Blood Glucose Control With A Software-Algorithm In Intensive Care Unit (ICU) Patients

NCT00735163 | Completed

• 1 other identifier: HC-G-H-0806
• Study Type: interventional
• Target: 20
• Locations: 1 country
• Sites: 1

Brief Summary

Hyperglycemia is common in critically ill patients and associated with an adverse outcome. Recently, large randomized controlled trials have demonstrated that tight glycaemic control (TGC) reduces morbidity and mortality in this population. Based on this emerging evidence intensive insulin therapy is currently finding its way into the critical care practice. In the meantime numerous insulin infusion protocols, which are based on frequent bedside glucose monitoring, have been implemented. Recent reviews comparing different types of protocols describe widely ranging practice and difficulties in achieving TGC despite extensive efforts of the intensive care unit (ICU) staff. A fully automated algorithm may help to overcome some of these limitations by excluding intuitive interventions and integrating relevant clinical data in the decision-making process. The primary objective of the current study is to investigate the performance (efficacy) of a control algorithm for glycaemic control in ICU patients for the whole length of ICU stay.

Conditions

Critical Illness

Keywords

algorithm; tight glycemic control; glucose control; intensive care; insulin; ICU

Outcome Measures

Primary Outcomes (1)

• percentage of time within the predefined glucose target range of 80-110 mg/dL

  from start of treatment to the last glucose measurement under treatment

Secondary Outcomes (3)

• hypoglycemias (lab) and possible attendant clinical symptoms (e.g. convulsions)

  from start of treatment to the last glucose measurement under treatment

• Usability parameters like convenience of alarming function; workload; blood sampling frequency

  from start of treatment to the last glucose measurement under treatment

• Concomitant medication including insulin infusion rate, parenteral/enteral nutrition

  from start of treatment to the last glucose measurement under treatment

Study Arms (1)

A

EXPERIMENTAL

improved model predictive control algorithm (eMPC) for glycaemic control in ICU patients

Other: enhanced model predictive control algorithm (eMPC)

Interventions

enhanced model predictive control algorithm (eMPC) OTHER

eMPC (software on a bedside computer) advised insulin titration to establish tight glycaemic control

A

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Age: \> 18 years of age
• Stay in the ICU expected to be \> 120 h
• Blood glucose \> 110 mg/dl or patient on insulin treatment

You may not qualify if:

• Patients with hyperglycaemic crisis/ketoacidosis due to insulin deficiency.
• Known or suspected allergy to insulin
• Any disease or condition which the investigator or treating physician feels would interfere with the trial or the safety of the patient (i.e., liver failure, other fatal organ failures)
• Moribund patients likely to die within 24 hours

Sponsors & Collaborators

• B. Braun Melsungen AG: lead

Study Sites (1)

Medical University Graz

Graz, 8036, Austria

Location

Related Publications (2)

• Plank J, Blaha J, Cordingley J, Wilinska ME, Chassin LJ, Morgan C, Squire S, Haluzik M, Kremen J, Svacina S, Toller W, Plasnik A, Ellmerer M, Hovorka R, Pieber TR. Multicentric, randomized, controlled trial to evaluate blood glucose control by the model predictive control algorithm versus routine glucose management protocols in intensive care unit patients. Diabetes Care. 2006 Feb;29(2):271-6. doi: 10.2337/diacare.29.02.06.dc05-1689.

  PMID: 16443872 BACKGROUND

• Pachler C, Plank J, Weinhandl H, Chassin LJ, Wilinska ME, Kulnik R, Kaufmann P, Smolle KH, Pilger E, Pieber TR, Ellmerer M, Hovorka R. Tight glycaemic control by an automated algorithm with time-variant sampling in medical ICU patients. Intensive Care Med. 2008 Jul;34(7):1224-30. doi: 10.1007/s00134-008-1033-8. Epub 2008 Feb 23.

  PMID: 18297268 BACKGROUND

MeSH Terms

Conditions

Critical Illness; Insulin Resistance

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Hyperinsulinism; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases

Study Officials

• Thomas R Pieber, Prof. Dr.

  Medical University of Graz

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: SINGLE GROUP
• Sponsor Type: INDUSTRY

Study Record Dates

• First Submitted: August 13, 2008
• First Posted: August 14, 2008
• Study Start: September 1, 2008
• Primary Completion: February 1, 2009
• Study Completion: February 1, 2009
• Last Updated: April 16, 2009

Record last verified: 2009-04

Locations

AU (1)