Integrated closed-loop medication delivery with error model and safety check

Abstract

A closed-loop system for insulin infusion overnight uses a model predictive control algorithm (“MPC”). Used with the MPC is a glucose measurement error model which was derived from actual glucose sensor error data. That sensor error data included both a sensor artifacts component, including dropouts, and a persistent error component, including calibration error, all of which was obtained experimentally from living subjects. The MPC algorithm advised on insulin infusion every fifteen minutes. Sensor glucose input to the MPC was obtained by combining model-calculated, noise-free interstitial glucose with experimentally-derived transient and persistent sensor artifacts associated with the FreeStyle Navigator® Continuous Glucose Monitor System (“FSN”). The incidence of severe and significant hypoglycemia reduced 2300- and 200-fold, respectively, during simulated overnight closed-loop control with the MPC algorithm using the glucose measurement error model suggesting that the continuous glucose monitoring technologies facilitate safe closed-loop insulin delivery.

What is claimed is: 
     
         1 . A system comprising:
 a glucose sensor configured to provide a sensor glucose measurement signal representative of sensed glucose level; and   a controller programmed to receive the sensor glucose measurement signal and to provide an insulin delivery parameter as a function of the received sensor glucose measurement signal in accordance with a model predictive control and a glucose measurement error model, the glucose measurement error model derived from actual glucose sensor measurement data.   
     
     
         2 . The system of  claim 1 , wherein the model predictive control is based on a glucoregulatory model. 
     
     
         3 . The system of  claim 1 , wherein the glucose measurement error model is derived from at least sensor dropout. 
     
     
         4 . The system of  claim 1 , wherein the glucose measurement error model is derived from at least sensor calibration error. 
     
     
         5 . The system of  claim 4 , wherein the sensor calibration error comprises a difference between a plasma glucose level and a sensor glucose level. 
     
     
         6 . The system of  claim 1 , wherein the glucose measurement error model is derived from at least a combination of sensor dropout and sensor calibration error. 
     
     
         7 . The system of  claim 1 , wherein the actual glucose sensor measurement data excludes at least sensor noise data. 
     
     
         8 . The system of  claim 1 , wherein the actual glucose sensor measurement data excludes at least randomly generated variable data. 
     
     
         9 . The system of  claim 1 , wherein the actual glucose sensor measurement data excludes at least a combination of sensor noise data and randomly generated variable data. 
     
     
         10 . The system of  claim 1 , wherein the insulin delivery parameter is at least an insulin basal rate. 
     
     
         11 . The system of  claim 1 , wherein the insulin delivery parameter is at least an insulin bolus amount. 
     
     
         12 . The system of  claim 1 , wherein the controller is further programmed to adjust the insulin delivery parameter in accordance with a safety check. 
     
     
         13 . The system of  claim 12 , wherein the safety check is a maximum basal rate. 
     
     
         14 . The system of  claim 12 , wherein the safety check is a rapidly decreasing sensed glucose level. 
     
     
         15 . A method comprising:
 sensing, with a glucose sensor, a glucose level and providing a sensor glucose measurement signal representative of the sensed glucose to a controller; and   providing, with the controller, an insulin delivery parameter as a function of the sensor glucose measurement signal in accordance with a model predictive control and a glucose measurement error model, the glucose measurement error model derived from actual glucose sensor measurement data.   
     
     
         16 . The system of  claim 1 , wherein the model predictive control is based on a glucoregulatory model. 
     
     
         17 . The system of  claim 1 , wherein the glucose measurement error model is derived from at least sensor dropout, at least sensor calibration error, or at least a combination of sensor dropout and sensor calibration error. 
     
     
         18 . The system of  claim 1 , wherein the actual glucose sensor measurement data excludes at least sensor noise data, excludes at least randomly generated variable data, or excludes at lease a combination of sensor noise data and randomly generated variable data. 
     
     
         19 . The system of  claim 1 , wherein the insulin delivery parameter is at least an insulin basal rate. 
     
     
         20 . The system of  claim 1 , wherein the insulin delivery parameter is at least an insulin bolus amount.