Evaluation of Implementation of a Fully Automated Algorithm (Enhanced Model Predictive Control) in an Interacting Infusion Pump System for Establishment of Tight Glycemic Control in Medical Intensive Care Unit Patients

Abstract

Background: The objective of this study was to investigate the performance of a newly developed decision support system for the establishment of tight glycemic control in medical intensive care unit (ICU) patients for a period of 72 hours. Methods: This was a single-center, open, non-controlled feasibility trial including 10 mechanically ventilated ICU patients. The CS-1 decision support system (interacting infusion pumps with integrated enhanced model predictive control algorithm and user interface) was used to adjust the infusion rate of administered insulin to normalize blood glucose. Efficacy and safety were assessed by calculating the percentage of values within the target range (80-110 mg/dl), hyperglycemic index, mean glucose, and hypoglycemic episodes (<40 mg/dl).

A comparison of two insulin infusion protocols in the medical intensive care unit by continuous glucose monitoring

“…Multiple IIPs have been developed, but to the best of our knowledge this is the first study comparing the clinical efficacy (% time in target glycemia) and safety (hypoglycemia, glycemic variability) of two IIPs in MICU patients by means of CGM. Overall, compared to existing data (see Table 4, [3–15, 29–38], both our IIPs were able to obtain reasonably strict glucose control without excessive risk of hypoglycemia. The percentage of time in normoglycemia was higher (37 vs. 26%), and percentage of time in hypoglycemia lower (0 vs. 5%) and glycemic variability was less pronounced in patients treated with the Yale IIP.…”

“…Earlier observational studies and randomized controlled trials (RCTs) in medical ICU (MICU) or mixed ICU settings, and targeting a glycemia between 80 and 110 mg/dl, reported that 22–60% of all blood glucose values were in target for paper-based IIPs [3–14], compared to 42–69% for computerized decision-supported algorithms [4, 10, 11, 14, 15]. The efficacy and safety of different IIPs on glycemic control have recently been investigated using computer simulation models [16, 17] and in RCTs in cardiac surgery patients [18, 19].…”

Accuracy and optimization of a subcutaneous insulin model for less acute critical care patients

“…Hyperglycaemia worsens outcomes, leading to further risk of complications, including sepsis [5], myocardial infarction [1], polyneuropathy, and multiple organ failure [2]. Adaptive model-based protocols for accurate glycaemic control (AGC) that modulate insulin and nutrition have shown considerable promise in the intensive care unit (ICU) [6][7][8][9][10]. However, they are not suitable for less acute wards because of the demands they place on clinical staff [11][12][13][14] and the lack of intravenous (IV) lines for IV delivery.…”

AP@home: A Novel European Approach to Bring the Artificial Pancreas Home

“…23,24 Margins for improvement are still wide. Exercise and meals are additional obstacles to perfect glucose control.…”