Providing alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods.

Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

Diabetes is a major public health challenge affecting more than 451 million people. Physiological and experimental factors influence the accuracy of non-invasive glucose monitoring, and these need to be overcome before replacing the finger prick method. Also, the suitable employment of machine learning techniques can significantly improve the accuracy of glucose predictions. One aim of this study is to use light sources with multiple wavelengths to enhance the sensitivity and selectivity of glucose detection in an aqueous solution. Multiple wavelength measurements have the potential to compensate for errors associated with inter- and intra-individual differences in blood and tissue components. In this study, the transmission measurements of a custom built optical sensor are examined using 18 different wavelengths between 410 and 940 nm. Results show a high correlation value (0.98) between glucose concentration and transmission intensity for four wavelengths (485, 645, 860 and 940 nm). Five machine learning methods are investigated for glucose predictions. When regression methods are used, 9% of glucose predictions fall outside the correct range (normal, hypoglycemic or hyperglycemic). The prediction accuracy is improved by applying classification methods on sets of data arranged into 21 classes. Data within each class corresponds to a discrete 10 mg/dL glucose range. Classification based models outperform regression, and among them, the support vector machine is the most successful with F1-score of 99%. Additionally, Clarke error grid shows that 99.75% of glucose readings fall within the clinically acceptable zones. This is an important step towards critical diagnosis during an emergency patient situation.

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1-5 min, the current blood glucose concentration and its rate-of-change, two key pieces of information for improving the determination of exogenous insulin administration and the prediction of forthcoming adverse events, such as hypo-/hyper-glycemia. The current research in diabetes technology is putting considerable effort into developing decision support systems for patient use, which automatically analyze the patient's data collected by CGM sensors and other portable devices, as well as providing personalized recommendations about therapy adjustments to patients. Due to the large amount of data collected by patients with T1D and their variety, artificial intelligence (AI) techniques are increasingly being adopted in these decision support systems. In this paper, we review the state-of-the-art methodologies using AI and CGM sensors for decision support in advanced T1D management, including techniques for personalized insulin bolus calculation, adaptive tuning of bolus calculator parameters and glucose prediction.

Design of a Real-time Self-adjusting Calibration Algorithm to Improve the Accuracy of Continuous Blood Glucose Monitoring

The aim of this study is to establish a real-time self-adjusting calibration algorithm to compensate for signal drift and sensitivity attenuation of subcutaneous implantable glucose sensors. A real-time self-adjusting in vivo calibration method was designed based on the one-point calibration model. The current signal was compensated in real-time and the sensitivity was calibrated regularly. The least squares method was used to fit the initial parameters of the model, and then, the in vivo monitored current data was calibrated. Comparing with the mean absolute relative difference (MARD) of the blood glucose concentration by the traditional one-point calibration model (22.85 ± 5.76%), the MARD of the blood glucose concentration calibrated by the real-time self-adjusting in vivo calibration method was 6.28 ± 2.31%. The accuracy of the dynamic blood glucose monitoring was effectively improved. This calibration algorithm could compensate the signal drift in real time and correct sensitivity regularly to improve the accuracy of dynamic glucose monitoring, thus significantly enhancing diabetic self-management.

Fibrotic Encapsulation Is the Dominant Source of Continuous Glucose Monitor Delays

Continuous glucose monitor (CGM) readings are delayed relative to blood glucose, and this delay is usually attributed to the latency of interstitial glucose levels. However, CGM-independent data suggest rapid equilibration of interstitial glucose. This study sought to determine the loci of CGM delays. Electrical current was measured directly from CGM electrodes to define sensor kinetics in the absence of smoothing algorithms. CGMs were implanted in mice, and sensor versus blood glucose responses were measured after an intravenous glucose challenge. Dispersion of a fluorescent glucose analog (2-NBDG) into the CGM microenvironment was observed in vivo using intravital microscopy. Tissue deposited on the sensor and nonimplanted subcutaneous adipose tissue was then collected for histological analysis. The time to half-maximum CGM response in vitro was 35 ± 2 s. In vivo, CGMs took 24 ± 7 min to reach maximum current versus 2 ± 1 min to maximum blood glucose (P = 0.0017). 2-NBDG took 21 ± 7 min to reach maximum fluorescence at the sensor versus 6 ± 6 min in adipose tissue (P = 0.0011). Collagen content was closely correlated with 2-NBDG latency (R = 0.96, P = 0.0004). Diffusion of glucose into the tissue deposited on a CGM is substantially delayed relative to interstitial fluid. A CGM that resists fibrous encapsulation would better approximate real-time deviations in blood glucose.

Changes in Accuracy of Continuous Glucose Monitoring Using Dexcom G4 Platinum Over the Course of Moderate Intensity Aerobic Exercise in Type 1 Diabetes

Continuous glucose monitoring (CGM) systems help diabetes management in patients with type 1 diabetes (T1D) but could have lower accuracy during exercise. We aim to evaluate the dynamics of CGM accuracy during exercise in patients with T1D. Secondary analysis of data was carried out on 22 patients with T1D (glycated hemoglobin [HbA_1c]: 7.3% ± 1.0%, diabetes duration: 23 ± 13 years), who did three exercise sessions (45 min at 60% VO_2max on an ergocycle, 3 h postmeal) with paired Dexcom G4 Platinum, and capillary glucose values that were collected every 5 min. Dexcom accuracy was evaluated using sensor bias (SB) and absolute relative difference (ARD). For dynamics of SB analysis, data pairs following hypoglycemia correction were excluded. The analyzed data included 792 pairs (594 during 66 exercise sessions, 198 at rest before exercise). Median ARD was 8.44 (5.35-12.13)% at rest and increased to 16.77 (10.75-26.72)% during exercise (P < 0.001). During exercise, mean SB values evolved from T0 minutes = 5.95 ± 16.04 mg/dL (exercise start); T5 = 9.55 ± 16.40; T10 = 13.51 ± 18.02; T15 = 15.32 ± 20.36; T20 = 17.30 ± 18.92; T25 = 19.46 ± 17.48; T30 = 21.08 ± 19.64; T35 = 19.10 ± 20.36; T40 = 19.82 ± 20.18; and T45 = 18.02 ± 20.90 (exercise end). CGM overestimated capillary at a mean SB of 14.23 ± 16.76 mg/dL over the whole exercise session. CGM accuracy decreased during moderate aerobic exercise as previously described. However, the trend to overestimate capillary glucose was maintained at relatively stable values within 15 min of exercise initiation, which could help patients in their clinical decisions. Similar analyses would be needed for other types of exercise.

Windows Into Human Health Through Wearables Data Analytics

Background

Wearable sensors (wearables) have been commonly integrated into a wide variety of commercial products and are increasingly being used to collect and process raw physiological parameters into salient digital health information. The data collected by wearables are currently being investigated across a broad set of clinical domains and patient populations. There is significant research occurring in the domain of algorithm development, with the aim of translating raw sensor data into fitness- or health-related outcomes of interest for users, patients, and health care providers.

Objectives

The aim of this review is to highlight a selected group of fitness- and health-related indicators from wearables data and to describe several algorithmic approaches used to generate these higher order indicators.

Methods

A systematic search of the Pubmed database was performed with the following search terms (number of records in parentheses): Fitbit algorithm (18), Apple Watch algorithm (3), Garmin algorithm (5), Microsoft Band algorithm (8), Samsung Gear algorithm (2), Xiaomi MiBand algorithm (1), Huawei Band (Watch) algorithm (2), photoplethysmography algorithm (465), accelerometry algorithm (966), ECG algorithm (8287), continuous glucose monitor algorithm (343). The search terms chosen for this review are focused on algorithms for wearable devices that dominated the commercial wearables market between 2014-2017 and that were highly represented in the biomedical literature. A second set of search terms included categories of algorithms for fitness-related and health-related indicators that are commonly used in wearable devices (e.g. accelerometry, PPG, ECG). These papers covered the following domain areas: fitness; exercise; movement; physical activity; step count; walking; running; swimming; energy expenditure; atrial fibrillation; arrhythmia; cardiovascular; autonomic nervous system; neuropathy; heart rate variability; fall detection; trauma; behavior change; diet; eating; stress detection; serum glucose monitoring; continuous glucose monitoring; diabetes mellitus type 1; diabetes mellitus type 2. All studies uncovered through this search on commercially available device algorithms and pivotal studies on sensor algorithm development were summarized, and a summary table was constructed using references generated by the literature review as described (Table 1).

Conclusions

Wearable health technologies aim to collect and process raw physiological or environmental parameters into salient digital health information. Much of the current and future utility of wearables lies in the signal processing steps and algorithms used to analyze large volumes of data. Continued algorithmic development and advances in machine learning techniques will further increase analytic capabilities. In the context of these advances, our review aims to highlight a range of advances in fitness- and other health-related indicators provided by current wearable technologies.

In Vitro Sugar Interference Testing With Amperometric Glucose Oxidase Sensors

BACKGROUND

Electrochemical enzymatic glucose sensors are intended to measure blood or interstitial fluid glucose concentrations. One class of these glucose sensors are continuous glucose monitors (CGMs), indicated for tracking and trending of glucose concentrations in interstitial fluid and as an adjunct to blood glucose testing. Currently approved CGMs employ a glucose oxidase (GOx) electrochemical detection scheme. Potential interfering agents can impact the accuracy of results obtained by glucose sensors, including CGMs.

METHODS

Seven sugars, seven sugar alcohols, and three artificial sweeteners were in vitro screened for interference with amperometric glucose oxidase (GOx) sensors at concentrations greater than physiologic concentrations. Galactose was investigated further at physiologically relevant concentrations using a custom amperometric system. Furthermore, glucose and galactose calibration experiments were conducted to facilitate multiple enzyme kinetic analysis approaches (Michaelis-Menten and Hill equation) to understand the potential source and mechanism of interference from galactose.

RESULTS

Under in vitro testing, except for galactose, xylose and mannose, all screened compounds exhibited interference bias, expressed in mean absolute relative difference (MARD), of ⩽ 20% even at concentrations significantly higher than normal physiologic concentrations. Galactose exhibited, CGM-dependent, MARD of 47-72% and was subjected to further testing. The highest recorded mean relative difference (MRD) was 6.9 ± 1.3% when testing physiologically relevant galactose concentrations (0.1-10 mg/dL). Enzyme kinetic analysis provided calculations of maximum reaction rates ( imax ), apparent Michaelis constants ( Kmapp ), and Hill equation h parameters for glucose and galactose substrates for the enzymes in the CGMs.

