Historical data enhances safety supervision system performance in T1DM insulin therapy risk management

• Published in: Computer methods and programs in biomedicine Vol. 109; no. 2; pp. 220 - 225
• Main Authors: Hughes-Karvetski, Colleen, Patek, Stephen D, Breton, Marc D, Kovatchev, Boris P
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.02.2013
• Subjects: Alarms; Algorithms; Artificial pancreas; Behavioral profiles; Blood Glucose - analysis; Clinical Alarms; Continuous glucose monitoring; Diabetes; Diabetes Mellitus, Type 1 - drug therapy; Diabetes Mellitus, Type 1 - metabolism; Humans; Hypoglycemia - prevention & control; Hypoglycemic Agents - therapeutic use; Insulin - therapeutic use; Insulin Infusion Systems; Internal Medicine; Models, Biological; Monitoring, Physiologic - methods; Other; Patient Safety; Pump attenuation; Retrospective Studies; Risk Assessment - methods; Safety; Safety Management; Artificial pancreas; Continuous glucose monitoring; Behavioral profiles; Alarms; Diabetes; Safety; Pump attenuation
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2011.12.016

Abstract Safety measures to prevent or mitigate hypoglycemia are an important component of open loop, closed loop, and advisory mode insulin therapy control settings in type 1 diabetes. In recent work, we introduce a method for the automatic, gradual attenuation of the insulin pump delivery rate when a risk of hypoglycemia is detected, a method that we refer to as brakes . In the methods presented here, we demonstrate the use of historical glucose measurement data to inform and enhance the ability of the brakes to prevent hypoglycemia in real-time. The updated brakes are based on a patient-specific, time-varying model that reflects the typical trajectory of glycemic fluctuations throughout the day. Historical heightened risk of hypoglycemia throughout the day prompts an increase in the aggressiveness of insulin attenuation as compared to the original brakes that are based on real-time data alone. Through the use of available real-time data supplemented with historical glucose information to assess hypoglycemic risk, we are able to better anticipate and prevent hypoglycemia.