Shahar Miller1, Revital Nimri, Eran Atlas, Eli A Grunberg, Moshe Phillip. 1. The Jesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, The National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel.
Abstract
BACKGROUND: Applying real-time learning into an artificial pancreas system could effectively track the unpredictable behavior of glucose-insulin dynamics and adjust insulin treatment accordingly. We describe a novel learning algorithm and its performance when integrated into the MD-Logic Artificial Pancreas (MDLAP) system developed by the Diabetes Technology Center, Schneider Children's Medical Center of Israel, Petah Tikva, Israel. METHODS: The algorithm was designed to establish an initial patient profile using open-loop data (Initial Learning Algorithm component) and then make periodic adjustments during closed-loop operation (Runtime Learning Algorithm component). The MDLAP system, integrated with the learning algorithm, was tested in seven different experiments using the University of Virginia/Padova simulator, comprising adults, adolescents, and children. The experiments included simulations using the open-loop and closed-loop control strategy under nominal and varying insulin sensitivity conditions. The learning algorithm was automatically activated at the end of the open-loop segment and after every day of the closed-loop operation. Metabolic control parameters achieved at selected time points were compared. RESULTS: The percentage of time glucose levels were maintained within 70-180 mg/dL for children and adolescents significantly improved when open-loop was compared with day 6 of closed-loop control (P<0.0001) and remained unaltered for the adult group (P=0.11) during nominal conditions. In varying insulin sensitivity conditions, the percentage of time glucose levels were below 70 mg/dL was significantly reduced by approximately sevenfold (P<0.001). These observations were correlated with significant reduction in the Low Blood Glucose Index (P<0.001). CONCLUSIONS: The new algorithm was effective in characterizing the patient profiles from open-loop data and in adjusting treatment to provide better glycemic control during closed-loop control in both conditions. These findings warrant corroboratory clinical trials.
BACKGROUND: Applying real-time learning into an artificial pancreas system could effectively track the unpredictable behavior of glucose-insulin dynamics and adjust insulin treatment accordingly. We describe a novel learning algorithm and its performance when integrated into the MD-Logic Artificial Pancreas (MDLAP) system developed by the Diabetes Technology Center, Schneider Children's Medical Center of Israel, Petah Tikva, Israel. METHODS: The algorithm was designed to establish an initial patient profile using open-loop data (Initial Learning Algorithm component) and then make periodic adjustments during closed-loop operation (Runtime Learning Algorithm component). The MDLAP system, integrated with the learning algorithm, was tested in seven different experiments using the University of Virginia/Padova simulator, comprising adults, adolescents, and children. The experiments included simulations using the open-loop and closed-loop control strategy under nominal and varying insulin sensitivity conditions. The learning algorithm was automatically activated at the end of the open-loop segment and after every day of the closed-loop operation. Metabolic control parameters achieved at selected time points were compared. RESULTS: The percentage of time glucose levels were maintained within 70-180 mg/dL for children and adolescents significantly improved when open-loop was compared with day 6 of closed-loop control (P<0.0001) and remained unaltered for the adult group (P=0.11) during nominal conditions. In varying insulin sensitivity conditions, the percentage of time glucose levels were below 70 mg/dL was significantly reduced by approximately sevenfold (P<0.001). These observations were correlated with significant reduction in the Low Blood Glucose Index (P<0.001). CONCLUSIONS: The new algorithm was effective in characterizing the patient profiles from open-loop data and in adjusting treatment to provide better glycemic control during closed-loop control in both conditions. These findings warrant corroboratory clinical trials.
Authors: Fraser Cameron; Günter Niemeyer; Darrell M Wilson; B Wayne Bequette; Kari S Benassi; Paula Clinton; Bruce A Buckingham Journal: Diabetes Technol Ther Date: 2014-09-26 Impact factor: 6.118