CONCLUSION

Under the conditions of in vitro screening, 14 of the 17 compounds did not exhibit measuarable interference. Galactose exhibited the highest interference during screening, but did not substantially interfere with CGMs under the conditions of in vitro testing at physiologically relevant concentrations. Enzyme kinetic analysis conducted with galactose supported the notion that (1) the reactivity of GOx enzyme toward nonglucose sugars and (2) the presence of enzymatic impurities (such as galactose oxidase) are two potential sources for sugar interference with GOx glucose sensors, and thus, should be considered during device development.

Bayesian Model Selection Framework to Improve Calibration of Continuous Glucose Monitoring Sensors for Diabetes Management

Minimally-invasive continuous glucose monitoring (CGM) sensors have revolutionized perspectives in the treatment of type 1 diabetes (T1D). Their accuracy relies on an internal calibration function that transforms the raw, physically measured, electrical data into blood glucose concentration values. Usually, a unique, pre-determined, calibration functional is adopted, with parameters periodically updated in individual patients by using "gold standard" references suitably collected by finger prick devices. However, retrospective analysis of CGM data suggests that variability of sensor-subject characteristics is often inefficiently coped with. In the present study, we propose a conceptual Bayesian model- selection framework aimed at guaranteeing wide margins of flexibility for both the determination of the most appropriate calibration functional and the numerical values of its unknown parameters. The calibration model is determined among a finite specified set of candidates, each one depending on a set of unknown model parameters, for which a priori statistical expectations are available. Model selection is based on predictive distributions carrying out asymptotic calculations through Monte Carlo integration methods. Performance of the proposed approach is assessed on synthetic data generated by a well-established T1D simulation model.

Reduction of measurement noise in a continuous glucose monitor by coating the sensor with a zwitterionic polymer

Continuous glucose monitors (CGMs), used by patients with diabetes mellitus, can autonomously track fluctuations in blood glucose over time. However, the signal produced by CGMs during the initial recording period following sensor implantation contains substantial noise, requiring frequent recalibration via fingerprick tests. Here, we show that coating the sensor with a zwitterionic polymer, found via a combinatorial-chemistry approach, significantly reduces signal noise and improves CGM performance. We evaluated the polymer-coated sensors in mice as well as in healthy and diabetic non-human primates, and show that the sensors accurately record glucose levels without the need for recalibration. We also show that the polymer-coated sensors significantly abrogated immune responses to the sensor, as indicated by histology, fluorescent whole-body imaging of inflammation-associated protease activity, and gene expression of inflammation markers. The polymer coating may allow CGMs to become standalone measuring devices.

Limits to the Evaluation of the Accuracy of Continuous Glucose Monitoring Systems by Clinical Trials

Systems for continuous glucose monitoring (CGM) are evolving quickly, and the data obtained are expected to become the basis for clinical decisions for many patients with diabetes in the near future. However, this requires that their analytical accuracy is sufficient. This accuracy is usually determined with clinical studies by comparing the data obtained by the given CGM system with blood glucose (BG) point measurements made with a so-called reference method. The latter is assumed to indicate the correct value of the target quantity. Unfortunately, due to the nature of the clinical trials and the approach used, such a comparison is subject to several effects which may lead to misleading results. While some reasons for the differences between the values obtained with CGM and BG point measurements are relatively well-known (e.g., measurement in different body compartments), others related to the clinical study protocols are less visible, but also quite important. In this review, we present a general picture of the topic as well as tools which allow to correct or at least to estimate the uncertainty of measures of CGM system performance.

Calibration of Minimally Invasive Continuous Glucose Monitoring Sensors: State-of-The-Art and Current Perspectives

Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide real-time measurement of subcutaneous glucose concentration. This can be of great help in the daily management of diabetes. Most of the commercially available CGM devices have a wire-based sensor, usually placed in the subcutaneous tissue, which measures a "raw" current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be translated in real-time to glucose concentration through a calibration process. For such a scope, the first commercialized CGM sensors implemented simple linear regression techniques to fit reference glucose concentration measurements periodically collected by fingerprick. On the one hand, these simple linear techniques required several calibrations per day, with the consequent patient's discomfort. On the other, only a limited accuracy was achieved. This stimulated researchers to propose, over the last decade, more sophisticated algorithms to calibrate CGM sensors, resorting to suitable signal processing, modelling, and machine-learning techniques. This review paper will first contextualize and describe the calibration problem and its implementation in the first generation of CGM sensors, and then present the most recently-proposed calibration algorithms, with a perspective on how these new techniques can influence future CGM products in terms of accuracy improvement and calibration reduction.

