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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| A | Experimental | improved model predictive control algorithm (eMPC) for glycaemic control in ICU patients |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| enhanced model predictive control algorithm (eMPC) | Other | eMPC (software on a bedside computer) advised insulin titration to establish tight glycaemic control |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Thomas R Pieber, Prof. Dr. | Medical University of Graz | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Medical University Graz | Graz | 8036 | Austria |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 16443872 | Background | 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. | |
| 18297268 |
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| ID | Term |
|---|---|
| D016638 | Critical Illness |
| D007333 | Insulin Resistance |
| ID | Term |
|---|---|
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D006946 | Hyperinsulinism |
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| Concomitant medication including insulin infusion rate, parenteral/enteral nutrition | from start of treatment to the last glucose measurement under treatment |
| 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. |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |