Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems

Iman Hajizadeh, Mudassir Rashid, Sediqeh Samadi, Jianyuan Feng, Mert Sevil, Nicole Hobbs, Caterina Lazaro, Zacharie Maloney, Rachel Brandt, Xia Yu, Kamuran Turksoy, Elizabeth Littlejohn, Eda Cengiz, Ali Cinar
  • Journal of Diabetes Science and Technology, March 2018, SAGE Publications
  • DOI: 10.1177/1932296818763959

Plasma Insulin Concentration Estimation for People with Type 1 Diabetes and the Artificial Pancreas

What is it about?

People with type 1 diabetes (T1D) regulate their blood glucose concentration by administering insulin by injection or infusion. An accurate estimation of the amount of active insulin already present in the body prevents overdosing. An adaptive and personalized plasma insulin concentration (PIC) estimator is designed to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka’s glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach.

Why is it important?

The accurate real-time estimation of plasma insulin concentration will improve accuracy of computing the optimal dose of insulin to be infused and reduce the probability of hypoglycemia.This will also benefit the artificial pancreas systems by preventing over-delivery of insulin when significant insulin is present in the bloodstream.

Perspectives

Ali Cinar (Author)
Illinois Institute of Technology

A major concern in the use of artificial pancreas systems is to induce too much insulin and the potential for hypoglycemia due to excess insulin in the bloodstream. Better estimation of insulin in the bloodstream will reduce the potential for hypoglycemia. The proposed method is evaluated by using clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of plasma insulin concentration. The collaborative effort of engineering researchers and endocrinologists enabled this important achievement.

The following have contributed to this page: Ali Cinar