Toward Calibration-Free Continuous Glucose Monitoring Sensors: Bayesian Calibration Approach Applied to Next-Generation Dexcom Technology

BACKGROUND

Continuous glucose monitoring (CGM) sensors need to be calibrated twice/day by using self-monitoring of blood glucose (SMBG) samples. Recently, to reduce the calibration frequency, we developed an online calibration algorithm based on a multiple-day model of sensor time variability and Bayesian parameter estimation. When applied to Dexcom G4 Platinum (DG4P) sensor data, the algorithm allowed the frequency of calibrations to be reduced to one-every-four-days without significant worsening of sensor accuracy. The aim of this study is to assess the same methodology on raw CGM data acquired by a next-generation Dexcom CGM sensor prototype and compare the results with that obtained on DG4P.

METHODS

The database consists of 55 diabetic subjects monitored for 10 days by a next-generation Dexcom CGM sensor prototype. The new calibration algorithm is assessed, retrospectively, by simulating an online procedure using progressively fewer SMBG samples until zero. Accuracy is evaluated with mean absolute relative differences (MARD) between blood glucose versus CGM values.

RESULTS

The one-per-day and one-every-two-days calibration scenarios in the next-generation CGM data have an accuracy of 8.5% MARD (vs. 11.59% of DG4P) and 8.4% MARD (vs. 11.63% of DG4P), respectively. Accuracy slightly worsens to 9.2% (vs. 11.62% of DG4P) when calibrations are reduced to one-every-four-days. The calibration-free scenario results in 9.3% MARD (vs. 12.97% of DG4P).

CONCLUSIONS

In next-generation Dexcom CGM sensor data, the use of an online calibration algorithm based on a multiple-day model of sensor time variability and Bayesian parameter estimation aids in the shift toward a calibration-free scenario with even better results than those obtained in present-generation sensors.

[Design and implementation of real-time continuous glucose monitoring instrument]

Real-time continuous glucose monitoring can help diabetics to control blood sugar levels within the normal range. However, in the process of practical monitoring, the output of real-time continuous glucose monitoring system is susceptible to glucose sensor and environment noise, which will influence the measurement accuracy of the system. Aiming at this problem, a dual-calibration algorithm for the moving-window double-layer filtering algorithm combined with real-time self-compensation calibration algorithm is proposed in this paper, which can realize the signal drift compensation for current data. And a real-time continuous glucose monitoring instrument based on this study was designed. This real-time continuous glucose monitoring instrument consisted of an adjustable excitation voltage module, a current-voltage converter module, a microprocessor and a wireless transceiver module. For portability, the size of the device was only 40 mm × 30 mm × 5 mm and its weight was only 30 g. In addition, a communication command code algorithm was designed to ensure the security and integrity of data transmission in this study. Results of experiments in vitro showed that current detection of the device worked effectively. A 5-hour monitoring of blood glucose level in vivo showed that the device could continuously monitor blood glucose in real time. The relative error of monitoring results of the designed device ranged from 2.22% to 7.17% when comparing to a portable blood meter.

Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters

Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.

A Personalized Week-to-Week Updating Algorithm to Improve Continuous Glucose Monitoring Performance

BACKGROUND

Continuous glucose monitoring (CGM) systems are increasingly becoming essential components in type 1 diabetes mellitus (T1DM) management. Current CGM technology requires frequent calibration to ensure accurate sensor performance. The accuracy of these systems is of great importance since medical decisions are made based on monitored glucose values and trends.

METHODS

In this work, we introduce a calibration strategy that is augmented with a weekly updating feature. During the life cycle of the sensor, the calibration mechanism periodically estimates the parameters of a calibration model to fit self-monitoring blood glucose (SMBG) measurements. At the end of each week of use, an optimization problem that minimizes the sum of squared residuals between past reference and predicted blood glucose values is solved remotely to identify personalized calibration parameters. The newly identified parameters are used to initialize the calibration mechanism of the following week.

RESULTS

The proposed method was evaluated using two sets of clinical data both consisting of 6 weeks of Dexcom G4 Platinum CGM data on 10 adults with T1DM (over 10 000 hours of CGM use), with seven SMBG data points per day measured by each subject in an unsupervised outpatient setting. Updating the calibration parameters using the history of calibration data indicated a positive trend of improving CGM performance.

CONCLUSIONS

Although not statistically significant, the updating framework showed a relative improvement of CGM accuracy compared to the non-updating, static calibration method. The use of information collected for longer periods is expected to improve the performance of the sensor over time.

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine.

Reduction of Blood Glucose Measurements to Calibrate Subcutaneous Glucose Sensors: A Bayesian Multiday Framework

OBJECTIVE

In most continuous glucose monitoring (CGM) devices used for diabetes management, the electrical signal measured by the sensor is transformed to glucose concentration by a calibration function whose parameters are estimated using self-monitoring of blood glucose (SMBG) samples. The calibration function is usually a linear model approximating the nonlinear relationship between electrical signal and glucose concentration in certain time intervals. Thus, CGM devices require frequent calibrations, usually twice a day. The aim here is to develop a new method able to reduce the frequency of calibrations.

METHODS

The algorithm is based on a multiple-day model of sensor time-variability with second-order statistical priors on its unknown parameters. In an online setting, these parameters are numerically determined by the Bayesian estimation exploiting SMBG sparsely collected by the patient. The method is assessed retrospectively on 108 CGM signals acquired for 7 days by the Dexcom G4 Platinum sensor, testing progressively less-calibration scenarios.

