Chee F, Fernando T, van Heerden PV. Closed-loop glucose control in critically ill patients using continuous glucose monitoring system (CGMS) in real time.
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE : A PUBLICATION OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY 2003;
7:43-53. [PMID:
12670018 DOI:
10.1109/titb.2003.808509]
[Citation(s) in RCA: 104] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
A study was conducted to determine if continuous subcutaneous glucose monitoring (from MiniMed CGMS) could be used in real-time to control blood sugar level (BSL) in patients with critical illness. A closed-loop control system was constructed to use CGMS in a real-time manner, coupled with a proportional integral (PI) control algorithm based on a sliding scale approach, for automatic intravenous infusion of insulin to patients. A total of five subjects with high BSL (> 10 mmol/L) participated in formal studies of the closed-loop control system. Subjects were recruited from critically ill patients in the intensive care unit (ICU) after informed consent was obtained. Error grid analysis showed that 64.6% of the BSL readings as determined in real time using CGMS sensor, when compared to conventional BSL measurements on blood drawn from an arterial line, was clinically accurate (i.e., < 20% deviation from glucometer value). In the five patients who underwent closed-loop control, the controller managed to control only one patient's glycaemia without any manual intervention. Manual intervention was required due to the real-time sensor reading deviating more than 20% from the glucometer value, and also as a safety mechanism. Test on equality of mean and variance for BSL attained prior to, during, and post trial showed that the controller's performance was comparable to manual control. We conclude that the automatic sliding scale approach of closed-loop BSL control is feasible in patients in intensive care. More work is needed in the refinement of the algorithm and the improvement of real-time sensor accuracy.
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