In this study, we used 20 continuous glucose monitoring sensor datasets obtained in subjects with type 1 diabetes to examine the effect of different background currents and calibration frequency on sensor accuracy. We found that, compared to a background current of zero, the use of a non-zero background current yielded a clear benefit in sensor accuracy. This benefit was seen both with analysis of post hoc processed data in which differing background currents were compared and also when directly compared to the manufacturer's displayed data. The magnitude of this improvement was quite substantial, approximately 3.5–5.5 MARD units, depending on calibration frequency. There are several sources of background current in amperometric glucose sensors. One is the presence of certain non-glucose compounds that diffuse from the interstitial fluid to the indicating electrode where they are oxidized, leading to a current that masquerades as a glucose current. A number of materials have been used in the construction of sensors for the purpose of eliminating such interferents, including Nafion®
(DuPont, Wilmington, DE),11
other charged membranes,12
cellulose, sulfone-type polymers,2
or other electron shuttle mediators.15,16
Many of these mediators allow electrode polarization at a voltage low enough to minimize interferent oxidation. Despite the multitude of methods that have been proposed, it is often difficult to completely eliminate the effect of these oxidizable compounds. One explanation is that, in glucose oxidase-based sensors, common interferents such as ascorbic acid, uric acid, and acetaminophen, unlike glucose, do not require enzymatic conversion to be measured at an anode. For this reason, even small amounts of these compounds are efficiently oxidized, thus creating a relatively large signal. Another potentially problematic issue is the fact that slight alterations in chemical handling or manufacturing procedures can impact the effectiveness of layers designed to prevent passage of interfering compounds. It should be noted that interference is much less of an issue for sensors that are not positively polarized, such as those that exploit the reduction of oxygen as opposed to the oxidation of an electron shuttle or peroxide.17
Compared to a background current of zero, we found that a widely used commercial amperometric glucose sensor functioned with greater accuracy with a background current of 4
nA. This background current was accounted for during the process of calibration and during calculation of glucose in unknowns. This finding suggests that near-perfect physical exclusion of interfering compounds by the sensor may not be necessary. Instead, if it can be measured and accounted for in human pilot studies, mathematical correction of background current may be sufficient to optimize accuracy. Of course, if the magnitude of the background current is quite large compared to the glucose current (not the case in this study), the effect of measurement noise might prevent such a correction from being successful. Background current correction between 3 and 5
nA was also found to optimize detection of hypoglycemia. This range led to much better hypoglycemia detection sensitivity than in a lower range of background current, with only a minimal loss of specificity and positive predictive value.
We also addressed the benefit of calibration frequency, knowing that recalibration helps to compensate for sensor drift. When a very accurate blood glucose reference measuring instrument is used, the process of recalibration returns the sensor glucose value to the blood glucose value (unless prior sensed glucose or blood glucose data are also taken into account, which was not the case here). Compared to the adjustment for background current, the benefit of increasing calibration frequency in our study was modest. In the validation dataset, there was no significant benefit of increasing frequency from every 12
h to every 8
h; only when the frequency was increased further to every 4
h, with background current correction, was a benefit appreciated, and, even then, the benefit was small. Such a finding suggests that the sensors under study were relatively stable and did not undergo rapid drift. In addition to the process of recalibration, there are other methods that can improve the fidelity of the sensor signal by mathematically filtering out noise. Clarke and Kovatchev18
and the Rensselaer group19
have proposed such methods, which might help to minimize the need for frequent recalibration. Review of the Medtronic patent suggests variable background current correction, based upon the magnitude of the sensitivity.20
There is a general agreement that the signals from subcutaneously implanted glucose sensors lag behind blood values, although the estimates of the lag magnitude are dependent on the particular device. The causes of lag include the physiologic duration required for glucose to reach interstitial fluid from blood (likely very short21
), the time required for glucose to pass through sensor membranes, and delays that result from data smoothing and filtering. Kovatchev et al.8
discussed in some detail each of these sources of lag and found that the lag of the Abbott Navigator sensor, typically in the 12–17
min range, was greater during rising glucose than during falling glucose. Kamath et al.22
found a somewhat shorter lag duration in their recent study of the DexCom™ (San Diego, CA) Seven®
Plus sensor. A recent report from Medtronic found that software processing and filtering can lead to an apparent lag, although such a delay cannot be considered a true physical lag.23
Medtronic uses a “Wiener filter” for compensation of sensor lag,24
although details are not provided. The autoregression techniques used by the Reifman/Gani group appear to be a promising method of minimizing sensor inaccuracy and possibly lag.25
Reported Medtronic lag times include 4.0
min during glucose changes,26
up to 18
min during insulin-induced hypoglycemia,27
and up to 12
min during normalization of glucose after administration of exogenous insulin.28
An interesting result of our study was that error from an incorrect background current can sometimes have the appearance of a time lag. We found that before the background current correction was carried out, inspection of the time series curve (reference and sensor glucose vs. time) often suggested a marked lag, especially under certain conditions such as rising glucose after meals. In contrast, after this correction was carried out, the time series curves often suggested a much smaller lag. Consider the situation during which background current is underestimated during calibration at a low or normal glucose level. As glucose rises, the calibration error will lead to an apparent lag of sensor glucose behind the reference blood glucose. However, if the data are analyzed with the correct background current, the sensor glucose values rise appropriately with the meal, and the apparent “lag” is reduced. For this reason, before estimation of sensor lag, it is important to measure glucose with the correct background current.
In order to determine the background current in this study, we used data collected at many glucose levels and extrapolated the current versus glucose data line to the y-intercept at zero glucose. Of course, calibrating at many glucose levels for patients in the hospital or outpatient setting is impractical as a means of determining background current, and we acknowledge that, in some ways, the research setting with a highly accurate glucose monitor gives us an unfair advantage when we compare sensor accuracy to values typically obtained during unsupervised use by outpatients. Ideally, the background current for each sensor would be measured in human studies in order to optimize the algorithm used to transform electrical current into an estimated glucose value. We believe that determination of background current is more accurately determined during such in vivo testing rather than during in vitro testing. Humans possess oxidizable interfering compounds that enter into the interstitial fluid (perhaps even many that have not yet been identified), and for this reason it may well be impossible to determine background current from in vitro studies.
In conclusion, we found that with an amperometric glucose sensor commonly used in clinical settings, accuracy was markedly improved by the use of a correction for background current. This correction can be carried out in human studies by obtaining in vivo preliminary training sensor data accompanied by corresponding reference glucose values. When tested in a validation study, the background current correction was robust. We also found improvements in accuracy, although lesser in magnitude, with an increase in calibration frequency. In some cases, sensor lag can appear falsely high when background current is underestimated. The accuracy-optimizing scheme presented here can be implemented in real time.