RESULTS

Despite the reduction of calibration frequency (on average from 2/day to 0.25/day), the method shows a statistically significant accuracy improvement compared to manufacturer calibration, e.g., mean absolute relative difference when compared to a laboratory reference decreases from 12.83% to 11.62% (p-value of 0.006).

CONCLUSION

The methodology maintains (sometimes improves) CGM sensor accuracy compared to that of the original manufacturer, while reducing the frequency of calibrations.

SIGNIFICANCE

Reducing the need of calibrations facilitates the adoption of CGM technology both in terms of ease of use and cost, an obvious prerequisite for its use as replacement of traditional SMBG devices.

Factory-Calibrated Continuous Glucose Sensors: The Science Behind the Technology

The use of commercially available continuous glucose monitors for diabetes management requires sensor calibrations, which until recently are exclusively performed by the patient. A new development is the implementation of factory calibration for subcutaneous glucose sensors, which eliminates the need for user calibrations and the associated blood glucose tests. Factory calibration means that the calibration process is part of the sensor manufacturing process and performed under controlled laboratory conditions. The ability to move from a user calibration to factory calibration is based on several technical requirements related to sensor stability and the robustness of the sensor manufacturing process. The main advantages of factory calibration over the conventional user calibration are: (a) more convenience for the user, since no more fingersticks are required for calibration and (b) elimination of use errors related to the execution of the calibration process, which can lead to sensor inaccuracies. The FreeStyle Libre^™ and FreeStyle Libre Pro^™ flash continuous glucose monitoring systems are the first commercially available sensor systems using factory-calibrated sensors. For these sensor systems, no user calibrations are required throughout the sensor wear duration.

Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator

BACKGROUND

Computer simulation has been shown over the past decade to be a powerful tool to study the impact of medical devices characteristics on clinical outcomes. Specifically, in type 1 diabetes (T1D), computer simulation platforms have all but replaced preclinical studies and are commonly used to study the impact of measurement errors on glycemia.

METHOD

We use complex mathematical models to represent the characteristics of 3 continuous glucose monitoring systems using previously acquired data. Leveraging these models within the framework of the UVa/Padova T1D simulator, we study the impact of CGM errors in 6 simulation scenarios designed to generate a wide variety of glycemic conditions. Assessment of the simulated accuracy of each different CGM systems is performed using mean absolute relative deviation (MARD) and precision absolute relative deviation (PARD). We also quantify the capacity of each system to detect hypoglycemic events.

RESULTS

The simulated Roche CGM sensor prototype (RCGM) outperformed the 2 alternate systems (CGM-1 & CGM-2) in accuracy (MARD = 8% vs 11.4% vs 18%) and precision (PARD = 6.4% vs 9.4% vs 14.1%). These results held for all studied glucose and rate of change ranges. Moreover, it detected more than 90% of hypoglycemia, with a mean time lag less than 4 minutes (CGM-1: 86%/15 min, CGM-2: 57%/24 min).

CONCLUSION

The RCGM system model led to strong performances in these simulation studies, with higher accuracy and precision than alternate systems. Its characteristics placed it firmly as a strong candidate for CGM based therapy, and should be confirmed in large clinical studies.

Comparison of Two Continuous Glucose Monitoring Systems, Dexcom G4 Platinum and Medtronic Paradigm Veo Enlite System, at Rest and During Exercise

BACKGROUND

Despite technological advances, the accuracy of continuous glucose monitoring (CGM) systems may not always be satisfactory with rapidly changing glucose levels, as is notable during exercise. We compare the performance of two current and widely used CGM systems, Dexcom G4 Platinum (Dexcom) and Medtronic Paradigm Veo Enlite system (Enlite), during both rest and exercise in adults with type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

Paired sensor and plasma glucose (PG) values (total of 431 data pairs for Dexcom and 425 for Enlite) were collected from 17 adults (37.3 ± 13.6 years) with T1D. To evaluate and compare the accuracy of sensor readings, criteria involving sensor bias (sensor minus PG levels), absolute relative difference (ARD), and percentage of readings meeting International Organization for Standardization (ISO) criteria were considered.

RESULTS

Both Dexcom and Enlite performed equally well during the rest period, with respective mean/median biases of -0.12/-0.02 mmol/L versus -0.18/-0.40 (P = 0.78, P = 0.66) mmol/L and ARDs of 13.77/13.34% versus 12.38/11.95% (P = 0.53, P = 0.70). During exercise, sensor bias means/medians were -0.40/-0.21 mmol versus -0.26/-0.24 mmol/L (P = 0.67, P = 0.62) and ARDs were 22.53/15.13% versus 20.44/14.11% (P = 0.58, P = 0.68) for Dexcom and Enlite, respectively. Both sensors demonstrated significantly lower performance during exercise; median ARD comparison at rest versus exercise for both Dexcom and Enlite showed a P = 0.02. More data pairs met the ISO criteria for Dexcom and Enlite at rest, 73.6% and 76.9% compared with exercise 48.2% and 53.9%.

