While tight glucose control has been shown to improve the outcomes of some critical care patients, much controversy regarding its overall benefit persists; in part due an unacceptable incidence of hypoglycemia, or low blood sugar.
A decision support system for glucose control in critical care, much like an artificial pancreas, is comprised of three essential components:
(1) a glucose measuring device,
(2) an algorithm that interprets this measurement and recommends a treatment strategy, and
(3) a delivery device that implements this strategy,
delivering insulin, glucose, or some other agent (e.g., glucagon) to a patient.
This project uses systems engineering tools to provide a robust answer to the following questions: given the characteristics of a minimally invasive glucose measuring device, what is the tightest glucose control achievable while avoiding hypoglycemia, and what is the strategy to achieve this control?
We will use a very large multi-center dataset of critically ill patients receiving insulin, aiming to:
(1) calibrate and validate a mathematical model of glucose and insulin dynamics and
(2) characterize between-patient variations as embodied in model parameters. Such a model will then be used to
(3) design and deliver a patient-tailored decision support system, in the form of a portable interface, that would forewarn clinical practitioners of potential hypoglycemic episodes and recommend insulin or dextrose dose administration.
The ultimate goal of this project is to put all necessary tools in place for a randomized clinical trial of tight glucose control in critically ill patients, while completely avoiding episodes of hypoglycemia. It is expected that a successful completion of this project will have high translational impact and contribute to systems engineering science, specifically in the tailoring of sophisticated algorithms to patient–specific needs.
This project is supported by the National Institute of Diabetes and Digestive and Kidney Diseases - 1R21DK092813-01