Sensors & Algorithms for Continuous Glucose Monitoring: Reconstruction of Glucose in Plasma from Interstitial Fluid Continuous Glucose Monitoring Data: Role of Sensor Calibration

Background

Continuous glucose monitoring (CGM) sensors measure glucose concentration in the interstitial fluid (ISF). Equilibration between plasma and ISF glucose is not instantaneous. Therefore, ISF and plasma glucose concentrations exhibit different dynamic patterns, particularly during rapid changes. The purpose of this work was to investigate how well plasma glucose can be reconstructed from ISF CGM data.

Methods

Six diabetic volunteers were monitored for 2 days using the TheraSense FreeStyle Navigator (Abbott Diabetes Care, Alameda, CA), a minimally invasive device that, on the basis of an initial calibration procedure (hereafter referred to as standard calibration), returns ISF glucose concentration. Simultaneously, plasma glucose concentration was also measured every 15 minutes. First we identified, in each subject, the linear time-invariant (LTI) two-compartment model of plasma-interstitium kinetics. Then, a nonparametric regularization deconvolution method was used to reconstruct plasma from ISF glucose.

Results

Deconvoluted profiles were always closer to plasma glucose than ISF ones. However, the quality of the reconstruction is unsatisfactory. Some visible discrepancies between average plasma and ISF time series suggest problems either in the applicability of the LTI model of plasma-interstitium kinetics to normal life conditions or in the standard calibration with which ISF glucose is determined from the sensor internal readings. Assuming that the LTI model of plasma-interstitium kinetics is correct, we focused on the influence of calibration and we employed a recently proposed method to recalibrate ISF data.

Conclusions

After the recalibration step, the relative error in reconstructing plasma glucose was reduced significantly. Results also demonstrate that further margins of improvement of plasma glucose reconstruction are possible by developing more sophisticated recalibration procedures.     

Mathematical Models of the Metabolic System in Health and in Diabetes: Modeling of Calibration Effectiveness and Blood-to-Interstitial Glucose Dynamics as Potential Confounders of the Accuracy of Continuous Glucose Sensors during Hyperinsulinemic Clamp

Background

Models of the dynamics of interstitial fluid-based continuous glucose sensors imply a variable sensor deviation from reference blood glucose (BG), depending on both sensor calibration procedure and BG dynamics. These effects could have a significant effect on the cross-interpretation of nonidentical accuracy studies.

Methods

Hyperinsulinemic euglycemic and hypoglycemic clamps were performed on 39 subjects with type 1 diabetes wearing the Medtronic Continuous Glucose Monitoring System®. Sensor calibration and interstitial glucose (IG) dynamics were modeled and analyzed as potential confounders of sensor deviation from reference BG.

Results

The mean absolute deviation (MAD) of sensor data was 20.9 mg/dl during euglycemia and 24.5 mg/dl during descent into and recovery from hypoglycemia. Computer-generated recalibration reduced MAD to 10.6 and 14.6 mg/dl, respectively. Modeling of IG dynamics reduced the MAD further to 10.0 and 10.4 mg/dl (using idiosyncratic parameters) or to 10.6 and 11.5 mg/dl (using model parameters common for all subjects), respectively.

Conclusions

The sensor MAD from reference is strongly influenced by the choice of calibration points. Thus, cross-experiment comparisons of sensor accuracy are likely to be heavily dependent on the employed calibration procedures. Demanding calibration points substantially differing in value was found to improve calibration effectiveness. Simulation using existing IG models and population parameters reduced the bias resulting from BG–IG dynamics.     

Sensors & Algorithms for Continuous Glucose Monitoring: Accuracy Requirements for a Hypoglycemia Detector: An Analytical Model to Evaluate the Effects of Bias, Precision, and Rate of Glucose Change

Background

There has been considerable debate on what constitutes a good hypoglycemia (Hypo) detector and what is the accuracy required from the continuous monitoring sensor to meet the requirements of such a detector. The performance of most continuous monitoring sensors today is characterized by the mean absolute relative difference (MARD), whereas Hypo detectors are characterized by the number of false positive and false negative alarms, which are more relevant to the performance of a Hypo detector. This article shows that the overall accuracy of the system and not just the sensor plays a key role.

Methods

A mathematical model has been developed to investigate the relationship between the accuracy of the continuous monitoring system as described by the MARD, and the number of false negatives and false positives as a function of blood glucose rate change is established. A simulation method with N = 10,000 patients is used in developing the model and generating the results.

Results

Based on simulation for different scenarios for rate of change (0.5, 1.0, and 5.0 mg/dl per minute), sampling rate (from 1, 2.5, 5, and 10 minutes), and MARD (5, 7.5, 10, 12.5, and 15%), the false positive and false negative ratios are computed. The following key results are from these computations.1. For a given glucose rate of change, there is an optimum sampling time.2. The optimum sampling time as defined in the critical sampling rate section gives the best combination of low false positives and low false negatives.3. There is a strong correlation between MARD and false positives and false negatives.4. For false positives of <10% and false negatives of <5%, a MARD of <7.5% is needed.

Conclusions

Based on the model, assumptions in the model, and the simulation on N = 10,000 patients for different scenarios for rate of glucose change, sampling rate, and MARD, it is concluded that the false negative and false positive ratio will vary depending on the alarm Hypo threshold set by the patient and the MARD value. Also, to achieve a false negative ratio <5% and a false positive ratio <10% would require continuous glucose monitoring to have an MARD ≤7.5%.