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Human Pseudoislet System for Synchronous Assessment of Fluorescent Biosensor Dynamics and Hormone Secretory Profiles

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A closed-loop artificial pancreas based on risk management.

Fraser Cameron¹, B Wayne Bequette, Darrell M Wilson

¹Department of Aeronautics and Astronautics, Stanford University, Stanford, California, USA. fmccamer@yahoo.com

Journal of Diabetes Science and Technology

|April 30, 2011

Summary

This summary is machine-generated.

A new extended model predictive controller (EMPC) effectively manages blood glucose (BG) in type 1 diabetes by minimizing hypoglycemia and hyperglycemia risks. This advanced control strategy significantly improves BG control compared to existing methods.

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Area of Science:

Biomedical Engineering
Control Systems
Diabetes Technology

Background:

Type 1 diabetes management presents challenges due to asymmetric risks of hypoglycemia and hyperglycemia.
The irreversible nature of insulin action complicates blood glucose regulation.
Existing control algorithms often adopt conservative strategies due to these inherent complexities.

Purpose of the Study:

To develop a novel extended model predictive controller (EMPC) that explicitly addresses the challenges of asymmetric glycemic risk and insulin action irreversibility.
To improve blood glucose regulation in type 1 diabetes by integrating prediction uncertainties into the control strategy.

Main Methods:

The EMPC algorithm was developed to minimize combined hypoglycemia and hyperglycemia risks, incorporating prediction uncertainties.
Simulations were conducted using the University of Virginia/Padova Simulator.
The EMPC was compared against optimized proportional-integral-derivative (PID), traditional model predictive control (MPC), basal/bolus (BB) controllers, and an independent MPC (IMPC).

Main Results:

The EMPC achieved a blood glucose risk index (BGRI) of 2.51, outperforming PID (2.99), MPC (3.05), and IMPC (4.10).
The EMPC resulted in 84% of blood glucose values within the euglycemic range (70-180 mg/dl), with a mean BG of 147 mg/dl.
The basal/bolus controller, optimized retrospectively, showed the best performance with a BGRI of 1.27 and 92% euglycemic values.

Conclusions:

The novel EMPC strategy effectively addresses asymmetric glycemic risk and insulin action irreversibility using prediction uncertainties and a defined risk function.
The EMPC significantly reduces avoidable blood glucose risk by 56% compared to a published MPC algorithm.
This advanced control approach offers a promising advancement in artificial pancreas technology for type 1 diabetes management.