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Tight Glycemic Control by eMPC Algorithm in Medical ICU Patients.

This study has been completed.
Information provided by:
Medical University of Graz Identifier:
First received: April 12, 2007
Last updated: NA
Last verified: April 2007
History: No changes posted

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.

Condition Intervention
Critically Ill Patients
Mechanical Ventilation
Procedure: insulin titration applying a computer algorithm

Study Type: Interventional
Study Design: Allocation: Randomized
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.

Resource links provided by NLM:

Further study details as provided by Medical University of Graz:

Primary Outcome Measures:
  • Hyperglycemic Index

Secondary Outcome Measures:
  • Number of hypoglycaemic episodes (BG < 40 mg/dl (< 2.2mM) [10] )
  • Mean glucose
  • Sampling interval
  • Insulin need

Estimated Enrollment: 50
Study Start Date: May 2006
Study Completion Date: November 2006
Detailed Description:

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.


Ages Eligible for Study:   18 Years to 90 Years   (Adult, Senior)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No

Inclusion Criteria:

  • Mechanical ventilation
  • Increased blood glucose levels during ICU care (> 110 mg/dL; > 6.1 mM) or patient on current insulin treatment.
  • Age of patients in the range from 18 to 90 years.

Exclusion Criteria:

  • Known or suspected allergy against insulin.
  • Any disease or condition which the Investigator or the treating physician feels would interfere with the trial or the safety of the patient.
  • Patients participating in another study.
  • Moribund patients likely to die in the next 24 hours.
  • Disabled patients
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Please refer to this study by its identifier: NCT00460252

Medical University Graz
Graz, Austria, 8036
Sponsors and Collaborators
Medical University of Graz
Principal Investigator: Thomas R Pieber, MD. Medical University of Graz
  More Information

Additional Information:
Publications: Identifier: NCT00460252     History of Changes
Other Study ID Numbers: CM4
Study First Received: April 12, 2007
Last Updated: April 12, 2007

Keywords provided by Medical University of Graz:
tight glycemic control
intensive care

Additional relevant MeSH terms:
Critical Illness
Disease Attributes
Pathologic Processes
Glucose Metabolism Disorders
Metabolic Diseases
Hypoglycemic Agents
Physiological Effects of Drugs processed this record on April 28, 2017