Tight Glycemic Control by eMPC Algorithm in Medical ICU Patients.
This is an open mono-centre randomised controlled trial performed at the Medical University Graz including a treatment visit (V1). In the treatment visit (V1) after admittance to the ICU arterial blood glucose values will be monitored and either the software-algorithm eMPC will be used to adjust the infusion rate of intravenously administered human soluble insulin to normalise arterial blood glucose or routine treatment will be used to establish tight glycaemic control. The treatment visit will last for 72 hours.
The primary hypothesis of the study is that blood glucose control by the eMPC algorithm is not inferior compared to the implemented routine protocol.
Critically Ill Patients
Procedure: insulin titration applying a computer algorithm
|Study Design:||Allocation: Randomized
Endpoint Classification: Safety/Efficacy Study
Intervention Model: Parallel Assignment
Masking: Open Label
Primary Purpose: Treatment
|Official Title:||An Open, Mono-Centre Randomised Controlled Trial to Investigate the Feasibility of Blood Glucose Control With the Software-Algorithm eMPC (Enhanced Model Predictive Control) Via the Arterial-Intravenous Route in Patients at the Medical Intensive Care Unit.|
- Hyperglycemic Index
- Number of hypoglycaemic episodes (BG < 40 mg/dl (< 2.2mM)  )
- Mean glucose
- Sampling interval
- Insulin need
|Study Start Date:||May 2006|
|Study Completion Date:||November 2006|
Hyperglycaemia is commonly found in critically ill patients. The stress of critical illness induces glucose counterregulatory hormones and a number of alterations in carbohydrate metabolism, including increased peripheral glucose demands, enhanced hepatic glucose production, insulin resistance and relative insulin deficiency. Moreover, clinical interventions, such as corticoids, vasopressors, and enteral or parenteral nutrition, further predispose these patients to elevated blood glucose levels. In patients in intensive care as well as in general hospital settings patients with hyperglycemia have higher mortality rates. Recent studies demonstrated that tight blood glucose control in ICU patients results in a significant better outcome for the patients.
Based on this emerging clinical evidence, there are increasing efforts world-wide to maintain strict glycaemic control in critically ill patients. However, achieving this goal requires extensive nursing efforts, including frequent bedside glucose monitoring and the implementation of complex intensive insulin infusion protocols and such increased work demands may not be readily accepted by a busy ICU nursing staff.
The development of a closed loop control system that automatically infuses insulin on the basis of glucose measurements could permit strict glycaemic control and improve clinical outcome without increasing workload of the ICU nursing staff. The EU founded project CLINICIP (Closed Loop Infusion in Critically ill patients) aims to develop a low–risk monitoring and control system which allows maintaining metabolic control in intensive care units. As a first step a local bedside semi-closed system will be developed. Based on arterial spot measurement, an adaptive control algorithm will generate advice and thus represent a decision supporting system for the ICU nursing staff. This control algorithm was adapted for patients in the ICU. The first study using this algorithm was performed at the Medical University Hospital in Graz. In all six patients, who were included in this feasibility trial, blood glucose levels could be normalised and maintained in the narrow target range for up to 24 hours without a single hypoglycaemic episode. Subsequently, the algorithm was tested in a multicentric randomized controlled trial setting and showed superiority by means of a reduced number of hypoglycaemic events and a higher percentage of glucose values within the target range as compared to routine care glucose management protocols. In this study it was a fact that the hourly sampling frequency in the algorithm group has positively influenced the outcome in the algorithm group. Therefore in an enhanced version of the algorithm (eMPC) the sampling interval is expanded up to 240 min.
The purpose of this study is to evaluate the feasibility of this enhanced model predictive control algorithm for glycaemic control in critically ill patients compared to routine treatment.