The performance difference between YSI and meter calibration was less than 2%. A greater error margin was expected, but by simply imposing CF limits, the potential for significant error was averted. This marginal difference may be the result of careful finger stick measurements taken in the clinic prior to frequent sampling. Sensor calibrations produced a MARD of 14%, which is considered reasonable. However, the tails of the distributions of have outliers of 58% to 80%. Such errors can appear at the extremes if a sensor is under- or over-reading and are not comparable to points that reside in the upper E zone of the Clarke error grid. In such cases, the error will likely decrease as glucose moves closer to the middle of the glycemic range.
The time lags introduced are not pure time delays but rather simulated physiological time lags involved in the diffusion of the blood glucose from the plasma to the subcutaneous tissue. It was observed that time lag had minimal effect on closed-loop control performance in silico, where a greater influence was expected with the inclusion of meal responses. Additive noise had the effect of biasing control high, where HBGI increased with some reduction in LBGI, thereby further decreasing the risk of severe hypoglycemia. This was an interesting finding where one may expect the risk of a severe adverse event to increase for both metrics.
In silico analysis revealed a strong correlation between hyperglycemia, HBGI, and sensors that under-read with negative bias. The reverse is true for sensors that over-read with a positive bias. Similarly, there is a strong correlation among highly biased sensors, MARD, and poor glucose control. Sensor 12 provides a good example of how sensor bias can affect glucose control. In this case the sensor was calibrated during a glucose rate-of-change of 2.13 mg/dl/min. As a result, MARD (34.3%) and mean bias (-61.2 mg/dl) are significant, resulting in 55.1% of the overnight period above 180 mg/dl. Conversely, sensor 11 produced reasonable control with a high mean bias and error, and sensor 9 resulted in superior control to that achieved with plasma glucose readings. Improved control with sensor 9 is a direct result of the sensor moderately over-reading, thereby making the controller more aggressive with a relatively conservative control setting.
We ran simulations with noise and errors that were extracted from the new Enlite sensor and we added results from clinical trials that were conducted with the Sof-sensor. The purpose of this contribution was to show that closed loop is feasible with the current sensor technology and, therefore, the overnight clinical trials results are presented to reinforce our hypothesis. Evidently, with no challenges prior to the overnight period, excellent control is achievable, which was demonstrated in the clinical adult study with nearly 100% time in target. Physical activity obviously stresses the system by producing greater variability and, in one instance, a hypoglycemic event that required rescue during the overnight period following exercise. It should be noted that the open-loop control arm for this study generated 14 hypoglycemic events for the same period. Once again, control was excellent with a few outliers, where LBGI reached a high level (5.9) in one case and reached moderate levels (2.5 and 3.4) in two cases. However, in the remaining nine subjects, a low risk of severe hypoglycemia was maintained with over half of the subjects spending 100% time in target. It is hypothesized that the exercise-induced hypoglycemic events could be further mitigated with a higher set point as demonstrated in . Here it is evident that with a lower set point of 110 mg/dl, LBGI can increase to a more serious category with moderate risk of severe hypoglycemia, but decreases to almost zero at 140 mg/dl. We performed a similar exercise in silico to validate that variability is not overly affected by an increase in set point, and observed a SD increase of only 4 mg/dl.
Although the exercise study used a different sensor, the system was calibrated manually with YSI samples producing a MARD of 10.9%. This is significantly better than one-point meter calibrations of 14%. We chose the one-point method to create greater variability intentionally and demonstrate the effect of bias. While sensor performance typically tracked control performance, in one example, the control sensor for the exercise experiment that suffered a hypoglycemic event below 60 mg/dl requiring a rescue had a low MARD and negative bias. This demonstrates that good sensor performance is not always enough to guarantee no incidence of severe hypoglycemia.