Glucose control in intensive care: usability, efficacy and safety of Space GlucoseControl in two medical European intensive care units

Amrein, Karin; Kachel, Norman; Fries, Heike; Hovorka, Roman; Pieber, Thomas R.; Plank, Johannes; Wenger, Urs; Lienhardt, Barbara; Maggiorini, Marco

doi:10.1186/1472-6823-14-62

Cited by 22 publications

(27 citation statements)

References 42 publications

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“…Výsledkem bylo více uvolněné doporučení udržovat glykémii na hladinách mezi 8 až 10 mmol/l (144 mg/dL -180 mg/dL). Celá řada dalších prací přinesla navzájem kontroverzní výsledky (101)(102)(103)(104)(105). Jedna skupina doporučuje co nejnižší, ale zároveň ještě bezpečné glykémie, druhá se nebrání i hodnotám kolem 10 -12mmol/l (97).…”

Section: Hyperglykémie U Kriticky Nemocných a Její Terapeutické Ovlivunclassified

The Central Role of Glucose in Metabolism and Nutrition of Critically Ill Patients

Skořepa¹,

Sobotka²,

Fortunato³

et al. 2017

MMSL

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Section: Hyperglykémie U Kriticky Nemocných a Její Terapeutické Ovlivunclassified

The Central Role of Glucose in Metabolism and Nutrition of Critically Ill Patients

Skořepa¹,

Sobotka²,

Fortunato³

et al. 2017

MMSL

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“…1 Micro-dialysis has often been used in the clinical environment for continuously harvesting body fluids in a minimal invasive manner, but a drawback of this process are variable recovery rates. 2,3 Some general considerations in microdialysis have been reviewed also by Nandi and Lunte.…”

Section: Introductionmentioning

confidence: 99%

Ex-vivo glucose sensors using micro-dialysis: importance of on-line recovery rate determination by multi-analyte infrared spectrometry

et al. 2015

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Micro-dialysis has been established in the clinical environment for continuously harvesting body fluids, but a drawback of this process are variable recovery rates, which can be observed especially for subcutaneously implanted catheters. Perfusates with either acetate or mannitol have been investigated as recovery markers. The latter substance is suggested for application with external cavity tuneable quantum cascade lasers, rendering a limited wavenumber interval in contrast to FTIR-spectrometers. Despite the overlap of mannitol and glucose spectra, their simultaneous quantification was successful. By investigating the depletion of the marker substances from the perfusates using different micro-dialysis devices, the theoretical nonlinear relationship between the relative dialysate marker concentration and glucose recovery rate was confirmed for the marker substance-analyte pair of acetate and glucose, rendering a basis for reliable blood glucose measurements. For the pair of mannitol and glucose an almost linear dependency was expected for the microdialysate catheters and experimentally verified, which provides a straightforward correction of any dialysis recovery rate variation during patient monitoring.

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“…They tend to dismiss expert system controllers as they are not mathematically derived, but rather rely on heuristic reasoning. This study shows that when an AI-based controller is carefully designed by a domain expert 8 who also has decades of clinical experience, that it may have the potential to improve on the results achieved by either PID or MPC controllers, 45,46 although strictly speaking it is not valid to compare in silico results with clinical studies. Specifically, this simulation shows that this AI-based controller may be capable of achieving exceptional results, including (1) time in control range > 90%, (2) time in range 70-140 mg/dl > 95%, (3) severe hypoglycemia (<40 mg/dl) rate of 0, (4) hypoglycemia (40-69 mg/dl) rate < 0.1%, (5) CV < 13%, and (6) hyperglycemia (>140 mg/dl) rate < 3%.…”

Section: Discussionmentioning

confidence: 99%

In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting

DeJournett

2016

J Diabetes Sci Technol

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The importance of glucose control in the intensive care unit (ICU) setting was first demonstrated by Furnary 1 and later confirmed by Van den Berghe 2 in a large, prospective, randomized control study. However, almost all of the clinical studies that have been published since then have struggled to achieve superior glucose control while avoiding the glucose metrics that actually increase mortality rates-hypoglycemia, hyperglycemia, variability, and low time in range.3-6 A Japanese study that utilized a closed loop glucose control system that minimized hypoglycemia while at the same time achieving a high percentage of time in range showed the true promise of effective glucose control.7 To achieve tight glucose control while at the same time minimizing glucose metrics that increase mortality rates, a closed loop system will need to be created. This system should mimic the workings of the native system which have been previously reviewed. 8 To date, most attempts at creating a closed loop glucose control system for use in the ICU setting have utilized the standard engineering control techniques of proportional integral derivative (PID) or model predictive control (MPC). [9][10][11][12] PID controllers typically control to a set point and are challenged by the fact they rely solely on insulin for control, thus they do not take advantage of the counter-regulatory effects of intravenous (IV) glucose. MPC controllers rely on a complex mathematical model of the glucose-insulin system. MPC model parameters are refitted every 1 to 4 hours, in an iterative fashion, based on the difference between the predicted and measured glucose level. Control recommendations are made from the resulting best fit model. MPC controllers can be designed to control to a set point or to a desired range and Background: Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates which should in turn lead to decreased health care expenditures. Current ICU-based glucose controllers are mathematically derived, and tend to be based on proportional integral derivative (PID) or model predictive control (MPC). Artificial intelligence (AI)-based closed loop glucose controllers may have the ability to achieve control that improves on the results achieved by either PID or MPC controllers. Method:We conducted an in silico analysis of an AI-based glucose controller designed for use in the ICU setting. This controller was tested using a mathematical model of the ICU patient's glucose-insulin system. A total of 126 000 unique 5-day simulations were carried out, resulting in 107 million glucose values for analysis. Results:For the 7 control ranges tested, with a sensor error of ±10%, the following average results were achieved: (1) time in control range, 94.2%, (2) time in range 70-140 mg/dl, 97.8%, (3) time in hyperglycemic range (>140 mg/dl), 2.1%, and (4) time in hypoglycemic range (<70 mg/dl), 0.09%. In addition, the average coefficient of variation (CV) was 11.1%. Conclusions:This in sili...

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Glucose control in intensive care: usability, efficacy and safety of Space GlucoseControl in two medical European intensive care units

Cited by 22 publications

References 42 publications

The Central Role of Glucose in Metabolism and Nutrition of Critically Ill Patients

The Central Role of Glucose in Metabolism and Nutrition of Critically Ill Patients

Ex-vivo glucose sensors using micro-dialysis: importance of on-line recovery rate determination by multi-analyte infrared spectrometry

In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting

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