Computerized Glucose Control in Critically Ill Patients (CGAO-REA)
Device: CGAO-based Glucose Control
Device: Standard-Care Glucose Control
|Study Design:||Allocation: Randomized
Endpoint Classification: Safety/Efficacy Study
Intervention Model: Parallel Assignment
Masking: Open Label
Primary Purpose: Treatment
|Official Title:||Impact of the Use of a Computerized Protocol for Glucose Control Named CGAOtm on the Outcome of Critically Ill Patients|
- All-cause 90-day Mortality [ Time Frame: Day 90 ] [ Designated as safety issue: Yes ]
- All-cause 28-day Mortality [ Time Frame: Day 28 ] [ Designated as safety issue: Yes ]
- All-cause Intensive Care Unit Mortality [ Time Frame: Date of discharge from the ICU ] [ Designated as safety issue: No ]
- All-cause In-hospital Mortality [ Time Frame: Day of discharge from the hospital ] [ Designated as safety issue: No ]
- Intensive Care Unit Free Days [ Time Frame: 28 days ] [ Designated as safety issue: No ]Intensive care unit free days was 28-day-ICU-free-days i.e. was calculated by subtracting the actual ICU duration in days from 28 with patients who died at day 28 or before being assigned 0 free-days and those who had a stay in ICU of 28 days or more being also assigned 0 free-days
- Time Spent in Blood Glucose Target [ Time Frame: Day of discharge from the ICU ] [ Designated as safety issue: No ]
- Severe Hypoglycemia [ Time Frame: Date of discharge from the ICU ] [ Designated as safety issue: Yes ]Number of patients with severe biological hypoglycemia (defined as blood glucose of 40 mg per deciliter or less)regardless of clinical signs
- Hospital Length of Stay [ Time Frame: Date of discharge from the hospital ] [ Designated as safety issue: No ]
- Intensive Care Unit Length of Stay [ Time Frame: Date of discharge from the ICU ] [ Designated as safety issue: No ]
- Incidence of Nosocomial Bacteriemia [ Time Frame: Date of discharge from the ICU ] [ Designated as safety issue: Yes ]
|Study Start Date:||October 2009|
|Study Completion Date:||April 2013|
|Primary Completion Date:||December 2012 (Final data collection date for primary outcome measure)|
Experimental: CGAO-based Glucose Control
Use of a Computerized Protocol fot Tight Glycemic Control named CGAO software in order to maintain Blood Glucose Levels between 4.4 and 6.1 mmol/l.
Device: CGAO-based Glucose Control
Use of a clinical computerized decision-support system named CGAOtm designed to achieve tight glucose control in various ICU settings, and fine-tuned to reduce glucose variability without increasing the incidence of severe hypoglycemia or nurse workload.
CGAOtm is based on explicit replicable recommendations following each blood glucose measurement for insulin rates and time to next measurement, and reminders, alerts, graphic tools, trends, and individual on-line data aimed at increasing confidence of the nursing staff in the computer protocol and giving care staff a method for controlling the process during the whole ICU stay, according to a "human-in-the-loop" approach.
The algorithm used in the CGAOtm software for the calculation of the recommended insulin rates derived from a PID (Proportional-integral-derivative) controller, a generic control loop feedback mechanism widely used in industrial control.
Other Name: CGAO, LC_CGAO version1
Active Comparator: Standard-Care Glucose Gontrol
Use of Standard-Care Methods for Glucose Control targeting Blood Glucose Levels inferior to 10 mmol/l.
Device: Standard-Care Glucose Control
Patients in the control group will receive conventional insulin therapy using the "usual care" protocol of each participating centre (already used in the centre before the beginning of the trial and targeting blood glucose levels inferior to 180 mg/dl).
Other Name: Usual care
Hide Detailed Description
Hyperglycemia in response to critical illness has long been associated with adverse outcomes.
In 2001, the first "Leuven study", a randomized controlled trial conducted in surgical intensive care patients comparing a strategy based on a nurse-driven protocol for insulin therapy in order to maintain normal blood glucose levels [80 - 110 mg/dl] with standard care defined at the time as intravenous insulin started only when blood glucose level exceeded 215 mg/dl and then adjusted to keep blood glucose level between 180 and 200 mg/dl, showed a reduction in hospital mortality by one third.
The results of this trial have been enthusiastically received and rapidly incorporated into guidelines, such as the Surviving Sepsis Campaign in 2004, and now endorsed internationally by numerous professional societies.
However, subsequent randomized controlled trials have failed to confirm a mortality benefit with intensive insulin therapy among critically ill patients, in whom stress hypoglycemia is common. Moreover the Normoglycemia in Intensive Care Evaluation - Survival Using Glucose Algorithm Regulation (NICE-SUGAR) study, an international multicentre trial involving 6104 patients, the largest trial of insulin therapy to date, showed a lower 90-day mortality in the control group targeted blood glucose levels inferior to 180 mg/dl when compared to the intervention group with tight glucose control [80 - 110 mg/dl].
In addition, many studies and meta-analyses have reported high rates of hypoglycemia with tight glucose control. Consequently, considerable controversy has emerged as to whether tight glucose control is warranted in all critically ill patients especially as tight glucose control (without appropriate computer protocol) causes a significant increase in nurse workload.
The conflicting results between the first Leuven study and the NICE-SUGAR study could be explained by numerous differences between the two trials : the specific method (algorithms, compliance of nurses and physicians with recommendations, etc) used to achieve tight glucose control in each randomized control trial could be a major issue.
Several experimental and observational studies have highlighted the possible negative impact of glucose variability (large fluctuations in blood glucose possibly with undetected hypoglycemia and hypokalemia alternating with hyperglycemia) when implementing tight glucose control, be it due to the intrinsic properties of the algorithms used, technical factors (errors in measurements of the blood glucose level or lack of control over intravenous insulin therapy) or human factors (delay in performing glucose measurements or non respect of recommendations not based on clinical expertise but as a consequence of insufficient training inducing a lack of confidence in the algorithms by inexperienced nurses).
Therefore, remaining concerns about the best way to achieve glucose control in the ICU reduce the impact of conclusions of all of the recent randomized controlled trials on tight glucose control : are the negative results due to the concept, tight glucose control with intensive insulin therapy in critically ill patients in order to reduce the toxicity of high blood glucose levels, or are the negative results mainly due to specific methods used for achieving tight glucose control ? In most cases the methods used in clinical trials were never tested in numerical patients according to existing and validated models (in SILICO expertise) before implementing them in clinical practice on real patients.
Particularly, whether the use of a clinical computerized decision-support system (CDSS) designed for achieving tight glucose control in various ICU settings, and fine-tuned to reduce glucose variability, without increasing the incidence of severe hypoglycemia nor the nurse workload, has an impact on the outcome of patients staying at least three days in an ICU remains to be tested.
Among the different CDSS, the CGAOtm software has been developed to standardize different aspects of glucose control in an ICU setting based on 1) explicit replicable recommendations following each blood glucose level measurement concerning insulin rates and time to next measurement, 2) reminders and alerts and 3) various graphic tools, trends, and individual on-line data aiming to increase the confidence of the nursing staff in the computer protocol and therefore their adherence, to reduce necessary training time, and to give physicians and nurses a way to control the tight glucose control process during the whole ICU stay. Moreover, the CGAOtm software is designed to take into account irregular sampling, saturations, and some precision and stability issues.
The aim of the study is to evaluate the capability of the CGAOtm software to reduce 90-day mortality in a mixed ICU population of patients requiring intensive care for at least three days.
Sample size and power calculations. The expected all cause 90-day mortality in the control group is 25 % (identical to the observed all cause 90-day mortality in the control group of the NICE-SUGAR trial). Considering that all cause 90-day mortality in the experimental group (computer protocol group) is expected to be 22 % (absolute reduction of 3 %), considering an alpha risk and a beta risk respectively of 0.05 and 0.20 and three intermediate analyses performed according to the O'Brien-Fleming design, 3,211 patients per treatment arms are needed and will be recruited from the participating 60 centres, all located in France.
Please refer to this study by its ClinicalTrials.gov identifier: NCT01002482
|C.H.U. Hôpital Nord|
|Amiens, France, 80054|
|Avignon, France, 84902|
|G.H.U. Nord Hôpital Jean Verdier|
|Bondy, France, 93143|
|Polyclinique Jean Vilar|
|Bruges, France, 33520|
|Bry sur Marne, France, 94366|
|C.H. de Chartres|
|Chartres, France, 28018|
|Chateauroux, France, 36019|
|Hôpital Sud-Francilien - Site Corbeil|
|Corbeil-Essonnes, France, 91006|
|Clinique des Cèdres|
|Cornebarrieu, France, 31700|
|C.H. Victor Jousselin|
|Dreux, France, 28012|
|Garches, France, 92380|
|Centre Hospitalier Départemental Les Oudairies|
|La Roche Sur Yon, France, 85925|
|G.H.U. Sud Bicêtre|
|Le Kremlin Bicêtre, France, 94275|
|Hôpital de Mantes-La-Jolie|
|Mantes-La-Jolie, France, 78200|
|Hôpital Paul Desbief|
|Marseille, France, 13002|
|C.H.U. La Timone|
|Marseille, France, 13005|
|Hôpital Ambroise Paré|
|Marseille, France, 13291|
|C.H.U. de -Hôpital Saint-Eloi|
|Montpellier, France, 34295|
|Montpellier, France, 34925|
|C.H.U. Nantes - Hôpital Laennec|
|Nantes, France, 44093|
|C.H.U. de Nice - Hôpital Saint-Roch|
|Nice, France, 06006|
|Hôpital Européen Georges Pompidou|
|Paris, France, 75015|
|Paris, France, 75651|
|Institut Mutualiste Montsouris|
|Paris, France, 75674|
|G.H.U. Nord Claude Bernard|
|Paris, France, 75877|
|C.H. de Pau|
|Pau, France, 64046|
|CHU de Bordeaux - Groupe Hospitalier Sud, Hôpital Haut Lévêque|
|Pessac, France, 33604|
|C.H. René Dubos|
|Pontoise, France, 95301|
|Rodez, France, 12000|
|C.H.U. Hôpitaux de Rouen|
|Rouen, France, 76031|
|Suresnes, France, 92151|
|C.H. Intercommunal - Hôpital Font-Pré|
|Toulon, France, 83100|
|Toulouse, France, 31059|
|Toulouse, France, 31059|
|C.H.R.U. de Tours|
|Tours, France, 37044|
|Principal Investigator:||Pierre Kalfon, MD||Centre Hospitalier de Chartres|
|Study Director:||Bruno Riou, MD PhD||G.H.U. Est, C.H.U. Pitié-Salpétriêre|
|Study Chair:||Djillali Annane, MD PhD||G.H.U. Ouest, Hôpital Raymond Poincaré|
|Study Chair:||Jean Chastre, MD PhD||G.H.U. Est, Pitié-Salpétriêre|
|Study Chair:||Pierre-François Dequin, MD PhD||CHRU Tours|
|Study Chair:||Hervé Dupont, MD PhD||CHRU Amiens|
|Study Chair:||Carole Ichai, MD PhD||CHRU de Nice|
|Study Chair:||Yannick Malledant, MD PhD||CHRU Rennes|
|Study Chair:||Philippe Montravers, MD PhD||G.H.U. Nord Bichat-Claude Bernard|