Tight glycaemic control by an automated algorithm with time-variant sampling in medical ICU patients
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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.
Open randomised controlled trial.
Nine-bed medical intensive care unit (ICU) in a tertiary teaching hospital.
Patients and participants
Fifty mechanically ventilated medical ICU patients.
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.
Measurements and main 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 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.
The eMPC algorithm was effective in maintaining tight glycaemic control in severely ill medical ICU patients.
KeywordsCritically ill Insulin resistance Tight glycemic control Computer algorithm
The authors want to express their special thanks to the nursing staff of the Intensive Care Unit of the Department of Internal Medicine (Medical University Graz), who performed all study-related activities in addition to their routine workload.
The study is part of CLINICIP, an IST (Information Society and Technology) project funded by the European Community under the Sixth Framework Programme, Action Line eHealth, Project Reference 506965.
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