CONCLUSION

Dexcom and Enlite demonstrated comparable overall performances during rest and physical activity. However, a lower accuracy was observed during exercise for both sensors, necessitating a fine-tuning of their performance with physical activity.

Using meta-differential evolution to enhance a calculation of a continuous blood glucose level

We developed a new model of glucose dynamics. The model calculates blood glucose level as a function of transcapillary glucose transport. In previous studies, we validated the model with animal experiments. We used analytical method to determine model parameters. In this study, we validate the model with subjects with type 1 diabetes. In addition, we combine the analytic method with meta-differential evolution. To validate the model with human patients, we obtained a data set of type 1 diabetes study that was coordinated by Jaeb Center for Health Research. We calculated a continuous blood glucose level from continuously measured interstitial fluid glucose level. We used 6 different scenarios to ensure robust validation of the calculation. Over 96% of calculated blood glucose levels fit A+B zones of the Clarke Error Grid. No data set required any correction of model parameters during the time course of measuring. We successfully verified the possibility of calculating a continuous blood glucose level of subjects with type 1 diabetes. This study signals a successful transition of our research from an animal experiment to a human patient. Researchers can test our model with their data on-line at https://diabetes.zcu.cz.

From Two to One Per Day Calibration of Dexcom G4 Platinum by a Time-Varying Day-Specific Bayesian Prior

BACKGROUND

In the DexCom G4 Platinum (DG4P) continuous glucose monitoring (CGM) sensor, the raw current signal generated by glucose-oxidase is transformed to glucose concentration by a calibration function whose parameters are periodically updated by matching self-monitoring of blood glucose references, usually twice a day, to compensate for sensor variability in time. The aim of this work is to reduce DG4P calibration frequency to once a day by a recently proposed Bayesian calibration algorithm, which employs a time-varying calibration function and suitable day-specific priors.

METHODS

The database consists of 57 CGM signals that are collected by the DG4P for 7 days. The Bayesian calibration algorithm is used to calibrate the raw current signal following two different schedules, that is, two and one calibration per day. Calibrated glycemic profiles are compared with those originally acquired by the manufacturer, on days 1, 4, and 7, where frequent blood glucose references were available, by using standard metrics, that is, mean absolute relative difference (MARD), percentage of accurate glucose estimates, and percentage of data in the A-zone of Clarke Error Grid.

RESULTS

The one per day Bayesian calibration algorithm has accuracy similar to that of two per day (11.8% vs. 11.7% MARD, respectively), and it is statistically better (P-value of 0.0411) than the manufacturer calibration algorithm, which requires two calibrations per day (13.1% MARD).

CONCLUSIONS

A Bayesian calibration algorithm employing a time-varying calibration function and suitable priors enables a reduction of the calibrations of DG4P sensor from two to one per day.

Diurnal glycemic fluctuation is associated with severity of coronary artery disease in prediabetic patients: Possible role of nitrotyrosine and glyceraldehyde-derived advanced glycation end products

BACKGROUND

Glucose fluctuation (GF) is a risk factor for coronary artery disease (CAD). However, it remains unknown whether specific indices of GF are risk factors for CAD. Therefore, we evaluated the relationship between GF, as determined by a continuous glucose monitoring system (CGMS) or the glucose level at 2h after a 75-g oral glucose tolerance test (75g OGTT 120), and the severity of CAD in prediabetic patients. We also evaluated whether nitrotyrosine (NT) and glyceraldehyde-derived advanced glycation end-products (Glycer-AGE) were induced by GF.

METHODS

Twenty-eight prediabetic patients underwent coronary angiography (CAG), and the Gensini score and the SYNTAX score were evaluated as the severity of CAD, while the mean amplitude of glycemic excursions (MAGE) by CGMS and 75g OGTT 120 were evaluated. Serum NT and Glycer-AGE were measured.

RESULTS

The MAGE was closely associated with the Gensini score (r=0.742, p<0.001) and the SYNTAX score (r=0.776, p<0.001), respectively. The 75g OGTT 120 was not associated with the Gensini score (r=0.36, p=0.06), but it was significantly associated with the SYNTAX score (r=0.413, p=0.036). Multiple linear regression analysis showed that the MAGE was the only independent determinant for the severity of CAD. The levels of NT and Glycer-AGE were significantly higher in the high MAGE group than in the low MAGE group.

CONCLUSIONS

Diurnal GF is associated with the severity of CAD, even in prediabetic patients. GF, NT, and Glycer-AGE may play a pathological role in the progression of CAD.

Long-Term Home Study on Nocturnal Hypoglycemic Alarms Using a New Fully Implantable Continuous Glucose Monitoring System in Type 1 Diabetes

BACKGROUND

This study analyzed the overall nocturnal performance during home use of a long-term subcutaneous implantable continuous glucose monitoring (CGM) sensor.

SUBJECTS AND METHODS

In this study, 12 subjects with type 1 diabetes mellitus (T1DM) (mean±SD age, 37±8 years; mean±SD disease duration, 11±6 years) were implanted with an investigational continuous glucose sensor in the upper arm for up to 90 days. All subjects received full access to real-time glucose display and user programmable hypo- and hyperglycemic alarms. Subjects calibrated the sensors with a self-monitoring of blood glucose (SMBG) meter and continued to rely on their regular SMBG measurements for their diabetes management. Accuracy of the sensors during the home-use study was calculated using SMBG as the reference. The nocturnal sensor attenuation (NSA) concept was tested. Sensitivity and specificity of the nocturnal hypoglycemic alarm were calculated.

