Hypoglycemia Prediction and Detection Using Optimal Estimation

Abstract

Patients with diabetes play with a double-edged sword when it comes to deciding glucose and A1c target levels. On the one side, tight control has been shown to be crucial in avoiding long-term complications; on the other, tighter control leads to an increased risk of iatrogenic hypoglycemia, which is compounded when hypoglycemia unawareness sets in. Development of continuous glucose monitoring systems has led to the possibility of being able not only to detect hypoglycemic episodes, but to make predictions based on trends that would allow the patient to take preemptive action to entirely avoid the condition. Using an optimal estimation theory approach to hypoglycemia prediction, we demonstrate the effect of measurement sampling frequency, threshold level, and prediction horizon on the sensitivity and specificity of the predictions. We discuss how optimal estimators can be tuned to trade-off the false alarm rate with the rate of missed predicted hypoglycemic episodes. We also suggest the use of different alarm levels as a function of current and future estimates of glucose and the hypoglycemic threshold and prediction horizon.

Detection of a Meal Using Continuous Glucose Monitoring

“…The second approach is based on optimal estimation theory, using a Kalman filter (14,15), an established method that has been used as part of different algorithms in the context of glucose management such as the following: hypoglycemic/hyperglycemic prediction (16,17), improved glucose monitoring (18,19), and feedback control (7). This method assumes that the glucose sensor signal varies primarily through two contributions: 1) real changes to the underlying glucose value (g k ) and 2) measurement noise (v k ).…”

Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring

“…Palerm et al [12] proposed an algorithm to predict glucose levels based on the estimation of glucose and its rate of change, using a Kalman filter. They applied the algorithm to predict hypoglycemia using data from a series of hypoglycemic clamps in which a CGMS® System (Medtronic-Minimed, Northridge, CA) was employed [13].…”

Modeling of Calibration Effectiveness and Blood-to-Interstitial Glucose Dynamics as Potential Confounders of the Accuracy of Continuous Glucose Sensors during Hyperinsulinemic Clamp

“…16 So far, we have no compelling results showing any of the aforementioned as superior to the minimal assumptions outlined in our methodology. However, continued experiments are needed, particularly regarding the application of SIG methodology to nonclamp field data where the parameters likely vary in time and may have much higher variance than in our relatively small study in a well-controlled environment.…”