Use of Subcutaneous Interstitial Fluid Glucose to Estimate Blood Glucose: Revisiting Delay and Sensor Offset

Abstract

Background: Estimates for delays in the interstitial fluid (ISF) glucose response to changes in blood glucose (BG) differ substantially among research groups. We review these findings along with arguments that continuous glucose monitoring (CGM) devices used to measure ISF delay contribute to the variability. We consider the impact of the ISF delay and review approaches to correct for it, including strategies pursued by the manufacturers of these devices. The focus on how the manufacturers have approached the problem is motivated by the observation that clinicians and researchers are often unaware of how the existing CGM devices process the ISF glucose signal. Methods: Numerous models and simulations were used to illustrate problems related to measurement and correction of ISF glucose delay. Results: We find that (1) there is no evidence that the true physiologic ISF glucose delay is longer than 5–10 min and that the values longer than this can be explained by delays in CGM filtering routines; (2) the primary impact of the true ISF delay is on sensor calibration algorithms, making it difficult to estimate calibration factors and offset (OS) currents; (3) inaccurate estimates of the sensor OS current result in overestimation of sensor glucose at low values, making it difficult to detect hypoglycemia; (4) many device companies introduce nonlinear components into their filters, which can be expected to confound attempts by investigators to reconstruct BG using linear deconvolution; and (5) algorithms advocated by academic groups are seldom compared to algorithms pursued by industry, making it difficult to ascertain their value. Conclusions: The absence of any direct comparisons between existing and new algorithms for correcting ISF delay and sensor OS current is, in part, due to the difficulty in extracting relevant details from industry patents and/or extracting unfiltered sensor signals from industry products. The model simulation environment, where all aspects of the signal can be derived, may be more appropriate for developing new filtering and calibration strategies. Nevertheless, clinicians, academic researchers, and the industry would benefit from collaborating when evaluating those strategies.

The Identifiable Virtual Patient Model: Comparison of Simulation and Clinical Closed-Loop Study Results

“…12 Underestimation of sensor glucose can be due to nonlinearities in the sensor response, intrinsic offset in sensor current, or delays in glucose equilibration with ISF. 24 In our simulations, we included the delay in glucose equilibration with the SC ISF but did not simulate an offset in sensor current or nonlinearity in the sensor response. This resulted in a better MARD for the simulation than was reported in the pediatric study (5.1 vs 13.2%).…”

Wearable Bio and Chemical Sensors

“…In recent years, significant effort has been focused on sampling and analysis of alternative body fluids, such as interstitial fluid [5], tears [6], saliva [7], and sweat [8]. The transition from blood to other body fluids provides a less invasive means of sampling, which in turn may provide a route to facilitate longer-term continuous monitoring.…”

Comparison Between One-Point Calibration and Two-Point Calibration Approaches in a Continuous Glucose Monitoring Algorithm

“…An important cause of the insufficient CGM calibration is the inaccurate estimation of the sensor background current in calibration, [7][8][9][10] which causes large CGM inaccuracy particularly in hypoglycemia, and as a result, the hypoglycemic BG values are overestimated by CGM readings. [13][14][15] This can lead to failure in detection of hypoglycemia, and consequently causes critical situations for the patient. 9,10,16 The invivo sources of the sensor background current, which is a glucose-nonspecific current, are 3 main interfering substances in interstitial fluid (ISF) including ascorbic acid, acetaminophen, and uric acid.…”

“…9,10,16 The invivo sources of the sensor background current, which is a glucose-nonspecific current, are 3 main interfering substances in interstitial fluid (ISF) including ascorbic acid, acetaminophen, and uric acid. 7,10,11,17 These 3 components are oxidized and generate current, in the presence of the voltage applied between the sensor electrodes 7,11 ISIG I I g = + 0 (1) where ISIG (interstitial signal) is the current measured by the sensor, I g is the true glucose current, and I 0 is the sensor background current. It is difficult to directly measure the sensor background current in vivo; 7 therefore, if the current is not negligible, it has to be estimated mathematically by means of a 2-point calibration algorithm where the background current is estimated by measuring the sensor output at 2 or more different BG concentrations and finding the y-intercept at zero glucose level.…”

“…Therefore, the time lag is a major cause of inaccurate estimation of the sensor background current especially if the calibration points are taken at the non-steady state between BG and IG (eg, dropping BG to hypoglycemia, and after a meal), which the effect of lag is maximized. 4,[26][27][28][29] Rebrin et al 13 demonstrated that any mismatch between the true background current and the estimated current by the calibration results in a CGM sensor that underestimates BG values above the calibration point and overestimates the values below the calibration point. Therefore, although theoretically the sensor background current can be estimated by a 2-point calibration, the error of the estimation itself affects the accuracy of the calibrated sensor readings to a large extent.…”