RESULTS

Mean±SD glucose sensor life span was 87±7 days. The mean±SE absolute relative difference over the range of 40-400 mg/dL for the sensors in this home-use study was 12.3±0.7% using SMBG as the reference. The hypoglycemia alarms were set to be triggered when the glucose level went below 70 mg/dL. Percentage of nights with hypoglycemic alarms triggered for at least 10 min was 13.6%. Recovery into euglycemia within 30 min from the timestamp of the immediate confirmatory SMBG testing was obtained in 74% of all episodes (n=20). The implanted continuous glucose sensor showed a hypoglycemia detection sensitivity and specificity of 77% and 96%, respectively. The NSA-associated high negative rate of change of at least -4 mg/dL/min was not encountered during night use of the system.

CONCLUSIONS

This home-use study of a fully implantable, long-term continuous glucose sensor shows excellent performance in nocturnal hypoglycemia detection in T1DM patients. The apparent lack of NSA affecting the implanted sensor and the high specificity of the hypoglycemic alarm expedite the recovery from nighttime hypoglycemia.

Time Delay of CGM Sensors: Relevance, Causes, and Countermeasures

BACKGROUND

Continuous glucose monitoring (CGM) is a powerful tool to support the optimization of glucose control of patients with diabetes. However, CGM systems measure glucose in interstitial fluid but not in blood. Rapid changes in one compartment are not accompanied by similar changes in the other, but follow with some delay. Such time delays hamper detection of, for example, hypoglycemic events. Our aim is to discuss the causes and extent of time delays and approaches to compensate for these.

METHODS

CGM data were obtained in a clinical study with 37 patients with a prototype glucose sensor. The study was divided into 5 phases over 2 years. In all, 8 patients participated in 2 phases separated by 8 months. A total number of 108 CGM data sets including raw signals were used for data analysis and were processed by statistical methods to obtain estimates of the time delay.

RESULTS

Overall mean (SD) time delay of the raw signals with respect to blood glucose was 9.5 (3.7) min, median was 9 min (interquartile range 4 min). Analysis of time delays observed in the same patients separated by 8 months suggests a patient dependent delay. No significant correlation was observed between delay and anamnestic or anthropometric data. The use of a prediction algorithm reduced the delay by 4 minutes on average.

CONCLUSIONS

Prediction algorithms should be used to provide real-time CGM readings more consistent with simultaneous measurements by SMBG. Patient specificity may play an important role in improving prediction quality.

Fully Implantable Arterial Blood Glucose Device for Metabolic Research Applications in Rats for Two Months

BACKGROUND

Chronic continuous glucose monitoring options for animal research have been very limited due to various technical and biological challenges. We provide an evaluation of a novel telemetry device for continuous monitoring of temperature, activity, and plasma glucose levels in the arterial blood of rats for up to 2 months.

METHODS

In vivo testing in rats including oral glucose tolerance tests (OGTTs) and intraperitoneal glucose tolerance tests (IPGTTs) and ex vivo waterbath testing were performed to evaluate acute and chronic sensor performance. Animal studies were in accordance with the guidelines for the care and use of laboratory animals and approved by the corresponding animal care and use committees (Data Sciences International, Eli Lilly).

RESULTS

Results demonstrated the ability to record continuous measurements for 75 days or longer. Bench testing demonstrated a high degree of linearity over a range of 20-850 mg/dL with R(2) = .998 for linear fit and .999 for second order fit (n = 8 sensors). Evaluation of 6 rats over 28 days with 52 daily and OGTT test strip measurements each resulted in mean error of 3.8% and mean absolute relative difference of 16.6%.

CONCLUSIONS

This device provides significant advantages in the quality and quantity of data that can be obtained relative to existing alternatives such as intermittent blood sampling. These devices provide the opportunity to expand the understanding of both glucose metabolism and homeostasis and to work toward improved therapies and cures for diabetes.

Preclinical Performance Evaluation of Percutaneous Glucose Biosensors: Experimental Considerations and Recommendations

The utility of continuous glucose monitoring devices remains limited by an obstinate foreign body response (FBR) that degrades the analytical performance of the in vivo sensor. A number of novel materials that resist or delay the FBR have been proposed as outer, tissue-contacting glucose sensor membranes as a strategy to improve sensor accuracy. Traditionally, researchers have examined the ability of a material to minimize the host response by assessing adsorbed cell morphology and tissue histology. However, these techniques do not adequately predict in vivo glucose sensor function, necessitating sensor performance evaluation in a relevant animal model prior to human testing. Herein, the effects of critical experimental parameters, including the animal model and data processing methods, on the reliability and usefulness of preclinical sensor performance data are considered.

