Calibration and performance of a Raman-based device for non-invasive glucose monitoring in type 2 diabetes

Raman spectroscopy has been demonstrated as a viable technique for non-invasive glucose monitoring (NIGM). However, its clinical utility is limited by an extended calibration period lasting several weeks. In this study, we address this limitation by employing a pre-trained calibration model, which is individualized through a brief calibration phase consisting of 10 measurements. The performance of the Raman-based NIGM device was evaluated in a clinical trial involving 50 individuals with type 2 diabetes over a 2-day study period. The protocol included a 4-h calibration phase on the first day, followed by validation phases of 4 h and 8 h on days 1 and 2, respectively. NIGM glucose readings were compared with capillary blood glucose measurements, with glucose fluctuations induced by standardized meal challenges. The numerical and clinical accuracy of the NIGM device was evaluated on 1918 paired points and expressed by mean absolute relative difference of 12.8% (95% CI 12.4, 13.2) and consensus error grid analysis showing 100% of NIGM readings in zones A and B. These results highlight the ability to reliably track blood glucose levels in people with type 2 diabetes. The successful introduction of a practical calibration scheme underlines Raman spectroscopy as a promising technology for NIGM and constitutes an important step towards factory calibration.

Accuracy of a continuous glucose monitoring system applied before, during, and after an intense leg-squat session with low- and high-carbohydrate availability in young adults without diabetes

PURPOSE

The aim was to assess the accuracy of a continuous blood glucose monitoring (CGM) device (Abbott FreeStyle Libre 3) against capillary blood glucose measurement (BGM) before, during, and after an intense lower body strength training session in connection with high- versus low-carbohydrate breakfasts.

METHODS

Nine adults (22 ± 2 years) completed a strength training session (10 × 10 at 60% 1RM) twice after high-carbohydrate and twice after low-carbohydrate breakfasts. CGM accuracy versus BGM was assessed across four phases: post-breakfast, pre-exercise, exercise, and post-exercise.

RESULTS

Overall fed state mean BGM levels were 84.4 ± 20.6 mg/dL. Group-level Bland-Altman analysis showed acceptable agreement between CGM and BGM across all phases, with mean biases between - 7.95 and - 17.83 mg/dL; the largest discrepancy was in the post-exercise phase. Mean absolute relative difference was significantly higher post-exercise compared to pre-exercise and exercise phases, for overall data and after the high-carbohydrate breakfast (all p ≤ 0.02). Clark Error Grid analysis showed 50.5-64.3% in Zone A and 31.7-44.6% in Zone B, with an increase in treatment errors during and after exercise.

CONCLUSION

In this group of healthy participants undergoing strength training, CGM showed satisfactory accuracy in glucose monitoring but varied substantially between individuals compared to BGM and fails in meeting clinical criteria for diabetic monitoring. CGM could aid non-diabetic athletes by tracking glucose fluctuations due to diet and exercise. Although utilization of CGM shows potential in gathering, analyzing, and interpreting interstitial glucose for improving performance, the application in sports nutrition is not yet validated, and challenges in data interpretation could limit its adoption.

Implantable and transcutaneous continuous glucose monitoring system: a randomized cross over trial comparing accuracy, efficacy and acceptance

AIM

To compare accuracy, efficacy and acceptance of implantable and transcutaneous continuous glucose monitoring (CGM) systems.

METHODS

In a randomized crossover trial we compared 12 weeks with Eversense implantable sensor (EVS) and 12 weeks with Dexcom G5 transcutaneous sensor (DG5) in terms of accuracy, evaluated as Mean Absolute Relative Difference (MARD) vs capillary glucose (SMBG), time of CGM use, adverse events, efficacy (as HbA1c, time in range, time above and below range) and psychological outcomes evaluated with Diabetes Treatment Satisfaction Questionnaire (DTSQ), Glucose Monitoring Satisfaction Survey (GMSS), Hypoglycemia Fear Survey (HFS2), Diabetes Distress Scale (DDS).

