Tight glycaemic control by an automated algorithm with time-variant sampling in medical ICU patients.
OBJECTIVE
Tight glycaemic control (TGC) in critically ill patients improves clinical outcome, but is difficult to establish The primary objective of the present study was to compare glucose control in medical ICU patients applying a computer-based enhanced model predictive control algorithm (eMPC) extended to include time-variant sampling against an implemented glucose management protocol.


METHODS
Open randomised controlled trial.


METHODS
Nine-bed medical intensive care unit (ICU) in a tertiary teaching hospital.


METHODS
Fifty mechanically ventilated medical ICU patients.


METHODS
Patients were included for a study period of up to 72 h. Patients were randomised to the control group (n = 25), treated by an implemented insulin algorithm, or to the eMPC group (n = 25), using the laptop-based algorithm. Target range for blood glucose (BG) was 4.4-6.1 mM. Efficacy was assessed by mean BG, hyperglycaemic index (HGI) and BG sampling interval. Safety was assessed by the number of hypoglycaemic-episodes < 2.2 mM. Each participating nurse filled-in a questionnaire regarding the usability of the algorithm.


RESULTS
BG and HGI were significantly lower in the eMPC group [BG 5.9 mM (5.5-6.3), median (IQR); HGI 0.4 mM (0.2-0.9)] than in control patients [BG 7.4 mM (6.9-8.6), p < 0.001; HGI 1.6 mM (1.1-2.4), p < 0.001]. One hypoglycaemic episode was detected in the eMPC group; no such episodes in the control group. Sampling interval was significantly shorter in the eMPC group [eMPC 117[Symbol: see text]min (+/- 34), mean (+/- SD), vs 174 min (+/- 27); p < 0.001]. Thirty-four nurses filled-in the questionnaire. Thirty answered the question of whether the algorithm could be applied in daily routine in the affirmative.


CONCLUSIONS
The eMPC algorithm was effective in maintaining tight glycaemic control in severely ill medical ICU patients.
Pachler
Christoph
C
Department of Internal Medicine, Medical University Graz, Stiftingtalstrasse 24, 8010, Graz, Austria. christoph.pachler@healthsite.at
Plank
Johannes
J
Weinhandl
Heinz
H
Chassin
Ludovic J
LJ
Wilinska
Malgorzata E
ME
Kulnik
Roman
R
Kaufmann
Peter
P
Smolle
Karl-Heinz
KH
Pilger
Ernst
E
Pieber
Thomas R
TR
Ellmerer
Martin
M
Hovorka
Roman
R
D018806:APACHE; D000465:Algorithms; D001786:Blood Glucose / Q000187:drug effects*; D003422:Critical Care / Q000379:methods*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000145:classification / Q000188:drug therapy*; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D005260:Female; D038321:Glycemic Index; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000627:therapeutic use*; D007328:Insulin / Q000008:administration & dosage / Q000627:therapeutic use*; D007333:Insulin Resistance; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D013997:Time Factors
D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin