Clinical performance of a continuous viscometric affinity sensor for glucose.

OBJECTIVE: A viscometric affinity sensor has been developed to measure the interstitial glucose concentration continuously. In a pilot clinical study its performance was assessed under conditions close to everyday life. Additionally, different insertion sites were tested for their suitability to apply subcutaneous glucose sensors. METHODS: Twelve subjects, 10 of whom with type 1 diabetes, were examined for 8 h. Sensors were applied subcutaneously at the forearm and the abdomen of each subject. Capillary blood glucose references were obtained from the finger tip every 30 min. Retrospective calibration was carried out individually with Deming regression. RESULTS: After retrospective calibration the 95% limits of agreement in the plot of the differences between sensor signals and references versus their means were +/-60 mg/dL. The sensitivity of the sensors remained stable over the entire measuring period, without any significant differences between the sensors at forearm and abdomen. Correcting for the observed time delay of 15 min between references and sensor values the limits of agreements were reduced to +/-38 mg/dL. Furthermore, error grid analysis showed 89.3% of the paired values in zone A and 9.6% in zone B. Only 1.1% were clinically unacceptable (zone D). CONCLUSIONS: The performance of the viscometric affinity sensor shows the potential of the measuring principle under in vivo conditions. Forearm and abdomen seem to be similarly well suited for the application of subcutaneous sensors. The signal stability over time and the absence of enzymatic, chemical, or electrode reactions are advantages of the viscometric affinity principle.

Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes.

A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.