RESULTS

16 subjects (13 males, 48.8 ± 10.1 years, HbA1c 55.8 ± 7.9 mmol/mol, mean ± SD) completed the study. DG5 was used more than EVS [percentage of use 95.7 ± 3.6% vs 93.5 ± 4.3% (p = 0.02)]. MARD was better with EVS (12.2 ± 11.5% vs. 13.1 ± 14.7%, p< 0.001). No differences were found in HbA1c. While using EVS time spent in range increased and time spent in hyperglycemia decreased, but these data were not confirmed by analysis of retrofitted data based on SMBG values. EVS reduced perceived distress, without significant changes in other psychological outcomes.

CONCLUSIONS

CGM features may affect glycemic control and device acceptance.

In Vivo Monitoring of Glucose Using Ultrasound-Induced Resonance in Implantable Smart Hydrogel Microstructures

A novel glucose sensor is presented using smart hydrogels as biocompatible implantable sensing elements, which eliminates the need for implanted electronics and uses an external medical-grade ultrasound transducer for readout. The readout mechanism uses resonance absorption of ultrasound waves in glucose-sensitive hydrogels. In vivo glucose concentration changes in the interstitial fluid lead to swelling or deswelling of the gels, which changes the resonance behavior. The hydrogels are designed and shaped such as to exhibit specific mechanical resonance frequencies while remaining sonolucent to other frequencies. Thus, they allow conventional and continued ultrasound imaging, while yielding a sensing signal at specific frequencies that correlate with glucose concentration. The resonance frequencies can be tuned by changing the shape and mechanical properties of the gel structures, such as to allow for multiple, colocated implanted hydrogels with different sensing characteristics or targets to be employed and read out, without interference using the same ultrasound transducer, by simply toggling frequencies. The fact that there is no need for any implantable electronics, also opens up the path toward future use of biodegradable hydrogels, thus creating a platform that allows injection of sensors that do not need to be retrieved when they reach the end of their useful lifespan.

Model individualization for artificial pancreas

BACKGROUND AND OBJECTIVE

The inter-subject variability characterizing the patients affected by type 1 diabetes mellitus makes automatic blood glucose control very challenging. Different patients have different insulin responses, and a control law based on a non-individualized model could be ineffective. The definition of an individualized control law in the context of artificial pancreas is currently an open research topic. In this work we consider two novel identification approaches that can be used for individualizing linear glucose-insulin models to a specific patient.

METHODS

The first approach belongs to the class of black-box identification and is based on a novel kernel-based nonparametric approach, whereas the second is a gray-box identification technique which relies on a constrained optimization and requires to postulate a model structure as prior knowledge. The latter is derived from the linearization of the average nonlinear adult virtual patient of the UVA/Padova simulator. Model identification and validation are based on in silico data collected during simulations of clinical protocols designed to produce a sufficient signal excitation without compromising patient safety. The identified models are evaluated in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean square error.

RESULTS

Both identification approaches were used to identify a linear individualized glucose-insulin model for each adult virtual patient of the UVA/Padova simulator. The resulting model simulation performance is significantly improved with respect to the performance achieved by a linear average model.

CONCLUSIONS

The approaches proposed in this work have shown a good potential to identify glucose-insulin models for designing individualized control laws for artificial pancreas.

Fluorescent Biocompatible Platinum-Porphyrin-Doped Polymeric Hybrid Particles for Oxygen and Glucose Biosensing

Near infrared (NIR) fluorophores like Pt-porphyrin along with analyte specific enzymes require co-encapsulation in biocompatible and biodegradable carriers in order to be transformed into implantable biosensors for efficient and continuous monitoring of analytes in patients. The main objective of this research is to develop natural, biodegradable, biocompatible and a novel co-encapsulated system of Pt-porphyrin encapsulated polymeric nanoparticle and nano-micro hybrid carriers. A sequential emulsification-solvent evaporation and an air driven atomization technique was used for developing above matrices and testing them for fluorescence based oxygen and glucose biosensing. The results indicate Pt-porphyrin can be efficiently encapsulated in Poly-lactic acid (PLA) nanoparticles and PLA-alginate nano-micro particles with sizes ~450 nm and 10 µm, respectively. Biosensing studies have showed a linear fluorescent response in oxygen concentrations ranging from of 0–6 mM (R² = 0.992). The Oxygen sensitivity was transformed into a linear response of glucose catalytic reaction in the range of 0–10 mM (R² = 0.968) with a response time of 4 minutes and a stability over 15 days. We believe that the investigated NIR fluorophores like Pt-Porphyrin based nano/nano-micro hybrid carrier systems are novel means of developing biocompatible biodegradable carriers for developing implantable glucose biosensors which can efficiently manage glucose levels in diabetes.

Differences Between Flash Glucose Monitor and Fingerprick Measurements

Freestyle Libre (FL) is a factory calibrated Flash Glucose Monitor (FGM). We investigated Mean Absolute Relative Difference (MARD) between Self Monitoring of Blood Glucose (SMBG) and FL measurements in the first day of sensor wear in 39 subjects with Type 1 diabetes. The overall MARD was 12.3%, while the individual MARDs ranged from 4% to 25%. Five participants had a MARD ≥ 20%. We estimated bias and lag between the FL and SMBG measurements. The estimated biases range from -1.8 mmol / L to 1.4 mmol / L , and lags range from 2 min to 24 min . Bias is identified as a main cause of poor individual MARDs. The biases seem to persist in days 2⁻7 of sensor usage. All cases of MARD ≥ 20% in the first day are eliminated by bias correction, and overall MARD is reduced from 12.3% to 9.2%, indicating that adding support for voluntary user-supplied bias correction in the FL could improve its performance.

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.

Insulin Sensitivity Index-Based Optimization of Insulin to Carbohydrate Ratio: In Silico Study Shows Efficacious Protection Against Hypoglycemic Events Caused by Suboptimal Therapy

BACKGROUND AND AIM

The insulin to carbohydrate ratio (CR) is a parameter used by patients with type 1 diabetes (T1D) to calculate the premeal insulin bolus. Usually, it is estimated by the physician based on patient diary, but modern diabetes technologies, such as subcutaneous glucose sensing (continuous glucose monitoring, CGM) and insulin delivery (continuous subcutaneous insulin infusion, CSII) systems, can provide important information for its optimization. In this study, a method for CR optimization based on CGM and CSII data is presented and its efficacy and robustness tested in several in silico scenarios, with the primary aim of increasing protection against hypoglycemia.

METHODS

The method is based on a validated index of insulin sensitivity calculated from sensor and pump data (S_I^SP), area under CGM and CSII curves. The efficacy and robustness of the method are tested in silico using the University of Virginia/Padova T1D simulator, in several suboptimal therapy scenarios: with nominal CR variation, over/underestimation of meal size or suboptimal basal insulin infusion. Simulated CGM and CSII data were used to calculate the optimal CR. The same scenarios were then repeated using the estimated CR and glycemic control was compared.

RESULTS

The optimized CR was efficacious in protecting against hypoglycemic events caused by suboptimal therapy. The method was also robust to possible error in carbohydrate count and suboptimal basal insulin infusion.

CONCLUSIONS

A novel method for CR optimization in T1D, implementable in daily life using CGM and CSII data, is proposed. The method can be used both in open- and closed-loop insulin therapy.

Automated blood glucose control in type 1 diabetes: A review of progress and challenges

Since the 2000s, research teams worldwide have been working to develop closed-loop (CL) systems able to automatically control blood glucose (BG) levels in patients with type 1 diabetes. This emerging technology is known as artificial pancreas (AP), and its first commercial version just arrived in the market. The main objective of this paper is to present an extensive review of the clinical trials conducted since 2011, which tested various implementations of the AP for different durations under varying conditions. A comprehensive table that contains key information from the selected publications is provided, and the main challenges in AP development and the mitigation strategies used are discussed. The development timelines for different AP systems are also included, highlighting the main evolutions over the clinical trials for each system.

Retrofitting Real-Life Dexcom G5 Data

BACKGROUND

We proposed in 2014 a retrofitting algorithm to retrospectively increase the accuracy of continuous glucose monitoring (CGM) data by using some blood glucose (BG) measurements. The method proved effective on Dexcom SEVEN Plus when about 10 highly accurate YSI measurements/session were available. In this study, we test the method on Dexcom G5 sensor in a more realistic setup, where only five capillary BG measurements (self-monitoring blood glucose [SMBG]) per 12 h-session are available. Furthermore, we investigate how accuracy is affected by the number of BG measurements.

METHOD

The algorithm was tested in 51 adults and 46 adolescents studied for 7 days with Dexcom G5. Each patient also underwent an ∼12-h hospital admission where frequent SMBG and YSI measurements were collected. First, five SMBGs per 12-h session were used to retrofit the CGM. Then, we varied the number of SMBGs provided to the method from 2 to 10 per 12-h session.

RESULT

Retrofitted CGM traces with five SMBGs per 12-h session have lower mean absolute difference than original CGM, reduced from 16.2 to 10.7 mg/dL (P < 0.001) in adults and from 17.6 to 11.5 mg/dL (P < 0.001) in adolescents, and mean absolute relative difference is reduced from 9.0% to 6.4% (P < 0.001) in adults and from 10.3% to 6.8% (P < 0.001) in adolescents. Reducing the number of BG measurements reduces improvement in the accuracy from >30% with 10 SMBGs per 12-h session to <16% with 2 SMBGs/day.

CONCLUSION

The retrofitting method retrospectively improves the accuracy of CGM data, even if applied to one of the most accurate CGM sensors currently available on the market.

Significance and Reliability of MARD for the Accuracy of CGM Systems

BACKGROUND

There is a need to assess the accuracy of continuous glucose monitoring (CGM) systems for several uses. Mean absolute relative difference (MARD) is the measure of choice for this. Unfortunately, it is frequently overlooked that MARD values computed with data acquired during clinical studies do not reflect the accuracy of the CGM system only, but are strongly influenced by the design of the study. Thus, published MARD values must be understood not as precise values but as indications with some uncertainty.

DATA AND METHODS

Data from a recent clinical trial, Monte Carlo simulations, and assumptions about the error distribution of the reference measurements have been used to determine the confidence region of MARD as a function of the number and the accuracy of the reference measurements.

RESULTS

The uncertainty of the computed MARD values can be quantified by a newly introduced MARD reliability index (MRI), which independently mirrors the reliability of the evaluation. Thus MARD conveys information on the accuracy of the CGM system, while MRI conveys information on the uncertainty of the computed MARD values.

CONCLUSIONS

MARD values from clinical studies should not be used blindly but the reliability of the evaluation should be considered as well. Furthermore, it should not be ignored that MARD does not take into account the key feature of CGM sensors, the frequency of the measurements. Additional metrics, such as precision absolute relative difference (PARD) should be used as well to obtain a better evaluation of the CGM performance for specific uses, for example, for artificial pancreas.

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.

Modeling Transient Disconnections and Compression Artifacts of Continuous Glucose Sensors

BACKGROUND

Modeling the various error components affecting continuous glucose monitoring (CGM) sensors is very important (e.g., to generate realistic scenarios for developing and testing CGM-based applications in type 1 diabetes simulators). Recent work has focused on some error components (i.e., blood-to-interstitium delay, calibration, and random noise), but key events such as transient faults have not been investigated in depth. We propose two mathematical models that describe the disconnections and compression artifacts.

MATERIALS AND METHODS

A dataset of 72 subjects monitored with the Dexcom (San Diego, CA) G4(®) Platinum sensor is considered. Disconnections and compression artifacts have been isolated, and some basic statistical parameters (e.g., frequency and duration) have been extracted. A Markov chain model is proposed to describe the dynamics of a disconnection, and the effect of a compression artifact in the CGM profile is modeled as the output of a first-order linear dynamic system driven by a rectangular function.

RESULTS

The great majority of disconnections (approximately 90%) lasted less than 20 min. Compression artifact median (5(th)-95(th) percentiles) values were 45 (30-70) min for the duration and 24 (10-48) mg/dL for the amplitude. Both disconnections and compression artifacts happened with almost equal probability during the 7 days of monitoring. Disconnections were more frequent during the day and compression artifacts during the night. A three-state Markov model is shown to be effective to describe the single disconnection. The asymmetric shape of compression artifact is well fitted by the proposed model.