Hypo- and Hyperglycemic Alarms: Devices and Algorithms

Soon after the discovery that insulin regulates blood glucose by Banting and Best in 1922, the symptoms and risks associated with hypoglycemia became widely recognized. This article reviews devices to warn individuals of impending hypo- and hyperglycemia; biosignals used by these devices include electroencephalography, electrocardiography, skin galvanic resistance, diabetes alert dogs, and continuous glucose monitors (CGMs). While systems based on other technology are increasing in performance and decreasing in size, CGM technology remains the best method for both reactive and predictive alarming of hypo- or hyperglycemia.

Online Calibration of Glucose Sensors From the Measured Current by a Time-Varying Calibration Function and Bayesian Priors

GOAL

Minimally invasive continuous glucose monitoring (CGM) sensors measure in the subcutis a current signal, which is converted into interstitial glucose (IG) concentration by a calibration process periodically updated using fingerstick blood glucose (BG) references. Though important in diabetes management, CGM sensors still suffer from accuracy problems. Here, we propose a new online calibration method improving accuracy of CGM glucose profiles with respect to manufacturer calibration.

METHOD

The proposed method fits CGM current signal against the BG references collected twice a day for calibration purposes, by a time-varying calibration function whose parameters are identified in a Bayesian framework using a priori second-order statistical knowledge. The distortion introduced by BG-to-IG kinetics is compensated before parameter identification via nonparametric deconvolution.

RESULTS

The method was tested on a database where 108 CGM signals were collected for 7 days by the Dexcom G4 Platinum sensor. Results show the new method drives to a statistically significant accuracy improvement as measured by three commonly used metrics: mean absolute relative difference reduced from 12.73% to 11.47%; percentage of accurate glucose estimates increased from 82.00% to 89.19%; and percentage of values falling in the "A" zone of the Clark error grid increased from 82.22% to 88.86%.

CONCLUSION

The new calibration method significantly improves CGM glucose profiles accuracy with respect to manufacturer calibration.

SIGNIFICANCE

The proposed algorithm provides a real-time improvement of CGM accuracy, which can be crucial in several CGM-based applications, including the artificial pancreas, thus providing a potential great impact in the diabetes technology research community.

Evaluation of a clinical tool to test and adjust the programmed overnight basal profiles for insulin pump therapy: a pilot study

OBJECTIVE

Clinical protocols for basal rate testing and adjustment are needed for effective insulin pump therapy. We evaluated the effects of a continuous glucose monitoring (CGM)-based semiautomated basal algorithm on glycemia.

METHODS

We developed and piloted a basal rate analyzer that interpreted CGM data from overnight fasts and recommended dose changes for subsequent nights. Subjects uploaded data online using sensor-augmented pumps for evaluation by the analyzer after each of 5 overnight fasts conducted over 2 to 8 weeks. It was designed to be conservative and iterative, making changes that did not exceed 10% at each iteration. The standard deviation and interquartile range of CGM values from midnight to 7 am (SD12-7am and IQR12-7am) over 3 baseline and 3 postintervention nights, hypoglycemia incidence (CGM values <4.0 mmol/L), and glycated hemoglobin (A1C) were compared.

RESULTS

Twenty subjects with mean ages of 38±13 years and A1C 7.6%±0.8% (60±8.7 mmol/mol) underwent the 5 iterations of basal assessments over 5±3 weeks. SD12-7am and IQR12-7am did not change from baseline to postintervention (1.57±0.8 to 1.63±0.8 mmol/L; p=0.35; 3.66±2.07 to 3.47±2.26 mmol/L; p=0.90). However, mean glucose values were lower between 2 to 3 am at baseline compared to postintervention; 3-night hypoglycemia incidence declined from 1.6±1.8 to 0.5±0.7 episodes (p=0.01), and A1C improved from 7.6%±0.8% to 7.4%±0.9% (60%±8.7% to 57%±9.8% mmol/mol; p=0.03).

CONCLUSIONS

The use of a basal rate analyzer was associated with reduced hypoglycemia and improved A1C. However, overnight glycemic stability was not improved. Further research into the efficacy of the CGM-based semiautomated algorithm is warranted.

Jump neural network for real-time prediction of glucose concentration

Prediction of the future value of a variable is of central importance in a wide variety of fields, including economy and finance, meteorology, informatics, and, last but not least important, medicine. For example, in the therapy of Type 1 Diabetes (T1D), in which, for patient safety, glucose concentration in the blood should be maintained in a defined normoglycemic range, the ability to forecast glucose concentration in the short-term (with a prediction horizon of around 30 min) might be sufficient to reduce the incidence of hypoglycemic and hyperglycemic events. Neural Network (NN) approaches are suitable for prediction purposes because of their ability to model nonlinear dynamics and handle in their inputs signals coming from different domains. In this chapter we illustrate the design of a jump NN glucose prediction algorithm that exploits past glucose concentration data, measured in real-time by a minimally invasive continuous glucose monitoring (CGM) sensor, and information on ingested carbohydrates, supplied by the patient himself or herself. The methodology is assessed by tuning the NN on data of ten T1D individuals and then testing it on a dataset of ten different subjects. Results with a prediction horizon of 30 min show that prediction of glucose concentration in T1D via NN is feasible and sufficiently accurate. The average time anticipation obtained is compatible with the generation of preventive hypoglycemic and hyperglycemic alerts and the improvement of artificial pancreas performance.