CONCLUSIONS

The provided models are sufficiently accurate for simulation purposes (e.g., to create more challenging and realistic scenarios) to test real-time fault detection algorithms and artificial pancreas closed-loop controllers.

One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator

OBJECTIVE

The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution.

METHODS

The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed.

RESULTS

The model well describes glucose traces (coefficient of determination R² = 0.962 ± 0.027 ) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern.

CONCLUSION

The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic.

SIGNIFICANCE

The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator.

Perioperative glycemic control with a computerized algorithm versus conventional glycemic control in cardiac surgical patients undergoing cardiopulmonary bypass with blood cardioplegia

OBJECTIVE

In critical illness, hypoglycemia and hyperglycemia seem to influence outcome. While hypoglycemia can lead to organ dysfunction, hyperglycemia can lead to surgical site infections (SSI). In cardiac surgery, the use of blood cardioplegia is associated with high blood glucose levels. A computer-based algorithm (CBA) for guiding insulin towards normoglycemia might be beneficial. The authors' primary study end-point was the duration in a predefined blood glucose target range of 80 mg/dL to 150 mg/dL. Patients with conventional therapy served as controls.

DESIGN

Prospective, randomized trial.

SETTING

University hospital.

PARTICIPANTS

Seventy-five patients.

INTERVENTIONS

The start of therapy was the beginning of cardiopulmonary bypass. Group A: Therapy with CBA and measurement of blood glucose every 30 minutes. Group B: Measurement of blood glucose every 15 minutes using the identical CBA. Group C: Conventional therapy using a fixed insulin dosing scheme. End of therapy was defined as discharge from ICU.

MEASUREMENT AND MAIN RESULTS

Glucose administration during cardioplegia did not differ between groups (A: 33 ± 12 g; B: 32 ± 12 g; C: 38 ± 20 g). Glucose levels in groups A and B stayed significantly longer in the target interval compared with group C (A: 75 ± 20%; B: 72 ± 19%; C: 50 ± 34%, p < 0.01 n = 25, respectively). There were no significant differences regarding ICU stay and SSI rates.

CONCLUSIONS

Early computer-based insulin therapy allows practitioners to better achieve normoglycemia in patients undergoing major cardiac surgery with the use of blood cardioplegia.

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.

Retrofitting of continuous glucose monitoring traces allows more accurate assessment of glucose control in outpatient studies

BACKGROUND

Glucose control in artificial pancreas (AP) studies is commonly assessed by metrics such as the percentage of time with blood glucose (BG) concentration below 70 mg/dL or in the nearly normal range 70-180 mg/dL (in brief, time in hypoglycemia and time in target, respectively). In outpatient studies these control metrics can be computed only from continuous glucose monitoring (CGM) sensor data, with the risk of an unfair assessment because of their inaccuracy. The aim of the present article is to show that the control metrics can be much more accurately determined if CGM data are preprocessed by a recently proposed retrofitting algorithm.

SUBJECTS AND METHODS

Data from 47 type 1 diabetes subjects are considered. Subjects were studied in a closed-loop control trial prescribing three 24-h admissions. Glucose concentration was monitored using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus CGM sensor. Frequent BG reference values were collected in parallel with the YSI analyzer (Yellow Springs Instrument, Yellow Springs, OH). To simulate the few reference values available in outpatient conditions, we down-sampled the YSI data and provided to the retrofitting algorithm only the reference values that would have been collected in outpatient protocols. Time in hypoglycemia, time in target, mean, and SD of glucose profile were computed on the basis of both the original and the retrofitted CGM traces and compared with those computed using the frequently obtained YSI data.

RESULTS

Using the retrofitted traces, the average error affecting the estimation of time in hypoglycemia and time in target was approximately halved with respect to the original CGM traces (from 4.5% to 1.9% and from 8.7% to 4.4%, respectively). Error in mean and SD was reduced even further, from 10.0 mg/dL to 3.5 mg/dL and from 8.6 mg/dL to 2.9 mg/dL, respectively.

CONCLUSIONS

The improved accuracy of retrofitted CGM with respect to the original CGM traces allows a more reliable assessment of glucose control in outpatient AP studies.

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.